> ## Documentation Index
> Fetch the complete documentation index at: https://anaconda.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Environments
export const Comments = ({children}) => {
return ;
};
export const TroubleshootSolution = ({children}) => <>
Solution
{children}
;
export const TroubleshootCause = ({children}) =>
Cause
{children}
;
export const TroubleshootTitle = ({children}) => <>
{children}
;
export const Troubleshoot = ({children}) => ;
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 version of Toolbox](/tools/anaconda-notebooks/anaconda-toolbox/local-toolbox).
## System requirements
* Anaconda Navigator version 2.6 or later.
* Anaconda Toolbox version 4.20.0 or later.
* An internet connection.
* An [Anaconda.com](https://auth.anaconda.com/ui/registration) account.
## Creating an account
[Sign up for an Anaconda.com account](https://auth.anaconda.com/ui/registration)
1. Click **Sign Up with Email**.
2. Enter your email address and a password that meets the password requirements.
3. Check your email for a verification message.
4. Click the verification link in your message to complete account verification.
5. (Optional) Complete the user survey. This survey helps Anaconda get to know you as a user and improve your experience in the future.
1. Click the button for the service you want to use to authenticate.
2. Follow the on-screen instructions.
If your organization has [configured Single Sign-On](/anaconda-platform/cloud/admin/integrations/esso) (SSO) for your email domain, you'll be redirected to your organization's SSO login page.
* Adequate disk space for environments.
Installation times vary based on your internet connection speed.
***
Each quick start environment's size is displayed on its card in Anaconda Toolbox.
The `anaconda-ml-ai` environment requires a large amount of disk space. To free up space, Anaconda recommends opening a notebook terminal and running `conda clean --all` to free up space after installation.
## 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 Name | Description | Subscription plan\* |
| ------------------------- | -------------------------------------------------------------------------------------------------------------------------- | :----------------------------: |
| `anaconda-2025.12-py3.13` | 250+ popular data science packages from the [Anaconda metapackage](/getting-started/advanced-install/install-metapackage). | Free |
| `anaconda-finance` | Essential packages for financial analysis, modeling, and market data visualization. | Free |
| `anaconda-ml-ai` | Core packages for building and training machine learning and AI models. | Free |
| `python-starter` | Start from scratch with Python and [install your own packages](#installing-additional-packages). | Free |
| `anaconda-banking` | Credit risk modeling, regulatory compliance, fraud detection, and stress testing tools. | Business |
| `anaconda-devops` | Infrastructure automation and deployment tools for modern DevOps workflows. | Business |
| `anaconda-etl` | Extract, transform, and load data pipelines with Spark, boto3, and modern processing frameworks. | Business |
| `anaconda-insurance` | Actuarial modeling, claims analytics, and catastrophe risk assessment tools. | Business |
| `anaconda-life-sciences` | Complete genomics, bioinformatics, and molecular analysis toolkit for life sciences and pharma research. | Business |
| `anaconda-manufacturing` | Industrial IoT, predictive maintenance, and quality control analytics tools. | Business |
| `anaconda-nlp` | Natural language processing with transformers, spaCy, NLTK, and text analysis tools. | Business |
| `anaconda-pytorch` | PyTorch deep learning framework for neural network development and training. | Business |
| `anaconda-snowflake` | Native Snowflake connectivity with optimized data processing, analytics, and machine learning. | Business |
| `anaconda-web-dev` | Modern Python web development with FastAPI, Django, Flask, and database integration. | Business |
\*Some quick start environments are restricted by subscription plan. See our [pricing page](https://www.anaconda.com/pricing) for subscription plan details.
### Installing quick start environments
More of a visual learner? [Sign in to Anaconda.com](https://auth.anaconda.com/ui/login?return_to=https://anaconda.com/app/) and enroll in our free [Quick Start Environments](https://learning.anaconda.com/courses/take/simplifying-python-environment-set-up/lessons/67414331-introduction-to-quick-start-environments) course.
To install a new quick start environment:
1. Select **Create a New Environment**.
2. Identify the environment you want to install. You can view the packages included in an environment by hovering over the additional packages tag:
3. Click **Install** on the environment you want to install.
### Activating quick start environments
To activate a quick start environment:
1. Open the Launcher by selecting 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.
1. Select **Create a New Environment**.
2. Select **Open New Notebook** on the installed environment card.
A new notebook opens with the selected environment assigned as the runtime.
Select the runtime in the top right, then switch to your desired environment in the **Select Runtime** modal.
## Editing environments with Gator
The Gator JupyterLab extension is an environment and package management tool that provides a GUI for managing your conda environments.
You can use the Gator extension to add, remove, or update packages in an environment. Gator comes bundled with Anaconda Toolbox, so there's no need to install anything to get started.
To edit the packages in a quick start environment:
1. Select **Create a New Environment** in Anaconda Toolbox.
2. Locate the installed environment you want to edit.
3. Select **Edit Environment**.
The Gator environment manager opens in a new tab. From the Gator environment manager, you can [add](#installing-additional-packages), [update](#updating-packages), or [remove](#removing-packages) packages in the environments you've installed.
### Viewing installed packages
Open the filter dropdown menu beside the search bar and select *Installed* to display the packages that are currently installed in the environment.
### Installing additional packages
1. Open the filter dropdown beside the search bar and select *All* or *Not installed* to display packages that are available to install in the environment.
2. Use the search bar to locate the package you want to install.
3. Select the checkbox beside the package you want to install in the environment.
The *Change To*\* field displays **Install** for selected packages.
4. Select **Apply package modifications**.
The selected packages are installed into the environment.
### Updating packages
1. Open the filter dropdown beside the search bar and select *Installed* to display packages that are currently installed in the environment.
2. Use the search bar to locate the package you want to update, or find it in the Packages list.
3. In the **Change To** column, select the version you want to use.
4. Select **Apply package modifications**.
The package version is changed to the selected version.
### Removing packages
1. Open the filter dropdown beside the search bar and select *Installed* to display packages that are currently installed in the environment.
2. Use the search bar to locate the package you want to remove, or find it in the Packages list.
3. In the **Change To** column, select *Remove*.
4. Select **Apply package modifications**.
The package is removed from the selected environment.
## Uninstalling quick start environments
Anaconda recommends uninstalling quick start environments directly 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](#cli-or-navigator-gives-error-starting-kernel-message) below for details.
To uninstall an environment from Anaconda Toolbox, select **Uninstall**.
## Troubleshooting
### Toolbox environment card says the environment is installed, but no environment in the Launcher or runtime selector
The environment might not have fully synchronized with the Launcher or runtime selector yet.
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 two minutes, close all open Jupyter sessions and restart Jupyter from Navigator or the command line.
### Quickstart environment installation errored
Installation errors can occur due to misconfigured channels in your `.condarc` file, organizational policies blocking installation, connectivity issues, or stale Jupyter kernel states.
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
CLI or Navigator gives "error starting kernel" message
Uninstalling a quick start environment using either the CLI or Navigator can result in an `Error starting kernel: [Errno 2] No such file or directory` message, because the Jupyter kernel references aren't properly removed when the environment is deleted.
To resolve this issue:
1. Open Anaconda Prompt (Terminal in macOS/Linux)
2. Run:
```sh theme={null}
jupyter kernelspec list
```
3. Find the name of the quick start environment you deleted.
4. Then run:
```sh theme={null}
jupyter kernelspec uninstall
```
Replace \ with the name of the deleted environment.