Quickstart¶
Before using
dstack, ensure you've installed the server, or signed up for dstack Sky .
Initialize a repo¶
Before using dstack's CLI in a directory, initialize the directory as a repo with dstack init.
$ mkdir quickstart && cd quickstart
$ dstack init
Apply a configuration¶
A dev environment lets you provision an instance and access it with your desktop IDE.
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:py3.13-0.7-cuda-12.1
ide: vscode
# Uncomment to request resources
#resources:
# gpu: 24GB
Apply 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.
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:py3.13-0.7-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 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.
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:py3.13-0.7-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 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, rate limits, 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 applyautomatically provisions instances, uploads the contents of the repo (incl. your local uncommitted changes), and runs the configuration.
Troubleshooting¶
Something not working? See the troubleshooting guide.