dstack 0.10.3: A preview of Lambda Cloud support

With the 0.10.3 update, dstack now allows provisioning infrastructure in Lambda Cloud while storing state and artifacts in an S3 bucket.

See the Reference for detailed instructions on how to configure a project that uses Lambda Cloud.

Note, there are a few limitations in the preview:

1. Since Lambda Cloud does not have its own object storage, dstack requires you to specify an S3 bucket, along with AWS credentials for storing state and artifacts.
2. At the moment, there is no possibility to create a Lambda project via the UI. Currently, you can only create a Lambda project through an
   API request.

In other news, we have pre-configured the base Docker image with the required Conda channel, enabling you to install additional CUDA tools like nvcc using conda install cuda. Note that you only need it for building a custom CUDA kernel; otherwise, the essential CUDA drivers are already pre-installed and not necessary.

The documentation and examples are updated to reflect the changes.

Give it a try and share feedback

Go ahead, and install the update, give it a spin, and share your feedback in our Slack community.

What's Changed

• Updated fields in run details page by @olgenn in #515
• Update gcp.md by @axitkhurana in #517
• Warn user about long git diff by @Egor-S in #518
• Close #523 Implemented building run status by @olgenn in #525
• Implement Lambda Labs backend by @r4victor in #528
• Restore cache functionality by @Egor-S in #527
• Runner storages refactoring by @Egor-S in #532
• Refactor backends by @r4victor in #533
• Add multi-region compute support for AWS by @r4victor in #536
• Check if the build exists before starting an instance by @Egor-S in #534
• Pre-configure the nvidia/label/cuda-11.4.3 channel for conda in the CUDA image by @peterschmidt85 in #541

New Contributors

• @axitkhurana made their first contribution in #517

Changelog: 0.10.2...0.10.3