Distributed Jobs on Many VMs¶

SkyPilot supports multi-node cluster provisioning and distributed execution on many VMs.

For example, here is a simple PyTorch Distributed training example:

name: resnet-distributed-app

resources:
  accelerators: V100:4

num_nodes: 2

setup: |
  pip3 install --upgrade pip
  git clone https://github.com/michaelzhiluo/pytorch-distributed-resnet
  cd pytorch-distributed-resnet
  # SkyPilot's default image on AWS/GCP has CUDA 11.6 (Azure 11.5).
  pip3 install -r requirements.txt torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
  mkdir -p data  && mkdir -p saved_models && cd data && \
    wget -c --quiet https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
  tar -xvzf cifar-10-python.tar.gz

run: |
  cd pytorch-distributed-resnet

  num_nodes=`echo "$SKYPILOT_NODE_IPS" | wc -l`
  master_addr=`echo "$SKYPILOT_NODE_IPS" | head -n1`
  python3 -m torch.distributed.launch --nproc_per_node=$SKYPILOT_NUM_GPUS_PER_NODE \
    --nnodes=$num_nodes --node_rank=${SKYPILOT_NODE_RANK} --master_addr=$master_addr \
    --master_port=8008 resnet_ddp.py --num_epochs 20

In the above, num_nodes: 2 specifies that this task is to be run on 2 nodes, with each node having 4 V100s.

Environment variables¶

SkyPilot exposes these environment variables that can be accessed in a task’s run commands:

SKYPILOT_NODE_RANK: rank (an integer ID from 0 to num_nodes-1) of the node executing the task.
SKYPILOT_NODE_IPS: a string of IP addresses of the nodes reserved to execute the task, where each line contains one IP address.
- You can retrieve the number of nodes by echo "$SKYPILOT_NODE_IPS" | wc -l and the IP address of the third node by echo "$SKYPILOT_NODE_IPS" | sed -n 3p.
- To manipulate these IP addresses, you can also store them to a file in the run command with echo $SKYPILOT_NODE_IPS >> ~/sky_node_ips.
SKYPILOT_NUM_GPUS_PER_NODE: number of GPUs reserved on each node to execute the task; the same as the count in accelerators: <name>:<count> (rounded up if a fraction).

Launching a multi-node task (new cluster)¶

When using sky launch to launch a multi-node task on a new cluster, the following happens in sequence:

Nodes are provisioned. (barrier)
Workdir/file_mounts are synced to all nodes. (barrier)
setup commands are executed on all nodes. (barrier)
run commands are executed on all nodes.

Launching a multi-node task (existing cluster)¶

When using sky launch to launch a multi-node task on an existing cluster, the cluster may have more nodes than the current task’s num_nodes requirement.

The following happens in sequence:

SkyPilot checks the runtime on all nodes are up-to-date. (barrier)
Workdir/file_mounts are synced to all nodes. (barrier)
setup commands are executed on all nodes of the cluster. (barrier)
run commands are executed on the subset of nodes scheduled to execute the task, which may be fewer than the cluster size.

Tip

To skip rerunning the setup commands, use either sky launch --no-setup ... (performs steps 1, 2, 4 above) or sky exec (performs step 2 (workdir only) and step 4).

Executing a task on the head node only¶

To execute a task on the head node only (a common scenario for tools like mpirun), use the SKYPILOT_NODE_RANK environment variable as follows:

...

num_nodes: <n>

run: |
  if [ "${SKYPILOT_NODE_RANK}" == "0" ]; then
      # Launch the head-only command here.
  fi

Distributed Jobs on Many VMs

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