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Multi-node Training

Multi-node training can be performed easily on Gaudi with DeepSpeed for any training script as follows:

python gaudi_spawn.py \
    --hostfile path_to_my_hostfile --use_deepspeed \
    path_to_my_script.py --args1 --args2 ... --argsN \
    --deepspeed path_to_my_deepspeed_config

where --argX is an argument of the script to run.

Setup

Check out the documentation to know how to set up your Gaudi instances for multi-node runs on premises or on AWS.

We provide two Dockerfile to easily start your multi-node runs:

  • A Dockerfile provided here for multi-node runs on AWS.
  • A Dockerfile provided here for multi-node runs using GaudiNIC.

The Dockerfile is based on an image compatible with Ubuntu 22.04 but you can easily adapt it to another OS.

To build the Docker image, run:

docker build -t gaudi_multi_node PATH

where PATH is the path to the folder containing the Dockerfile.

To run a Docker container with the image you just built, execute:

docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host gaudi_multi_node:latest

For AWS DL1 instances, --privileged must be passed to the docker run command so that EFA interfaces are visible.

You will need to copy the leader node Docker's id_rsa.pub key to every other node Docker's ~/.ssh/authorized_keys to enable password-less SSH:

a. Copy id_rsa.pub to ~/.ssh/authorized_keys on each node

cat id_rsa.pub > authorized_keys
vi authorized_keys

b. Copy the leader node's id_rsa.pub key contents to other systems' authorized_keys.

Finally, on each system, add all hosts (including itself) to known_hosts. The IP addresses used below are just for illustration:

ssh-keyscan -p 3022 -H 10.10.100.101 >> ~/.ssh/known_hosts
ssh-keyscan -p 3022 -H 10.10.100.102 >> ~/.ssh/known_hosts
ssh-keyscan -p 3022 -H 10.10.100.103 >> ~/.ssh/known_hosts
ssh-keyscan -p 3022 -H 10.10.100.104 >> ~/.ssh/known_hosts

You can check if ssh port is working with the following command:

  1. Run lsof -i inside docker of each node to make sure sshd is up. It should be something like below.
COMMAND PID USER   FD   TYPE   DEVICE SIZE/OFF NODE NAME
sshd     35 root    3u  IPv4 23262521      0t0  TCP *:3022 (LISTEN)
sshd     35 root    4u  IPv6 23262523      0t0  TCP *:3022 (LISTEN)

If no sshd, then do the following to restart sshd.

sed -i 's/#Port 22/Port 3022/g' /etc/ssh/sshd_config
sed -i 's/#   Port 22/    Port 3022/g' /etc/ssh/ssh_config
sed -i 's/#PermitRootLogin prohibit-password/PermitRootLogin yes/' /etc/ssh/sshd_config
service ssh restart

  1. Test ssh with command ssh -p 3022 IP-address to each other to make sure the nodes can communicate with each other.

  2. Try gaudi_spawn.py training command with world_size 8 for few steps to make sure the command works for 8 ranks on each node.

  3. Start gaudi_spawn.py with multi-nodes run on main node docker. (the node with the 1st ip address in the hostfile)

Hostfile

DeepSpeed requires a hostfile to know the addresses of and the number of devices to use on each node. You can specify its path with --hostfile. This file should look like this:

ip_1 slots=8
ip_2 slots=8
...
ip_n slots=8

You can find a template here.

Environment variables

If you need to set environment variables for all nodes, you can specify them in a .deepspeed_env file which should be located in the local path you are executing from or in your home directory. It is formatted as follows:

env_variable_1_name=value
env_variable_2_name=value
...

You can find an example for GaudiNIC instances here.

Note above environment variables refers to /etc/profile.d/habanalabs.sh inside docker, and should set only on GaudiNIC master node.

You can find an example for AWS instances here.

Note that one should set HCCL_OVER_OFI=1 and LD_LIBRARY_PATH=/root/hccl_ofi_wrapper:/opt/amazon/openmpi/lib:/opt/amazon/efa/lib only on AWS DL1 instances. These should not be used otherwise.

Recommendations

  • It is strongly recommended to use gradient checkpointing for multi-node runs to get the highest speedups. You can enable it with --gradient_checkpointing in all these examples or with gradient_checkpointing=True in your GaudiTrainingArguments.
  • Larger batch sizes should lead to higher speedups.
  • Multi-node inference is not recommended and can provide inconsistent results.
  • On AWS DL1 instances, run your Docker containers with the --privileged flag so that EFA devices are visible.