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README.md

torchelastic on AWS

This directory contains scripts and libraries that help users run torchelastic jobs on AWS.

Prerequisites

  1. Familiarity with basic AWS (EC2, Auto Scaling Groups, S3, EFS).
  2. (suggested) install and setup awscli.
  3. Basic knowledge of containers (we use Docker in our examples).

Requirements

  1. git clone https://github.com/pytorch/elastic.git
  2. cd elastic/aws && pip install -r requirements.txt
  3. The following AWS resources:
    1. EC2 instance profile
    2. Subnet(s)
    3. Security group
    4. EFS volume
    5. S3 Bucket
  4. Install the AWS Session Manager plugin

Quickstart

This guide shows how to get a simple torchelastic job running on AWS. petctl is a commandline tool that helps run distributed jobs written with torchelastic on EC2 instances.

python3 petctl.py setup

This will bootstrap all the AWS resources required to run a torchelastic job. For details take a look at the CloudFormation template .

Use --s3_bucket and --efs_id to use an existing S3 bucket and EFS file system. Otherwise an S3 bucket and EFS volume will be created.

IMPORTANT when specifying --efs_id you MUST ensure that NO mount targets exist on the EFS file system. torchelastic's cfn stack will attempt to create mount targets for the subnets it creates and WILL FAIL if the file system already has mount targets on a different VPC. For more information refer to the EFS docs.

TIP: If the stack creation fails, log into the CloudFormation console, inspect the failure reason, address the failure, then manually delete the stack and re-run petctl configure.

If you are familiar with AWS or already have the resources specified in the Requirements section above, then you can follow the Manual Setup instructions below and simply copy the sample specs file and fill in the template, then run python petctl.py configure.

Write a script

If you already have a script that uses torchelastic to run distributed training, great! Otherwise you can use the provided imagenet example or classy vision. Or... this is a great time to work on one.

If you are using examples/imagenet/main.py you must download the imagenet dataset from here onto the EFS volume you specified. The dataset will be available to the workers on /mnt/efs/fs1/<download_dir>.

Run the script

We will assume that you are working with the imagenet example and that you have downloaded the imagenet dataset to /mnt/efs/fs1/data/imagenet/train. To run the script we'll use petctl,

python3 aws/petctl.py run_job --size 2 --min_size 1 --max_size 3 --name ${USER}-job examples/imagenet/main.py -- --input_path /mnt/efs/fs1/data/imagenet/train

In the example above, the named arguments, such as, --size , --min_size, and --max_size are parameters to the run_job sub-command of petctl. In the example above, we created an elastic job where the initial worker --size=2, we are allowed to scale down to --min_size=1 and up to --max_size=3. This is used by torchelastic's rendezvous algorithm to determine how many nodes to admit on each re-rendezvous before considering the group final and start the train_step.

The other positional arguments have the form:

[local script] -- [script args ...]
  -- or -- 
[local directory] -- [script] [script args...]

If the first positional argument is a path to a script file, then the script is uploaded to S3 and the script arguments specified after the -- delimiter are passed through to the script.

If the first positional argument is a directory, then a tarball of the directory is created and uploaded to S3 and is extracted on the worker-side. In this case the first argument after the -- delimiter is the path to the script relative to the specified directory and the rest of the arguments after the delimiter is passed to the script.

In our example we specified

petctl.py run_job [...] examples/imagenet/main.py -- --input_path /mnt/efs/fs1/data/imagenet/train

We could have decided to specify the directory instead

petctl.py run_job [...] examples -- imagenet/main.py --input_path /mnt/efs/fs1/data/imagenet/train

TIP 1: Besides a local script or directory you can run with scripts or tar files that have already been uploaded to S3 or directly point it to a file or directory on the container.

python3 petctl.py run_job [...] s3://my-bucket/my_script.py
python3 petctl.py run_job [...] s3://my-bucket/my_dir.tar.gz -- my_script.py

# or
python3 petctl.py run_job [...] docker:///abs/path/in/container/dir -- my_script.py
python3 petctl.py run_job [...] docker://rel/path/in/container/dir/my_script.py

TIP 2: To iterate quickly, simply make changes to your local script and upload the script to S3 using

python3 petctl.py upload examples/imagenet/main.py s3://<bucket>/<prefix>/<job_name> 

TIP 3: Use the EFS volume attached on /mnt/efs/fs1 on all the workers to save input data, checkpoints and job output.

Once the run_job command returns log into the EC2 console, you will see two Auto Scaling Groups

  1. etcd server
  2. workers

Inspect the logs

Log into the AWS CloudWatch Logs console. You should see a log group called torchelastic/$USER. Under it there will be a log stream per instance with the name $job_name/$instance_id (e.g. my_job/i0b938EXAMPLE).

