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0.18.0

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@peterschmidt85 peterschmidt85 released this 10 Apr 15:46
· 499 commits to master since this release

RunPod

The update adds the long-awaited integration with RunPod, a distributed GPU cloud that offers GPUs at affordable prices.

To use RunPod, specify your RunPod API key in ~/.dstack/server/config.yml:

projects:
- name: main
  backends:
  - type: runpod
    creds:
      type: api_key
      api_key: US9XTPDIV8AR42MMINY8TCKRB8S4E7LNRQ6CAUQ9

Once the server is restarted, go ahead and run workloads.

Clusters

Another major change with the update is the ability to run multi-node tasks over an interconnected cluster of instances.

type: task

nodes: 2

commands:
  - git clone https://github.com/r4victor/pytorch-distributed-resnet.git
  - cd pytorch-distributed-resnet
  - mkdir -p data
  - cd data
  - wget -c --quiet https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
  - tar -xvzf cifar-10-python.tar.gz
  - cd ..
  - pip3 install -r requirements.txt torch
  - mkdir -p saved_models
  - torchrun --nproc_per_node=$DSTACK_GPUS_PER_NODE 
     --node_rank=$DSTACK_NODE_RANK 
     --nnodes=$DSTACK_NODES_NUM
     --master_addr=$DSTACK_MASTER_NODE_IP
     --master_port=8008 resnet_ddp.py 
     --num_epochs 20

resources:
  gpu: 1

Currently supported providers for this feature include AWS, GCP, and Azure.

Other

  • The commands property is now not required for tasks and services if you use an image that has a default entrypoint configured.
  • The permissions required for using dstack with GCP are more granular.

What's changed

Full changelog: 0.17.0...0.18.0