Rithwik Ediga Lakhamsani created SPARK-41775: ------------------------------------------------
Summary: Implement training functions as input Key: SPARK-41775 URL: https://issues.apache.org/jira/browse/SPARK-41775 Project: Spark Issue Type: Sub-task Components: PySpark Affects Versions: 3.4.0 Reporter: Rithwik Ediga Lakhamsani Currently, `Distributor().run(...)` takes only files as input. Now we will add in additional functionality to take in functions as well. This will require us to go through the following process on each task in the executor nodes: 1. take the input function and args and pickle them 2. Create a temp train.py file that looks like ```python import cloudpickle import os if __name__ == "__main__": train, args = cloudpickle.load(f"\{tempdir}/train_input.pkl") output = train(*args) if output and os.environ.get("RANK", "") == "0": # this is for partitionId == 0 cloudpickle.dump(f"\{tempdir}/train_output.pkl") ``` 3. Run that train.py file with `torchrun` 4. Check if `train_output.pkl` has been created on process on partitionId == 0, if it has, then deserialize it and return that output -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org