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---
title: Run distributed training jobs
---
<Note>Only the PyTorch framework is supported distributed experiment currently.</Note>
### 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.
<Warning>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.</Warning>
#### 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.
#### Using CLI
To run a distributed experiment using CLI, the number of nodes must be set to an integer greater than one.
```bash
vessl experiment create --worker-count 2 --framework-type pytorch
```
### Examples: Distributed CIFAR
You can find the full example codes [here](https://github.com/savvihub/examples/tree/main/distributed\_cifar).
#### Step 1: Prepare CIFAR-10 dataset
Download the CIFAR dataset with the scripts below. and add a vessl type dataset to your organization.
```bash
wget -c --quiet https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
tar -xvzf cifar-10-python.tar.gz
```
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`](https://pytorch.org/docs/stable/distributed.html) 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.
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