Anaconda Notebooks is a hosted JupyterLab service that enables you to run JupyterLab notebooks online while simultaneously taking advantage of conda’s robust environment management tools. With Anaconda Toolbox’s quick start environments, you can select from a curated collection of packages tailored for specific industries or common workflows. These ready-to-install environments eliminate the need to manually configure dependencies and let you focus on extracting insights from your data rather than managing package compatibility.
Quick start environments are currently only available for the Local Toolbox.

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System requirements

  • Anaconda Navigator version 2.6 or later.
  • Anaconda Toolbox version 4.20.0 or later.
  • An internet connection.
  • An Anaconda.com account.
  • Adequate disk space for environments.
    Installation times vary based on your internet connection speed.
    Each environment’s size is listed on its quick start environment card in Anaconda Toolbox.
    The anaconda-ml-ai environment requires a large amount of disk space. To free up space, we recommend opening a notebook terminal and running conda clean --all to free up space after installation.

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Installing a quick start environment

To install a new quick start environment:
  1. Click Create new Environment in Anaconda Toolbox:
  2. Click Install on the environment you want to install.

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Quick start environments

Anaconda Toolbox comes with the following quick start environments ready to be installed and used. Each environment includes curated packages for fields that rely on data analytics, helping you discover new tools to optimize workflows and ensuring teams work with identical configurations for seamless collaboration.
Environment NameDescriptionSubscription Tier*
anaconda-py3.11250+ popular data science packages from the Anaconda metapackage that are compatible with Python 3.11.Free
anaconda-financeEssential packages for financial analysis, modeling, and market data visualization.Free
anaconda-ml-aiCore packages for building and training machine learning and AI models.Free
python-starterStart from scratch with Python 3.11 and install your own packages.Free
anaconda-bankingCredit risk modeling, regulatory compliance, fraud detection, and stress testing tools.Business
anaconda-devopsInfrastructure automation and deployment tools for modern DevOps workflows.Business
anaconda-etlExtract, transform, and load data pipelines with Spark, boto3, and modern processing frameworks.Business
anaconda-insuranceActuarial modeling, claims analytics, and catastrophe risk assessment tools.Business
anaconda-life-sciencesComplete genomics, bioinformatics, and molecular analysis toolkit for life sciences and pharma research.Business
anaconda-manufacturingIndustrial IoT, predictive maintenance, and quality control analytics tools.Business
anaconda-nlpNatural language processing with transformers, spaCy, NLTK, and text analysis tools.Business
anaconda-pytorchPyTorch deep learning framework for neural network development and training.Business
anaconda-snowflakeNative Snowflake connectivity with optimized data processing, analytics, and machine learning.Business
anaconda-statisticsAdvanced statistical modeling with PyMC, Stan, and Bayesian analysis frameworks.Business
anaconda-web-devModern Python web development with FastAPI, Django, Flask, and database integration.Business
*Some quick start environments are restricted by subscription tier. See our pricing page for subscription tier details.
View the packages included in an environment by hovering over the additional packages tag:

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Activating an environment

To activate a new environment:
  1. Open a new Launcher by clicking the blue plus in the top-left corner of the File Browser.
  2. Select the environment under Notebook.
A new notebook opens with the selected environment assigned as the runtime.

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Adding packages to an environment

To add packages to an environment, use the ! syntax to access the system shell and run conda commands. In a notebook cell, run:
# Replace <PACKAGE_NAME> with the name of the package you want to install
!conda install <PACKAGE_NAME>

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Updating packages

To update a package in your environment, use the ! syntax to access the system shell and run conda commands. In a notebook cell, run:
# Replace <PACKAGE> with the name of the package you want to update
!conda update <PACKAGE>

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Deactivating environments

It is best practice to deactivate your environment when you are finished working in it.
To deactivate your active environment, run the following command in a notebook cell:
!conda deactivate

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Uninstalling environments

We recommend uninstalling quick start environments from Anaconda Toolbox. Uninstalling a quick start environment via the CLI or Navigator can result in an “Error starting kernel” message. See the frequently asked question below for details.
To uninstall an environment from Anaconda Toolbox, click Uninstall.

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Frequently asked questions

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Are package versions specified in the quick start environments?

No, quick start environments include a list of packages to install, but rely on the conda solver to select the most up-to-date versions that are compatible with the other packages and their dependencies. After installation, you can verify installed package versions by running !conda list in a notebook cell in the active environment. You can also view package versions on the Environments page in Navigator.

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Can I view security information like CVEs for the installed packages?

While security information isn’t directly accessible, installation security is managed through channel configurations. When installing a quick start environment, conda uses the channels list available in your .condarc file. For business tier customers, this means that you are only able to install quick start environments if your organization’s policies allow it.

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Which channels do I need?

All environments require either the Anaconda defaults channel or conda-forge to be included in your .condarc file. You can update your channels in Navigator or with conda via the CLI.

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How long does an environment take to install?

The python-starter environment, which includes the fewest packages, is the quickest to install and should complete in fewer than five minutes. The anaconda-ai-ml environment, on the other hand, could take up to 15 minutes to install, due to the large size of some of the packages and complexity of the package dependency resolution process. Exact installation times vary depending on your internet download speeds and hardware specifications.

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Can I install more than one quick start environment?

Yes! You can install one or all of these environments, provided your computer has the available disk space.

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How do I stop or interrupt an environment from installing?

To stop or interrupt a quick start environment during installation, completely close your Jupyter instance (not just the tab or window), then reopen it via Navigator or the command line.

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The environment card in Toolbox says the environment is installed, but I don’t see it in the Launcher or runtime selector.

First, try refreshing the browser page. If the environment still doesn’t appear after refreshing, check the Environments page in Navigator to see if it’s available. If the environment is still unavailable after waiting 2 minutes, close all open Jupyter sessions and restart Jupyter from Navigator or the command line.

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I got an error message while installing. What should I do?

First, check your .condarc file and verify that your channels are set to either defaults or conda-forge. If your channels are set correctly and you’re still experiencing issues, check with your administrator to see if they have defined any policies that could be blocking installation. If neither of the above resolves the issue, you can try:
  • Refreshing the page
  • Checking your internet connection
  • Shutting down all running Jupyter kernels and restarting your Jupyter instance

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If I’ve uninstalled an environment, can I continue working?

You can continue working in the same notebook, but you’ll need to select an existing runtime. This can be done through the runtime selector, the Launcher, or by using the terminal. Any previously executed cells might need to be re-run in the new runtime.

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I’m getting an error saying, “Error starting kernel: [Errno 2] No such file or directory”.

Uninstalling a quick start environment via the CLI or Navigator can result in this message, as the Jupyter kernel references won’t be properly removed when the environment is deleted. To resolve this issue:
  1. Open Anaconda Prompt (Terminal in macOS/Linux)
  2. Run: 
    jupyter kernelspec list
  3. Find the name of the quick start environment you deleted.
  4. Then run: 
    # Replace <ENV_NAME> with the name of the deleted environment
jupyter kernelspec uninstall <ENV_NAME>