What are the common causes of OOM errors in JAX

1. Preallocation of GPU Memory: JAX preallocates 75% of the total GPU memory by default when the first JAX operation is run. This can lead to OOM errors if the system does not have enough memory. To mitigate this, you can adjust the preallocation fraction using the `XLA_PYTHON_CLIENT_MEM_FRACTION` environment variable or disable preallocation by setting `XLA_PYTHON_CLIENT_PREALLOCATE=false`[1][3].

2. Running Multiple JAX Processes Concurrently: When multiple JAX processes are run at the same time, they may collectively consume more memory than available, leading to OOM errors. Adjusting the memory fraction for each process or disabling preallocation can help manage this issue[1][3].

3. Running JAX and GPU TensorFlow Concurrently: Both JAX and TensorFlow preallocate GPU memory by default, which can cause conflicts and lead to OOM errors. Using CPU-only TensorFlow for certain tasks or adjusting memory allocations can resolve this[1][3].

4. Incompatible CUDA and CUDNN Versions: Sometimes, OOM errors might not be directly related to memory but could be due to version mismatches between CUDA and CUDNN, which can cause internal errors that appear as memory issues[5].

5. Memory Leaks or Excessive Memory Usage: JAX programs can accumulate memory over time if arrays are not properly managed. Using the JAX device memory profiler can help identify such issues[7].