Protips¶
Below are tips and tricks to use dstack
more efficiently.
Fleets¶
By default, when running dev environments, tasks, or services, dstack apply
reuses idle
instances from existing fleets. If no idle
instances match the requirements, it creates a new fleet automatically.
To avoid creating new fleet automatically,
set creation_policy
to reuse
in the configuration.
Use fleets configurations to create fleets manually. This reduces startup time for dev environments, tasks, and services, and is very convenient if you want to reuse fleets across runs.
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 vs. services¶
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
.
Services require a gateway but they also provide additional features for production-grade service deployment not offered by tasks, such as HTTPS domains and auto-scaling. If you run a web app as a task and it works, go ahead and run it as a service.
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:
- HUGGING_FACE_HUB_TOKEN
ide: vscode
Then, you can pass the environment variable either via the shell:
HUGGING_FACE_HUB_TOKEN=... dstack apply -f .dstack.yml
Or via the -e
option of the dstack apply
command:
dstack apply -f .dstack.yml -e HUGGING_FACE_HUB_TOKEN=...
.env
A better way to configure environment variables not hardcoded in YAML is by specifying them in a .env
file:
HUGGING_FACE_HUB_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.
Data and models¶
dstack
has support for volumes
to persist data across different runs and instance interruptions.
Volumes are ideal for storing intermediate work and data that should be quickly accessible.
You can also load and save data using an object storage like S3 or HuggingFace Datasets.
For models, it's best to use services like HuggingFace Hub.
dstack
has no explicit support for object storage.
You can load and save data directly from your code.
Idle duration¶
If you run a dev environment, task, or service via dstack apply
,
and it creates a new fleet, it sets the idle duration to 5m
. If instances of the fleet are idle
for this time, dstack
terminates them.
If you create a fleet manually, the idle duration is not set.
You can override idle duration for fleets, dev environment, tasks, and services by setting
termination_idle_time
in the configuration file.
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 logs -a RUN_NAME
.
GPU¶
dstack
natively supports NVIDIA GPU, AMD GPU, and Google Cloud TPU accelerator chips.
The gpu
property withing 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)
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.
AMD
Currently, when an AMD GPU is specified, either by name or by vendor, the image
property must be specified as well.
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.