verl on ROCm installation

verl on ROCm installation#

2026-09-25

2 min read time

Applies to Linux

System requirements#

To use verl 0.9.0, you need the following prerequisites:

  • ROCm version: 10.0.0

  • Operating system: Ubuntu 24.04

  • GPU platform: AMD Instinct™ MI300X, MI325X, MI350X and MI355X

  • PyTorch: 2.12.0

  • Python: 3.12

  • vLLM: 0.27.0

Install verl#

To run verl with ROCm enabled, build from source using the provided Dockerfile.

Build verl from source#

  1. Clone the verl repository containing the Dockerfile:

    git clone --recursive --branch release/0.9.0.amd0 https://github.com/AMD-Ecosystem/verl.git
cd verl/docker/rocm

  2. Build the Docker image:

    docker build -t verl-release-v0.9.0amd0 -f Dockerfile.rocm .

    This builds an image with verl 0.9.0 and the required dependencies, including PyTorch 2.12.0, vLLM 0.27.0, SGLang 0.5.19, and Ray 2.58.0.

  3. Run the Docker container:

    mkdir -p $HOME/verl-workspace
docker run -it \
   --name verl-release \
   --device /dev/kfd \
   --device /dev/dri \
   --privileged \
   --network=host \
   --group-add video \
   --cap-add=SYS_PTRACE \
   --security-opt seccomp=unconfined \
   --shm-size=2048g \
   --ulimit memlock=-1 \
   --ulimit stack=67108864 \
   -v $HOME/verl-workspace:/verl-workspace \
   -w /workspace \
   verl-release-v0.9.0amd0 \
   /bin/bash

    Note

    --shm-size=2048g is the recommended shared-memory allocation for large multi-GPU ROCm training in this image.

Test the verl installation#

After starting the container, verify that verl and ROCm are working correctly.

  1. Confirm that verl is installed:

    pip list | grep verl

    Expected output:

    verl    0.9.0

  2. Confirm GPU visibility from the container. On ROCm, PyTorch reports AMD GPUs through the CUDA-compatible API:

    python -c "import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))"

    This should print True and the name of your AMD Instinct GPU (for example, AMD Instinct MI300X, MI325X, MI350X or MI355X).

  3. Verify key dependency versions:

    python -c "import torch; print('PyTorch', torch.__version__)"
pip show vllm ray | grep -E '^(Name|Version):'

If all checks pass, proceed to run a verl example or run a PPO or GRPO training workflow.