forked from mlrun/marketplace
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcatalog.json
1 lines (1 loc) · 297 KB
/
catalog.json
1
{"functions": {"development": {"tf2_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "tf2-serving", "platformVersion": "", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.0"}}, "load_dataset": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dataset", "platformVersion": "3.5.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dataset", "platformVersion": "3.2.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dataset", "platformVersion": "3.5.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "load-dataset", "platformVersion": "", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dataset", "platformVersion": "3.2.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "model_server_tester": {"latest": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-server-tester", "platformVersion": "3.5.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server-tester", "platformVersion": "3.2.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server-tester", "platformVersion": "3.2.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-server-tester", "platformVersion": "3.5.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "model-server-tester", "platformVersion": "", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server-tester", "platformVersion": "3.2.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "tf2_serving_v2": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving-v2", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving-v2", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving-v2", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving-v2", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "tf2-serving-v2", "platformVersion": "", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving-v2", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.0"}}, "sql_to_file": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "sql-to-file", "platformVersion": "3.5.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sql-to-file", "platformVersion": "3.2.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sql-to-file", "platformVersion": "3.2.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "sql-to-file", "platformVersion": "3.5.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "sql-to-file", "platformVersion": "", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sql-to-file", "platformVersion": "3.2.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "feature_selection": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-selection", "platformVersion": "3.5.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.9.0", "name": "feature-selection", "platformVersion": "3.2.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "feature-selection", "platformVersion": "3.2.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection/feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.1": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-selection", "platformVersion": "3.5.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.1"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-selection", "platformVersion": "3.5.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.5.4", "name": "feature-selection", "platformVersion": "2.10.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection/feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.9.0", "name": "feature-selection", "platformVersion": "3.2.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection/feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "aggregate": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "aggregate", "platformVersion": "3.5.2", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2021-05-19:22-31", "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "aggregate", "platformVersion": "3.2.0", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "1.2.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "aggregate", "platformVersion": "3.5.2", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2021-05-19:22-31", "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "aggregate", "platformVersion": "3.2.0", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "aggregate", "platformVersion": "3.5.2", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.1"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "aggregate", "platformVersion": "3.5.2", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2021-05-19:22-31", "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.6.2", "name": "aggregate", "platformVersion": "3.0.0", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.0.2": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2021-05-19:22-31", "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.7.1", "name": "aggregate", "platformVersion": "3.2.0", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.2"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "aggregate", "platformVersion": "3.2.0", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "bert_embeddings": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "bert-embeddings", "platformVersion": "3.5.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "1.1.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "bert-embeddings", "platformVersion": "3.2.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "0.8.0"}, "1.1.1": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "bert-embeddings", "platformVersion": "3.5.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "1.1.1"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "bert-embeddings", "platformVersion": "3.5.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.5.4", "name": "bert-embeddings", "platformVersion": "2.10.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "bert-embeddings", "platformVersion": "3.2.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "0.9.0"}}, "describe": {"latest": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "describe", "platformVersion": "3.5.0", "spec": {"filename": "describe.py", "handler": "analyze", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2022-04-07:14-20", "icon": "", "labels": {"author": "Davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe", "platformVersion": "3.2.0", "spec": {"filename": "describe.py", "handler": "analyze", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "Iguazio"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe", "platformVersion": "3.2.0", "spec": {"filename": "describe.py", "handler": "summarize", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "0.9.2": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2022-04-26:10-20", "icon": "", "labels": {"author": "Davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe", "platformVersion": "3.2.0", "spec": {"filename": "describe.py", "handler": "analyze", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.2"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "describe", "platformVersion": "3.5.0", "spec": {"filename": "describe.py", "handler": "analyze", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "Iguazio"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.5.4", "name": "describe", "platformVersion": "2.10.0", "spec": {"filename": "describe.py", "handler": "summarize", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe", "platformVersion": "3.2.0", "spec": {"filename": "describe.py", "handler": "summarize", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "model_server": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-server", "platformVersion": "3.5.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.1.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server", "platformVersion": "3.2.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server", "platformVersion": "3.2.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-server", "platformVersion": "3.5.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "model-server", "platformVersion": "", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server", "platformVersion": "3.2.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.9.0"}}, "pandas_profiling_report": {"latest": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "pandas-profiling-report", "platformVersion": "3.5.0", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "pandas-profiling-report", "platformVersion": "3.2.0", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "pandas-profiling-report", "platformVersion": "3.5.0", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "pandas-profiling-report", "platformVersion": "", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "pandas-profiling-report", "platformVersion": "3.2.0", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "0.9.0"}}, "load_dask": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dask", "platformVersion": "3.5.0", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dask", "platformVersion": "3.2.0", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dask", "platformVersion": "3.5.0", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "load-dask", "platformVersion": "", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dask", "platformVersion": "3.2.0", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "0.9.0"}}, "slack_notify": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "slack-notify", "platformVersion": "3.5.0", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "slack-notify", "platformVersion": "3.2.0", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "slack-notify", "platformVersion": "3.5.0", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "slack-notify", "platformVersion": "", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "slack-notify", "platformVersion": "3.2.0", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "0.9.0"}}, "xgb_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "xgb_serving", "platformVersion": "3.5.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "1.1.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "xgb_serving", "platformVersion": "3.2.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "xgb_serving", "platformVersion": "3.2.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "xgb_serving", "platformVersion": "3.5.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.6.2", "name": "xgb_serving", "platformVersion": "3.0.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "xgb_serving", "platformVersion": "3.2.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "0.9.0"}}, "model_monitoring_batch": {"latest": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-monitoring-batch", "platformVersion": "3.5.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-batch", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-batch", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-monitoring-batch", "platformVersion": "3.5.