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9 changes: 5 additions & 4 deletions README.md
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`dstack` supports `NVIDIA`, `AMD`, `Google TPU`, `Intel Gaudi`, and `Tenstorrent` accelerators out of the box.

## Latest news ✨

- [2025/07] [dstack 0.19.17: Secrets, Files, Rolling deployment](https://github.com/dstackai/dstack/releases/tag/0.19.17)
- [2025/06] [dstack 0.19.16: Docker in Docker, CloudRift](https://github.com/dstackai/dstack/releases/tag/0.19.16)
- [2025/06] [dstack 0.19.13: InfiniBand support in default images](https://github.com/dstackai/dstack/releases/tag/0.19.13)
- [2025/06] [dstack 0.19.12: Simplified use of MPI](https://github.com/dstackai/dstack/releases/tag/0.19.12)
- [2025/05] [dstack 0.19.10: Priorities](https://github.com/dstackai/dstack/releases/tag/0.19.10)
- [2025/05] [dstack 0.19.8: Nebius clusters, GH200 on Lambda](https://github.com/dstackai/dstack/releases/tag/0.19.8)
- [2025/04] [dstack 0.19.6: Tenstorrent, Plugins](https://github.com/dstackai/dstack/releases/tag/0.19.6)
- [2025/04] [dstack 0.19.5: GCP A3 High clusters](https://github.com/dstackai/dstack/releases/tag/0.19.5)
- [2025/04] [dstack 0.19.3: GCP A3 Mega clusters](https://github.com/dstackai/dstack/releases/tag/0.19.3)
- [2025/03] [dstack 0.19.0: Prometheus](https://github.com/dstackai/dstack/releases/tag/0.19.0)

## How does it work?

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2 changes: 1 addition & 1 deletion docs/blog/posts/amd-on-runpod.md
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description: "dstack, the open-source AI container orchestration platform, adds support for AMD accelerators, with RunPod as the first supported cloud provider."
slug: amd-on-runpod
categories:
- Releases
- Changelog
---

# Supporting AMD accelerators on RunPod
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---
title: "Rolling deployment, Secrets, Files, Tenstorrent, and more"
date: 2025-07-10
description: "TBA"
slug: changelog-07-25
image: https://dstack.ai/static-assets/static-assets/images/changelog-07-25.png
categories:
- Changelog
---

# Rolling deployment, Secrets, Files, Tenstorrent, and more

Thanks to feedback from the community, `dstack` continues to evolve. Here’s a look at what’s new.

#### Rolling deployments

Previously, updating running services could cause downtime. The latest release fixes this with [rolling deployments](../../docs/concepts/services.md/#rolling-deployment). Replicas are now updated one by one, allowing uninterrupted traffic during redeployments.

<div class="termy">

```shell
$ dstack apply -f .dstack.yml

Active run my-service already exists. Detected changes that can be updated in-place:
- Repo state (branch, commit, or other)
- File archives
- Configuration properties:
- env
- files

Update the run? [y/n]: y
⠋ Launching my-service...

NAME BACKEND PRICE STATUS SUBMITTED
my-service deployment=1 running 11 mins ago
replica=0 deployment=0 aws (us-west-2) $0.0026 terminating 11 mins ago
replica=1 deployment=1 aws (us-west-2) $0.0026 running 1 min ago
```

</div>

<!-- more -->

#### Secrets

Secrets let you centrally manage sensitive data like API keys and credentials. They’re scoped to a project, managed by project admins, and can be [securely referenced](../../docs/concepts/secrets.md) in run configurations.

<div editor-title=".dstack.yml">

```yaml hl_lines="7"
type: task
name: train

image: nvcr.io/nvidia/pytorch:25.05-py3
registry_auth:
username: $oauthtoken
password: ${{ secrets.ngc_api_key }}

commands:
- git clone https://github.com/pytorch/examples.git pytorch-examples
- cd pytorch-examples/distributed/ddp-tutorial-series
- pip install -r requirements.txt
- |
torchrun \
--nproc-per-node=$DSTACK_GPUS_PER_NODE \
--nnodes=$DSTACK_NODES_NUM \
multinode.py 50 10

resources:
gpu: H100:1..2
shm_size: 24GB
```

</div>

#### Files

By default, `dstack` mounts the repo directory (where you ran `dstack init`) to all runs.

