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@peterschmidt85 peterschmidt85 released this 25 Jul 09:06
· 1057 commits to master since this release

Port mapping

Any task that is running on dstack can expose ports. Here's an example:

type: task

ports:
  - 7860

commands:
  - pip install -r requirements.txt
  - gradio app.py

When you run it with dstack run, by default, dstack forwards the traffic from the specified port to the same port on your local machine.

With this update, you now have the option to override the local machine's port for traffic forwarding.

dstack run . -f serve.dstack.yml --port 3000:7860

This command forwards the traffic to port 3000 on your local machine.

If you specify a port on your local machine already taken by another process, dstack will notify you before provisioning cloud resources.

Max duration

Previously, when running a dev environment or task with dstack and forgetting about it, it would continue indefinitely. Now, you can use the max_duration property in .dstack/profiles.yml to set a maximum time for workloads.

Example:

profiles:
  - name: gcp-t4
    project: gcp
    resources:
      memory: 24GB
      gpu:
        name: T4
    max_duration: 2h

With this profile, dstack will automatically stop the workload after 2 hours.

If you don't specify max_duration, dstack defaults to 6h for dev environments and 72h for tasks.

To disable max duration, you can set it to off.

Imagine the amount of money your team can save with this minor configuration.

More supported GPUs

With the CUDA version updated to 11.8, dstack now supports additional GPU types, including NVIDIA T4 and NVIDIA L4. These GPUs are highly efficient for LLM development, offering excellent performance at low costs!

If you are using a custom Docker image, you can now utilize a CUDA version up to 12.2.

Last but not least, the K80 GPU is no longer supported.

Examples

Make sure to check the new page with examples.

The documentation is updated to reflect the changes in the release.

What's changed

Changelog: 0.10.5...0.10.6