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "model-monitoring-batch", "platformVersion": "", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-batch", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "stream_to_parquet": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "stream-to-parquet", "platformVersion": "3.5.0", "spec": {"customFields": {"max_replicas": 1, "min_replicas": 1}, "filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": []}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "stream-to-parquet", "platformVersion": "3.2.0", "spec": {"customFields": {"max_replicas": 1, "min_replicas": 1}, "filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "stream-to-parquet", "platformVersion": "3.5.0", "spec": {"customFields": {"max_replicas": 1, "min_replicas": 1}, "filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "stream-to-parquet", "platformVersion": "", "spec": {"filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": [], "customFields": {"min_replicas": 1, "max_replicas": 1}}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "stream-to-parquet", "platformVersion": "3.2.0", "spec": {"customFields": {"max_replicas": 1, "min_replicas": 1}, "filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.9.0"}}, "describe_spark": {"latest": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "describe-spark", "platformVersion": "3.5.0", "spec": {"filename": "describe_spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe-spark", "platformVersion": "3.2.0", "spec": {"filename": "describe_spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe-spark", "platformVersion": "3.2.0", "spec": {"filename": "describe-spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "describe-spark", "platformVersion": "3.5.0", "spec": {"filename": "describe_spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "describe-spark", "platformVersion": "", "spec": {"filename": "describe-spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe-spark", "platformVersion": "3.2.0", "spec": {"filename": "describe-spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "gen_class_data": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "gen_class_data", "platformVersion": "3.5.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "gen_class_data", "platformVersion": "3.2.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "0.10.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "gen_class_data", "platformVersion": "3.2.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.10.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "gen_class_data", "platformVersion": "3.5.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.6.2", "name": "gen_class_data", "platformVersion": "3.0.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "gen_class_data", "platformVersion": "3.2.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "open_archive": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "open-archive", "platformVersion": "3.5.0", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "open-archive", "platformVersion": "3.2.0", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "open-archive", "platformVersion": "3.5.0", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "open-archive", "platformVersion": "", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "open-archive", "platformVersion": "3.2.0", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "send_email": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "send-email", "platformVersion": "3.5.0", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "send-email", "platformVersion": "3.2.0", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "send-email", "platformVersion": "3.5.0", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "send-email", "platformVersion": "", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "send-email", "platformVersion": "3.2.0", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "concept_drift": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "concept-drift", "platformVersion": "", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.0.2": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "concept-drift", "platformVersion": "", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "0.0.2"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "0.9.0"}}, "sentiment_analysis_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "sentiment-analysis-serving", "platformVersion": "3.5.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "1.1.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.0", "name": "sentiment-analysis-serving", "platformVersion": "3.4.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sentiment-analysis-serving", "platformVersion": "3.2.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "sentiment-analysis-serving", "platformVersion": "3.5.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.6.3", "name": "sentiment-analysis-serving", "platformVersion": "3.0.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sentiment-analysis-serving", "platformVersion": "3.2.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "0.9.0"}}, "tf1_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf1-serving", "platformVersion": "3.5.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf1-serving", "platformVersion": "3.2.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf1-serving", "platformVersion": "3.2.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf1-serving", "platformVersion": "3.5.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "tf1-serving", "platformVersion": "", "spec": {"filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": [], "env": {"MODEL_CLASS": "TFModel", "ENABLE_EXPLAINER": false}}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf1-serving", "platformVersion": "3.2.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.9.0"}}, "churn_server": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "churn-server", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "1.1.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "churn-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "churn-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "churn-server", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "churn-server", "platformVersion": "", "spec": {"filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": [], "env": {"ENABLE_EXPLAINER": "False"}, "customFields": {"default_class": "ChurnModel"}}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "churn-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "0.9.0"}}, "model_monitoring_stream": {"latest": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-monitoring-stream", "platformVersion": "3.5.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-stream", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-stream", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-monitoring-stream", "platformVersion": "3.5.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "model-monitoring-stream", "platformVersion": "", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-stream", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.9.0"}}, "virtual_drift": {"latest": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "virtual-drift", "platformVersion": "3.5.0", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "virtual-drift", "platformVersion": "3.2.0", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "virtual-drift", "platformVersion": "3.5.0", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "virtual-drift", "platformVersion": "", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "virtual-drift", "platformVersion": "3.2.0", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "0.9.0"}}, "rnn_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "rnn-serving", "platformVersion": "3.5.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "1.1.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "rnn-serving", "platformVersion": "3.2.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "rnn-serving", "platformVersion": "3.2.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "rnn-serving", "platformVersion": "3.5.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "rnn-serving", "platformVersion": "", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["keras"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "rnn-serving", "platformVersion": "3.2.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "0.9.0"}}, "feature_perms": {"latest": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-perms", "platformVersion": "3.5.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "feature-perms", "platformVersion": "3.2.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "feature-perms", "platformVersion": "3.2.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-perms", "platformVersion": "3.5.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "feature-perms", "platformVersion": "", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "feature-perms", "platformVersion": "3.2.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "v2_model_tester": {"latest": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "v2-model-tester", "platformVersion": "3.5.0", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-tester", "platformVersion": "3.2.0", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "v2-model-tester", "platformVersion": "3.5.0", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "v2-model-tester", "platformVersion": "", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-tester", "platformVersion": "3.2.0", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "coxph_test": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "coxph-test", "platformVersion": "3.5.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "coxph-test", "platformVersion": "3.2.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "coxph-test", "platformVersion": "3.2.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "coxph-test", "platformVersion": "3.5.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "coxph-test", "platformVersion": "", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "coxph-test", "platformVersion": "3.2.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "arc_to_parquet": {"latest": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "arc-to-parquet", "platformVersion": "3.5.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "1.2.0": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "arc-to-parquet", "platformVersion": "3.5.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "arc-to-parquet", "platformVersion": "3.2.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "arc-to-parquet", "platformVersion": "3.5.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.5.4", "name": "arc-to-parquet", "platformVersion": "2.10.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "arc-to-parquet", "platformVersion": "3.2.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "github_utils": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "github-utils", "platformVersion": "3.5.0", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "github-utils", "platformVersion": "3.2.0", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "github-utils", "platformVersion": "3.5.0", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "github-utils", "platformVersion": "", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "github-utils", "platformVersion": "3.2.0", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "v2_model_server": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "v2-model-server", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "1.1.