dstack is an open-source tool for managing dev environments and automating ML tasks on any cloud.
Project description
dstack
makes it very easy for ML teams to automate running dev environments and tasks in their cloud.
Installation and setup
To use dstack
, install it with pip
and start the Hub application.
pip install "dstack[aws,gcp,azure]"
dstack start
The server will set up a default project to run dev environments and tasks locally. To run dev environments and tasks in the cloud, log into the UI, create the corresponding project, and configure the CLI to use it.
Configurations
A configuration is a YAML file that describes what you want to run.
Note: All configuration files must be named with the suffix
.dstack.yml
. For example, you can name the configuration file.dstack.yml
orapp.dstack.yml
. You can define these configurations anywhere within your project.
Configurations can be of two types: dev-environment
and task
.
Below is a configuration that runs a dev environment with a pre-built environment to which you can connect via VS Code Desktop.
type: dev-environment
ide: vscode
Here's an example of a task configuration. A task can be either a batch job, such as training or fine-tuning a model, or a web application.
type: task
ports:
- 7860
commands:
- pip install -r requirements.txt
- gradio app.py
CLI
To run a configuration, use the dstack run
command and pass the path to the
directory with the configuration.
$ dstack run .
RUN CONFIGURATION USER PROJECT INSTANCE RESOURCES SPOT
fast-moth-1 .dstack.yml admin local - 5xCPUs, 15987MB auto
Starting SSH tunnel...
To open in VS Code Desktop, use this link:
vscode://vscode-remote/ssh-remote+fast-moth-1/workflow
To exit, press Ctrl+C.
The CLI automatically provisions the required cloud resources and forwards the ports to your local machine. If you interrupt the run, the cloud resources will be released automatically.
Profiles
The .dstack/profiles.yml
file allows to describe multiple profiles.
ach profile can configure the project to use and the resources required for the run.
profiles:
- name: gpu-large
project: gcp
resources:
memory: 48GB
gpu:
memory: 24GB
default: true
If you have configured the default profile, the dstack run
command will use it automatically.
Otherwise, you can always specify the profile using --profile PROFILE
.
More information
For additional information and examples, see the following links:
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