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Releases: dstackai/dstack-enterprise

0.18.10-v1

13 Aug 15:20
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0.18.10-v1
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0.18.10-v1

0.18.10

The update includes all the features and bug fixes from version 0.18.10.

Environment variables interpolation

Previously, it wasn't possible to use environment variables to configure credentials for a private Docker registry. With this update, you can now use the following interpolation syntax to avoid hardcoding credentials in the configuration.

type: dev-environment
name: train

env:
  - DOCKER_USER
  - DOCKER_USERPASSWORD

image: dstackai/base:py3.10-0.4-cuda-12.1
registry_auth:
  username: ${{ env.DOCKER_USER }}
  password: ${{ env.DOCKER_USERPASSWORD }}

Network interfaces for port forwarding

When you run a dev environment or a task with dstack apply, it automatically forwards the remote ports to localhost. However, these ports are, by default, bound to 127.0.0.1. If you'd like to make a port available on an arbitrary host, you can now specify the host using the --host option.

For example, this command will make the port available on all network interfaces:

dstack apply --host 0.0.0.0 -f my-task.dstack.yml

Major bugfixes

Other

All changes: dstackai/dstack@0.18.9...0.18.10

Contributors

un-def, r4victor, and 2 other contributors
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0.18.9-v1

07 Aug 16:13
@peterschmidt85 peterschmidt85
0.18.9-v1
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0.18.9-v1

0.18.9

The update includes all the features and bug fixes from version 0.18.9.

Base Docker image with nvcc

If you don't specify a custom Docker image, dstack uses its own base image with essential CUDA drivers, python, pip, and conda (Miniforge). Previously, this image didn't include nvcc, needed for compiling custom CUDA kernels (e.g., Flash Attention).

With version 0.18.9, you can now include nvcc.

type: task

python: "3.10"
# This line ensures `nvcc` is included into the base Docker image
nvcc: true

commands:
  - pip install -r requirements.txt
  - python train.py

resources:
  gpu: 24GB

Environment variables for on-prem fleets

When you create an on-prem fleet, it's now possible to pre-configure environment variables. These variables will be used when installing the dstack-shim service on hosts and running workloads.

For example, these environment variables can be used to configure dstack to use a proxy:

type: fleet
name: my-fleet

placement: cluster

env:
- HTTP_PROXY=http://proxy.example.com:80
- HTTPS_PROXY=http://proxy.example.com:80
- NO_PROXY=localhost,127.0.0.1

ssh_config:
  user: ubuntu
  identity_file: ~/.ssh/id_rsa
  hosts:
    - 3.255.177.51
    - 3.255.177.52

Examples

New examples include:

  • Llama 3.1 recipes for inference and fine-tuning
  • Spark cluster setup
  • Ray cluster setup

Other

Full changelog: https://github.com/dstackai/dstack/releases/0.18.9

Contributors

un-def, r4victor, and 2 other contributors
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0.18.8-v1

01 Aug 15:39
@peterschmidt85 peterschmidt85
0.18.8-v1
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0.18.8-v1

0.18.8

The update includes all the features and bug fixes from version 0.18.8.

GCP volumes

Now, volumes are also supported for the gcp backend:

type: volume
name: my-gcp-volume
backend: gcp
region: europe-west1
size: 100GB

Previously, volumes were only supported for aws and runpod.

Major bugfixes

The update fixes a major bug introduced in 0.18.7 that could prevent instances from being terminated in the cloud.

Other

Full changelog: https://github.com/dstackai/dstack/releases/0.18.8

Contributors

un-def, r4victor, and peterschmidt85
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0.18.7-v1

29 Jul 14:12
@peterschmidt85 peterschmidt85
0.18.7-v1
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0.18.7-v1

0.18.7

The update brings all the features and bug fixes introduced in version 0.18.7.

Fleets

With fleets, you can now describe clusters declaratively and create them in both cloud and on-prem with a single command. Once a fleet is created, it can be used with dev environments, tasks, and services.