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "v2-model-server", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "v2-model-server", "platformVersion": "", "spec": {"filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": [], "customFields": {"default_class": "ClassifierModel"}}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "0.9.0"}}, "concept_drift_streaming": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift-streaming", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift-streaming", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "0.9.1"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift-streaming", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift-streaming", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "concept-drift-streaming", "platformVersion": "", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "0.0.1"}, "0.0.2": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "concept-drift-streaming", "platformVersion": "", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "0.0.2"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift-streaming", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "0.9.0"}}, "onnx_utils": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "onnx_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "with_mlrun": false}}, "filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.13.0", "onnxruntime~=1.14.0", "onnxoptimizer~=0.3.0", "onnxmltools~=1.11.0", "tf2onnx~=1.13.0"]}, "url": "", "version": "1.1.1"}, "0.10.1": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "onnx_utils", "platformVersion": "3.2.0", "spec": {"filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.10.1"}, "0.10.2": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "onnx_utils", "platformVersion": "3.2.0", "spec": {"filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.10.2"}, "0.8.0": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2021-10-25:00-15", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "onnx_utils", "platformVersion": "3.2.0", "spec": {"filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.1": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "onnx_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "with_mlrun": false}}, "filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.13.0", "onnxruntime~=1.14.0", "onnxoptimizer~=0.3.0", "onnxmltools~=1.11.0", "tf2onnx~=1.13.0"]}, "url": "", "version": "1.1.1"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "onnx_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "with_mlrun": false}}, "filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "onnxoptimizer~=0.2.0", "onnxmltools~=1.9.0", "tf2onnx~=1.9.0"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2021-10-25:00-15", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "onnx_utils", "platformVersion": "", "spec": {"filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "onnx_utils", "platformVersion": "3.2.0", "spec": {"filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.1": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "onnx_utils", "platformVersion": "3.2.0", "spec": {"filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.1"}}, "ingest": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "ingest", "platformVersion": "3.5.0", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "ingest", "platformVersion": "3.5.0", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "ingest", "platformVersion": "", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "ingest", "platformVersion": "", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "get_offline_features": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "get_offline_features", "platformVersion": "3.5.0", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.1": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-05-25:10-58", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.1", "name": "get_offline_features", "platformVersion": "", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "1.0.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-05-25:10-58", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.1", "name": "get_offline_features", "platformVersion": "", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "get_offline_features", "platformVersion": "3.5.0", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "1.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-05-25:10-58", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.1", "name": "get_offline_features", "platformVersion": "", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.1"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-01-17:17-56", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.9.1", "name": "get_offline_features", "platformVersion": "", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-01-17:17-56", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.9.1", "name": "get_offline_features", "platformVersion": "", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "azureml_utils": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "azureml_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "commands": ["python -m pip install pip==22.1.2", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true}}, "filename": "azureml_utils.py", "handler": "train", "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.40.0", "azureml-train-automl-client==1.40.0", "plotly~=5.4"]}, "url": "", "version": "1.1.0"}, "0.9.5": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2021-04-20:15-18", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "azureml_utils", "platformVersion": "", "spec": {"filename": "azureml_utils.py", "handler": "train", "extra_spec": {"build": {"commands": ["python -m pip install pip==21.2.4", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true, "auto_build": true}, "allow_empty_resources": true}, "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.40.0", "azureml-train-automl-client==1.40.0", "plotly~=5.4"]}, "url": "", "version": "0.9.5"}, "0.9.4": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "azureml_utils", "platformVersion": "", "spec": {"filename": "azureml_utils.py", "handler": "train", "extra_spec": {"build": {"commands": ["python -m pip install pip==21.2.4", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true, "auto_build": true}, "allow_empty_resources": true}, "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.33.0", "azureml-train-automl-client==1.33.0", "plotly~=5.4"]}, "url": "", "version": "0.9.4"}, "0.9.3": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "azureml_utils", "platformVersion": "", "spec": {"filename": "azureml_utils.py", "handler": "train", "extra_spec": {"build": {"commands": ["python -m pip install pip==21.2.4", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true, "auto_build": true}, "allow_empty_resources": true}, "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.33.0", "azureml-train-automl-client==1.33.0", "plotly~=5.4"]}, "url": "", "version": "0.9.3"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "azureml_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "commands": ["python -m pip install pip==22.1.2", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true}}, "filename": "azureml_utils.py", "handler": "train", "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.40.0", "azureml-train-automl-client==1.40.0", "plotly~=5.4"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "azureml_utils", "platformVersion": "", "spec": {"filename": "azureml_utils.py", "handler": "train", "commands": null, "image": "", "kind": "job", "requirements": ["azureml-core==1.33.0", "azureml-train-automl-client==1.33.0"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "azureml_utils", "platformVersion": "", "spec": {"filename": "azureml_utils.py", "handler": "train", "commands": null, "image": "", "kind": "job", "requirements": ["azureml-core==1.33.0", "azureml-train-automl-client==1.33.0"]}, "url": "", "version": "0.9.0"}}, "auto_trainer": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "auto_trainer", "platformVersion": "3.5.0", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.10.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-02-06:10-18", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "", "kind": "job", "requirements": []}, "url": "", "version": "0.10.1"}, "0.10.3": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-04-26:10-43", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "", "kind": "job", "requirements": []}, "url": "", "version": "0.10.3"}, "0.10.2": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-02-06:10-18", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "", "kind": "job", "requirements": []}, "url": "", "version": "0.10.2"}, "1.0.6": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-04-26:10-43", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.6"}, "0.10.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-02-06:10-18", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "", "kind": "job", "requirements": []}, "url": "", "version": "0.10.0"}, "1.0.2": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-04-26:10-43", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.2"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "auto_trainer", "platformVersion": "3.5.0", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "1.0.4": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-04-26:10-43", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.4"}, "1.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-04-26:10-43", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "", "kind": "job", "requirements": []}, "url": "", "version": "1.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-02-06:10-18", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "snowflake_dask": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Snowflake Dask - Ingest snowflake data in parallel with Dask cluster", "doc": "", "example": "snowflake-dask-mlrun.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "xingsheng", "framework": "dask"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "snowflake_dask", "platformVersion": "3.5.0", "spec": {"filename": "snowflake_dask.py", "handler": "load_results", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Snowflake Dask - Ingest snowflake data in parallel with Dask cluster", "doc": "", "example": "snowflake-dask-mlrun.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "xingsheng", "framework": "dask"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "snowflake_dask", "platformVersion": "3.5.0", "spec": {"filename": "snowflake_dask.py", "handler": "load_results", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Snowflake Dask - Ingest snowflake data in parallel with Dask cluster", "doc": "", "example": "snowflake-dask-mlrun.ipynb", "generationDate": "2022-03-20:12-28", "icon": "", "labels": {"author": "xingsheng", "framework": "dask"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.9.1", "name": "snowflake_dask", "platformVersion": "3.2.0", "spec": {"filename": "snowflake_dask.py", "handler": "load_results", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}}, "azureml_serving": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "AzureML serving function", "doc": "", "example": "azureml_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "azureml_serving", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "mlrun.frameworks.sklearn.PickleModelServer"}, "filename": "azureml_serving.py", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["azureml-automl-runtime~=1.38.1"]}, "url": "", "version": "1.1.