task¶
The task configuration type allows running tasks.
Configuration files must be inside the project repo, and their names must end with
.dstack.yml(e.g..dstack.ymlortrain.dstack.ymlare both acceptable). Any configuration can be run viadstack apply.
Examples¶
Python version¶
If you don't specify image, dstack uses its base Docker image pre-configured with
python, pip, conda (Miniforge), and essential CUDA drivers.
The python property determines which default Docker image is used.
type: task
# The name is optional, if not specified, generated randomly
name: train
# If `image` is not specified, dstack uses its base image
python: "3.10"
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
nvcc
By default, the base Docker image doesn’t include nvcc, which is required for building custom CUDA kernels.
If you need nvcc, set the corresponding property to true.
type: task
# The name is optional, if not specified, generated randomly
name: train
# If `image` is not specified, dstack uses its base image
python: "3.10"
# Ensure nvcc is installed (req. for Flash Attention)
nvcc: true
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
Ports¶
A task can configure ports. In this case, if the task is running an application on a port, dstack run
will securely allow you to access this port from your local machine through port forwarding.
type: task
# The name is optional, if not specified, generated randomly
name: train
python: "3.10"
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- tensorboard --logdir results/runs &
- python fine-tuning/qlora/train.py
# Expose the port to access TensorBoard
ports:
- 6000
When running it, dstack run forwards 6000 port to localhost:6000, enabling secure access.
Docker image¶
type: dev-environment
# The name is optional, if not specified, generated randomly
name: train
# Any custom Docker image
image: dstackai/base:py3.10-0.5-cuda-12.1
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
Private registry
Use the registry_auth property to provide credentials for a private Docker registry.
type: dev-environment
# The name is optional, if not specified, generated randomly
name: train
# Any private Docker image
image: dstackai/base:py3.10-0.5-cuda-12.1
# Credentials of the private Docker registry
registry_auth:
username: peterschmidt85
password: ghp_e49HcZ9oYwBzUbcSk2080gXZOU2hiT9AeSR5
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
Resources¶
If you specify memory size, you can either specify an explicit size (e.g. 24GB) or a
range (e.g. 24GB.., or 24GB..80GB, or ..80GB).
type: task
# The name is optional, if not specified, generated randomly
name: train
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
resources:
# 200GB or more RAM
memory: 200GB..
# 4 GPUs from 40GB to 80GB
gpu: 40GB..80GB:4
# Shared memory (required by multi-gpu)
shm_size: 16GB
# Disk size
disk: 500GB
The gpu property allows specifying not only memory size but also GPU vendor, names
and their quantity. Examples: nvidia (one NVIDIA GPU), A100 (one A100), A10G,A100 (either A10G or A100),
A100:80GB (one A100 of 80GB), A100:2 (two A100), 24GB..40GB:2 (two GPUs between 24GB and 40GB),
A100:40GB:2 (two A100 GPUs of 40GB).
Google Cloud TPU
To use TPUs, specify its architecture via the gpu property.
type: task
# The name is optional, if not specified, generated randomly
name: train
python: "3.10"
# Commands of the task
commands:
- pip install torch~=2.3.0 torch_xla[tpu]~=2.3.0 torchvision -f https://storage.googleapis.com/libtpu-releases/index.html
- git clone --recursive https://github.com/pytorch/xla.git
- python3 xla/test/test_train_mp_imagenet.py --fake_data --model=resnet50 --num_epochs=1
resources:
gpu: v2-8
Currently, only 8 TPU cores can be specified, supporting single host workloads. Multi-host support is coming soon.
Shared memory
If you are using parallel communicating processes (e.g., dataloaders in PyTorch), you may need to configure
shm_size, e.g. set it to 16GB.
Environment variables¶
type: task
python: "3.10"
# Environment variables
env:
- HUGGING_FACE_HUB_TOKEN
- HF_HUB_ENABLE_HF_TRANSFER=1
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
If you don't assign a value to an environment variable (see
HUGGING_FACE_HUB_TOKENabove),dstackwill require the value to be passed via the CLI or set in the current process.
