Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Revert "Adds CUDA_MODULE_LOADING=EAGER to core jax container env vars" #842

Merged
merged 3 commits into from
Jun 13, 2024
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion .github/container/Dockerfile.jax
Original file line number Diff line number Diff line change
Expand Up @@ -75,7 +75,6 @@ ENV XLA_FLAGS="${XLA_FLAGS} --xla_gpu_enable_latency_hiding_scheduler=true"
ENV XLA_FLAGS="${XLA_FLAGS} --xla_gpu_enable_triton_gemm=false"
ENV CUDA_DEVICE_MAX_CONNECTIONS=1
ENV NCCL_NVLS_ENABLE=0
ENV CUDA_MODULE_LOADING=EAGER

COPY --from=builder ${BUILD_PATH_JAXLIB} ${BUILD_PATH_JAXLIB}
COPY --from=builder ${SRC_PATH_JAX} ${SRC_PATH_JAX}
Expand Down
1 change: 0 additions & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -306,7 +306,6 @@ The [JAX image](https://github.com/NVIDIA/JAX-Toolbox/pkgs/container/jax) is emb
| -------------------- | ----- | ----------- |
| `CUDA_DEVICE_MAX_CONNECTIONS` | `1` | use a single queue for GPU work to lower latency of stream operations; OK since XLA already orders launches |
| `NCCL_NVLS_ENABLE` | `0` | Disables NVLink SHARP ([1](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html#nccl-nvls-enable)). Future releases will re-enable this feature. |
| `CUDA_MODULE_LOADING` | `EAGER` | Disables lazy-loading ([1](https://docs.nvidia.com/cuda/cuda-c-programming-guide/#cuda-environment-variables)) which uses slightly more GPU memory. |

There are various other XLA flags users can set to improve performance. For a detailed explanation of these flags, please refer to the [GPU performance](./rosetta/docs/GPU_performance.md) doc. XLA flags can be tuned per workflow. For example, each script in [contrib/gpu/scripts_gpu](https://github.com/google/paxml/tree/main/paxml/contrib/gpu/scripts_gpu) sets its own [XLA flags](https://github.com/google/paxml/blob/93fbc8010dca95af59ab615c366d912136b7429c/paxml/contrib/gpu/scripts_gpu/benchmark_gpt_multinode.sh#L30-L33).

Expand Down
Loading