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "AzureML serving function", "doc": "", "example": "azureml_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "azureml_serving", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "mlrun.frameworks.sklearn.PickleModelServer"}, "filename": "azureml_serving.py", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["azureml-automl-runtime~=1.38.1"]}, "url": "", "version": "1.1.0"}}, "batch_inference": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/ml-models", "kind": "job", "requirements": null}, "url": "", "version": "1.2.0"}, "1.2.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/ml-models", "kind": "job", "requirements": null}, "url": "", "version": "1.2.0"}, "1.1.1": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/ml-models", "kind": "job", "requirements": null}, "url": "", "version": "1.1.1"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference ( also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "plotly"]}, "url": "", "version": "1.1.0"}}, "hugging_face_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "Generic Hugging Face model server.", "doc": "", "example": "hugging_face_serving.ipynb", "generationDate": "2022-09-05:17-00", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "hugging_face_serving", "platformVersion": "", "spec": {"customFields": {"default_class": "HuggingFaceModelServer"}, "filename": "hugging_face_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==4.21.3", "tensorflow==2.9.2"]}, "url": "", "version": "1.0.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "Generic Hugging Face model server.", "doc": "", "example": "hugging_face_serving.ipynb", "generationDate": "2022-09-05:17-00", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "hugging_face_serving", "platformVersion": "", "spec": {"customFields": {"default_class": "HuggingFaceModelServer"}, "filename": "hugging_face_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==4.21.3", "tensorflow==2.9.2"]}, "url": "", "version": "1.0.0"}}, "hugging_face_classifier_trainer": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train and optimize functions for HuggingFace framework", "doc": "", "example": "hugging_face_classifier_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "hugging_face_classifier_trainer", "platformVersion": "3.5.0", "spec": {"filename": "hugging_face_classifier_trainer.py", "handler": "train", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "optimum~=1.6.4", "transformers~=4.26.1", "datasets~=2.10.1", "scikit-learn~=1.0.2"]}, "url": "", "version": "0.0.1"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train and optimize functions for HuggingFace framework", "doc": "", "example": "hugging_face_classifier_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "hugging_face_classifier_trainer", "platformVersion": "3.5.0", "spec": {"filename": "hugging_face_classifier_trainer.py", "handler": "train", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "optimum~=1.6.4", "transformers~=4.26.1", "datasets~=2.10.1", "scikit-learn~=1.0.2"]}, "url": "", "version": "0.0.1"}}, "validate_great_expectations": {"latest": {"apiVersion": "v1", "categories": ["data-validation", "data-analysis"], "description": "Validate a dataset using Great Expectations", "doc": "", "example": "validate_great_expectations.ipynb", "generationDate": "2022-04-26:12-28", "hidden": false, "icon": "", "labels": {"author": "nicks", "framework": "great-expectations"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "validate-great-expectations", "platformVersion": "3.5.2", "spec": {"filename": "validate_great_expectations.py", "handler": "validate_expectations", "image": "mlrun/mlrun", "kind": "job", "requirements": ["great-expectations==0.15.41"]}, "url": "", "version": "1.1.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-validation", "data-analysis"], "description": "Validate a dataset using Great Expectations", "doc": "", "example": "validate_great_expectations.ipynb", "generationDate": "2022-04-26:12-28", "hidden": false, "icon": "", "labels": {"author": "nicks", "framework": "great-expectations"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "validate-great-expectations", "platformVersion": "3.5.2", "spec": {"filename": "validate_great_expectations.py", "handler": "validate_expectations", "image": "mlrun/mlrun", "kind": "job", "requirements": ["great-expectations==0.15.41"]}, "url": "", "version": "1.1.0"}}}, "master": {"tf2_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "tf2-serving", "platformVersion": "", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0"}}, "load_dataset": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dataset", "platformVersion": "3.5.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "load-dataset", "platformVersion": "", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dataset", "platformVersion": "3.2.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dataset", "platformVersion": "3.2.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dataset", "platformVersion": "3.5.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "model_server_tester": {"latest": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-server-tester", "platformVersion": "3.5.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "model-server-tester", "platformVersion": "", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server-tester", "platformVersion": "3.2.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server-tester", "platformVersion": "3.2.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server-tester", "platformVersion": "3.2.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["monitoring", "model-serving"], "description": "test model servers", "doc": "", "example": "model_server_tester.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-server-tester", "platformVersion": "3.5.0", "spec": {"filename": "model_server_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "tf2_serving_v2": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving-v2", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "tf2-serving-v2", "platformVersion": "", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving-v2", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving-v2", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving-v2", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server v2", "doc": "", "example": "tf2_serving_v2.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving-v2", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving_v2.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0"}}, "sql_to_file": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "sql-to-file", "platformVersion": "3.5.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "sql-to-file", "platformVersion": "", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sql-to-file", "platformVersion": "3.2.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sql-to-file", "platformVersion": "3.2.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sql-to-file", "platformVersion": "3.2.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "SQL To File - Ingest data using SQL query", "doc": "", "example": "sql_to_file.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "adih"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "sql-to-file", "platformVersion": "3.5.0", "spec": {"filename": "sql_to_file.py", "handler": "sql_to_file", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "feature_selection": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-selection", "platformVersion": "3.5.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.5.4", "name": "feature-selection", "platformVersion": "2.10.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection/feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.9.0", "name": "feature-selection", "platformVersion": "3.2.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "1.1.1": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-selection", "platformVersion": "3.5.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.9.0", "name": "feature-selection", "platformVersion": "3.2.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection/feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "1.2.0": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-selection", "platformVersion": "3.5.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "feature-selection", "platformVersion": "3.2.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection/feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Select features through multiple Statistical and Model filters", "doc": "", "example": "feature_selection.ipynb", "generationDate": "2022-08-28:17-25", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-selection", "platformVersion": "3.5.0", "spec": {"filename": "feature_selection.py", "handler": "feature_selection", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "aggregate": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "aggregate", "platformVersion": "3.5.2", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2021-05-19:22-31", "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.6.2", "name": "aggregate", "platformVersion": "3.0.0", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "aggregate", "platformVersion": "3.2.0", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "1.2.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "aggregate", "platformVersion": "3.5.2", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "aggregate", "platformVersion": "3.2.0", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Rolling aggregation over Metrics and Lables according to specifications", "doc": "", "example": "aggregate.ipynb", "generationDate": "2022-08-28:17-25", "icon": "", "labels": {"author": "avia"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "aggregate", "platformVersion": "3.5.0", "spec": {"filename": "aggregate.py", "handler": "aggregate", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "bert_embeddings": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "bert-embeddings", "platformVersion": "3.5.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "1.1.1"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.5.4", "name": "bert-embeddings", "platformVersion": "2.10.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "0.0.1"}, "1.1.1": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "bert-embeddings", "platformVersion": "3.5.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "1.1.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "bert-embeddings", "platformVersion": "3.2.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "bert-embeddings", "platformVersion": "3.2.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Get BERT based embeddings for given text", "doc": "", "example": "bert_embeddings.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "bert-embeddings", "platformVersion": "3.5.0", "spec": {"filename": "bert_embeddings.