For instance, you can define environment variables in a .envrc file and utilize tools like direnv.
System environment variables¶
The following environment variables are available in any run and are passed by dstack by default:
| Name | Description |
|---|---|
DSTACK_RUN_NAME |
The name of the run |
DSTACK_REPO_ID |
The ID of the repo |
DSTACK_GPUS_NUM |
The total number of GPUs in the run |
DSTACK_NODES_NUM |
The number of nodes in the run |
DSTACK_NODE_RANK |
The rank of the node |
DSTACK_MASTER_NODE_IP |
The internal IP address the master node |
Distributed tasks¶
By default, the task runs on a single node. However, you can run it on a cluster of nodes.
type: task
# The name is optional, if not specified, generated randomly
name: train-distrib
# The size of the cluster
nodes: 2
python: "3.10"
# Commands of the task
commands:
- pip install -r requirements.txt
- torchrun
--nproc_per_node=$DSTACK_GPUS_PER_NODE
--node_rank=$DSTACK_NODE_RANK
--nnodes=$DSTACK_NODES_NUM
--master_addr=$DSTACK_MASTER_NODE_IP
--master_port=8008 resnet_ddp.py
--num_epochs 20
resources:
gpu: 24GB
If you run the task, dstack first provisions the master node and then runs the other nodes of the cluster.
All nodes are provisioned in the same region.
dstackis easy to use withaccelerate,torchrun, and other distributed frameworks. All you need to do is pass the corresponding environment variables such asDSTACK_GPUS_PER_NODE,DSTACK_NODE_RANK,DSTACK_NODES_NUM,DSTACK_MASTER_NODE_IP, andDSTACK_GPUS_NUM(see System environment variables).
Backends
Running on multiple nodes is supported only with the aws, gcp, azure, oci backends, or
SSH fleets.
Web applications¶
Here's an example of using ports to run web apps with tasks.
type: task
# The name is optional, if not specified, generated randomly
name: streamlit-hello
python: "3.10"
# Commands of the task
commands:
- pip3 install streamlit
- streamlit hello
# Expose the port to access the web app
ports:
- 8501
Spot policy¶
You can choose whether to use spot instances, on-demand instances, or any available type.
type: task
# The name is optional, if not specified, generated randomly
name: train
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
# Use either spot or on-demand instances
spot_policy: auto
The spot_policy accepts spot, on-demand, and auto. The default for tasks is on-demand.
Queueing tasks¶
By default, if dstack apply cannot find capacity, the task fails.
To queue the task and wait for capacity, specify the retry
property:
type: task
# The name is optional, if not specified, generated randomly
name: train
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
retry:
# Retry on no-capacity errors
on_events: [no-capacity]
# Retry within 1 day
duration: 1d
Backends¶
By default, dstack provisions instances in all configured backends. However, you can specify the list of backends:
type: task
# The name is optional, if not specified, generated randomly
name: train
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
# Use only listed backends
backends: [aws, gcp]
Regions¶
By default, dstack uses all configured regions. However, you can specify the list of regions:
type: task
# The name is optional, if not specified, generated randomly
name: train
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
# Use only listed regions
regions: [eu-west-1, eu-west-2]
Volumes¶
Volumes allow you to persist data between runs.
To attach a volume, simply specify its name using the volumes property and specify where to mount its contents:
type: task
# The name is optional, if not specified, generated randomly
name: vscode
python: "3.10"
# Commands of the task
commands:
- pip install -r fine-tuning/qlora/requirements.txt
- python fine-tuning/qlora/train.py
# Map the name of the volume to any path
volumes:
- name: my-new-volume
path: /volume_data
Once you run this configuration, the contents of the volume will be attached to /volume_data inside the task,
and its contents will persist across runs.
Limitations
When you're running a dev environment, task, or service with dstack, it automatically mounts the project folder contents
to /workflow (and sets that as the current working directory). Right now, dstack doesn't allow you to
attach volumes to /workflow or any of its subdirectories.
The task configuration type supports many other options. See below.