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
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 contents of the repo (incl. your local uncommitted changes),
and runs the configuration.
Troubleshooting¶
Something not working? See the troubleshooting guide.