GPU memory allocation¶
JAX will preallocate 90% of currently-available GPU memory when the first JAX operation is run. Preallocating minimizes allocation overhead and memory fragmentation, but can sometimes cause out-of-memory (OOM) errors. If your JAX process fails with OOM, the following environment variables can be used to override the default behavior:
This disables the preallocation behavior. JAX will instead allocate GPU memory as needed, potentially decreasing the overall memory usage. However, this behavior is more prone to GPU memory fragmentation, meaning a JAX program that uses most of the available GPU memory may OOM with preallocation disabled.
If preallocation is enabled, this makes JAX preallocate XX% of currently-available GPU memory, instead of the default 90%. Lowering the amount preallocated can fix OOMs that occur when the JAX program starts.
This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed (note that this is the only configuration that will deallocate GPU memory, instead of reusing it). This is very slow, so is not recommended for general use, but may be useful for running with the minimal possible GPU memory footprint or debugging OOM failures.
Common causes of OOM failures¶
- Running multiple JAX processes concurrently.
XLA_PYTHON_CLIENT_MEM_FRACTIONto give each process an appropriate amount of memory, or set
- Running JAX and GPU TensorFlow concurrently.
TensorFlow also preallocates by default, so this is similar to running multiple JAX processes concurrently.
One solution is to use CPU-only TensorFlow (e.g. if you’re only doing data loading with TF). You can prevent TensorFlow from using the GPU with the command
XLA_PYTHON_CLIENT_PREALLOCATE. There are also similar options to configure TensorFlow’s GPU memory allocation (gpu_memory_fraction and allow_growth in TF1, which should be set in a
tf.Session. See Using GPUs: Limiting GPU memory growth for TF2).
- Running JAX on the display GPU.