> ## 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. # Glossary export const Comments = ({children}) => { return
{children}
; }; ## AI Catalyst AI Catalyst is Anaconda AI development suite that provides comprehensive tools for developing, managing, and deploying AI models at enterprise scale, while maintaining the security and governance standards required by modern organizations. ## Anaconda.org Anaconda.org is a public package hosting service, established and maintained by Anaconda. It is a web-based repository that provides a method for hosting your packages so they can be publicly accessed and used. For more information and usage instructions, see [Anaconda.org](/tools/anaconda-org/getting-started). ## Anaconda Client CLI The Anaconda Client command line interface (CLI) allows you to log in to Anaconda.org directly from your terminal window and manage your account. It is not necessary for downloading or installing packages from Anaconda.org. ## Anaconda Core Core is the enterprise-grade Python package management suite that serves as the foundation for secure, scalable Python development across the entire delivery lifecycle. ## Anaconda Distribution A downloadable, open-source, high-performance, optimized Python distribution with hundreds of packages automatically included. Anaconda Distribution provides the option to easily install an additional 7,500+ open-source packages for data science, including advanced and scientific analytics. Anaconda Distribution includes [conda](#conda), Python, [Anaconda Navigator](#anaconda-navigator), and over 300 additional data science and machine learning packages. Anaconda Distribution is available for Windows, macOS, and Linux. After installing Anaconda Distribution, you can install thousands of additional open source packages contained in the Anaconda repository with the following command: ```bash theme={null} conda install ``` Replace \ with the name of the desired package. For more information on Anaconda Distribution, see [Anaconda Distribution](/getting-started/anaconda/main). For information on licensing, see our [Terms of Service](https://anaconda.com/legal). ## Anaconda metapackage A collection of packages at specific versions that are associated with Anaconda Distribution installers. Anaconda metapackage is used to pull all of the other packages into the installer. It contains several core, low-level libraries, including compression, encryption, linear algebra, and some GUI libraries. The Anaconda metapackage is useful for creating environments that have all of the Anaconda Distribution packages in them and it has strong effects on conda's solver behavior. For more information on the Anaconda metapackage, see [Anaconda metapackage](https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/packages.html#meta-package). For more information about the distinctions between Anaconda Distribution and the Anaconda metapackage, see the Anaconda blog, [What's in a Name? Clarifying the Anaconda Metapackage](https://www.anaconda.com/blog/whats-in-a-name-clarifying-the-anaconda-metapackage). ## Anaconda Navigator A desktop graphical user interface (GUI) included in all versions of Anaconda that allows you to easily manage [conda packages](#packages), [environments](#environments), [channels](#channels), and [notebooks](#anaconda-notebooks) without the need to use the [command line interface (CLI)](#command-line-interface-cli). For more information on Anaconda Navigator, see [Anaconda Navigator](/tools/anaconda-navigator/main). ## Anaconda Notebooks Anaconda Notebooks is a hosted JupyterLab service, powered by PythonAnywhere, that enables you to run JupyterLab notebooks reliably online. The Notebooks service provides you with a hosted JupyterLab instance running in a dedicated JupyterHub environment, persistent cloud storage, pre-configured conda environments with common data science packages, and the ability to create your own custom environments.