If the directory is large or you need files outside of it, use the new [files](../../docs/concepts/dev-environments/#files) property to map specific local paths into the container.

<div editor-title=".dstack.yml">

```yaml
type: task
name: trl-sft

files:
- .:examples # Maps the directory where `.dstack.yml` to `/workflow/examples`
- ~/.ssh/id_rsa:/root/.ssh/id_rsa # Maps `~/.ssh/id_rsa` to `/root/.ssh/id_rs

python: 3.12

env:
- HF_TOKEN
- HF_HUB_ENABLE_HF_TRANSFER=1
- MODEL=Qwen/Qwen2.5-0.5B
- DATASET=stanfordnlp/imdb

commands:
- uv pip install trl
- |
trl sft \
--model_name_or_path $MODEL --dataset_name $DATASET
--num_processes $DSTACK_GPUS_PER_NODE

resources:
gpu: H100:1
```

</div>

#### Tenstorrent

`dstack` remains committed to supporting multiple GPU vendors—including NVIDIA, AMD, TPUs, and more recently, [Tenstorrent :material-arrow-top-right-thin:{ .external }](https://tenstorrent.com/){:target="_blank"}. The latest release improves Tenstorrent support by handling hosts with multiple N300 cards and adds Docker-in-Docker support.

<img src="https://dstack.ai/static-assets/static-assets/images/dstack-tenstorrent-n300.png" width="630"/>

Huge thanks to the Tenstorrent community for testing these improvements!

#### Docker in Docker

Using Docker inside `dstack` run configurations is now even simpler. Just set `docker` to `true` to [enable the use of Docker CLI](../../docs/concepts/tasks.md#docker-in-docker) in your runs—allowing you to build images, run containers, use Docker Compose, and more.

<div editor-title=".dstack.yml">

```yaml
type: task
name: docker-nvidia-smi

docker: true

commands:
- |
docker run --gpus all \
nvidia/cuda:12.3.0-base-ubuntu22.04 \
nvidia-smi

resources:
gpu: H100:1
```

</div>

#### AWS EFA

EFA is a network interface for EC2 that enables low-latency, high-bandwidth communication between nodes—crucial for scaling distributed deep learning. With `dstack`, EFA is automatically enabled when using supported instance types in fleets. Check out our [example](../../examples/clusters/efa/index.md)

#### Default Docker images

If no `image` is specified, `dstack` uses a base Docker image that now comes pre-configured with `uv`, `python`, `pip`, essential CUDA drivers, InfiniBand, and NCCL tests (located at `/opt/nccl-tests/build`).

<div editor-title="examples/clusters/nccl-tests/.dstack.yml">

```yaml
type: task
name: nccl-tests

nodes: 2

startup_order: workers-first
stop_criteria: master-done

env:
- NCCL_DEBUG=INFO
commands:
- |
if [ $DSTACK_NODE_RANK -eq 0 ]; then
mpirun \
--allow-run-as-root \
--hostfile $DSTACK_MPI_HOSTFILE \
-n $DSTACK_GPUS_NUM \
-N $DSTACK_GPUS_PER_NODE \
--bind-to none \
/opt/nccl-tests/build/all_reduce_perf -b 8 -e 8G -f 2 -g 1
else
sleep infinity
fi

resources:
gpu: nvidia:1..8
shm_size: 16GB
```

</div>

These images are optimized for common use cases and kept lightweight—ideal for everyday development, training, and inference.

#### Server performance

Server-side performance has been improved. With optimized handling and background processing, each server replica can now handle more runs.

#### Google SSO

Alongside the open-source version, `dstack` also offers [dstack Enterprise :material-arrow-top-right-thin:{ .external }](https://github.com/dstackai/dstack-enterprise){:target="_blank"} — which adds dedicated support and extra integrations like Single Sign-On (SSO). The latest release introduces support for configuring your company’s Google account for authentication.

<img src="https://dstack.ai/static-assets/static-assets/images/dstack-enterprise-google-sso.png" width="630"/>

If you’d like to learn more about `dstack` Enterprise, [let us know](https://calendly.com/dstackai/discovery-call).

