Installation
============

Requirements
------------

- **Python**: Version >= 3.9
- **CUDA**: Version >= 12.1

verl supports various backends. Currently, the following configurations are available:

- **FSDP** and **Megatron-LM** (optional) for training.
- **SGLang**, **vLLM** and **TGI** for rollout generation.

Choices of Backend Engines
----------------------------

1. Training:

We recommend using **FSDP** backend to investigate, research and prototype different models, datasets and RL algorithms. The guide for using FSDP backend can be found in :doc:`FSDP Workers<../workers/fsdp_workers>`.

For users who pursue better scalability, we recommend using **Megatron-LM** backend. Currently, we support `Megatron-LM v0.12.1 <https://github.com/NVIDIA/Megatron-LM/tree/core_v0.12.1>`_. The guide for using Megatron-LM backend can be found in :doc:`Megatron-LM Workers<../workers/megatron_workers>`.


2. Inference:

For inference, vllm 0.8.3 and later versions have been tested for stability. We recommend turning on env var `VLLM_USE_V1=1` for optimal performance.

For SGLang, refer to the :doc:`SGLang Backend<../workers/sglang_worker>` for detailed installation and usage instructions. SGLang rollout is under extensive development and offers many advanced features and optimizations. We encourage users to report any issues or provide feedback via the `SGLang Issue Tracker <https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial/issues/106>`_.

For huggingface TGI integration, it is usually used for debugging and single GPU exploration.

Install from docker image
-------------------------

We provide pre-built Docker images for quick setup. And from this version,
we utilize a new image release hierarchy for productivity and stability.

The image types are divided into three large categories:

- **Base Image**: Without inference and training frameworks, only basic dependencies are installed.
  Can directly install vllm or SGLang on top of it, without need of reinstall torch or CUDA.
- **Application Image**: Stable version with inference and training frameworks installed.
- **Community Image**: Unstable version with the latest frameworks and features.

The first two types of images are hosted on dockerhub `verlai/verl <https://hub.docker.com/r/verlai/verl>`_ repository, while the preview images are hosted on community repository.

.. note::

    The image versions are mapped with verl releases, for example, image with tag ``verl0.4`` is built for verl release ``v0.4.x``.

Base Image
::::::::::

The stable base image is ``verlai/verl:base-verl0.4-cu124-cudnn9.8-torch2.6-fa2.7.4-te2.3``. The installed package versions can be found from tags, and the Dockerfile can be found in ``docker/verl[version]-[packages]/Dockerfile.base``.

The base images for preview are ``verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.8.0` and ``verlai/verl:base-verl0.5-preview-cu128-cudnn9.8-torch2.7.1-fa2.8.0`` with different CUDA versions. From verl0.5, images are built with `Deep-EP <https://github.com/deepseek-ai/DeepEP>`_ for efficient EP communication.

The update of base image is not frequent, and the app image can be built on top of it without reinstalling base packages.

Application Image
:::::::::::::::::

From this version, we divide images built for vLLM and SGLang as the divergence of dependent packages like FlashInfer.

There are four types of application images available:

- **vLLM with FSDP and Megatron**: ``verlai/verl:app-verl0.4-vllm0.8.5-mcore0.12.1``, with Deep-EP support: ``verlai/verl:app-verl0.4-vllm0.8.5-mcore0.12.1-deepep``.
- **SGLang with FSDP and Megatron**: ``verlai/verl:app-verl0.4-sglang0.4.6.post5-vllm0.8.5-mcore0.12.1`` (need vLLM support, but can have some package conflicts), with Deep-EP support: ``verlai/verl:app-verl0.4-sglang0.4.6.post5-vllm0.8.5-mcore0.12.1-deepep``.
- **Preview version of SGLang with FSDP and Megatron, CUDA 12.6**: ``verlai/verl:app-verl0.5-sglang0.4.8-mcore0.12.1``
- **Preview version of SGLang with FSDP and Megatron, CUDA 12.8**: ``verlai/verl:app-preview-verl0.5-sglang0.4.8-mcore0.12.1``

The latest vLLM support is coming soon.

Docker images with Megatron backends are runnable with large language model like ``Qwen/Qwen3-235B-A22B``, ``deepseek-ai/DeepSeek-V3-0324`` post-training. Refer to the :doc:`Large Language Model Post-Training documentation<../perf/dpsk>` for more details.

Application images can be updated frequently, and the Dockerfile can be found in ``docker/verl[version]-[packages]/Dockerfile.app.[frameworks]``. Based on the base image, it is easy to build your own application image with the desired inference and training frameworks.