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["torch==1.6.0"]}, "url": "", "version": "1.1.0"}}, "describe": {"latest": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "describe", "platformVersion": "3.5.0", "spec": {"filename": "describe.py", "handler": "analyze", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "Iguazio"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.5.4", "name": "describe", "platformVersion": "2.10.0", "spec": {"filename": "describe.py", "handler": "summarize", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2022-04-07:14-20", "icon": "", "labels": {"author": "Davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe", "platformVersion": "3.2.0", "spec": {"filename": "describe.py", "handler": "analyze", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe", "platformVersion": "3.2.0", "spec": {"filename": "describe.py", "handler": "summarize", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.9.2": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2022-04-26:10-20", "icon": "", "labels": {"author": "Davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe", "platformVersion": "3.2.0", "spec": {"filename": "describe.py", "handler": "analyze", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.2"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe", "platformVersion": "3.2.0", "spec": {"filename": "describe.py", "handler": "summarize", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "describe and visualizes dataset stats", "doc": "", "example": "describe.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "describe", "platformVersion": "3.5.0", "spec": {"filename": "describe.py", "handler": "analyze", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "model_server": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-server", "platformVersion": "3.5.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "model-server", "platformVersion": "", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server", "platformVersion": "3.2.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.9.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server", "platformVersion": "3.2.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-server", "platformVersion": "3.2.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "model_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-server", "platformVersion": "3.5.0", "spec": {"filename": "model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.1.0"}}, "pandas_profiling_report": {"latest": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "pandas-profiling-report", "platformVersion": "3.5.0", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "pandas-profiling-report", "platformVersion": "", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "pandas-profiling-report", "platformVersion": "3.2.0", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "pandas-profiling-report", "platformVersion": "3.2.0", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "Create Pandas Profiling Report from Dataset", "doc": "", "example": "pandas_profiling_report.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "nicks"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "pandas-profiling-report", "platformVersion": "3.5.0", "spec": {"filename": "pandas_profiling_report.py", "handler": "pandas_profiling_report", "image": "mlrun/mlrun", "kind": "job", "requirements": ["pandas_profiling"]}, "url": "", "version": "1.1.0"}}, "load_dask": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dask", "platformVersion": "3.5.0", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "load-dask", "platformVersion": "", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dask", "platformVersion": "3.2.0", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dask", "platformVersion": "3.2.0", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation", "etl"], "description": "load dask cluster with data", "doc": "", "example": "load_dask.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dask", "platformVersion": "3.5.0", "spec": {"filename": "load_dask.py", "handler": "load_dask", "image": "mlrun/ml-models", "kind": "dask", "requirements": []}, "url": "", "version": "1.1.0"}}, "slack_notify": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "slack-notify", "platformVersion": "3.5.0", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "slack-notify", "platformVersion": "", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "slack-notify", "platformVersion": "3.2.0", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "slack-notify", "platformVersion": "3.2.0", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Slack notification", "doc": "", "example": "slack_notify.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "mdl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "slack-notify", "platformVersion": "3.5.0", "spec": {"filename": "slack_notify.py", "handler": "slack_notify", "image": "python:3.6-jessie", "kind": "job", "requirements": ["requests"]}, "url": "", "version": "1.1.0"}}, "xgb_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "xgb_serving", "platformVersion": "3.5.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.6.2", "name": "xgb_serving", "platformVersion": "3.0.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "xgb_serving", "platformVersion": "3.2.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "0.9.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "xgb_serving", "platformVersion": "3.2.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "xgb_serving", "platformVersion": "3.2.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an XGBoost model server.", "doc": "", "example": "xgb_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "xgb_serving", "platformVersion": "3.5.0", "spec": {"filename": "xgb_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "remote", "requirements": []}, "url": "", "version": "1.1.0"}}, "model_monitoring_batch": {"latest": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-monitoring-batch", "platformVersion": "3.5.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "model-monitoring-batch", "platformVersion": "", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-batch", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-batch", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-batch", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_batch.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-monitoring-batch", "platformVersion": "3.5.0", "spec": {"filename": "model_monitoring_batch.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "stream_to_parquet": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "stream-to-parquet", "platformVersion": "3.5.0", "spec": {"customFields": {"max_replicas": 1, "min_replicas": 1}, "filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "stream-to-parquet", "platformVersion": "", "spec": {"filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": [], "customFields": {"min_replicas": 1, "max_replicas": 1}}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "stream-to-parquet", "platformVersion": "3.2.0", "spec": {"customFields": {"max_replicas": 1, "min_replicas": 1}, "filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "stream-to-parquet", "platformVersion": "3.2.0", "spec": {"customFields": {"max_replicas": 1, "min_replicas": 1}, "filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "Saves a stream to Parquet and can lunch drift detection task on it", "doc": "", "example": "stream_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "stream-to-parquet", "platformVersion": "3.5.0", "spec": {"customFields": {"max_replicas": 1, "min_replicas": 1}, "filename": "stream_to_parquet.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": []}, "url": "", "version": "1.1.0"}}, "describe_spark": {"latest": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "describe-spark", "platformVersion": "3.5.0", "spec": {"filename": "describe_spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "describe-spark", "platformVersion": "", "spec": {"filename": "describe-spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe-spark", "platformVersion": "3.2.0", "spec": {"filename": "describe_spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe-spark", "platformVersion": "3.2.0", "spec": {"filename": "describe-spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "describe-spark", "platformVersion": "3.2.0", "spec": {"filename": "describe-spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "", "doc": "", "example": "describe_spark.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "describe-spark", "platformVersion": "3.5.0", "spec": {"filename": "describe_spark.py", "handler": "describe_spark", "image": "iguazio/shell:3.0_b5565_20201026062233_wsdf", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "gen_class_data": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "gen_class_data", "platformVersion": "3.5.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.6.2", "name": "gen_class_data", "platformVersion": "3.0.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "gen_class_data", "platformVersion": "3.2.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "gen_class_data", "platformVersion": "3.2.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "0.10.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "gen_class_data", "platformVersion": "3.2.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.10.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Create a binary classification sample dataset and save.", "doc": "", "example": "gen_class_data.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "gen_class_data", "platformVersion": "3.5.0", "spec": {"filename": "gen_class_data.py", "handler": "gen_class_data", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "open_archive": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "open-archive", "platformVersion": "3.5.0", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "open-archive", "platformVersion": "", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "open-archive", "platformVersion": "3.2.0", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "open-archive", "platformVersion": "3.2.0", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Open a file/object archive into a target directory", "doc": "", "example": "open_archive.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "open-archive", "platformVersion": "3.5.0", "spec": {"filename": "open_archive.py", "handler": "open_archive", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "send_email": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "send-email", "platformVersion": "3.5.0", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "send-email", "platformVersion": "", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "send-email", "platformVersion": "3.2.0", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "send-email", "platformVersion": "3.2.0", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Send Email messages through SMTP server", "doc": "", "example": "send_email.