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Using Docker and Docker Compose inside GPU-enabled containers

To run containers with dstack, you can use your own Docker image (or the default one) without a need to interact directly with Docker. However, some existing code may require direct use of Docker or Docker Compose. That's why, in our latest release, we've added this option.

type: task
name: chat-ui-task

image: dstackai/dind
privileged: true

working_dir: examples/misc/docker-compose
commands:
  - start-dockerd
  - docker compose up
ports: [9000]

resources:
  gpu: 16GB..24GB

Using TPUs for fine-tuning and deploying LLMs

If you’re using or planning to use TPUs with Google Cloud, you can now do so via dstack. Just specify the TPU version and the number of cores (separated by a dash), in the gpu property under resources.

Read below to find out how to use TPUs with dstack for fine-tuning and deploying LLMs, leveraging open-source tools like Hugging Face’s Optimum TPU and vLLM.

Supporting AMD accelerators on RunPod

While dstack helps streamline the orchestration of containers for AI, its primary goal is to offer vendor independence and portability, ensuring compatibility across different hardware and cloud providers.

Inspired by the recent MI300X benchmarks, we are pleased to announce that RunPod is the first cloud provider to offer AMD GPUs through dstack, with support for other cloud providers and on-prem servers to follow.

Using volumes to optimize cold starts on RunPod

Deploying custom models in the cloud often faces the challenge of cold start times, including the time to provision a new instance and download the model. This is especially relevant for services with autoscaling when new model replicas need to be provisioned quickly.

Let's explore how dstack optimizes this process using volumes, with an example of deploying a model on RunPod.