0.15.1

Kubernetes

With the latest update, it's now possible to configure a Kubernetes backend. In this case, if you run a workload, dstack will provision infrastructure within your Kubernetes cluster. This may work with both self-managed and managed clusters.

Specifying a custom VPC for AWS

If you're using dstack with AWS, it's now possible to configure a vpc_name via ~/.dstack/server/config.yml.

** Learn more about the new features in detail on the changelog page.**

What's changed

• Print total offers count in run plan by @Egor-S in #862
• Add OpenAPI reference to the docs by @Egor-S in #863
• Fixes #864 by pinning the APScheduler dep to < 4 by @tleyden in #867
• Support gateway creation for Kubernetes by @r4victor in #870
• Improve get_latest_runner_build by @Egor-S in #871
• Added ruff by @TheBits in #850
• Handle ResourceNotExistsError instead of 404 by @r4victor in #875
• Simplify Kubernetes backend config by @r4victor in #879
• Add SSH keys to GCP metadata by @Egor-S in #881
• Allow to configure VPC for an AWS backend by @r4victor in #883

New contributors

• @tleyden made their first contribution in #867

Full Changelog: 0.15.0...0.15.1

0.15.0

Resources

It is now possible to configure resources in the YAML configuration file:

type: dev-environment

python: 3.11
ide: vscode

# (Optional) Configure `gpu`, `memory`, `disk`, etc 
resources:
  gpu: 24GB

Supported properties include: gpu, cpu, memory, disk, and shm_size.

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).

The gpu property allows specifying not only memory size but also GPU names
and their quantity. Examples: 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), etc.

Authorization in services

Service endpoints now require the Authentication header with "Bearer <dstack token>". This also includes the OpenAI-compatible endpoints.

from openai import OpenAI


client = OpenAI(
  base_url="https://gateway.example.com",
  api_key="<dstack token>"
)

completion = client.chat.completions.create(
  model="mistralai/Mistral-7B-Instruct-v0.1",
  messages=[
    {"role": "user", "content": "Compose a poem that explains the concept of recursion in programming."}
  ]
)

print(completion.choices[0].message)

Authentication can be disabled by setting auth to false in the service configuration file.

OpenAI format in model mapping

Model mapping (required to enable OpenAI interact) now supports format: openai.

For example, if you run vLLM using the OpenAI mode, it's possible to configure model mapping for it.

type: service

python: "3.11"
env:
  - MODEL=NousResearch/Llama-2-7b-chat-hf
commands:
  - pip install vllm
  - python -m vllm.entrypoints.openai.api_server --model $MODEL --port 8000
port: 8000

resources:
  gpu: 24GB

model:
  format: openai
  type: chat
  name: NousResearch/Llama-2-7b-chat-hf

What's changed

• Configuration resources & ranges by @Egor-S in #844
• Range.str always returns a string by @Egor-S in #845
• Add infinity example by @deep-diver in #847
• error in documentation: use --url instead of --server by @promsoft in #852
• Support authorization on the gateway by @Egor-S in #851
• Implement Kubernetes backend by @r4victor in #853
• Add gpu support for kubernetes by @r4victor in #856
• Resources parse and store by @Egor-S in #857
• Use python3.11 in generate-json-schema by @r4victor in #859
• Implement OpenAI to OpenAI adapter for gateway by @Egor-S in #860

New contributors

• @deep-diver made their first contribution in #847
• @promsoft made their first contribution in #852

Full Changelog: 0.14.0...0.15.0

0.14.0

OpenAI-compatible endpoints

With the latest update, we are extending the service configuration in dstack to enable you to optionally map your custom LLM to an OpenAI-compatible endpoint.

To learn more about how the new feature, read our blog post on it.

What's changed

• Make gateway active by @Egor-S in #829
• Implement OpenAI streaming for TGI by @Egor-S in #833
• Make get_latest_runner_build robuster for editable installs by @Egor-S in #834
• Fix descending logs by @r4victor in #839
• Reraise Jinja2 TemplateError by @Egor-S in #840

Full Changelog: 0.13.1...0.14.0

0.13.1

Mounting repos via Python API

If you submit a task or a service via the Python API, you can now specify the repo with the Client.runs.submit method.

This argument accepts an instance of dstack.api.LocalRepo (which allows you to mount additional files to the run from a local folder), dstack.api.RemoteRepo (which allows you to mount additional files to the run from a remote Git repo), or dstack.api.VirtualRepo (which allows you to mount additional files to the run programmatically).

Here's an example:

repo=RemoteRepo.from_url(
    repo_url="https://github.com/dstackai/dstack-examples",
    repo_branch="main"
)
client.repos.init(repo)

run = client.runs.submit(
    configuration=...,
    repo=repo,
)

This allows you to access the additional files in your run from the mounted repo.

More examples are now available in the API documentation.

Note that the Python API is just one possible way to manage runs. Another one is the CLI. When using the CLI, it automatically mounts the repo in the current folder.

Bug-fixes

Among other improvements, the update addresses the issue that previously prevented the ability to pass custom arguments to the run using ${{ run.args }} in the YAML configuration.

