Skip to content

Protips

Below are tips and tricks to use dstack more efficiently.

Fleets

Creation policy

By default, when you run dstack apply with a dev environment, task, or service, dstack reuses idle instances from an existing fleet. If no idle instances match the requirements, dstack automatically creates a new fleet using configured backends.

To ensure dstack apply doesn't create a new fleet but reuses an existing one, pass -R (or --reuse) to dstack apply.

$ dstack apply -R -f examples/.dstack.yml

Termination policy

If a fleet is created automatically, it remains idle for 5 minutes and can be reused within that time. To change the default idle duration, set termination_idle_time in the run configuration (e.g., to 0 or a longer duration).

Fleets

For greater control over fleet provisioning, configuration, and lifecycle management, it is recommended to use fleets directly.

Volumes

To persist data across runs, it is recommended to use volumes. dstack supports two types of volumes: network (for persisting data even if the instance is interrupted) and instance (useful for persisting cached data across runs while the instance remains active).

If you use SSH fleets, you can mount network storage (e.g., NFS or SMB) to the hosts and access it in runs via instance volumes.

Dev environments

Before running a task or service, it's recommended that you first start with a dev environment. Dev environments allow you to run commands interactively.

Once the commands work, go ahead and run them as a task or a service.

Notebooks

VS Code

When you access a dev environment using your desktop VS Code, it allows you to work with Jupyter notebooks via its pre-configured and easy-to-use extension.

JupyterLab

If you prefer to use JupyterLab, you can run it as a task:

type: task

commands:
    - pip install jupyterlab
    - jupyter lab --allow-root

ports:
    - 8888

Tasks

Tasks can be used not only for batch jobs but also for web applications.

type: task
name: streamlit-task

python: "3.10"

commands:
  - pip3 install streamlit
  - streamlit hello
ports: 
  - 8501

While you run a task, dstack apply forwards the remote ports to localhost.

$ dstack apply -f app.dstack.yml

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

  Local URL: http://localhost:8501

This allows you to access the remote 8501 port on localhost:8501 while the CLI is attached.

Port mapping

If you want to override the local port, use the --port option:

$ dstack apply -f app.dstack.yml --port 3000:8501

This will forward the remote 8501 port to localhost:3000.

Tasks vs. services

Services provide external access, https, replicas with autoscaling, OpenAI-compatible endpoint and other service features. If you don't need them, you can use tasks for running apps.

Docker and Docker Compose

All backends except runpod, vastai, and kubernetes allow using Docker and Docker Compose inside dstack runs. To do that, additional configuration steps are required:

  1. Set the privileged property to true.
  2. Set the image property to dstackai/dind (or another DinD image).
  3. For tasks and services, add start-dockerd as the first command. For dev environments, add start-dockerd as the first command in the init property.

Note, start-dockerd is a part of dstackai/dind image, if you use a different DinD image, replace it with a corresponding command to start Docker daemon.

type: task
name: task-dind

privileged: true
image: dstackai/dind

commands:
  - start-dockerd
  - docker compose up

type: dev-environment
name: vscode-dind

privileged: true
image: dstackai/dind

ide: vscode

init:
  - start-dockerd
Volumes

To persist Docker data between runs (e.g. images, containers, volumes, etc), create a dstack volume and add attach it in your run configuration:

    type: dev-environment
    name: vscode-dind

    privileged: true
    image: dstackai/dind
    ide: vscode

    init:
      - start-dockerd

    volumes:
      - name: docker-volume
        path: /var/lib/docker

See more Docker examples here.

Environment variables

If a configuration requires an environment variable that you don't want to hardcode in the YAML, you can define it without assigning a value:

type: dev-environment
name: vscode

python: "3.10"

env:
  - HF_TOKEN
ide: vscode

Then, you can pass the environment variable either via the shell:

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

Or via the -e option of the dstack apply command:

$ dstack apply -e HF_TOKEN=... -f .dstack.yml
.envrc

A better way to configure environment variables not hardcoded in YAML is by specifying them in a .envrc file:

export HF_TOKEN=...

If you install direnv , it will automatically pass the environment variables from the .env file to the dstack apply command.

Remember to add .env to .gitignore to avoid pushing it to the repo.

