Q
how to deploy python as yarn job
I'm a seasoned industrial engineer with a keen interest in machine learning. Here to share insights on latest industry trends.
Deploying a Python job on YARN (Yet Another Resource Negotiator) requires wrapping your Python script as a Hadoop Streaming job or utilizing a framework like PySpark. With Hadoop Streaming, the Python script is treated as a mapper and/or reducer. Firstly, ensure your Hadoop cluster and YARN are well configured. Then, use the `hadoop` command with the `streaming` option, specifying your Python scripts for `mapper` and `reducer` parameters. For example:
```
hadoop jar /path/to/hadoop-streaming.jar \
-files yourMapper.py,yourReducer.py \
-mapper yourMapper.py \
-reducer yourReducer.py \
-input yourInputPath \
-output yourOutputPath
```
For PySpark jobs, submit them using `spark-submit` with the `--master yarn` flag to ensure they run on YARN. Ensure your Python environment is consistent across all nodes for smooth execution. Deploying Python jobs on YARN allows leveraging YARN's resource management and scheduling capabilities, ensuring efficient resource use across the cluster.
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