Troubleshooting

SSH

To SSH onto the worker nodes to debug/inspect the worker process use AWS Session Manager instead of the ec2 key pair. Install the Session Manager plugin and run

# get the instance ids of the workers
python3 petctl.py list_hosts <job_name>

# ssh onto one of the workers
awscli ssm start-session --target <instance_id>
 -- example --
awscli ssm start-session --target i-00b00EXAMPLE
Process Status and Logs

Once SSH'ed, the workers run in a docker container managed by systemd. You can take a look at their console outputs by running

# see the status of the worker
sudo systemctl status torchelastic_worker
# get the container id
sudo docker ps
# tail the container logs
sudo docker logs -f <container id>

Note since we have configured the log driver to be awslogs tailing the docker logs will not work. For more information see: https://docs.docker.com/config/containers/logging/awslogs/

You can also manually stop and start the workers by running

sudo systemctl stop torchelastic_worker
sudo systemctl start torchelastic_worker

EXERCISE: Try stopping or adding worker(s) to see elasticity in action! To add workers, simply increase the desired size of the worker autoscaling group.

Notable Directories
  1. torchelastic_worker systemd: /etc/systemd/service/torchelastic_worker.service
  2. torchelastic run scripts: /var/torchelastic

Note: by design, petctl tries to use the least number of AWS services. This was done intentionally to allow non-AWS users to easily transfer the functionality to their environment. Hence it currently does not have the functionality to query status of the job or to terminate the ASG when the job is done (there is nothing that is monitoring the job!). In practice consider using EKS, Batch, or SageMaker.

Stop the script

To stop the job and tear down the resources, use the kill_job command:

python3 petctl.py kill_job ${USER}-job

You'll notice that the two ASGs created with the run_job command are deleted.

Manual Setup

Create specs file

First, lets create a launch spec. This is a simple json file that specifies the launch configuration of EC2 instances. We have included a sample specs file so make a copy and fill it in. You only need to fill in the fields with {{ AWS_Resource_Id }}, you can leave the other fields alone for now.

# cd $torchelastic_repository_root
mkdir ~/torchelastic_workspace
cp aws/config/sample_specs.json ~/torchelastic_workspace/specs.json

The specs file is divided into two sections: rdzv and worker. As their names imply the rdzv section contains the launch specs for the instances of the rendezvous backend (e.g. etcd). The worker section contains the launch specs for the worker instances.

The following subsections describe the fields in the specs file.

Instance Type and Accelerator

    "instance_type" : "[ec2 instance type]",
    "accelerator" : "[none | gpu]",

The instance type specifies the EC2 instance type to use. The accelerator field can either be none or gpu. If an EC2 instance that has GPU capability is specified (e.g. g3, p2, p3 instance families) then you must specify accelerator = gpu.

If accelerator=gpu is not specified on a GPU capable instance type, petctl assumes you will only use CPU and will use an AMI that does not have CUDA nor NVIDIA drivers.

Subnet

Note that you can add multiple subnets. Each subnet belongs to an availability zone (AZ) so you can spread your instances across AZs by specifying multiple subnets:

    "subnets" : [
      "[subnet_in_us-west-2a]",
      "[subnet_in_us-west-2b]",
      ...
    ],

Some instances are not available in all AZs, make sure to create the subnet in the AZ that supports the instance type that you plan to run your jobs on.

Security Group

torchelastic jobs are distributed and hence require nodes to communicate with each other. Make sure that your security group allows all inbound traffic between instances within the same security group.

Optionally you may want to allow inbound SSH access in case you need to log into the instance for debugging.

EC2 Instance Profile

    "instance_role" : "[ec2 instance profile]",

This is the IAM role that is used when accessing other AWS services from within the EC2 instance (e.g. accessing S3 from the worker host). To learn more about instance profiles refer to the AWS documentation.

Refer to the Ref: IAMRoleRendezvous and Ref: IAMRoleWorker resources in the CloudFormation template for a full list of IAM policies that must be attached to this role.

You may wish to add other privileges to this role depending on what your workers do. For instance, if your job writes output to S3, then you will have to attach S3 Write IAM policy to this profile.

S3 Bucket and Prefix

petctl uploads your local script to S3 and pulls it down on the worker. Specify the S3 bucket and prefix for this purpose:

    "s3_bucket" : "<YOUR S3 BUCKET NAME>",
    "s3_prefix" : "<YOUR S3 PREFIX>"

Additional Worker Specs

Workers have a couple of additional specs compared to rdzv.

Docker Image
"docker_image" : "torchelastic/examples:0.1.0rc1",

Note that the worker section in the specs file has an extra docker_image configuration. This is because the workers run in a docker container whereas the rendezvous backend (etcd) runs directly on the instance. The torchelastic/aws image contains torchelastic (along with all of its dependencies) and a few runtime utilities such as the fetch_and_run script that allows us to run arbitrary user scripts. For production, you may consider creating your own docker image with a custom ENTRYPOINT specific to your application.

EFS

An Elastic File System volume is mounted on each worker instance (it is mounted through all the way to the container). EFS acts much like NFS in terms of semantics. Use it as if you were using NFS. You may store your input dataset, store model checkpoints, or job outputs here.

The specified EFS volume is mounted on /mnt/efs/fs1. On the host and container.

Configure petctl

After creating a specs file, configure petctl

python3 petctl.py configure

This will prompt for the absolute path of your specs file and the AWS region. You can run the configure sub-command as many times as you wish in case you made a mistake or you need to reconfigure petctl.

You'll notice that after configuration is done, there is a config file generated under $HOME/.petctl.

This is similar to how the aws cli is configured. If you decide to skip configuration, then you must pass --specs_file and --region arguments to petctl each time (e.g. petctl --sepcs_file /home/$USER/specs.json --region us-west-2).

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