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "saarc"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "send-email", "platformVersion": "3.5.0", "spec": {"filename": "send_email.py", "handler": "send_email", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "concept_drift": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "concept-drift", "platformVersion": "", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "1.1.0"}}, "sentiment_analysis_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "sentiment-analysis-serving", "platformVersion": "3.5.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.6.3", "name": "sentiment-analysis-serving", "platformVersion": "3.0.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sentiment-analysis-serving", "platformVersion": "3.2.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "0.9.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.0", "name": "sentiment-analysis-serving", "platformVersion": "3.4.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "sentiment-analysis-serving", "platformVersion": "3.2.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "BERT based sentiment classification model", "doc": "", "example": "sentiment_analysis_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "avia", "framework": "pytorch"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "sentiment-analysis-serving", "platformVersion": "3.5.0", "spec": {"filename": "sentiment_analysis_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==3.0.2"]}, "url": "", "version": "1.1.0"}}, "tf1_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf1-serving", "platformVersion": "3.5.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "tf1-serving", "platformVersion": "", "spec": {"filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": [], "env": {"MODEL_CLASS": "TFModel", "ENABLE_EXPLAINER": false}}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf1-serving", "platformVersion": "3.2.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf1-serving", "platformVersion": "3.2.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf1-serving", "platformVersion": "3.2.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf1 image classification server", "doc": "", "example": "tf1_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf1-serving", "platformVersion": "3.5.0", "spec": {"env": {"ENABLE_EXPLAINER": false, "MODEL_CLASS": "TFModel"}, "filename": "tf1_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": []}, "url": "", "version": "1.1.0"}}, "churn_server": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "churn-server", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "churn-server", "platformVersion": "", "spec": {"filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": [], "env": {"ENABLE_EXPLAINER": "False"}, "customFields": {"default_class": "ChurnModel"}}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "churn-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "0.9.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "churn-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "churn-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "churn classification and predictor", "doc": "", "example": "churn_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Iguazio", "framework": "churn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "churn-server", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "ChurnModel"}, "env": {"ENABLE_EXPLAINER": "False"}, "filename": "churn_server.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["xgboost==1.3.1", "lifelines==0.22.8"]}, "url": "", "version": "1.1.0"}}, "model_monitoring_stream": {"latest": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-monitoring-stream", "platformVersion": "3.5.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "model-monitoring-stream", "platformVersion": "", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-stream", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-stream", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "model-monitoring-stream", "platformVersion": "3.2.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["monitoring"], "description": "", "doc": "", "example": "model_monitoring_stream.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "model-monitoring-stream", "platformVersion": "3.5.0", "spec": {"filename": "model_monitoring_stream.py", "handler": "handler", "image": "livsmichael/mlrun-api:automation", "kind": "nuclio", "requirements": []}, "url": "", "version": "1.1.0"}}, "virtual_drift": {"latest": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "virtual-drift", "platformVersion": "3.5.0", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "virtual-drift", "platformVersion": "", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "virtual-drift", "platformVersion": "3.2.0", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "virtual-drift", "platformVersion": "3.2.0", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis", "machine-learning"], "description": "Compute drift magnitude between Time-Samples T and U", "doc": "", "example": "virtual_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "virtual-drift", "platformVersion": "3.5.0", "spec": {"filename": "virtual_drift.py", "handler": "drift_magnitude", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "scipy", "v3io_frames"]}, "url": "", "version": "1.1.0"}}, "rnn_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "rnn-serving", "platformVersion": "3.5.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "rnn-serving", "platformVersion": "", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["keras"]}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "rnn-serving", "platformVersion": "3.2.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "0.9.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "rnn-serving", "platformVersion": "3.2.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "rnn-serving", "platformVersion": "3.2.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "deploy an rnn based stock analysis model server.", "doc": "", "example": "rnn_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Daniel"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "rnn-serving", "platformVersion": "3.5.0", "spec": {"filename": "rnn_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": null}, "url": "", "version": "1.1.0"}}, "feature_perms": {"latest": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-perms", "platformVersion": "3.5.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "feature-perms", "platformVersion": "", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "feature-perms", "platformVersion": "3.2.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "feature-perms", "platformVersion": "3.2.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "feature-perms", "platformVersion": "3.2.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-analysis"], "description": "estimate feature importances using permutations", "doc": "", "example": "feature_perms.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "feature-perms", "platformVersion": "3.5.0", "spec": {"filename": "feature_perms.py", "handler": "permutation_importance", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "v2_model_tester": {"latest": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "v2-model-tester", "platformVersion": "3.5.0", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "v2-model-tester", "platformVersion": "", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-tester", "platformVersion": "3.2.0", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-tester", "platformVersion": "3.2.0", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-testing", "machine-learning"], "description": "test v2 model servers", "doc": "", "example": "v2_model_tester.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "v2-model-tester", "platformVersion": "3.5.0", "spec": {"filename": "v2_model_tester.py", "handler": "model_server_tester", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "coxph_test": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "coxph-test", "platformVersion": "3.5.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "coxph-test", "platformVersion": "", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "coxph-test", "platformVersion": "3.2.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "coxph-test", "platformVersion": "3.2.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "coxph-test", "platformVersion": "3.2.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-testing"], "description": "Test cox proportional hazards model", "doc": "", "example": "coxph_test.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "Iguazio", "framework": "survival"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "coxph-test", "platformVersion": "3.5.0", "spec": {"filename": "coxph_test.py", "handler": "cox_test", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "arc_to_parquet": {"latest": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "arc-to-parquet", "platformVersion": "3.5.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2021-05-19:22-04", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.5.4", "name": "arc-to-parquet", "platformVersion": "2.10.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "arc-to-parquet", "platformVersion": "3.2.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "1.2.0": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "arc-to-parquet", "platformVersion": "3.5.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "arc-to-parquet", "platformVersion": "3.2.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["etl"], "description": "retrieve remote archive, open and save as parquet", "doc": "", "example": "arc_to_parquet.ipynb", "generationDate": "2022-08-28:17-25", "icon": "", "labels": {"author": "yjb"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "arc-to-parquet", "platformVersion": "3.5.0", "spec": {"filename": "arc_to_parquet.py", "handler": "arc_to_parquet", "image": "mlrun/ml-base", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "github_utils": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "github-utils", "platformVersion": "3.5.0", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "github-utils", "platformVersion": "", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "github-utils", "platformVersion": "3.2.0", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "github-utils", "platformVersion": "3.2.0", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "add comments to github pull request", "doc": "", "example": "github_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "github-utils", "platformVersion": "3.5.0", "spec": {"filename": "github_utils.py", "handler": "run_summary_comment", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "v2_model_server": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "v2-model-server", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "v2-model-server", "platformVersion": "", "spec": {"filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": [], "customFields": {"default_class": "ClassifierModel"}}, "url": "", "version": "0.0.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "0.9.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "1.0.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "v2-model-server", "platformVersion": "3.2.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "generic sklearn model server", "doc": "", "example": "v2_model_server.