Anaconda's premium repository

A curated package repository that contains all of the packages in Anaconda's public repository, but with added security and governance features, including: * Identification of packages with known [CVEs](#common-vulnerabilities-and-exposures-cves), with scores and Anaconda-assigned statuses * Ongoing tracking of CVEs * Anti-tampering package signatures * Package software bills of materials (SBOMs) ## Anaconda Prompt Anaconda provides a set of [command line interface (CLI)](#command-line-interface-cli) applications with Windows installations of Anaconda Distribution and Miniconda stored in conda's `base` environment. Unlike Window's standard Command Prompt, Anaconda Prompt is pre-configured with the `base` environment activated by default for ease of use. Anaconda also provides Anaconda Powershell Prompt for Windows users who prefer to use Powershell instead of Command Prompt. ## Channels A channel is a location in a repository where conda searches for packages to install in environments. Channels can be defined as either URLs or file paths, depending on your setup. ## Command line interface (CLI) A CLI is a text-based method for interacting with software or a computer system. CLIs let you run programs by entering commands that the computer executes, as opposed to using graphical elements such as buttons or windows. Conda itself is a CLI tool, meaning you can interact with it through shell applications like [Anaconda Prompt](#anaconda-prompt) (Terminal on macOS/Linux). ## Common Vulnerabilities and Exposures (CVEs) A CVE is a public identifier for a known security vulnerability in software. Each CVE entry provides a standardized reference that makes it easier to share data across security tools and organizations. CVEs are paired with a severity score to help prioritize fixes. ## Conda Conda is an open-source package and environment manager included with [Anaconda Distribution](#anaconda-distribution) and [Miniconda](#miniconda). It installs, updates, and removes packages—along with their dependencies—inside isolated environments that it creates, allowing you to use multiple versions of the same software without conflicts. You can interface with conda at the command line or by using [Anaconda Navigator](#anaconda-navigator). For detailed technical information about conda, see the official [conda documentation](https://docs.conda.io). ## Conda-build A command line tool for building conda packages from recipes. ## Conda recipe Instructions used to tell `conda-build` how to build a package. ## Conda repository A cloud-based repository that contains 720+ open source certified packages that are easily installed locally via the `conda install` command. Can be viewed directly at [https://repo.anaconda.com/pkgs/](https://repo.anaconda.com/pkgs/) and used within [Anaconda Navigator](#anaconda-navigator) when downloading and installing packages from the Environments tab, or by using [conda](#conda) commands in a [CLI](#command-line-interface-cli). ## Deployment A deployed Anaconda project containing a Notebook, web app, dashboard, or machine learning model (exposed via an API). When you deploy a project, Data Science & AI Workbench builds a container with all the required dependencies and runtime components—the libraries on which the project depends in order to run—and launches it with the security and access permissions defined by the user. This allows you to easily run and share the application with others. ## Environments Environments in conda are self-contained, isolated spaces where you can install specific versions of software packages, including dependencies, libraries, and Python versions. This isolation helps avoid conflicts between package versions and ensures that your projects have the exact libraries and tools they need. ## Graphical user interface (GUI) A program with graphic images, icons, and menus into which commands are entered by clicking with a mouse and/or entering text in edit boxes. [Anaconda Navigator](#anaconda-navigator) is a graphical user interface that overlays the conda utility. ## Interactive data application Visualizations with sliders, dropdowns, and other widgets that allow users to interact with them. Interactive data applications can drive new computations, update plots, and connect to other programmatic functionality. ## Integrated development environment (IDE) A suite of software tools that combines everything a developer needs to write and test software. It typically includes a code editor, a compiler or interpreter, and a debugger that the developer accesses through a single Graphical User Interface (GUI). An IDE can be installed locally, or it can be included as part of one or more existing and compatible applications accessed through a web browser. ## Label Part of the URLs on Anaconda.org where conda looks for packages. Labels are searched only if you specify a label. The default label is `main`, so packages that are uploaded without specifying a label are automatically labeled `main`. The version labeled `main` is also downloaded by default, unless you specify a different label. For example, the following repositories are equivalent: ```sh theme={null} https://anaconda.org/sean/labels/main https://anaconda.org/sean ``` Commands such as `conda install` can be used with a channel or used with a channel and a label: ```sh theme={null} conda install --channel sean selenium conda install --channel sean/label/dev selenium conda install --channel sean/label/stable selenium ``` ## Miniconda Miniconda is