Cloud fleets

To provision a fleet in the cloud, specify the required resources, number of nodes, and other optional parameters.

type: fleet
name: my-fleet
placement: cluster
nodes: 2
resources:
  gpu: 24GB

On-prem fleets

To create a fleet from on-prem servers, specify their hosts along with the user, port, and SSH key for connection via SSH.

type: fleet
name: my-fleet
placement: cluster
ssh_config:
  user: ubuntu
  identity_file: ~/.ssh/id_rsa
  hosts:
    - 3.255.177.51
    - 3.255.177.52

To create or update the fleet, simply call the dstack apply command:

dstack apply -f examples/fleets/my-fleet.dstack.yml

Learn more about fleets in the documentation.

Deprecating dstack run

Now that we support dstack apply for gateways, volumes, and fleets, we have extended this support to dev environments, tasks, and services. Instead of using dstack run WORKING_DIR -f CONFIG_FILE, you can now use dstack apply -f CONFIG_FILE.

Also, it's now possible to specify a name for dev environments, tasks, and services, just like for gateways, volumes, and fleets.

type: dev-environment
name: my-ide

python: "3.11"

ide: vscode

resources:
  gpu: 80GB

This name is used as a run name and is more convenient than a random name. However, if you don't specify a name, dstack will assign a random name as before.

Major bugfixes

Important

This update fixes the broken kubernetes backend, which has been non-functional since a few previous updates.

Other

Full changelog: 0.18.7

Contributors

tamanobi, r4victor, and jvstme
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0.18.6-v1

22 Jul 07:57
@peterschmidt85 peterschmidt85
0.18.6-v1
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0.18.6-v1

0.18.6

The update brings all the features and bug fixes introduced in version 0.18.6.

Major fixes

Warning

If you have idle instances in your pool, it is recommended to re-create them after upgrading to version 0.18.6. Otherwise, there is a risk that these instances won't be able to execute jobs.

Other

For more details, check https://github.com/dstackai/dstack/releases/0.18.6

Logs

The logs in the control plane are now displayed properly, even if they include special characters (e.g., progress bars, color codes).

Contributors

deep-diver and jvstme
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0.18.5rc1-v1

08 Jul 14:00
@peterschmidt85 peterschmidt85
0.18.5rc1-v1
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0.18.5rc1-v1 Pre-release
Pre-release

This is a release candidate of the upcoming GA release 0.18.5-v1. This update brings the features introduced with dstack==0.18.5rc1:

0.18.5rc1

Volumes

When you run anything with dstack, it allows you to configure the disk size. However, once the run is finished, if you haven't stored your data in any external storage, all the data on disk will be erased. With 0.18.5, we're adding support for network volumes that allow data to persist across runs.

Once you've created a volume (e.g. named my-new-volume), you can attach it to a dev environment, task, or service.

type: dev-environment
ide: vscode
volumes:
  - name: my-new-volume
    path: /volume_data

The data stored in the volume will persist across runs.

dstack allows you to create new volumes and register existing ones. To learn more about how volumes work, check out the docs.

Important

Volumes are currently experimental and only work with the aws backend. Support for other backends is coming soon.

PostgreSQL

By default, dstack stores its state in /root/.dstack/server/data using SQLite. With this update, it's now possible to configure dstack to store its state in PostgreSQL. Just pass the DSTACK_DATABASE_URL environment variable.

docker run -it -p 3000:3000 \
  -v $HOME/.dstack-enterprise/server/:/root/.dstack/server \
  -e DSTACK_DATABASE_URL="postgresql+asyncpg://myuser:mypassword@myhostname:5432/mydatabase"
ghcr.io/dstackai/dstack-enterprise:latest

Note

If you are using PostgreSQL, mounting the /root/.dstack/server folder is optional and is only required if you plan to use a pre-configured /root/.dstack/server/config.yml.

Important

Despite PostgreSQL support, dstack still requires that you run only one instance of the dstack server. However, this requirement will be lifted in a future update.

On-prem clusters

Previously, dstack didn't allow the use of on-prem clusters (added via dstack pool add-ssh) if there were no backends configured. This update fixes that bug. Now, you don't have to configure any backends if you only plan to use on-prem clusters.