Attached mode

By default, dstack apply runs in attached mode. This means it streams the logs as they come in and, in the case of a task, forwards its ports to localhost.

To run in detached mode, use -d with dstack apply.

If you detached the CLI, you can always re-attach to a run via dstack attach.

GPU

dstack natively supports NVIDIA GPU, AMD GPU, and Google Cloud TPU accelerator chips.

The gpu property within resources (or the --gpu option with dstack apply) allows specifying not only memory size but also GPU vendor, names, their memory, and quantity.

Examples:

  • 1 (any GPU)
  • amd:2 (two AMD GPUs)
  • A100 (A100)
  • 24GB.. (any GPU starting from 24GB)
  • 24GB..40GB:2 (two GPUs between 24GB and 40GB)
  • A10G,A100 (either A10G or A100)
  • A100:80GB (one A100 of 80GB)
  • A100:2 (two A100)
  • MI300X:4 (four MI300X)
  • A100:40GB:2 (two A100 40GB)
  • tpu:v2-8 (v2 Google Cloud TPU with 8 cores)

The GPU vendor is indicated by one of the following case-insensitive values:

  • nvidia (NVIDIA GPUs)
  • amd (AMD GPUs)
  • tpu (Google Cloud TPUs)
AMD

Currently, when an AMD GPU is specified, either by name or by vendor, the image property must be specified as well.

TPU

Currently, you can't specify other than 8 TPU cores. This means only single host workloads are supported. Support for multiple hosts is coming soon.

Monitoring metrics

While dstack allows the use of any third-party monitoring tools (e.g., Weights and Biases), you can also monitor container metrics such as CPU, memory, and GPU usage using the built-in dstack stats CLI command or the corresponding API.

Service quotas

If you're using your own AWS, GCP, Azure, or OCI accounts, before you can use GPUs or spot instances, you have to request the corresponding service quotas for each type of instance in each region.

AWS

Check this guide on EC2 service quotas. The relevant service quotas include:

  • Running On-Demand P instances (on-demand V100, A100 80GB x8)
  • All P4, P3 and P2 Spot Instance Requests (spot V100, A100 80GB x8)
  • Running On-Demand G and VT instances (on-demand T4, A10G, L4)
  • All G and VT Spot Instance Requests (spot T4, A10G, L4)
  • Running Dedicated p5 Hosts (on-demand H100)
  • All P5 Spot Instance Requests (spot H100)
GCP

Check this guide on Compute Engine service quotas. The relevant service quotas include:

  • NVIDIA V100 GPUs (on-demand V100)
  • Preemtible V100 GPUs (spot V100)
  • NVIDIA T4 GPUs (on-demand T4)
  • Preemtible T4 GPUs (spot T4)
  • NVIDIA L4 GPUs (on-demand L4)
  • Preemtible L4 GPUs (spot L4)
  • NVIDIA A100 GPUs (on-demand A100)
  • Preemtible A100 GPUs (spot A100)
  • NVIDIA A100 80GB GPUs (on-demand A100 80GB)
  • Preemtible A100 80GB GPUs (spot A100 80GB)
  • NVIDIA H100 GPUs (on-demand H100)
  • Preemtible H100 GPUs (spot H100)
Azure

Check this guide on Azure service quotas. The relevant service quotas include:

  • Total Regional Spot vCPUs (any spot instances)
  • Standard NCASv3_T4 Family vCPUs (on-demand T4)
  • Standard NVADSA10v5 Family vCPUs (on-demand A10)
  • Standard NCADS_A100_v4 Family vCPUs (on-demand A100 80GB)
  • Standard NDASv4_A100 Family vCPUs (on-demand A100 40GB x8)
  • Standard NDAMSv4_A100Family vCPUs (on-demand A100 80GB x8)
  • Standard NCadsH100v5 Family vCPUs (on-demand H100)
  • Standard NDSH100v5 Family vCPUs (on-demand H100 x8)
OCI

Check this guide on requesting OCI service limits increase. The relevant service category is compute. The relevant resources include:

  • GPUs for GPU.A10 based VM and BM instances (on-demand A10)
  • GPUs for GPU2 based VM and BM instances (on-demand P100)
  • GPUs for GPU3 based VM and BM instances (on-demand V100)

Note, for AWS, GCP, and Azure, service quota values are measured with the number of CPUs rather than GPUs.