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DeepSpeed with Azure

This tutorial will help you get started running DeepSpeed on Azure virtual machines. Looking forward, we will be integrating these techniques and additional enhancements into the Azure ML platform to benefit all your large model training jobs.

If you don't already have an Azure account please see more details here: https://azure.microsoft.com/.

To help with launching Azure instances we suggest using the Azure CLI. We have created several helper scripts to get you quickly started using DeepSpeed with Azure.

Create an SSH key

Generate an SSH key that will be used across this tutorial to SSH into your VMs and between Docker containers. ssh-keygen is the recommended way of doing this. Our scripts assume your key is located inside the same directory as the Azure scripts.

Azure Config JSON

Our helper scripts depend on the following a configuration JSON for deployment and setup. We have provided a simple example JSON in azure_config.json that sets up a basic environment with two VMs. This config uses the NV6_Promo instance type which has one NVIDIA Tesla M60 GPU per VM. You can read more details about the VM on the Linux Virtual Machines Pricing page.

See the example below:

{
 "num_vms": 2,
 "location": "southcentralus",
 "azure_sku": "Standard_NV6_Promo",
 "ssh_private_key": "id_rsa",
 "docker_ssh_port": 2222
}

Dependencies

The scripts in this tutorial require jq to help with parsing JSON from the command line. Also it is recommended to install pdsh to help launch ssh connections in parallel.

Create Azure VMs

We first need to allocate the VMs. We provide a script

./create_vms.sh

to create VMs with the Azure SKU in the region specified in azure_config.json. Feel free to customize your JSON to your desired region/SKU. This step will take a few minutes to complete while it sets up all of your VMs on Azure.

Setup VM environment to use DeepSpeed

Next, we need to configure the VM environment for DeepSpeed. We provide a script

./setup_vms.sh

to generate a hostfile and SSH configuration on all of the VMs. This configuration will be used by the DeepSpeed Docker containers in the next step.

Start the DeepSpeed docker container

We now setup the DeepSpeed Docker containers on the VMs. We provide a script

./setup_docker.sh

to pull the DeepSpeed image onto all VMs and start a container instance in the background. This will take several minutes since it needs to pull the entire Docker image.

Access VMs

The tool azure_ssh.sh will let you SSH into any of the VMs with this syntax:

./azure_ssh.sh <node-id> [command]

where the node-id is a number between 0 and num_vms-1. This script will find the public IP address of your VM and use the SSH key provided in the Azure configuration JSON.

Access DeepSpeed container

Everything should be up and running at this point. Let's access the running DeepSpeed container on the first VM and make sure we can talk to the other containers in our deployment.

  • SSH into the first VM via: ./azure_ssh.sh 0
  • Change directories into the azure folder of this repo via: cd ~/workdir/DeepSpeed/azure
  • Attach the running docker container via: ./attach.sh
  • You should now be able to ssh into any other docker container, the containers can be accessed via their SSH alias of worker-N, where N is the VM number between 0 and num_vms-1. In this example we should be able to successfully run ssh worker-1 hostname which will return the hostname of worker-1.

Parallel SSH across containers

DeepSpeed comes installed with a helper script ds_ssh which is a wrapper around the pdsh command that lets you issue commands to groups of hosts (via SSH) in parallel. This wrapper simply connects with the hostfile that defines all the containers in your deployment. For example if you run ds_ssh hostname you should see a list of all the hostnames in your deployment.

Run CIFAR-10 example model

We will now run the DeepSpeed CIFAR-10 model example to test the VM setup. From inside the first DeepSpeed container:

  1. Install the python dependencies necessary to run the CIFAR-10 example model. You can do this across your cluster via:
ds_ssh pip install -r ~/workdir/DeepSpeed/DeepSpeedExamples/cifar/requirements.txt
  1. Now change directories to the CIFAR example:
cd ~/workdir/DeepSpeed/DeepSpeedExamples/cifar
  1. Finally, launch training across all VMs:
deepspeed cifar10_deepspeed.py --deepspeed --deepspeed_config ds_config.json

Megatron-LM GPT2

DeepSpeed includes an example model using Megatron-LM's GPT2. Please refer to the full Megatron tutorial for more details.

  • In order to fully train GPT2 with DeepSpeed and ZeRO we recommend using 8 instances of Azure's Standard_ND40rs_v2 SKU for a total of 64 NVIDIA V100 GPUs. With this setup and a batch size of 1536 you should be able to complete 100k training steps (153.6 million samples) in less than 2 weeks of training.