remove old configs leftover from removal of py3.5/py2 (#1551)

* rm old configs leftover from removal of py3.5

* black format

conda.recipe/build_and_upload.sh

@@ -26,5 +26,4 @@ conda config --set anaconda_upload no
 conda build --no-test --output-folder conda_build conda.recipe -c pytorch
 
 # Upload to Anaconda
-# We could use --all but too much platforms to uploaded
 ls conda_build/*/*.tar.bz2 | xargs -I {} anaconda -v -t $ANACONDA_TOKEN upload -u $UPLOAD_USER {}

conda.recipe/meta.yaml

@@ -15,12 +15,12 @@ build:
 # https://conda.io/docs/user-guide/tasks/build-packages/define-metadata.html#export-runtime-requirements
 requirements:
   build:
-    - python>=3.5
+    - python>=3.6
     - setuptools
     - pytorch>=1.3
 
   run:
-    - python>=3.5
+    - python>=3.6
     - pytorch>=1.3
 
 test:

examples/contrib/cifar10/main.py

@@ -151,7 +151,7 @@ def run(
     nproc_per_node=None,
     stop_iteration=None,
     with_trains=False,
-    **spawn_kwargs
+    **spawn_kwargs,
 ):
     """Main entry to train an model on CIFAR10 dataset.
 

ignite/contrib/engines/common.py

@@ -46,7 +46,7 @@ def setup_common_training_handlers(
     stop_on_nan: bool = True,
     clear_cuda_cache: bool = True,
     save_handler: Optional[Union[Callable, BaseSaveHandler]] = None,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> None:
     """Helper method to setup trainer with common handlers (it also supports distributed configuration):
 
@@ -149,7 +149,7 @@ def _setup_common_training_handlers(
     stop_on_nan: bool = True,
     clear_cuda_cache: bool = True,
     save_handler: Optional[Union[Callable, BaseSaveHandler]] = None,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> None:
     if output_path is not None and save_handler is not None:
         raise ValueError(
@@ -237,7 +237,7 @@ def _setup_common_distrib_training_handlers(
     stop_on_nan: bool = True,
     clear_cuda_cache: bool = True,
     save_handler: Optional[Union[Callable, BaseSaveHandler]] = None,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> None:
 
     _setup_common_training_handlers(
@@ -331,7 +331,7 @@ def setup_tb_logging(
     optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None,
     evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None,
     log_every_iters: int = 100,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> TensorboardLogger:
     """Method to setup TensorBoard logging on trainer and a list of evaluators. Logged metrics are:
 
@@ -363,7 +363,7 @@ def setup_visdom_logging(
     optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None,
     evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None,
     log_every_iters: int = 100,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> VisdomLogger:
     """Method to setup Visdom logging on trainer and a list of evaluators. Logged metrics are:
 
@@ -394,7 +394,7 @@ def setup_mlflow_logging(
     optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None,
     evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None,
     log_every_iters: int = 100,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> MLflowLogger:
     """Method to setup MLflow logging on trainer and a list of evaluators. Logged metrics are:
 
@@ -425,7 +425,7 @@ def setup_neptune_logging(
     optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None,
     evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None,
     log_every_iters: int = 100,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> NeptuneLogger:
     """Method to setup Neptune logging on trainer and a list of evaluators. Logged metrics are:
 
@@ -456,7 +456,7 @@ def setup_wandb_logging(
     optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None,
     evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None,
     log_every_iters: int = 100,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> WandBLogger:
     """Method to setup WandB logging on trainer and a list of evaluators. Logged metrics are:
 
@@ -487,7 +487,7 @@ def setup_plx_logging(
     optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None,
     evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None,
     log_every_iters: int = 100,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> PolyaxonLogger:
     """Method to setup Polyaxon logging on trainer and a list of evaluators. Logged metrics are:
 
@@ -518,7 +518,7 @@ def setup_trains_logging(
     optimizers: Optional[Union[Optimizer, Dict[str, Optimizer]]] = None,
     evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None,
     log_every_iters: int = 100,
-    **kwargs: Any
+    **kwargs: Any,
 ) -> TrainsLogger:
     """Method to setup Trains logging on trainer and a list of evaluators. Logged metrics are:
 