Supported GPUs

Previously, dstack didn't support L4 and H100 GPUs with AWS. Now you can use them.

Full changelog: 0.18.5rc1

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0.18.4-v1

03 Jul 13:12
@peterschmidt85 peterschmidt85
0.18.4-v1
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0.18.4-v1

Here's the list of what has changed in this update:

  • Gateways are now always read-only in the project settings UI. To create or update a gateway, you must now use dstack apply and dstack delete.
  • The UI now loads a lot faster and is more responsive.
  • Global administrators can now see running instances for all users across projects via Administration > Pools.

Additionally, the update includes all the new features introduced in versions 0.18.3 and 0.18.4. Please read below for more details.

0.18.3

Configuring VPCs for GCP

The gcp backend now also allows configuring VPCs:

projects:
  - name: main
    backends:
      - type: gcp

        project_id: my-awesome-project
        creds:
          type: default

        vpc_name: my-custom-vpc

The VPC should belong to the same project. If you would like to use a shared VPC from another project, you can also specify vpc_project_id.

Per-region VPC configuration with AWS

Last but not least, for the aws backend, it is now possible to configure VPCs for selected regions:

projects:
  - name: main
    backends:
      - type: aws
        creds:
          type: default

        vpc_ids:
          us-east-1: vpc-0a2b3c4d5e6f7g8h

        default_vpcs: true

To use the default VPC in other regions, just need to set default_vpcs to true.

Retry policy

We have reworked how to configure the retry policy and how it is applied to runs. Here's an example:

type: task

commands: 
  - python train.py

retry:
  on_events: [no-capacity]
  duration: 2h

Now, if you run such a task, dstack will keep trying to find capacity within 2 hours. Once capacity is found, dstack will run the task.

The on_events property also supports error (in case the run fails with an error) and interruption (if the run is using a spot instance and it was interrupted).

Oracle Cloud Infrastructure

With the new update, it is now possible to run workloads with your Oracle Cloud Infrastructure (OCI) account. The backend is called oci and can be configured as follows:

projects:
  - name: main
    backends:
      - type: oci
        creds:
          type: default

The supported credential types include default and client. In case default is used, dstack automatically picks the default OCI credentials from ~/.oci/config.

Just like other backends, oci supports dev environments, tasks, and services.

Important

Currently, the oci backend does not support creating gateways. This feature is coming soon.

Full changelog: 0.18.3

0.18.4

Private subnets with GCP

Additionally, the update allows configuring the gcp backend to use only private subnets. To achieve this, set public_ips to false.

projects:
  - name: main
    backends:
      - type: gcp
        creds:
          type: default

        public_ips: false

Google Cloud TPU

To request a TPU, specify the TPU architecture prefixed by tpu- (in gpu under resources):

type: task

python: "3.11"

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:  tpu-v2-8

Important

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

Full changelog: 0.18.4

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0.18.2-v2

24 May 13:10
@peterschmidt85 peterschmidt85
0.18.2-v2
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0.18.2-v2

0.18.2

This update brings all the new features introduced with 0.18.2:

  • On-prem clusters: The dstack pool add-ssh command now supports the --network argument. This argument allows you to configure the private network. If you add multiple on-prem instances sharing the same private network, you'll be able to use these instances as a cluster to run multi-node tasks.

  • Private subnets: The aws backend now allows to set public_ips to false. In this case, instances will be created in private subnets only to ensure maximum security.

  • Gateways: It's now possible to define a gateway configuration via YAML and create or update it using the dstack apply command. For AWS, gateways now support the public_ips and certificate properties. Use them to run gateways in a private network under the load balancer. The certificate property allows to specify the ARN of a certificate from AWS Certificate Manager.

Refer to the 0.18.2 release notes for more details.

Editing backend settings via UI

Additionally, the update introduces a YAML-based code editor to create and edit backend settings via the UI.

It's now possible to add backends using the same YAML syntax as in ~/.dstack/server/config.yml.

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