Skip to content

Latest commit

 

History

History
42 lines (25 loc) · 1.72 KB

README.md

File metadata and controls

42 lines (25 loc) · 1.72 KB
Raw

PyTorch GTC Workshop Repo

This repo contains the PyTorch Distributed Deep Learning workshop contents to run on the Nvidia DLI platform. It will simulate two host environment with 2 GPUs per host.

Docker Instructions

Use Dockerfile to build a docker image with docker build --no-cache -t ptgtc ..

Create private docker network for network connection across the hosts

docker network create -d bridge --subnet 192.168.0.0/24 --gateway 192.168.0.1 backend

Launch two docker containers (each simulating a host) using the docker image created.

Node 1:

docker run -d --name node1 --network=backend -p 8000:8888 --shm-size=1g -e NVIDIA_VISIBLE_DEVICES=0,1 --runtime=nvidia ptgtc

Node 2:

docker run -d --name node2 --network=backend -p 9000:8888 --shm-size=1g -e NVIDIA_VISIBLE_DEVICES=2,3 --runtime=nvidia ptgtc

Once the containers are running, visit the content in your browser at localhost:8000 and localhost:9000

Open a terminal window inside the juperlab browser window above and verify following commands are running.

ping node2

lsof -i -P -n to see the list of all the open ports

For testing the distributed data parallel across the two hosts, follow below sequence of steps:

  1. On node1, open two terminal windows
  2. From first terminal window run command export NCCL_DEBUG=info
  3. From first terminal window run command python ddp_tutorial.py 0 0
  4. From second terminal window run command python ddp_tutorial.py 1 1
  5. On node2, open two terminal windows
  6. From first terminal window run command export NCCL_DEBUG=info
  7. From first terminal window run command python ddp_tutorial.py 2 0
  8. From second terminal window run command python ddp_tutorial.py 3 1

The DDP training job should run and complete.