That’s all for now.

!!! info "What's next?"
Give dstack a try, and share your feedback—whether it’s [GitHub :material-arrow-top-right-thin:{ .external }](https://github.com/dstackai/dstack){:target="_blank"} issues, PRs, or questions on [Discord :material-arrow-top-right-thin:{ .external }](https://discord.gg/u8SmfwPpMd){:target="_blank"}. We’re eager to hear from you!
2 changes: 1 addition & 1 deletion docs/blog/posts/cursor.md
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slug: cursor
image: https://dstack.ai/static-assets/static-assets/images/dstack-cursor-v2.png
categories:
- Releases
- Changelog
---

# Accessing dev environments with Cursor
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categories:
- Releases
- Changelog
---

# Monitoring essential GPU metrics via CLI
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description: "With today's release, dstack Sky supports both options: accessing the GPU marketplace and using your own cloud accounts."
slug: dstack-sky-own-cloud-accounts
categories:
- Releases
- Changelog
---

# dstack Sky now supports your own cloud accounts
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description: A managed service that enables you to get GPUs at competitive rates from a wide pool of providers.
slug: dstack-sky
categories:
- Releases
- Changelog
---

# Introducing dstack Sky
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slug: gh200-on-lambda
image: https://dstack.ai/static-assets/static-assets/images/dstack-arm--gh200-lambda-min.png
categories:
- Releases
- Changelog
---

# Supporting ARM and NVIDIA GH200 on Lambda
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slug: gpu-blocks-and-proxy-jump
image: https://dstack.ai/static-assets/static-assets/images/data-centers-and-private-clouds.png
categories:
- Releases
- Changelog
---

# Introducing GPU blocks and proxy jump for SSH fleets
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slug: inactivity-duration
image: https://dstack.ai/static-assets/static-assets/images/inactive-dev-environments-auto-shutdown.png
categories:
- Releases
- Changelog
---

# Auto-shutdown for inactive dev environments—no idle GPUs
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image: https://dstack.ai/static-assets/static-assets/images/dstack-instance-volumes.png
slug: instance-volumes
categories:
- Releases
- Changelog
---

# Introducing instance volumes to persist data on instances
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slug: intel-gaudi
image: https://dstack.ai/static-assets/static-assets/images/dstack-intel-gaudi-and-intel-tiber-cloud.png-v2
categories:
- Releases
- Changelog
---

# Supporting Intel Gaudi AI accelerators with SSH fleets
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slug: metrics-ui
image: https://dstack.ai/static-assets/static-assets/images/dstack-metrics-ui-v3-min.png
categories:
- Releases
- Changelog
---

# Built-in UI for monitoring essential GPU metrics
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slug: mpi
image: https://dstack.ai/static-assets/static-assets/images/dstack-mpi-v2.png
categories:
- Releases
- Changelog
---

# Supporting MPI and NCCL/RCCL tests
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slug: nebius
image: https://dstack.ai/static-assets/static-assets/images/dstack-nebius-v2.png
categories:
- Releases
- Changelog
---

# Supporting GPU provisioning and orchestration on Nebius
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slug: nvidia-and-amd-on-vultr
image: https://dstack.ai/static-assets/static-assets/images/dstack-vultr.png
categories:
- Releases
- Changelog
---

# Supporting NVIDIA and AMD accelerators on Vultr
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slug: prometheus
image: https://dstack.ai/static-assets/static-assets/images/dstack-prometheus-v3.png
categories:
- Releases
- Changelog
---

# Exporting GPU, cost, and other metrics to Prometheus
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description: "Learn how to use TPUs with dstack for fine-tuning and deploying LLMs, leveraging open-source tools like Hugging Face’s Optimum TPU and vLLM."
slug: tpu-on-gcp
categories:
- Releases
- Changelog
---

# Using TPUs for fine-tuning and deploying LLMs
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description: "Learn how to use volumes with dstack to optimize model inference cold start times on RunPod."
slug: volumes-on-runpod
categories:
- Releases
- Changelog
---

# Using volumes to optimize cold starts on RunPod
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