a lightweight version of [Anaconda Distribution](#anaconda-distribution) run from a [CLI](#command-line-interface-cli) that only includes [conda](#conda), Python, and their dependencies. Once Miniconda is installed, you can install additional conda packages directly from the CLI with the following command: ```sh theme={null} conda install ``` Replace \ with the name of the package you want to install in your environment. For more information on Miniconda, see [Miniconda](/getting-started/miniconda/main). For information on licensing, see our [Terms of Service](https://anaconda.com/legal). ## Mirror Mirroring is the process of copying data from a source and then storing it in a new location, often on a scheduled basis. When the mirror's source updates its contents, the mirror's contents update to match the next time it runs. It is possible to mirror just a subset of the source's contents as well, if the source contains more data than you need or can store. ## Namespace Each user and organization has their own location called a "namespace" where they can host packages. You can view the public packages in a user or organization's namespace by navigating to their user page. EXAMPLE: The `travis` user namespace located at `https://anaconda.org/travis` contains packages that were uploaded and shared by the user whose account is named `travis`. ## Noarch package Noarch packages are not built specific to a particular CPU architecture or operating system, so they can be installed on any machine. ## Packages Packages are bundles of software files along with information about the software, such as its name, version, and description, that can be installed and managed by a package manager. Some packages consist only of metadata, with no software of their own. These are called metapackages. Their role is to pull in related packages so they can be installed and managed together. ## Package manager A collection of software tools that automates the process of installing, updating, configuring, and removing packages. Also known as a package management system. [Anaconda Navigator](#anaconda-navigator) includes the conda package manager with a [GUI](#graphical-user-interface-gui) overlay for ease of use. Anaconda.org supports two package managers: [conda](#conda) and pip. ## Parameters Model parameters are the weights and biases it learns during training. The more parameters a model has, the better its ability to learn, but the more tightly it will conform to its training data. ## Project template Contains all the base files and components to support a particular programming environment. For example, a Python Spark project template contains everything you need to write Python code that connects to Spark clusters. When creating a new project, you can select a template that contains a set of packages and their dependencies. ## Quantization Method Quantization balances a model's parameters. This is done to make the model either more efficient in terms of memory usage, or more robust and accurate. Think of this like taking a 300-page manual and condensing it down to 100, 50, or 30 pages of the most important information. ## R package An R package is a bundle of code, data, and documentation written in the R language that extends its functionality. For more information about R, see [Using R language with Anaconda](/getting-started/working-with-conda/packages/using-r-language). ## Repository Any storage location from which software or software assets, like packages, can be retrieved and installed on a local computer. See also: [Anaconda's Premium Repository](#anacondas-premium-repository) and [conda repository](#conda-repository). ## REST API A common way to operationalize a machine learning model is through a REST API. A REST API is a web server endpoint, or callable URL, which provides results based on a query. REST APIs allow developers to create applications that incorporate machine learning and prediction, without having to write models themselves. ## Session A time-bounded context that keeps user identity and state across requests. A session starts when a user (or service) authenticates or starts a runtime, and ends at sign-out, timeout, or termination. Sessions can hold permissions, environment settings, and runtime/process information. ## Token A token is a randomly generated string used for authentication or access control. It represents proof of identity and permissions, allowing systems to access resources such as repository channels or APIs. {/* Other tooltips without glossary definitions: Deregisters Removes the environment path from the list of known environments in environments.txt. Interpreter The software that runs your code. Kernel package A kernel is a programming-language-specific process that interprets your code, runs it, and gives you the results. For example, the `ipykernel` package enables interactive computing in Python. Non-scalar value A non-scalar value is a data structure that contains multiple values, such as a list, dictionary, or tuple. Online mode A mode where Navigator is connected to a network, usually the internet, so that it can download and install packages. Register Adds the full path to the directory containing the environment. Runtime The interpreter and other behind-the-scenes systems that run your code. Search string A sequence of characters, like letters, numbers, and spaces. Collections Project directories connected to (or shared with) your Anaconda account from nb.anaconda.com, pyscript.com, and PyExcel through the Anaconda Toolbox. */}