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Microsoft DeepSpeed#

Microsoft DeepSpeed is a library to enable efficient distributed training, fine-tuning and inference on CPUs and GPUs in a high configurable manner. The library is enabled on KSL systems as a module or can be installed in a personal `conda` environment. This documentation shows how to install DeepSpeed and launch mutliGPU/multicore jobs on KSL systems.

DeepSpeed on Shaheen III#

Note

section under construction

DeepSpeed on Ibex#

DeepSpeed integrates well with SLURM on Ibex. It can either be used by loading the managed software modules or in a conda environment managed by the users in their own directories.

For a conda environment, you can activate one with Python, Pytorch with CUDA and MPI library installed, and then install DeepSpeed using pip package manager.

pip install deepspeed

In case you don’t have a conda package manager, please refer to Using conda on Ibex.

Important

It is important to make a change in the installed deepspeed package when the mulitnode jobs are intended to be run. It is specially important when requesting GPUs on shared Ibex nodes.

In your shell, run the following to get the path to DeepSpeed installation:

python
Python 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import deepspeed as ds
[2024-05-14 11:54:06,648] [WARNING] [real_accelerator.py:162:get_accelerator] Setting accelerator to CPU. If you have GPU or other accelerator, we were unable to detect it.
[2024-05-14 11:54:06,651] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cpu (auto detect)
>>> ds.__file__
'/ibex/user/$USER/conda-environments/cifar/lib/python3.12/site-packages/deepspeed/__init__.py'
>>> exit()

Change directory to `/ibex/user/$USER/conda-environments/cifar/lib/python3.12/site-packages/deepspeed` and edit the file `constants.py` to make necessary changes to port selection:

# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

import os
from datetime import timedelta

#############################################
# Torch distributed constants
#############################################
s=socket.socket()
s.bind(("", 0))
port = s.getsockname()[1]
s.close()
TORCH_DISTRIBUTED_DEFAULT_PORT = port

# Default process group wide timeout, if applicable.
# This only applies to the gloo and nccl backends
# (only if NCCL_BLOCKING_WAIT or NCCL_ASYNC_ERROR_HANDLING is set to 1).
# To make an attempt at backwards compatibility with THD, we use an
# extraordinarily high default timeout, given that THD did not have timeouts.
default_pg_timeout = timedelta(minutes=int(os.getenv("DEEPSPEED_TIMEOUT", default=30)))
INFERENCE_GENERIC_MODE = 'generic'
INFERENCE_SPECIALIZED_MODE = 'specialized'

Running DeepSpeed on single node with multiGPUs#

Below is an example of a DeepSpeed job using 4 GPUs on a single node. The type of requested GPUs is `v100` with 32GB of GPU memory.

The example python script cifar10_deepspeed.py can be cloned from the `GitHub repository <microsoft/DeepSpeedExamples.git>

#!/bin/bash
#SBATCH --job-name=CIFAR_DS
#SBATCH --time=00:01:00
#SBATCH --gpus=4
#SBATCH --gpus-per-node=4
#SBATCH --constraint=v100
#SBATCH --ntasks=4
#SBATCH --ntasks-per-node=4
#SBATCH --cpus-per-task=8
#SBATCH --mem=100G

# Using pre-installed modules on Ibex
module load dl
module load pytorch
module load deepspeed

# Can use a conda environment by sourcing it as below. In this case, please comment the "module load" commands above.
#source /ibex/user/$USER/mambaforge/bin/activate ds_env


echo "Hostnames: $SLURM_NODELIST"
scontrol show job $SLURM_JOBID


export RUNDIR=${PWD}/result_${SLURM_JOB_NAME}
mkdir -p $RUNDIR
export OUTPUT=${SLURM_JOBID}
# Getting the node names
nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
nodes_array=($nodes)
echo "Node IDs of participating nodes ${nodes_array[*]}"


