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Quickstart

Before using dstack, ensure you've installed the server, or signed up for dstack Sky .

Initialize a repo

To use dstack's CLI in a folder, first run dstack init within that folder.

$ mkdir quickstart && cd quickstart
$ dstack init

Your folder can be a regular local folder or a Git repo.

Run a configuration

A dev environment lets you provision an instance and access it with your desktop IDE.

Define a configuration

Create the following configuration file inside the repo:

type: dev-environment
name: vscode

# If `image` is not specified, dstack uses its default image
python: "3.11"
#image: dstackai/base:py3.13-0.6-cuda-12.1

ide: vscode

# Uncomment to request resources
#resources:
#  gpu: 24GB
Run the configuration

Run the configuration via dstack apply:

$ dstack apply -f .dstack.yml

 #  BACKEND  REGION           RESOURCES                 SPOT  PRICE
 1  gcp      us-west4         2xCPU, 8GB, 100GB (disk)  yes   $0.010052
 2  azure    westeurope       2xCPU, 8GB, 100GB (disk)  yes   $0.0132
 3  gcp      europe-central2  2xCPU, 8GB, 100GB (disk)  yes   $0.013248

Submit the run vscode? [y/n]: y

Launching `vscode`...
---> 100%

To open in VS Code Desktop, use this link:
  vscode://vscode-remote/ssh-remote+vscode/workflow

Open the link to access the dev environment using your desktop IDE.

Alternatively, you can access it via ssh <run name>.

A task allows you to schedule a job or run a web app. Tasks can be distributed and can forward ports.

Define a configuration

Create the following configuration file inside the repo:

type: task
name: streamlit

# If `image` is not specified, dstack uses its default image
python: "3.11"
#image: dstackai/base:py3.13-0.6-cuda-12.1

# Commands of the task
commands:
  - pip install streamlit
  - streamlit hello
# Ports to forward
ports:
  - 8501

# Uncomment to request resources
#resources:
#  gpu: 24GB

By default, tasks run on a single instance. To run a distributed task, specify nodes, and dstack will run it on a cluster.

Run the configuration

Run the configuration via dstack apply:

$ dstack apply -f task.dstack.yml

 #  BACKEND  REGION           RESOURCES                 SPOT  PRICE
 1  gcp      us-west4         2xCPU, 8GB, 100GB (disk)  yes   $0.010052
 2  azure    westeurope       2xCPU, 8GB, 100GB (disk)  yes   $0.0132
 3  gcp      europe-central2  2xCPU, 8GB, 100GB (disk)  yes   $0.013248

Submit the run streamlit? [y/n]: y

Provisioning `streamlit`...
---> 100%

  Welcome to Streamlit. Check out our demo in your browser.

  Local URL: http://localhost:8501

If you specified ports, they will be automatically forwarded to localhost for convenient access.

A service allows you to deploy a model or any web app as an endpoint.

Define a configuration

Create the following configuration file inside the repo:

type: service
name: llama31-service

# If `image` is not specified, dstack uses its default image
python: "3.11"
#image: dstackai/base:py3.13-0.6-cuda-12.1

# Required environment variables
env:
  - HF_TOKEN
commands:
  - pip install vllm
  - vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct --max-model-len 4096
# Expose the vllm server port
port: 8000

# Specify a name if it's an OpenAI-compatible model
model: meta-llama/Meta-Llama-3.1-8B-Instruct

# Required resources
resources:
  gpu: 24GB
Run the configuration

Run the configuration via dstack apply:

$ HF_TOKEN=...
$ dstack apply -f service.dstack.yml

 #  BACKEND  REGION     INSTANCE       RESOURCES                    SPOT  PRICE
 1  aws      us-west-2  g5.4xlarge     16xCPU, 64GB, 1xA10G (24GB)  yes   $0.22
 2  aws      us-east-2  g6.xlarge      4xCPU, 16GB, 1xL4 (24GB)     yes   $0.27
 3  gcp      us-west1   g2-standard-4  4xCPU, 16GB, 1xL4 (24GB)     yes   $0.27

Submit the run llama31-service? [y/n]: y

Provisioning `llama31-service`...
---> 100%

Service is published at: 
  http://localhost:3000/proxy/services/main/llama31-service/
Model meta-llama/Meta-Llama-3.1-8B-Instruct is published at:
  http://localhost:3000/proxy/models/main/

Gateway

To enable auto-scaling, or use a custom domain with HTTPS, set up a gateway before running the service. If you're using dstack Sky , a gateway is pre-configured for you.

dstack apply automatically provisions instances, uploads the code from the current repo (incl. your local uncommitted changes).

Troubleshooting

Something not working? See the troubleshooting guide.

What's next?

  1. Read about dev environments, tasks, services, and fleets
  2. Join Discord
  3. Browse examples