Hi Dask Community,
We’ve been successfully using Dask for parallelizing computations in Python, especially when our Python code calls C++ libraries. This setup has worked flawlessly and has significantly improved our processing times.
Recently, we’ve encountered a new challenge. We have a set of algorithm libraries developed in Java, which we are integrating into our Python environment using JCC (a C++/Java bridge). When we run Python scripts that call these JCC-wrapped Java functions in a serial fashion (without Dask), everything works as expected. The Java components initialize correctly, load their necessary data files, and produce the correct results.
However, when we attempt to parallelize tasks that call these JCC-wrapped Java functions using Dask (e.g., dask.delayed or with a Client), we consistently run into issues, and the computations fail to execute successfully on the workers.
Our primary suspicion is that these failures are related to:
- JVM Initialization per Worker: How the JVM is (or isn’t) being initialized within each Dask worker process. Each worker might need its own properly configured JVM instance.
- Configuration/Data File Accessibility: The Java libraries often depend on specific configuration files or initial data files that need to be loaded at startup. It’s unclear how to ensure these are correctly found and loaded by the JVM running within each Dask worker.
We are wondering if the community has any experience or recommendations for such a scenario. Specifically:
- Are there known best practices or common pitfalls when using JCC-wrapped Java libraries with Dask?
- How can we ensure that each Dask worker correctly initializes its own JVM instance (if that’s the right approach) or properly manages JVM resources?
- What’s the recommended way to handle Java library dependencies (JARs) and essential initialization/configuration files that need to be accessible by each Dask worker’s JVM? (e.g.,
client.upload_file, shared file systems, environment variables, etc.) - Are there specific Dask configurations (e.g., for workers) or JCC settings we should be particularly mindful of for distributed execution?
- Has anyone successfully tackled a similar problem involving per-worker JVMs and their unique initialization requirements?
Any advice, pointers to examples, or insights would be greatly appreciated. We’re keen to leverage Dask’s power for these Java-based components as well.
Thanks in advance!
Best regards~