Note: Section 'Updates' lists the updates since the original release of the Milestone.
Mu has prepared a report template for your convenience here: Report Template.
sbt >= 1.4.7
openjdk@8
Should be available by default on iccluster028.iccluster.epfl.ch. Otherwise, refer to each project installation instructions. Prefer working locally on your own machine, you will have less interference in your measurements from other students.
If you work on iccluster028.iccluster.epfl.ch, you need to modify the PATH by default by adding the following line in ~/.bashrc:
export PATH=$PATH:/opt/sbt/sbt/bin
If you have multiple installations of openjdk, you need to specify the one to use as JAVA_HOME, e.g. on OSX with openjdk@8 installed through Homebrew, you would do:
export JAVA_HOME="/usr/local/Cellar/openjdk@8/1.8.0+282";
Download data.zip.
Unzip:
> unzip data.zip
It should unzip into data/ by default. If not, manually move ml-100k and ml-25m into data/.
Additional personal ratings are provided in the 'data/personal.csv' file in a
csv format with <movie>, <movie title>, <rating> to test your recommender.
You can copy this file and change the ratings, with values [1,5] to obtain
references more to your liking!
Entries with no rating are in the following format:
1,Toy Story (1995),
Entries with ratings are in the following format:
1,Toy Story (1995),5
src/main/scala/shared/predictions.scala:
All the functionalities of your code for all questions should be defined there.
This code should then be used in the following applications and tests.
1. ````src/main/scala/predict/Baseline.scala````: Output answers to questions **B.X**.
2. ````src/main/scala/distributed/DistributedBaseline.scala````: Output answers to questions **D.X**.
3. ````src/main/scala/predict/Personalized.scala````: Output answers to questions questions **P.X**.
4. ````src/main/scala/predict/kNN.scala````: Output answers to questions questions **N.X**.
5. ````src/main/scala/recommend/Recommender.scala````: Output answers to questions questions **R.X**.
Applications are separate from tests to make it easier to test with different inputs and permit outputting your answers and timings in JSON format for easier grading.
Corresponding unit tests for each application:
src/test/scala/predict/BaselineTests.scala
src/test/scala/distributed/DistributedBaselineTests.scala
src/test/scala/predict/PersonalizedTests.scala
src/test/scala/predict/kNNTests.scala
src/test/scala/recommend/RecommenderTests.scala
Your tests should demonstrate how to call your code to obtain the answers of the applications, and should make exactly the same calls as for the applications above. This structure intentionally encourages you to put as little as possible functionality in the application. This also gives the TA a clear and regular structure to check its correctness.
sbt "testOnly test.AllTests"
You should fill all tests and ensure they all succeed prior to submission.
On ml-100k:
sbt "runMain predict.Baseline --train data/ml-100k/u2.base --test data/ml-100k/u2.test --json baseline-100k.json"
On ml-25m:
sbt "runMain predict.Baseline --train data/ml-25m/r2.train --test data/ml-25m/r2.test --separator , --json baseline-25m.json"
sbt "runMain distributed.DistributedBaseline --train data/ml-25m/r2.train --test data/ml-25m/r2.test --separator , --json distributed-25m-4.json --master local[4]"
You can vary the number of executors used locally by using local[X] with X being an integer representing the number of cores you want to use locally.
sbt "runMain predict.Personalized --train data/ml-100k/u2.base --test data/ml-100k/u2.test --json personalized-100k.json"
sbt "runMain predict.kNN --train data/ml-100k/u2.base --test data/ml-100k/u2.test --json knn-100k.json"
sbt "runMain recommend.Recommender --data data/ml-100k/u.data --personal data/personal.csv --json recommender-100k.json"
For all the previous applications, you can set the number of measurements for timings by adding the following option --num_measurements X where X is an integer. The default value is 0.
Test your application locally as much as possible and only test on the iccluster once everything works, to keep the cluster and the driver node maximally available for other students.
sbt clean: clean up temporary files and previous assembly packages.
sbt assembly: create a new jar
target/scala-2.11/m1_yourid-assembly-1.0.jar that can be used with
spark-submit.
Prefer packaging your application locally and upload the tar archive of your application before running on cluster.
scp target/scala-2.11/m1_yourid-assembly-1.0.jar <username>@iccluster028.iccluster.epfl.ch:~
spark-submit --class distributed.DistributedBaseline --master yarn --num-executors 1 m1_yourid-assembly-1.0.jar --train TRAIN --test TEST --separator , --json distributed-25m-1.json --num_measurements 1
See config.sh for HDFS paths to pre-uploaded train and test datasets to replace TRAIN and TEST with in the command. For instance, if you want to run on ML-25m, you should first run config.sh and then use the above command adapted as such:
spark-submit --class distributed.DistributedBaseline --master yarn --num-executors 1 m1_yourid-assembly-1.0.jar --train $ML25Mr2train --test $ML25Mr2test --separator , --json distributed-25m-1.json --num_measurements 1
You can vary the number of executors with --num-executors X, and number of measurements with --num_measurements Y.
We will use the following scripts to grade your submission:
1. ````./test.sh````: Run all unit tests.
2. ````./run.sh````: Run all applications without timing measurements.
3. ````./timeTrials.sh````: Time applications to determine which student implementations are fastest.
4. ````./timeOthers.sh````: Time applications to check report answers against independent measurements.
5. ````./timeCluster.sh````: Package and time applications on Spark Cluster.
All scripts will produce execution logs in the logs
directory, including answers produced in the JSON format. Logs directories are
in the format logs/<scriptname>-<datetime>-<machine>/ and include at
least an execution log log.txt as well as possible JSON outputs from
applications.
Ensure all scripts run correctly locally before submitting. Avoid running
timeCluster.sh on iccluster as the packaging and measurements will
interfere with other students working on their Milestone at the same time. If
timeCluster.sh correctly runs locally on your machine, this should be
sufficient.
Steps:
1. Create a zip archive with all your code within ````src/````, as well as your report: ````zip sciper1-sciper2.zip -r src/ report.pdf````
2. Submit ````sciper1-sciper2.zip```` the TA for grading on
https://cs449-submissions.epfl.ch:8083/m1 using the passcode you have previously received by email.
Essential sbt: https://www.scalawilliam.com/essential-sbt/
Explore Spark Interactively (supports autocompletion with tabs!): https://spark.apache.org/docs/latest/quick-start.html
Scallop Argument Parsing: https://github.com/scallop/scallop/wiki
Spark Resilient Distributed Dataset (RDD): https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/rdd/RDD.html
Erick Lavoie (Design, Implementation, Tests)
Athanasios Xygkis (Requirements, Tests)