What are the alternatives to disabling preallocation in JAX

1. Adjusting Memory Fraction: Instead of disabling preallocation entirely, you can adjust the fraction of GPU memory that JAX preallocates. This is done by setting the `XLA_PYTHON_CLIENT_MEM_FRACTION` environment variable to a value less than the default 75%. For example, setting it to `0.5` would preallocate 50% of the total GPU memory. This approach can help mitigate out-of-memory errors while still benefiting from some preallocation[3][7].

2. Using a Different Allocator: JAX allows you to use a different memory allocator by setting `XLA_PYTHON_CLIENT_ALLOCATOR=platform`. This allocator allocates memory exactly as needed and deallocates it when no longer required, which can be useful for minimizing memory usage but is slower and not recommended for general use[3][7].

3. Buffer Donation: To optimize memory usage within JAX computations, you can use buffer donation. This involves specifying that certain input buffers can be reused for output, reducing the need for additional memory allocations. This is particularly useful when using functions like `jax.pmap` or `jax.jit` with the `donate_argnums` parameter[5].

4. Manual Memory Management: While not directly related to preallocation, manually managing memory by ensuring that large arrays are properly cleaned up after use can help prevent memory issues. This might involve using `del` to delete large objects or ensuring that computations are structured to minimize memory usage.

5. Running Multiple Processes with Care: If you need to run multiple JAX processes concurrently, consider using separate GPUs for each process to avoid memory conflicts. If this isn't feasible, carefully manage the memory preallocation settings to avoid race conditions[10].