SkyPilot: An intercloud broker for the clouds
Project description
SkyPilot is a framework for easily running machine learning[1] workloads on any cloud through a unified interface. No knowledge of cloud offerings is required or expected – you simply define the workload and its resource requirements, and SkyPilot will automatically execute it on AWS, Google Cloud Platform or Microsoft Azure.
Key features
- Run existing projects on the cloud with zero code changes
- No cloud lock-in – seamlessly run your code across different cloud providers (AWS, Azure or GCP)
- Minimize costs by leveraging spot instances and automatically stopping idle clusters
- Automatic recovery from spot instance failures
- Automatic fail-over to find resources across regions and clouds
- Store datasets on the cloud and access them like you would on a local file system
- Easily manage job queues across multiple clusters
Getting Started
You can find our documentation here.
Example SkyPilot Task
Tasks in SkyPilot are specified as a YAML file containing the resource requirements, data to be synced, setup commands and the task commands. Here is an example.
# my-task.yaml
resources:
# 1x NVIDIA V100 GPU
accelerators: V100:1
# Number of VMs to launch in the cluster
num_nodes: 1
# Working directory (optional) containing the project codebase.
# Its contents are synced to ~/sky_workdir/ on the cluster.
workdir: .
# Commands to be run before executing the job
# Typical use: pip install -r requirements.txt, git clone, etc.
setup: |
echo "Running setup."
# Commands to run as a job
# Typical use: make use of resources, such as running training.
run: |
echo "Hello, SkyPilot!"
conda env list
This task can be launched on the cloud with the sky launch
command.
$ sky launch my-task.yaml
SkyPilot will perform multiple functions for you:
- Find the lowest priced VM instance type across different clouds
- Provision the VM
- Copy the local contents of
workdir
to the VM - Run the task's
setup
commands to prepare the VM for running the task - Run the task's
run
commands
Please refer to Quickstart for more on how to use SkyPilot.
Issues, feature requests and questions
We are excited to hear your feedback! SkyPilot has two channels for engaging with the community - GitHub Issues and GitHub Discussions.
- For bug reports and issues, please open an issue.
- For feature requests or general questions, please join us on GitHub Discussions.
Contributing
We welcome and value all contributions to the project! Please refer to the contribution guide for more on how to get involved.
[1]: SkyPilot is primarily targeted at machine learning workloads, but it can also support many general workloads. We're excited to hear about your use case and would love to hear more about how we can better support your requirements - please join us in this discussion!
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