How can I configure JAX to avoid OOM errors

1. Disable Preallocation: By default, JAX preallocates 75% of the total GPU memory. You can disable this behavior by setting the environment variable `XLA_PYTHON_CLIENT_PREALLOCATE=false`. This will allocate memory as needed, reducing the likelihood of OOM errors but potentially increasing memory fragmentation[1][3].

2. Adjust Memory Fraction: If preallocation is enabled, you can adjust the fraction of GPU memory that JAX preallocates using `XLA_PYTHON_CLIENT_MEM_FRACTION=.XX`, where `.XX` is the desired percentage. Lowering this value can help prevent OOM errors at startup[1][3].

3. Use Platform Allocator: Setting `XLA_PYTHON_CLIENT_ALLOCATOR=platform` makes JAX allocate exactly what is needed on demand and deallocate unused memory. This is slow and not recommended for general use but can be useful for debugging OOM issues or minimizing memory usage[1][3].

4. Avoid Concurrent Processes: Running multiple JAX processes or using JAX alongside GPU TensorFlow can lead to OOM errors. Use `XLA_PYTHON_CLIENT_MEM_FRACTION` to allocate memory appropriately for each process, or set `XLA_PYTHON_CLIENT_PREALLOCATE=false`[1][3].

5. Manage Device Arrays: Ensure that `DeviceArray` objects are properly managed. Use `device_get` to fetch data to the host and ensure no references to `DeviceArray` objects remain to free device memory[2].

6. Optimize Model and Data: Consider batching or optimizing your model and data to reduce memory requirements. This can be particularly effective when working with large datasets or complex models[5].