Conda environments

Manage conda environments and their packages from within the app, so you can set up the Python environment your work needs without dropping to a separate terminal. You'll find Conda Environments in Settings, under General.

Why conda?

Scientific computing, machine learning, and AI work depends on more than Python packages. Libraries like NumPy, PyTorch, and TensorFlow rely on compiled code and native libraries — such as CUDA for GPUs, BLAS or MKL for linear algebra, and C, C++, and Fortran runtimes — that Python-only tools aren't designed to manage. Conda manages the full stack:

• Beyond Python — install native libraries, GPU toolkits, compilers, and packages for other languages such as R, alongside your Python packages. Conda manages the Python version itself, too.
• Prebuilt binaries — packages arrive precompiled for Windows, macOS, and Linux, so you don't need to build libraries from source or install developer tools.
• Compatible dependencies — conda's solver resolves the entire environment at once, so every package and native library it installs works together.
• Isolation — each conda environment is self-contained, so projects that need different versions of Python, PyTorch, or CUDA don't interfere with each other.
• Reproducibility — export an environment to an environment.yml file to recreate it on another machine or share it with a teammate.

To learn more, see 12 reasons to choose conda.

Conda concepts

New to conda? Learn more about fundamental conda concepts in the official Anaconda documentation:

• Environment — an isolated set of packages (and a Python version) for a project.
• Package — an installable library or tool.
• Channel — where packages are downloaded from.
• environment.yml — a file that describes an environment so it can be recreated or shared.

Use a conda environment in your work

Managing a conda environment here doesn't automatically "activate" it — there's no activate switch in the app. Instead, the agent connects it to your work. Ask an agent to find an appropriate conda environment for a notebook and point the notebook at it, then restart the notebook so it picks up the change.

The most durable approach is to have the agent write an environment.yml into the workspace. The notebooks you already have, and any new ones you create there, then use that environment automatically. See Notebooks for working with conda environments from a chat.

Browse your conda environments

The page lists your conda environments by name and prefix (the full path on disk). Search by name or path, sort the list, and page through it. Click the actions menu on an environment's row to Clone or Delete an environment. Select a row to open the environment and work with its packages.

Create a conda environment

Click New environment, then select Create New to open the Create environment dialog.

On the Create environment page, configure your environment:

• Location — where the environment is created.
• Name — the environment's name.
• Python version — a specific version, or Latest.

Under Select packages, search the list and check the packages to include. Each package has a version dropdown (defaulting to latest), and your choices collect in the Selected Packages panel. Use the Channels control to choose where packages come from (see Choose a channel). Select Create Environment to build it.

Import from a file

Click New environment, then select Import file to open the Import from file dialog. Navigate to or drop a conda environment file (.yml or .yaml). Optionally set a name override to use instead of the name declared in the file.

Work with a conda environment's packages

Selecting an environment opens its detail view. The header shows the environment name, its Python version, and the full prefix path. Below it, a table lists every installed package with its Version, Source (conda or pip), and Channel. Search by name and sort by any column.

Update packages

When newer versions are available, an Update button appears. Click the Update button to open the Update dialog. From there, you can update all packages at once, or filter the table down to just the packages that you want to update. A Dependency changes panel previews what the update pulls in and flags any dependency conflict it can't resolve, so you can adjust your selection and try again. Turn on Back up before updating to clone the environment first, so you can roll back if needed.

Alternatively, on the packages table on the environment's details page, you can click the actions menu on a package's row to Update or Remove just that one. Update is unavailable when a package is already at the latest available version.

Add packages

From an open environment, select + Add Packages to open the Add Packages dialog. Search for a package, and for each result pick a version (defaulting to latest). Your selections and their dependencies are added to the Selected packages list, and the Channels control sets where packages come from (see Choose a channel).

Before anything is applied, the Dependency changes panel previews the dependency changes the solver will make — packages that will be upgraded, downgraded, added, removed, or rebuilt — so there are no surprises. Select Install to apply the changes. A long-running install can be sent to Run in background so you can keep working while it finishes.

Choose a channel

When you select packages, you choose which channels they install from with the Channels control:

• Anaconda Main — secure, vetted packages; requires accepting Anaconda's terms of service.
• conda-forge — community-maintained and open-source.

Any channels configured in your conda settings appear here too.