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "v2-model-server", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "ClassifierModel"}, "filename": "v2_model_server.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "serving", "requirements": []}, "url": "", "version": "1.1.0"}}, "concept_drift_streaming": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift-streaming", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "1.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2021-05-19:22-41", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "concept-drift-streaming", "platformVersion": "", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "0.0.1"}, "0.9.1": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift-streaming", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "0.9.1"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift-streaming", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift-streaming", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "monitoring"], "description": "Deploy a streaming Concept Drift detector on a labeled stream. the nuclio part of the concept_drift function", "doc": "", "example": "concept_drift_streaming.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift-streaming", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift_streaming.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "nuclio", "requirements": ["scikit-multiflow==0.4.1", "v3io_frames"]}, "url": "", "version": "1.1.0"}}, "onnx_utils": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "onnx_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "with_mlrun": false}}, "filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.13.0", "onnxruntime~=1.14.0", "onnxoptimizer~=0.3.0", "onnxmltools~=1.11.0", "tf2onnx~=1.13.0"]}, "url": "", "version": "1.1.1"}, "1.1.1": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "onnx_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "with_mlrun": false}}, "filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.13.0", "onnxruntime~=1.14.0", "onnxoptimizer~=0.3.0", "onnxmltools~=1.11.0", "tf2onnx~=1.13.0"]}, "url": "", "version": "1.1.1"}, "0.10.2": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "onnx_utils", "platformVersion": "3.2.0", "spec": {"filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.10.2"}, "0.9.0": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "onnx_utils", "platformVersion": "3.2.0", "spec": {"filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "0.8.0": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "onnx_utils", "platformVersion": "3.2.0", "spec": {"filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun.", "doc": "", "example": "onnx_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "onnx_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "with_mlrun": false}}, "filename": "onnx_utils.py", "handler": "to_onnx", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "onnxoptimizer~=0.2.0", "onnxmltools~=1.9.0", "tf2onnx~=1.9.0"]}, "url": "", "version": "1.1.0"}}, "ingest": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "ingest", "platformVersion": "3.5.0", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "ingest", "platformVersion": "", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "ingest", "platformVersion": "3.5.0", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "get_offline_features": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "get_offline_features", "platformVersion": "3.5.0", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "1.0.2": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-05-25:10-58", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.1", "name": "get_offline_features", "platformVersion": "", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.2"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-01-17:17-56", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.9.1", "name": "get_offline_features", "platformVersion": "", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "1.2.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "get_offline_features", "platformVersion": "3.5.0", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.2.0"}, "1.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-05-25:10-58", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.1", "name": "get_offline_features", "platformVersion": "", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.1"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "retrieve offline feature vector results", "doc": "", "example": "get_offline_features.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "get_offline_features", "platformVersion": "3.5.0", "spec": {"filename": "get_offline_features.py", "handler": "get_offline_features", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "azureml_utils": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "azureml_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "commands": ["python -m pip install pip==22.1.2", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true}}, "filename": "azureml_utils.py", "handler": "train", "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.40.0", "azureml-train-automl-client==1.40.0", "plotly~=5.4"]}, "url": "", "version": "1.2.0"}, "0.9.4": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "azureml_utils", "platformVersion": "", "spec": {"filename": "azureml_utils.py", "handler": "train", "extra_spec": {"build": {"commands": ["python -m pip install pip==21.2.4", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true, "auto_build": true}, "allow_empty_resources": true}, "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.33.0", "azureml-train-automl-client==1.33.0", "plotly~=5.4"]}, "url": "", "version": "0.9.4"}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "azureml_utils", "platformVersion": "", "spec": {"filename": "azureml_utils.py", "handler": "train", "commands": null, "image": "", "kind": "job", "requirements": ["azureml-core==1.33.0", "azureml-train-automl-client==1.33.0"]}, "url": "", "version": "0.9.0"}, "1.2.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "azureml_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "commands": ["python -m pip install pip==22.1.2", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true}}, "filename": "azureml_utils.py", "handler": "train", "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.40.0", "azureml-train-automl-client==1.40.0", "plotly~=5.4"]}, "url": "", "version": "1.2.0"}, "0.9.5": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2021-04-20:15-18", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "azureml_utils", "platformVersion": "", "spec": {"filename": "azureml_utils.py", "handler": "train", "extra_spec": {"build": {"commands": ["python -m pip install pip==21.2.4", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true, "auto_build": true}, "allow_empty_resources": true}, "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.40.0", "azureml-train-automl-client==1.40.0", "plotly~=5.4"]}, "url": "", "version": "0.9.5"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom.", "doc": "", "example": "azureml_utils.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "azureml_utils", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "commands": ["python -m pip install pip==22.1.2", "apt-get update && apt-get install -y --no-install-recommends git"], "with_mlrun": true}}, "filename": "azureml_utils.py", "handler": "train", "image": "python:3.7.9-slim", "kind": "job", "requirements": ["azureml-core==1.40.0", "azureml-train-automl-client==1.40.0", "plotly~=5.4"]}, "url": "", "version": "1.1.0"}}, "auto_trainer": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.3.0", "name": "auto_trainer", "platformVersion": "3.5.0", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.3.0"}, "0.10.3": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-04-26:10-43", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "", "kind": "job", "requirements": []}, "url": "", "version": "0.10.3"}, "1.0.7": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-04-26:10-43", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.7"}, "0.10.2": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-02-06:10-18", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.10.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "", "kind": "job", "requirements": []}, "url": "", "version": "0.10.2"}, "1.0.6": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-04-26:10-43", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.6"}, "1.3.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.3.0", "name": "auto_trainer", "platformVersion": "3.5.0", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.3.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "auto_trainer", "platformVersion": "3.5.0", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "1.0.5": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM.", "doc": "", "example": "auto_trainer.ipynb", "generationDate": "2022-04-26:10-43", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.0.0", "name": "auto_trainer", "platformVersion": "", "spec": {"filename": "auto_trainer.py", "handler": "train", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.5"}}, "snowflake_dask": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Snowflake Dask - Ingest snowflake data in parallel with Dask cluster", "doc": "", "example": "snowflake-dask-mlrun.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "xingsheng", "framework": "dask"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "snowflake_dask", "platformVersion": "3.5.0", "spec": {"filename": "snowflake_dask.py", "handler": "load_results", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Snowflake Dask - Ingest snowflake data in parallel with Dask cluster", "doc": "", "example": "snowflake-dask-mlrun.ipynb", "generationDate": "2022-03-20:12-28", "icon": "", "labels": {"author": "xingsheng", "framework": "dask"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.9.1", "name": "snowflake_dask", "platformVersion": "3.2.0", "spec": {"filename": "snowflake_dask.py", "handler": "load_results", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "Snowflake Dask - Ingest snowflake data in parallel with Dask cluster", "doc": "", "example": "snowflake-dask-mlrun.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "xingsheng", "framework": "dask"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "snowflake_dask", "platformVersion": "3.5.0", "spec": {"filename": "snowflake_dask.py", "handler": "load_results", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0"}}, "azureml_serving": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "AzureML serving function", "doc": "", "example": "azureml_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "azureml_serving", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "mlrun.frameworks.sklearn.PickleModelServer"}, "filename": "azureml_serving.py", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["azureml-automl-runtime~=1.38.1"]}, "url": "", "version": "1.1.