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Artifacts

conda environments can be created in a few different ways. conda-store creates "artifacts" (corresponding to different environment creation options) for every environment, that can be shared with colleagues and used to reproduce environments. In the conda-store UI, these are available in the "Logs and Artifacts" section at the end of the environment page.

The following sections describe the various artifacts generated and how to create environments with them.

Environments in shared namespaces on conda-store can be accessed by everyone with access to that namespace, in which case you may not need to share the artifacts manually. Artifacts are used to share your environment with external collaborators who don't have access to conda-store.

note

The libraries (conda, conda-lock, conda-pack, etc.) mentioned in the following sections are separate projects in the conda ecosystem. The environments created using them are not managed by conda-store.

YAML file (pinned)​

YAML files that follow the conda specification are a common way to create environments. conda-store creates a "pinned" YAML, where all the exact versions of requested packages (including pip packages) as well as all their dependencies are specified, to ensure new environments created match the original environment as closely as possible.

info

In rare cases, building environments from "pinned" YAML files may not solve because packages are routinely marked as broken and removed at the repository level.

conda-forge (default channel in conda-store) has a policy that packages are never removed but are marked as broken. Most other channels do not have such a policy.

Click on "Show yml file" link in the conda-store UI to open the file in a new browser tab. You can copy-and-past this file in conda-store UI's YAML editor to create a new environment managed by conda-store in a different namespace.

You can download the file and share with someone or use it to create an environment on a different machine. Assuming conda is installed, run the CLI commands mentioned in the conda-documentation with the corresponding filename to create a conda environment (on any machine).

Lockfile​

A conda lockfile is a representation of all (conda and pip) dependencies in a given environment. conda-store creates lockfiles using the conda-lock project.

Click on "Show lockfile" to open the lockfile in a new browser tab. You can download the file and share with someone or use it to create an environment in a different space.

To create an environment att the new location, follow the commands in the conda-lock documentation.

Tarballs or archives​

warning

Building environments from archives is only supported on Linux machines because the tarballs are built on Linux machines.

A tarball or archive is a packaged environment that can be moved, unpacked, and used in a different location or on a different machine.

conda-store uses Conda-Pack, a library for creating tarballs of conda environments.

Click "Download archive" button to download the archive of your conda environment, and share/move it to the desired location.

To install the tarball, follow the instructions for the target machine in the conda-pack documentation.

Docker images​

warning

Docker image creation is currently not supported.

Authentication​

The conda-store docker registry requires authentication. You can use any username and your user token as the password.

docker login -u <any-username> -p <conda-store-token>

To get your user token:

  1. Visit your user page at <your-conda-store-domain>/admin/user
  2. Click on "Create token", which displays your token
  3. Click on "copy" to copy the token to your clipboard

Alternatively, you can set c.AuthenticationBackend.predefined_tokens in conda_store_config.py, which have environment read permissions on the given docker images required for pulling images.

General usage​

To use a specific environment build, click on the "Show Docker image" to get the URL to the docker image. For example: localhost:8080/analyst/python-numpy-env:583dd55140491c6b4cfa46e36c203e10280fe7e180190aa28c13f6fc35702f8f-20210825-180211-244815-3-python-numpy-env.

The URL consists of: <conda-store-domain>/<namespace>/<environment-name>:<build_key>

  • The conda-store domain (for example localhost:8080/) at the beginning tells Docker where the docker registry is located. Otherwise, Docker will try to use Docker Hub by default.
  • The <namespace>/<environment-name> refers to the specific conda environment
  • The "build key" is a combination of <specification-sha256>-<build date>-<build id>-<environment name> which points to specific build of the environment. For example, a past version of the environment.

To use a conda-store environment docker image:

docker run -it <docker-url>

On-demand (dynamic) docker image​

In conda-store, you can also specify the required packages within the docker image name itself, without needing an actual environment to be created by conda-store UI.

The URL format is: <registry-url>:<registry-port>/conda-store-dynamic/<package-constraint-1>/.../<package-constraint-n>.

After conda-store-dynamic, you can specify packages with constraints separated by slashes in the following format:

  • <=1.10 as .lt.1.10
  • >=1.10 as .gt.1.10

For example, if you need Python less than 3.10 and NumPy greater than 1.0, this would be the docker image name: <registry-url>:<registry-port>/conda-store-dynamic/python.lt.3.10/numpy.gt.1.0.

conda-store creates the environment ands builds the docker image, which you can then download.

Installers​

Installers are another way to share and use a set of (bundled) packages. conda-store uses constructor to generate an installer for the current platform (where the server is running):

  • on Linux and MacOS, it generates a .sh installer
  • on Windows, it generates a .exe installer using NSIS

conda-store automatically adds conda and pip to the target environment because these are required for the installer to work.

note

constructor uses a separate dependency solver instead of utilizing the generated lockfile, so the package versions used by the installer might be different compared to the environment available in conda-store. There are plans to address this issue in the future.