Community Image
:::::::::::::::

Community images are provided by the community, including the latest versions of vLLM and SGLang, and may include experimental features or configurations. And also works for other hardwares or platforms like AMD GPUs with ROCM or AWS EFA and Sagemaker.

For latest vLLM with FSDP, please refer to `hiyouga/verl <https://hub.docker.com/r/hiyouga/verl>`_ repository and the latest version is ``hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.4-flashinfer0.2.2-cxx11abi0``.

For latest SGLang with FSDP, please refer to `ocss884/verl-sglang <https://hub.docker.com/r/ocss884/verl-sglang>`_ repository and the latest version is ``ocss884/verl-sglang:ngc-th2.6.0-cu126-sglang0.4.6.post5`` which is provided by SGLang RL Group.

See files under ``docker/`` for NGC-based image or if you want to build your own.

Note that For aws instances with EFA net interface (Sagemaker AI Pod),
you need to install EFA driver as shown in ``docker/Dockerfile.extenstion.awsefa``

Installation from Docker
::::::::::::::::::::::::

After pulling the desired Docker image and installing desired inference and training frameworks, you can run it with the following steps:

1. Launch the desired Docker image and attach into it:

.. code:: bash

    docker create --runtime=nvidia --gpus all --net=host --shm-size="10g" --cap-add=SYS_ADMIN -v .:/workspace/verl --name verl <image:tag> sleep infinity
    docker start verl
    docker exec -it verl bash


2.	If you use the images provided, you only need to install verl itself without dependencies:

.. code:: bash

    # install the nightly version (recommended)
    git clone https://github.com/volcengine/verl && cd verl
    pip3 install --no-deps -e .

[Optional] If you hope to switch between different frameworks, you can install verl with the following command:

.. code:: bash

    # install the nightly version (recommended)
    git clone https://github.com/volcengine/verl && cd verl
    pip3 install -e .[vllm]
    pip3 install -e .[sglang]


Install from custom environment
---------------------------------------------

We recommend to use docker images for convenience. However, if your environment is not compatible with the docker image, you can also install verl in a python environment.


Pre-requisites
::::::::::::::

For training and inference engines to utilize better and faster hardware support, CUDA/cuDNN and other dependencies are required,
and some of the dependencies are easy to be overridden when installing other packages,
so we put them in the :ref:`Post-installation` step.

.. note::

    The installation steps below are recommended configurations for the latest version of verl.
    If you are trying to customize your own environment, please ignore the strict constraints.

We need to install the following pre-requisites:

- **CUDA**: Version >= 12.4
- **cuDNN**: Version >= 9.8.0
- **Apex**

CUDA above 12.4 is recommended to use as the docker image,
please refer to `NVIDIA's official website <https://developer.nvidia.com/cuda-toolkit-archive>`_ for other version of CUDA.

.. code:: bash

    # change directory to anywher you like, in verl source code directory is not recommended
    wget https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda-repo-ubuntu2204-12-4-local_12.4.1-550.54.15-1_amd64.deb
    dpkg -i cuda-repo-ubuntu2204-12-4-local_12.4.1-550.54.15-1_amd64.deb
    cp /var/cuda-repo-ubuntu2204-12-4-local/cuda-*-keyring.gpg /usr/share/keyrings/
    apt-get update
    apt-get -y install cuda-toolkit-12-4
    update-alternatives --set cuda /usr/local/cuda-12.4


cuDNN can be installed via the following command,
please refer to `NVIDIA's official website <https://developer.nvidia.com/rdp/cudnn-archive>`_ for other version of cuDNN.

.. code:: bash

    # change directory to anywher you like, in verl source code directory is not recommended
    wget https://developer.download.nvidia.com/compute/cudnn/9.8.0/local_installers/cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb
    dpkg -i cudnn-local-repo-ubuntu2204-9.8.0_1.0-1_amd64.deb
    cp /var/cudnn-local-repo-ubuntu2204-9.8.0/cudnn-*-keyring.gpg /usr/share/keyrings/
    apt-get update
    apt-get -y install cudnn-cuda-12

NVIDIA Apex is required for Megatron-LM and FSDP training.
You can install it via the following command, but notice that this steps can take a very long time.
It is recommended to set the ``MAX_JOBS`` environment variable to accelerate the installation process,
but do not set it too large, otherwise the memory will be overloaded and your machines may hang.

