Ben Gubler

Enroot on Slurm for Distributed ML: Part 2

How to use Enroot on Slurm for containerized multi-node training.

This is part 2 of a 2-part series. Part 1 is available here.

In part 1, we covered how to use Enroot on Slurm for containerized single-node training using salloc. In this post, we'll cover how to use Enroot on Slurm for containerized multi-node training, and transition to using sbatch.

Step 1: Slurm Launch Script

We'll end up creating several Bash files, all of which should be in the same directory as your training script. The first will be a Slurm launch file that we'll run with sbatch. This file will contain the same commands we ran with salloc in part 1, but declared using #SBATCH processing directives.

launch.sh

#!/bin/bash
#SBATCH -J "JOBNAME"
#SBATCH --nodes=2
#SBATCH --gpus-per-node=8
#SBATCH --cpus-per-task=128
#SBATCH --mem=2000G
#SBATCH --time=72:00:00
#SBATCH --qos=<qos>

export CUR_DIR=$(pwd)
srun --nodes=2 stage1.sh

Note that we create a variable CUR_DIR to store the current working directory (the directory where the sbatch command was run). I use this variable to share the location of my training directory between scripts, so I don't have to hard-code paths. But it's not required.

Slurm will automatically pass local environment variables through to the srun command, which will run the stage1.sh script on each node.

Step 2. Enroot Launch Script

Next, we'll create a script that will be run on each node. This script will be responsible for launching the container and running the training script. We'll call this script stage1.sh.

stage1.sh

#!/bin/bash

module load jq zstd pigz parallel libnvidia-container enroot

export MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1) # get the IP address of the first node in the list
export MASTER_PORT=6000 # set the port to use for communication between nodes

enroot create --name image-name /path/to/image-name.sqsh

enroot start --env SLURM_NODEID \
             --env MASTER_ADDR \
             --env MASTER_PORT \
             --env SLURM_JOB_NAME \
             --env CUR_DIR \
             --mount /local/file/path:/image/file/path \
             --rw image-name \
             bash ${CUR_DIR}/stage2.sh

Note that we pass several important environment variables provided by Slurm, along with CUR_DIR, into the container. The MASTER_ADDR and MASTER_PORT variables are used by PyTorch's distributed training backend to coordinate communication between nodes.

We also mount a local file path into the container (make sure it contains your training script!).

Step 3. Training Script

Finally, we'll create a training script that will be run inside the container. We'll call this script stage2.sh.

stage2.sh

#!/bin/bash

export NCCL_DEBUG=INFO # if you want to see NCCL logs
export NODE_RANK=$SLURM_NODEID # set the node rank to the node ID (0, 1, 2, etc.)
echo NODE_RANK: $NODE_RANK # print the node rank for debugging purposes

# Run training script
# NOTE: modify as desired if you're not using accelerate

accelerate launch --config_file ./accelerate_config.yaml --main_process_ip=$MASTER_ADDR --main_process_port=$MASTER_PORT --machine_rank $NODE_RANK ${CUR_DIR}/loop.py

Here I've used accelerate as a launcher for my distributed training script, but you can use whatever launcher you want. Just make sure you pass relevant environment variables through!

For the sake of completeness, here's my accelerate_config.yaml file. It utilizes FSDP (Fully Sharded Data Parallel) to split model parameters and gradients across processes. This is a great way to train large models that won't fit on just one GPU.

compute_environment: LOCAL_MACHINE
deepspeed_config: {}
distributed_type: FSDP
downcast_bf16: "no"
fsdp_config:
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_backward_prefetch_policy: BACKWARD_PRE
  fsdp_offload_params: false
  fsdp_sharding_strategy: 1
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
main_training_function: main
mixed_precision: "no"
num_machines: 2
num_processes: 16 # 8 GPUs per node * 2 nodes = 16 processes
use_cpu: false

Step 4. Submit the Job

Now that we've created all the necessary scripts, we can submit the job to Slurm using sbatch! From the directory containing the scripts, run:

sbatch launch.sh

Your job will be submitted to Slurm and run as soon as resources are available. Output logs will be stored at slurm-<jobid>.out in the current directory.

Conclusion

I hope this was helpful! There are many parts involved in getting distributed training working, but it's not too difficult once you get over the initial learning curve.

If you liked the article, don't forget to share it and follow me at @nebrelbug on Twitter.

© 2024 Ben Gubler