|
|
--- |
|
|
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. |
|
|
|
