Distributed Experiments

Early access feature

Only the PyTorch framework is supported distributed experiment currently.

What is a distributed experiment?

A distributed experiment is a single machine learning run on top of multi-node or multi-GPUs. The distributed experiment results are consist of logs, metrics, and artifacts for each worker which you can find under corresponding tabs.

Caveats

Multi-node training is not always an optimal solution. We recommend you try several experiments with a few epochs to see if multi-node training is the correct choice for you.

Environment variables

VESSL automatically sets the below environment variables based on the configuration.

NUM_NODES: Number of workers

NUM_TRAINERS: Number of GPUs per node

RANK: The global rank of node

MASTER_ADDR: The address of the master node service

MASTER_PORT: The port number on the master address

Creating a distributed experiment

Using Web Console

Running a distributed experiment on the web console is similar to a single node experiment. To create a distributed experiment, you only need to specify the number of workers. Other options are the same as those of a single node experiment.

Creating an Experiment

Using CLI

To run a distributed experiment using CLI, the number of nodes must be set to an integer greater than one.

vessl experiment create --worker-count 2 --framework-type pytorch

Examples: Distributed CIFAR

You can find the full example codes here.

Step 1: Prepare CIFAR-10 dataset

Download the CIFAR dataset with the scripts below. and add a vessl type dataset to your organization.

wget -c --quiet https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
tar -xvzf cifar-10-python.tar.gz

Adding New Datasets

Or, you can simply add an AWS S3 type dataset to your organization with the following public bucket URI.

s3://savvihub-public-apne2/cifar-10

Step 2: Create a distributed experiment

To run a distributed experiment we recommend to use torch.distributed.launch package. The example start command that runs on two nodes and one GPU for each node is as follows.

python -m torch.distributed.launch  \
  --nnodes=$NUM_NODES  \
  --nproc_per_node=$NUM_TRAINERS  \
  --node_rank=$RANK \
  --master_addr=$MASTER_ADDR \
  --master_port=$MASTER_PORT \
  examples/distributed_cifar/pytorch/main.py

VESSL will automatically set environment variables of --node_rank, --master_addr, --master_port, --nproc_per_node and --nnodes.

Files

In a distributed experiment, all workers share an output storage. Please be aware that files can be overrided by other workers when you use same output path.