.. code:: bash

    # change directory to anywher you like, in verl source code directory is not recommended
    git clone https://github.com/NVIDIA/apex.git && \
    cd apex && \
    MAX_JOB=32 pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./


Install dependencies
::::::::::::::::::::

.. note::

    We recommend to use a fresh new conda environment to install verl and its dependencies.

    **Notice that the inference frameworks often strictly limit your pytorch version and will directly override your installed pytorch if not paying enough attention.**

    As a countermeasure, it is recommended to install inference frameworks first with the pytorch they needed. For vLLM, if you hope to use your existing pytorch,
    please follow their official instructions
    `Use an existing PyTorch installation <https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#build-wheel-from-source>`_ .


1. First of all, to manage environment, we recommend using conda:

.. code:: bash

   conda create -n verl python==3.10
   conda activate verl


2. Then, execute the ``install.sh`` script that we provided in verl:

.. code:: bash

    # Make sure you have activated verl conda env
    # If you need to run with megatron
    bash scripts/install_vllm_sglang_mcore.sh
    # Or if you simply need to run with FSDP
    USE_MEGATRON=0 bash scripts/install_vllm_sglang_mcore.sh


If you encounter errors in this step, please check the script and manually follow the steps in the script.


Install verl
::::::::::::

For installing the latest version of verl, the best way is to clone and
install it from source. Then you can modify our code to customize your
own post-training jobs.

.. code:: bash

   git clone https://github.com/volcengine/verl.git
   cd verl
   pip install --no-deps -e .


Post-installation
:::::::::::::::::

Please make sure that the installed packages are not overridden during the installation of other packages.

The packages worth checking are:

- **torch** and torch series
- **vLLM**
- **SGLang**
- **pyarrow**
- **tensordict**
- **nvidia-cudnn-cu12**: For Magetron backend

If you encounter issues about package versions during running verl, please update the outdated ones.


Install with AMD GPUs - ROCM kernel support
------------------------------------------------------------------

When you run on AMD GPUs (MI300) with ROCM platform, you cannot use the previous quickstart to run verl. You should follow the following steps to build a docker and run it. 
If you encounter any issues in using AMD GPUs running verl, feel free to contact me - `Yusheng Su <https://yushengsu-thu.github.io/>`_.

Find the docker for AMD ROCm: `docker/Dockerfile.rocm <https://github.com/volcengine/verl/blob/main/docker/Dockerfile.rocm>`_
::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::

.. code-block:: bash

    #  Build the docker in the repo dir:
    # docker build -f docker/Dockerfile.rocm -t verl-rocm:03.04.2015 .
    # docker images # you can find your built docker
    FROM rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4

    # Set working directory
    # WORKDIR $PWD/app

    # Set environment variables
    ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942"

    # Install vllm
    RUN pip uninstall -y vllm && \
        rm -rf vllm && \
        git clone -b v0.6.3 https://github.com/vllm-project/vllm.git && \
        cd vllm && \
        MAX_JOBS=$(nproc) python3 setup.py install && \
        cd .. && \
        rm -rf vllm

    # Copy the entire project directory
    COPY . .

    # Install dependencies
    RUN pip install "tensordict<0.6" --no-deps && \
        pip install accelerate \
        codetiming \
        datasets \
        dill \
        hydra-core \
        liger-kernel \
        numpy \
        pandas \
        datasets \
        peft \
        "pyarrow>=15.0.0" \
        pylatexenc \
        "ray[data,train,tune,serve]" \
        torchdata \
        transformers \
        wandb \
        orjson \
        pybind11 && \
        pip install -e . --no-deps

Build the image
::::::::::::::::::::::::

.. code-block:: bash

    docker build -t verl-rocm .

Launch the container
::::::::::::::::::::::::::::

.. code-block:: bash

    docker run --rm -it \
      --device /dev/dri \
      --device /dev/kfd \
      -p 8265:8265 \
      --group-add video \
      --cap-add SYS_PTRACE \
      --security-opt seccomp=unconfined \
      --privileged \
      -v $HOME/.ssh:/root/.ssh \
      -v $HOME:$HOME \
      --shm-size 128G \
      -w $PWD \
      verl-rocm \
      /bin/bash

If you do not want to root mode and require assign yourself as the user,
Please add ``-e HOST_UID=$(id -u)`` and ``-e HOST_GID=$(id -g)`` into the above docker launch script. 

verl with AMD GPUs currently supports FSDP as the training engine, vLLM and SGLang as the inference engine. We will support Megatron in the future.
