Train on Multiple Nodes

Resource Specification

In the Getting Started tutorial, there is an example using the minimal resource specification in yaml format:

  - address: localhost
    gpus: [0,1]

However, we often need to train a large model with multiple nodes / virtual machines / pods etc., and at the same time with multiple accelerators. Here is an example to define a resource specification for multi-node training:

  - address:
    cpus: [0]
    gpus: [0,1,2,3]
    chief: true
  - address:
    gpus: [0,1,2,3]
    ssh_config: ssh_conf1
  - address:
    cpus: [0,1]
    gpus: [0,1]
    ssh_config: ssh_conf2

    key_file: '/home/ubuntu/.ssh/autodist.pem'
    username: 'ubuntu'
    python_venv: 'source /home/ubuntu/venvs/autodist/bin/activate'
    key_file: '/home/ubuntu/.ssh/my_private_key'
    username: 'username'
    python_venv: 'source /home/username/anaconda3/etc/profile.d/;conda activate autodist'
      LD_LIBRARY_PATH: '$LD_LIBRARY_PATH:/usr/local/cuda/lib64'
  • nodes: the computational nodes spec defined for distributed training.

    • address str: the ipv4 or ipv6 address to access the nodes. Note that in a multiple-node scenario the resource specification is shared across the node, and the address will be the identifier for communications across nodes in the cluster, so one should avoid using localhost in such case to prevent ambiguity.

    • cpus List[int] (optional): If not specified, it means only the first CPU will be utilized. One can also specify multiple CPUs. However, it requires some careful treatment. For example, if a strategy uses the extra CPU for data-parallel computation together with GPU, it can slow down the performance. Even though in the future AutoDist will support auto-generated strategies to utilize the extra CPUs smartly, the multiple CPUs strategy is still experimental in the current releases.

    • gpus List[int] (optional): the CUDA device IDs of GPUs.

    • chief bool (optional): Optional for single-node training. However, it is required to specify exactly one of the multiple nodes to be the chief. The chief node should be the node you launch the program from (it will also participate in the training, so make sure it is a node in your cluster).

  • ssh: You also need to create ssh_configs that can be used by the chief node to ssh into all other nodes. They must be named; this allows for having different ssh configs if, for example, different nodes have different private keys. ssh_conf1, ssh_conf2 can be any unique string to specify a group of configurations.

    • key_file str: key path on chief node to use to connect the remote node via ssh

    • username str: username to access the remote (non-chief) node with

    • python_venv str (optional): the command to activate the virtual environment on a remote (non-chief) node. If not provided, it will use the default system default python.

    • shared_envs pair (optional): the key-value environment variable pairs for a remote (non-chief) node to use

Multi-node coordination is currently limited to SSH and Python virtual environments. Other types are under development. For coordination on container level, please refer to the docker setup.

Environment Preparation

Follow the steps below to set up environments for multi-node training. If you are familiar with Docker, use these simple instructions to launch directly from Docker.

Before running your program distributed with AutoDist on multiple nodes,

  1. The model should be able to train on each node successfully with AutoDist, as in Getting Started.

  2. The working directory containing project files should share the same absolute path on all nodes. For example, if the working dir is /home/username/myproject on the node where you launch the program, it is also required that your project files are placed under the same absolute path for all the other nodes. Symbolic link is also allowed.

  3. Note that the field python_venv requires the command to source remote env, without sourcing ~/.bash_rc unless specified; while shared_envs is for passing the environment vars to the remote. For conda environments, must be sourced before conda environment can be activated.

Then one can launch the script from the chief machine.