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "AzureML serving function", "doc": "", "example": "azureml_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "azureml_serving", "platformVersion": "3.5.0", "spec": {"customFields": {"default_class": "mlrun.frameworks.sklearn.PickleModelServer"}, "filename": "azureml_serving.py", "image": "mlrun/mlrun", "kind": "serving", "requirements": ["azureml-automl-runtime~=1.38.1"]}, "url": "", "version": "1.1.0"}}, "batch_inference": {"latest": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/mlrun", "kind": "job", "requirements": null}, "url": "", "version": "1.4.0"}, "1.1.1": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "plotly"]}, "url": "", "version": "1.1.1"}, "1.4.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/mlrun", "kind": "job", "requirements": null}, "url": "", "version": "1.4.0"}, "1.2.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/ml-models", "kind": "job", "requirements": null}, "url": "", "version": "1.2.0"}, "1.3.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/ml-models", "kind": "job", "requirements": null}, "url": "", "version": "1.3.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["utils"], "description": "Batch inference ( also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference.ipynb", "generationDate": "2022-08-28:17-25", "icon": "", "labels": {"author": "guyl"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "batch_inference", "platformVersion": "3.5.0", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": true, "with_mlrun": false}}, "filename": "batch_inference.py", "handler": "infer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-learn", "plotly"]}, "url": "", "version": "1.1.0"}}, "hugging_face_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "Generic Hugging Face model server.", "doc": "", "example": "hugging_face_serving.ipynb", "generationDate": "2022-09-05:17-00", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "hugging_face_serving", "platformVersion": "", "spec": {"customFields": {"default_class": "HuggingFaceModelServer"}, "filename": "hugging_face_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==4.21.3", "tensorflow==2.9.2"]}, "url": "", "version": "1.0.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "Generic Hugging Face model server.", "doc": "", "example": "hugging_face_serving.ipynb", "generationDate": "2022-09-05:17-00", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "hugging_face_serving", "platformVersion": "", "spec": {"customFields": {"default_class": "HuggingFaceModelServer"}, "filename": "hugging_face_serving.py", "handler": "handler", "image": "mlrun/ml-models", "kind": "serving", "requirements": ["transformers==4.21.3", "tensorflow==2.9.2"]}, "url": "", "version": "1.0.0"}}, "hugging_face_classifier_trainer": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train and optimize functions for HuggingFace framework", "doc": "", "example": "hugging_face_classifier_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "hugging_face_classifier_trainer", "platformVersion": "3.5.0", "spec": {"filename": "hugging_face_classifier_trainer.py", "handler": "train", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "optimum~=1.6.4", "transformers~=4.26.1", "datasets~=2.10.1", "scikit-learn~=1.0.2"]}, "url": "", "version": "0.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train and optimize functions for HuggingFace framework", "doc": "", "example": "hugging_face_classifier_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "hugging_face_classifier_trainer", "platformVersion": "3.5.0", "spec": {"filename": "hugging_face_classifier_trainer.py", "handler": "train", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "optimum~=1.6.4", "transformers~=4.26.1", "datasets~=2.10.1", "scikit-learn~=1.0.2"]}, "url": "", "version": "0.0.1"}, "0.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train and optimize functions for HuggingFace framework", "doc": "", "example": "hugging_face_classifier_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "hugging_face_classifier_trainer", "platformVersion": "3.5.0", "spec": {"filename": "hugging_face_classifier_trainer.py", "handler": "train", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "optimum~=1.6.4", "transformers~=4.26.1", "datasets~=2.10.1", "scikit-learn~=1.0.2"]}, "url": "", "version": "0.1.0"}}, "validate_great_expectations": {"latest": {"apiVersion": "v1", "categories": ["data-validation", "data-analysis"], "description": "Validate a dataset using Great Expectations", "doc": "", "example": "validate_great_expectations.ipynb", "generationDate": "2022-04-26:12-28", "hidden": false, "icon": "", "labels": {"author": "nicks", "framework": "great-expectations"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "validate-great-expectations", "platformVersion": "3.5.2", "spec": {"filename": "validate_great_expectations.py", "handler": "validate_expectations", "image": "mlrun/mlrun", "kind": "job", "requirements": ["great-expectations==0.15.41"]}, "url": "", "version": "1.1.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-validation", "data-analysis"], "description": "Validate a dataset using Great Expectations", "doc": "", "example": "validate_great_expectations.ipynb", "generationDate": "2022-04-26:12-28", "hidden": false, "icon": "", "labels": {"author": "nicks", "framework": "great-expectations"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "validate-great-expectations", "platformVersion": "3.5.2", "spec": {"filename": "validate_great_expectations.py", "handler": "validate_expectations", "image": "mlrun/mlrun", "kind": "job", "requirements": ["great-expectations==0.15.41"]}, "url": "", "version": "1.1.0"}}, "question_answering": {"latest": {"apiVersion": "v1", "categories": ["machine-learning"], "description": "GenAI approach of question answering on a given data", "doc": "", "example": "question_answering.ipynb", "generationDate": "2023-08-07:11-30", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "question_answering", "platformVersion": "3.5.0", "spec": {"filename": "question_answering.py", "handler": "answer_questions", "image": "mlrun/mlrun", "kind": "job", "requirements": "transformers torch tqdm"}, "url": "", "version": "0.2.0"}, "0.2.0": {"apiVersion": "v1", "categories": ["machine-learning"], "description": "GenAI approach of question answering on a given data", "doc": "", "example": "question_answering.ipynb", "generationDate": "2023-08-07:11-30", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "question_answering", "platformVersion": "3.5.0", "spec": {"filename": "question_answering.py", "handler": "answer_questions", "image": "mlrun/mlrun", "kind": "job", "requirements": "transformers torch tqdm"}, "url": "", "version": "0.2.0"}, "0.1.0": {"apiVersion": "v1", "categories": ["machine-learning"], "description": "GenAI approach of question answering on a given data", "doc": "", "example": "question_answering.ipynb", "generationDate": "2023-08-07:11-30", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "question_answering", "platformVersion": "3.5.0", "spec": {"filename": "question_answering.py", "handler": "answer_questions", "image": "mlrun/mlrun", "kind": "job", "requirements": "transformers torch tqdm"}, "url": "", "version": "0.1.0"}}, "transcribe": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Transcribe audio files into text files", "doc": "", "example": "transcribe.ipynb", "generationDate": "2023-07-13:11-20", "hidden": false, "icon": "", "labels": {"author": "yonatans"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "transcribe", "platformVersion": "3.5.3", "spec": {"filename": "transcribe.py", "handler": "transcribe", "image": "mlrun/mlrun", "kind": "job", "requirements": ["openai-whisper", "tqdm"]}, "url": "", "version": "0.0.1"}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Transcribe audio files into text files", "doc": "", "example": "transcribe.ipynb", "generationDate": "2023-07-13:11-20", "hidden": false, "icon": "", "labels": {"author": "yonatans"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "transcribe", "platformVersion": "3.5.3", "spec": {"filename": "transcribe.py", "handler": "transcribe", "image": "mlrun/mlrun", "kind": "job", "requirements": ["openai-whisper", "tqdm"]}, "url": "", "version": "0.0.1"}}, "pii_recognizer": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "This function is used to recognize PII in a directory of text files", "doc": "", "example": "pii_recognizer.ipynb", "generationDate": "2023-08-15:10-24", "hidden": false, "icon": "", "labels": {"author": "pgw"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "pii-recognizer", "platformVersion": "3.5.3", "spec": {"filename": "pii_recognizer.py", "handler": "recognize_pii", "image": "mlrun/mlrun", "kind": "job", "requirements": ["nltk", "pandas", "presidio-anonymizer", "presidio-analyzer", "torch", "flair@git+https://github.com/flairNLP/flair.git@d4ed67bf663e4066517f00397412510d90043653", "st-annotated-text", "https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl"]}, "url": "", "version": "0.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "This function is used to recognize PII in a directory of text files", "doc": "", "example": "pii_recognizer.ipynb", "generationDate": "2023-08-15:10-24", "hidden": false, "icon": "", "labels": {"author": "pgw"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "pii-recognizer", "platformVersion": "3.5.3", "spec": {"filename": "pii_recognizer.py", "handler": "recognize_pii", "image": "mlrun/mlrun", "kind": "job", "requirements": ["nltk", "pandas", "presidio-anonymizer", "presidio-analyzer", "torch", "flair@git+https://github.com/flairNLP/flair.git@d4ed67bf663e4066517f00397412510d90043653", "st-annotated-text", "https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl"]}, "url": "", "version": "0.0.1"}, "0.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "This function is used to recognize PII in a directory of text files", "doc": "", "example": "pii_recognizer.ipynb", "generationDate": "2023-08-15:10-24", "hidden": false, "icon": "", "labels": {"author": "pgw"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "pii-recognizer", "platformVersion": "3.5.3", "spec": {"filename": "pii_recognizer.py", "handler": "recognize_pii", "image": "mlrun/mlrun", "kind": "job", "requirements": ["nltk", "pandas", "presidio-anonymizer", "presidio-analyzer", "torch", "flair@git+https://github.com/flairNLP/flair.git@d4ed67bf663e4066517f00397412510d90043653", "st-annotated-text", "https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl"]}, "url": "", "version": "0.1.0"}}, "huggingface_auto_trainer": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "fine-tune llm model with ease", "doc": "", "example": "huggingface_auto_trainer.ipynb", "generationDate": "2023-08-21:17-25", "hidden": false, "icon": "", "labels": {"author": "Zeevr"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "huggingface-auto-trainer", "platformVersion": "3.5.0", "spec": {"filename": "huggingface_auto_trainer.py", "handler": "finetune_llm", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "fine-tune llm model with ease", "doc": "", "example": "huggingface_auto_trainer.ipynb", "generationDate": "2023-08-21:17-25", "hidden": false, "icon": "", "labels": {"author": "Zeevr"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "huggingface-auto-trainer", "platformVersion": "3.5.0", "spec": {"filename": "huggingface_auto_trainer.py", "handler": "finetune_llm", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}}}}}