Using CPU-only TensorFlow can indeed help mitigate out-of-memory (OOM) issues when running concurrently with JAX. Here's why and how:
1. Preventing GPU Usage: By default, TensorFlow tends to use GPU resources if available, which can lead to memory conflicts when running alongside JAX. To prevent TensorFlow from using the GPU, you can use the following command:
python
tf.config.experimental.set_visible_devices([], "GPU")
This ensures that TensorFlow operates solely on the CPU, reducing the likelihood of GPU memory conflicts with JAX[2][4].
2. Reducing Memory Competition: When both TensorFlow and JAX are using GPU resources, they compete for memory, which can lead to OOM errors. By confining TensorFlow to CPU operations, you reduce this competition, allowing JAX to utilize GPU memory more efficiently[2][4].
3. Memory Management: While TensorFlow on CPU doesn't directly manage GPU memory, it reduces the overall system load by not competing for GPU resources. This can indirectly help manage memory more efficiently across your system, potentially reducing OOM issues when running JAX[1][3].
However, it's important to note that using CPU-only TensorFlow might not always be the best solution if your tasks require significant computational power, as GPUs are generally faster for complex computations. In such cases, optimizing memory allocation or using other memory management strategies might be more beneficial[5][6].
Citations:[1] https://ms.codes/blogs/computer-hardware/tensorflow-limit-cpu-memory-usage
[2] https://kolonist26-jax-kr.readthedocs.io/en/latest/gpu_memory_allocation.html
[3] https://stackoverflow.com/questions/38615121/limit-tensorflow-cpu-and-memory-usage
[4] https://docs.jax.dev/en/latest/gpu_memory_allocation.html
[5] https://www.tensorflow.org/guide/gpu
[6] https://datascience.stackexchange.com/questions/47274/why-do-i-get-an-oom-error-although-my-model-is-not-that-large
[7] https://github.com/tensorflow/tensorflow/issues/48881
[8] https://stackoverflow.com/questions/42495930/tensorflow-oom-on-gpu