# Get the IP address and set port for MASTER node
head_node="${nodes_array[0]}"
echo "Getting the IP address of the head node ${head_node}"
master_ip=$(srun -n 1 -N 1 --gpus=1 -w ${head_node} /bin/hostname -I | cut -d " " -f 2)
master_port=$(python -c 'import socket; s=socket.socket(); s.bind(("", 0)); print(s.getsockname()[1]); s.close()')
echo "head node is ${master_ip}:${master_port}"

workers=${SLURM_CPUS_PER_TASK}

echo "Hostname: $(/bin/hostname)"
echo "CPU workers: $workers"

for (( i=0; i< ${SLURM_NNODES}; i++ ))
do
    srun --cpu-bind=cores -n 1 -N 1 -c ${SLURM_CPUS_PER_TASK} -w ${nodes_array[i]} --gpus=${SLURM_GPUS_PER_NODE}  \
    python -m torch.distributed.launch --use_env --nproc_per_node=${SLURM_GPUS_PER_NODE} --nnodes=${SLURM_NNODES} --node_rank=${i} \
    --master_addr=${master_ip} --master_port=${master_port}  cifar10_deepspeed.py --deepspeed $@ &> $RUNDIR/$SLURM_JOBID.txt
done
wait

Running DeepSpeed on multiple node with multiGPUs#

Below is an example of a DeepSpeed job using 4 GPUs on 2 nodes with 2 GPUs on each. The type of requested GPUs again v100 with 32GB. The node also has GPUDirect RDMA connectivity enabled which moves data from GPU memory to the another GPU’s memory on a remote node, bypassing the CPUs. This is a very important feature to keep up the allreduce communication throughput in the distributed training process.

#!/bin/bash
#SBATCH --job-name=CIFAR_DS
#SBATCH --time=00:01:00
#SBATCH --gpus=4
#SBATCH --gpus-per-node=2
#SBATCH --constraint=v100,gpu_ai
#SBATCH --ntasks=4
#SBATCH --ntasks-per-node=2
#SBATCH --cpus-per-task=8
#SBATCH --mem=100G

# Using pre-installed modules on Ibex
module load dl
module load pytorch
module load deepspeed

# Can use a conda environment by sourcing it as below. In this case, please comment the "module load" commands above.
#source /ibex/user/$USER/mambaforge/bin/activate ds_env


echo "Hostnames: $SLURM_NODELIST"
scontrol show job $SLURM_JOBID


export RUNDIR=${PWD}/result_${SLURM_JOB_NAME}
mkdir -p $RUNDIR
export OUTPUT=${SLURM_JOBID}
# Getting the node names
nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
nodes_array=($nodes)
echo "Node IDs of participating nodes ${nodes_array[*]}"


# Get the IP address and set port for MASTER node
head_node="${nodes_array[0]}"
echo "Getting the IP address of the head node ${head_node}"
master_ip=$(srun -n 1 -N 1 --gpus=1 -w ${head_node} /bin/hostname -I | cut -d " " -f 2)
master_port=$(python -c 'import socket; s=socket.socket(); s.bind(("", 0)); print(s.getsockname()[1]); s.close()')
echo "head node is ${master_ip}:${master_port}"

workers=${SLURM_CPUS_PER_TASK}

echo "Hostname: $(/bin/hostname)"
echo "CPU workers: $workers"

for (( i=0; i< ${SLURM_NNODES}; i++ ))
do
    srun --cpu-bind=cores -n 1 -N 1 -c ${SLURM_CPUS_PER_TASK} -w ${nodes_array[i]} --gpus=${SLURM_GPUS_PER_NODE}  \
    python -m torch.distributed.launch --use_env --nproc_per_node=${SLURM_GPUS_PER_NODE} --nnodes=${SLURM_NNODES} --node_rank=${i} \
    --master_addr=${master_ip} --master_port=${master_port}  cifar10_deepspeed.py --deepspeed $@ &> $RUNDIR/$SLURM_JOBID.txt
done
wait
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