# Multi Node – Axolotl

# Multi Node

How to use Axolotl on multiple machines

The below are three ways to train multi-node in Axolotl.

Important

Each machine needs a copy of Axolotl, we suggest using the same commit to ensure compatibility.

You will also need to have the same configuration file for your model on each machine.

Make sure the main machine is reachable by other machines.

## Accelerate

You will need to create a configuration for accelerate, either by using `accelerate config` and follow the instructions or you can use one of the preset below:

~/.cache/huggingface/accelerate/default_config.yaml

    compute_environment: LOCAL_MACHINE
    debug: false
    distributed_type: FSDP
    downcast_bf16: 'no'
    machine_rank: 0 # Set to 0 for the main machine, increment by one for other machines
    main_process_ip: 10.0.0.4 # Set to main machine's IP
    main_process_port: 5000
    main_training_function: main
    mixed_precision: bf16
    num_machines: 2 # Change to the number of machines
    num_processes: 4 # That's the total number of GPUs, (for example: if you have 2 machines with 4 GPU, put 8)
    rdzv_backend: static
    same_network: true
    tpu_env: []
    tpu_use_cluster: false
    tpu_use_sudo: false
    use_cpu: false

Configure your model to use FSDP in the Axolotl yaml. For example:

    fsdp_version: 2
    fsdp_config:
      offload_params: true
      state_dict_type: FULL_STATE_DICT
      auto_wrap_policy: TRANSFORMER_BASED_WRAP
      transformer_layer_cls_to_wrap: LlamaDecoderLayer
      reshard_after_forward: true

All you have to do now is launch using accelerate as you would usually do on each machine and voila, the processes will start once you have launched accelerate on every machine.

## Raytrain

Please see ray train doc here.

## Torchrun

If you are using Infiniband, we recommend torchrun to utilize the full bandwidth.

Set the following env (change buffersize/socketname depending on your system):

    export NCCL_IB_DISABLE=0
    export NCCL_SOCKET_IFNAME="eth0,en,eth,em,bond"
    export NCCL_BUFFSIZE=2097152

Run the following on each node:

### Option 1: New Axolotl CLI with launcher args (Recommended)

    axolotl train config.yaml --launcher torchrun -- --nnodes $num_nodes --nproc_per_node $gpu_per_node --rdzv_id $rdzv_id --rdzv_backend c10d --rdzv_endpoint "$head_node_ip:$head_node_port"

### Option 2: Direct torchrun (Legacy)

    torchrun --nnodes $num_nodes --nproc_per_node $gpu_per_node --rdzv_id $rdzv_id --rdzv_backend c10d --rdzv_endpoint "$head_node_ip:$head_node_port" -m axolotl.cli.train config.yaml

Please make sure to substitute the placeholder variables:

- `num_nodes`: Number of nodes (containing GPUs)
- `gpu_per_node`: Number of gpus per node
- `head_node_ip`: IP of the head node (make sure other machines can connect to this)
- `head_node_port`: Port of the head node (make sure other machines can connect to this. Default 29400)
- `rdzv_id`: A unique job ID that is used by the job across nodes.

The new CLI approach (Option 1) is recommended as it provides consistent argument handling and works seamlessly with other Axolotl CLI features.

More info on the available configs can be found on the Pytorch docs here