Overcoming the limitations of dev environments for ML projects.
SSH is frequently an indispensable tool for ML engineers. In this discussion, we will delve into the drawbacks of using SSH manually and the advantages of embracing dev environments, which can significantly enhance your productivity as an ML engineer.
The latest update introduces Azure support, and includes other improvements.
At dstack, our goal is to create a simple and unified interface for ML engineers to run dev environments, pipelines, and
apps on any cloud. With the latest update, we take another significant step in this direction.
A preview of the new tool for teams to manage their ML workflows.
Last October, we open-sourced the dstack CLI for defining ML workflows as code and running them easily on any cloud or
locally. The tool abstracts ML engineers from vendor APIs and infrastructure, making it convenient to run scripts,
development environments, and applications.
The latest update of dstack now supports Google Cloud Platform (GCP).
With the release of version 0.2 of dstack, it is now possible to configure GCP as a remote. All features that were
previously available for AWS,
except real-time artifacts, are now available for GCP as
well.