@@ -560,7 +560,7 @@ def gen_save_best_models_by_val_score(
     n_saved: int = 3,
     trainer: Optional[Engine] = None,
     tag: str = "val",
-    **kwargs: Any
+    **kwargs: Any,
 ) -> Checkpoint:
     """Method adds a handler to ``evaluator`` to save ``n_saved`` of best models based on the metric
     (named by ``metric_name``) provided by ``evaluator`` (i.e. ``evaluator.state.metrics[metric_name]``).
@@ -619,7 +619,7 @@ def save_best_model_by_val_score(
     n_saved: int = 3,
     trainer: Optional[Engine] = None,
     tag: str = "val",
-    **kwargs: Any
+    **kwargs: Any,
 ) -> Checkpoint:
     """Method adds a handler to ``evaluator`` to save on a disk ``n_saved`` of best models based on the metric
     (named by ``metric_name``) provided by ``evaluator`` (i.e. ``evaluator.state.metrics[metric_name]``).

ignite/contrib/handlers/param_scheduler.py

@@ -579,7 +579,7 @@ def simulate_values(  # type: ignore[override]
         schedulers: List[ParamScheduler],
         durations: List[int],
         param_names: Optional[Union[List[str], Tuple[str]]] = None,
-        **kwargs: Any
+        **kwargs: Any,
     ) -> List[List[int]]:
         """Method to simulate scheduled values during num_events events.
 

ignite/contrib/handlers/tqdm_logger.py

@@ -104,7 +104,7 @@ def __init__(
         self,
         persist: bool = False,
         bar_format: str = "{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar}{postfix} [{elapsed}<{remaining}]",
-        **tqdm_kwargs: Any
+        **tqdm_kwargs: Any,
     ) -> None:
 
         try:

ignite/contrib/handlers/trains_logger.py

@@ -638,7 +638,7 @@ def __init__(
         output_uri: Optional[str] = None,
         dirname: Optional[str] = None,
         *args: Any,
-        **kwargs: Any
+        **kwargs: Any,
     ) -> None:
 
         self._setup_check_trains(logger, output_uri)

ignite/contrib/handlers/visdom_logger.py

@@ -145,7 +145,7 @@ def __init__(
         port: Optional[int] = None,
         num_workers: int = 1,
         raise_exceptions: bool = True,
-        **kwargs: Any
+        **kwargs: Any,
     ):
         try:
             import visdom

ignite/distributed/comp_models/horovod.py

@@ -129,7 +129,7 @@ def spawn(  # type: ignore[override]
             nproc_per_node: int = 1,
             hosts: Optional[str] = None,
             backend: str = HOROVOD,
-            **kwargs: Any
+            **kwargs: Any,
         ) -> None:
             c1 = "nnodes" in kwargs and kwargs["nnodes"] > 1
             c2 = "node_rank" in kwargs and kwargs["node_rank"] > 0

ignite/distributed/comp_models/native.py

@@ -283,7 +283,7 @@ def spawn(  # type: ignore[override]
             master_addr: str = "127.0.0.1",
             master_port: int = 2222,
             backend: str = "nccl",
-            **kwargs: Any
+            **kwargs: Any,
         ) -> None:
             world_size = nnodes * nproc_per_node
 

ignite/distributed/comp_models/xla.py

@@ -113,7 +113,7 @@ def spawn(  # type: ignore[override]
             nnodes: int = 1,
             node_rank: int = 0,
             backend: str = XLA_TPU,
-            **kwargs: Any
+            **kwargs: Any,
         ) -> None:
             if "start_method" not in kwargs:
                 kwargs["start_method"] = "fork"

ignite/distributed/launcher.py

@@ -181,7 +181,7 @@ def __init__(
         node_rank: Optional[int] = None,
         master_addr: Optional[str] = None,
         master_port: Optional[int] = None,
-        **spawn_kwargs: Any
+        **spawn_kwargs: Any,
     ) -> None:
         if backend is not None:
             if backend not in idist.available_backends():
@@ -216,7 +216,7 @@ def _setup_spawn_params(
         node_rank: Optional[int] = None,
         master_addr: Optional[str] = None,
         master_port: Optional[int] = None,
-        **spawn_kwargs: Any
+        **spawn_kwargs: Any,
     ) -> Dict:
         if nproc_per_node < 1:
             raise ValueError(f"Argument nproc_per_node should positive, but given {nproc_per_node}")