It’s important to remember that AI Projects are a container-based, and this may be different from what you’re used to. When you’re working in a container, files written to the container filesystem do not persist between restarts. AI Workbench automatically removes containers when they stop, which means that any changes you made to the container will be lost.
Persisting Data Between Restarts
If you want to persist data outside the Project repository between restarts, you should use a mount. A mount allows you to persist data even after the container is stopped or removed. This way, you can access your data again the next time you start the container. A Host mount will bind a directory from your host into the container. A Volume mount will create a Docker/Podman volume to store data.
Environment Configuration
AI Workbench aims to provide a natural and familiar environment configuration experience. You can edit a requirements.txt file or a bash script to make changes to your environment. However, behind the scenes, AI Workbench translates these changes into a Dockerfile/Containerfile and builds a container image for you. This allows you to work in a Linux-based environment, regardless of your host operating system.
Packages and Dependencies
Packages installed in the base environment may be installed in the root environment or the user environment, depending on how the base container was created. Packages installed from your requirements.txt or via the Packages interface will be installed in the user environment. If you need to install packages that require root privileges, you can use the sudo command in the preInstall.bash or postInstall.bash scripts.
Important to remember projects are git repositories, so you may want to be careful about what gets committed.
Sometimes you don’t want to commit intermediate data or other large files that you can easily recreate. Use
data/scratch, which is available by default or create a directory and add it to the.gitignore.AI Workbench will warn you when large files are about to be tracked by git, and suggest moving to a Git LFS tracked location or delete for example.
It’s important to remember that AI Workbench projects are Git repositories. Therefore, you may want to be careful about what you commit.
Sometimes, you don’t want to commit intermediate data or other large files that you can easily recreate to avoid bloating the repository. To avoid this, you can use the data/scratch directory, which is available by default and ignored by git, or create a directory and add it to the .gitignore file.
AI Workbench will warn you when large files are about to be tracked by Git. It will suggest moving them to a Git LFS tracked location or deleting them altogether. This will help you keep your repository manageable.