Here's an example:

type: task

python: "3.11" # (Optional) If not specified, your local version is used

commands:
  - pip install -r requirements.txt
  - python train.py ${{ run.args }}
 ``

Now, you can pass custom arguments to the run via `dstack run`:

```shell
dstack run . -f train.dstack.yml --gpu A100 --train_batch_size=1 --num_train_epochs=100

In this case --train_batch_size=1 --num_train_epochs=100 will be passed to python train.py.

Contribution guide

Last but not least, we've extended our contribution guide with a new wiki page that guides you through the steps of adding a custom backend. This can be helpful if you decide to extend dstack with support for a custom backend (cloud provider).

Feel free to check out this new wiki page and share your feedback. As always, if you need help with adding custom backend support, you can always ask for assistance from our team.

0.13.0

Disk size

Previously, dstack set the disk size to 100GB regardless of the cloud provider. Now, to accommodate larger language
models and datasets, dstack enables setting a custom disk size using --disk in dstack run or via the disk
property in .dstack/profiles.yml.

CUDA 12.1

We've upgraded the default Docker image's CUDA drivers to 12.1 (for better compatibility with modern libraries).

Mixtral 8x7B

Lastly, and most importantly, we've added the example showing how to deploy Mixtral 8x7B as a service.

0.12.4

Bug-fixes

• Resolves issues related to TensorDock.
• Enhances error handling. Previously, server errors were only visible when the debug log level was set. Now, errors appear regardless of the log level.
• The dstack.FineTuningTask failed because of a missing file
• Lastly, if you're using dstack Cloud, ensure you update to this version for compatibility.

0.12.3

Vast.ai

With dstack 0.12.3, you can now use dstack with Vast.ai, a marketplace providing GPUs from independent hosts at notably lower prices.

Configuring Vast.ai is very easy. Log into your Vast AI account, click Account in the sidebar, and copy your
API Key.

Then, go ahead and configure the backend via ~/.dstack/server/config.yml:

projects:
- name: main
  backends:
  - type: vastai
    creds:
      type: api_key
      api_key: d75789f22f1908e0527c78a283b523dd73051c8c7d05456516fc91e9d4efd8c5

Now you can restart the server and proceed to using the CLI or API for running development environments, tasks, and services.

$ dstack run --gpu 24GB --backend vastai --max-price 0.4

#  REGION            INSTANCE  RESOURCES                       PRICE
1  pl-greaterpoland  6244171   16xCPU, 32GB, 1xRTX3090 (24GB)  $0.18478
2  ee-harjumaa       6648481   16xCPU, 64GB, 1xA5000 (24GB)    $0.29583
3  pl-greaterpoland  6244172   32xCPU, 64GB, 2XRTX3090 (24GB)  $0.36678

Continue? [y/n]:

0.12.2

TensorDock

With dstack 0.12.2, you can now access TensorDock's cloud GPUs, leveraging their highly competitive pricing.

Configuring your TensorDock account with dstack is very easy. Simply generate an authorization key in your TensorDock
API settings and set it up in ~/.dstack/server/config.yml:

projects:
- name: main
  backends:
  - type: tensordock
    creds:
      type: api_key
      api_key: 248e621d-9317-7494-dc1557fa5825b-98b
      api_token: FyBI3YbnFEYXdth2xqYRnQI7hiusssBC

Now you can restart the server and proceed to using the CLI or API for running development environments, tasks, and services.

dstack run . -f .dstack.yml --gpu 40GB

 Min resources  1xGPU (40GB)
 Max price      -
 Max duration   6h
 Retry policy   no

 #  REGION        INSTANCE  RESOURCES                     SPOT  PRICE
 1  unitedstates  ef483076  10xCPU, 80GB, 1xA6000 (48GB)  no    $0.6235
 2  canada        0ca177e7  10xCPU, 80GB, 1xA6000 (48GB)  no    $0.6435
 3  canada        45d0cabd  10xCPU, 80GB, 1xA6000 (48GB)  no    $0.6435
    ...

Continue? [y/n]:

0.12.0

Server configuration

Previously, the only way to configure clouds for a project was through the UI. Additionally, you had to specify not only the credentials but also set up a storage bucket for each cloud to store metadata.

Now, you can configure clouds for a project via ~/.dstack/server/config.yml. Example:

projects:
- name: main
  backends:
  - type: aws
    creds:
      type: access_key
      access_key: AIZKISCVKUKO5AAKLAEH
      secret_key: QSbmpqJIUBn1V5U3pyM9S6lwwiu8/fOJ2dgfwFdW

Enhanced Python API

The earlier introduced Python API is now greatly refined.

Creating a dstack client is as easy as this:

from dstack.api import Client, ClientError

try:
    client = Client.from_config()
except ClientError:
    print("Can't connect to the server")

Now, you can submit a task or a service:

from dstack.api import Task, Resources, GPU

task = Task(
    image="ghcr.io/huggingface/text-generation-inference:latest",
    env={"MODEL_ID": "TheBloke/Llama-2-13B-chat-GPTQ"},
    commands=[
        "text-generation-launcher --trust-remote-code --quantize gptq",
    ],
    ports=["80"],
)

run = client.runs.submit(
    run_name="my-awesome-run",
    configuration=task,
    resources=Resources(gpu=GPU(memory="24GB")),
)

The dstack.api.Run instance provides methods for various operations including attaching to the run,
forwarding ports to localhost, retrieving status, stopping, and accessing logs. For more details, refer to
the example and reference.

Other changes

• Because we've prioritized CLI and API UX over the UI, the UI is no longer bundled.
  Please inform us if you experience any significant inconvenience related to this.
• Gateways should now be configured using the dstack gateway command, and their usage requires you to specify a domain.
  Learn more about how to set up a gateway.
• The dstack start command is now dstack server.
• The Python API classes were moved from the dstack package to dstack.api.

Migration

Unfortunately, when upgrading to 0.12.0, there is no automatic migration for data.
This means you'll need to delete ~/.dstack and configure dstack from scratch.

1. pip install "dstack[all]==0.12.0"
2. Delete ~/.dstack
3. Configure clouds via ~/.dstack/server/config.yml (see the new guide)
4. Run dstack server

0.11.3

Full Changelog: 0.11.2...0.11.3