Glossary#
Anaconda.org#
A web-based public repository hosting service in the cloud. Anaconda.org hosts hundreds of useful Python packages for a wide variety of applications. You do not need a Anaconda.org account, or to be logged in, to search for public packages or to download and install them.
You can also publish packages to Anaconda.org. These packages are hosted in a channel connected to your Anaconda.org account and can be shared with the public. Users with paid subscriptions to Anaconda.org can designate packages as private, to be shared only with authorized users.
For more information on Anaconda.org, see Anaconda.org.
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 Distribution#
A downloadable, open-source, high-performance, optimized Python distribution with 300+ 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, Python, 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:
# Replace <PACKAGE> with the name of the desired package conda install <PACKAGE>
For more information on Anaconda Distribution, see Anaconda Distribution.
For information on licensing, see our Terms of Service.
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.
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.
Anaconda Professional Repository#
A private enterprise server on your network where both open source and proprietary packages may be stored and retrieved for installation on a local computer. Anaconda Professional Repository is different from Anaconda.org or the default conda repository. The Anaconda Professional Repository is used to govern access to data science assets, including packages and notebooks. It is available for commercial use both on-premises and in the cloud.
For more information on Anaconda’s product tiers, see Anaconda pricing.
Channels#
A location in a repository where conda looks for packages. Channels may point to an Anaconda.org repository or a private location on a remote or local repository that you or your organization manage. The defaults channel list includes, by default, the following public repository channels:
https://repo.anaconda.com/pkgs/msys2 (for Windows computers only)
ckey#
An internal ID used to identify artifacts within Anaconda Server.
Command line interface (CLI)#
A program in which commands are entered as text, one line at a time, for a computer to execute. This is done in the Anaconda Prompt in Windows, and in a terminal in macOS and Linux. Conda is executed in a CLI. Contrast with Graphical User Interface (GUI).
Commit#
To make a set of local changes permanent by copying them to the remote server. Data Science & AI Workbench checks to see if your work will conflict with any commits that your colleagues have made on the same project, so the files will not be overwritten unless you so choose to do so.
Conda#
A package and environment manager program that is packaged with Anaconda Distribution and run in a CLI. Using conda, you can install and update conda packages and their dependencies, and switch between conda environments on your local computer. Contrast with Anaconda Navigator.
For more information on conda, see the conda documentation.
Conda-build#
A command line tool that you can use to build conda packages from recipes.
Conda environment#
A folder or directory that contains a specific collection of conda packages and their dependencies, so they can be maintained and run independently without interfering with each other. A conda environment maintains its own files, directories, and paths, so that you can work with specific versions of libraries and/or Python itself without affecting other Python projects. Changes to one environment do not affect other environments. For example, if you upgrade a program in one environment, this will not upgrade the same program in another environment.
For example, you may use one conda environment for only Python 2.7 and Python 2.7 packages, and maintain another conda environment with only Python 3.9 and Python 3.9 packages.
The environments in Anaconda Navigator are conda environments.
Conda package#
An archive file that contains everything that a software program needs in order to be installed and run, so you do not have to manually find and install each dependency separately. This can include system-level libraries, Python modules, executable programs, and other components. Conda tracks dependencies between specific packages and platforms, making it simple to create operating system-specific environments using different combinations of packages.
Conda packages can be managed with conda in the CLI or with Anaconda Navigator.
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/ and used within Anaconda Navigator when downloading and installing packages from the Environments tab, or by using conda commands in a CLI.
CVEs#
Common Vulnerabilities and Exposures found in software components. Because modern software is complex with its many layers, interdependencies, data input, and libraries, vulnerabilities tend to emerge over time. Ignoring a high CVE score can result in security breaches and unstable applications.
To learn more about CVE’s and how Anaconda mitigates and manages them, watch the State of Data Science webinar.
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#
A virtual environment allows multiple incompatible versions of the same (software) package to coexist on a single system. An environment is simply a file path containing a collection of mutually compatible packages. By isolating distinct versions of a given package (and their dependencies) in distinct environments, those versions are all available to work on particular projects or tasks.
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 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.
Interactive 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 may be installed locally, or it may 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 a user specifies a different label.
So, if a file is labeled main, then the label name may be omitted from the URL. For example, the following repositories are equivalent:
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:
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 run from a CLI that only includes conda, Python, and their dependencies. Once Miniconda is installed, you can install additional conda packages directly from the CLI with the following command:
# Replace <PACKAGE> with the name of the package you want to install in your environment
conda install <PACKAGE>
For more information on Miniconda, see Miniconda.
For information on licensing, see our Terms of Service.
Mirror#
Mirroring is the process of accurately copying data from a source and then storing it in a new location. A mirror can be either a subset of the original or an exact 1 to 1. Mirroring can be in real-time, on a fixed schedule, or a one-time event.
Namespace#
Each user and organization has their own location called a “namespace” where they may 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#
A conda package that contains nothing specific to any system architecture, so it may be installed on any system. When conda searches for packages on any system in a channel, conda always checks both the system-specific subdirectory—for example, linux-64—and the noarch directory.
For a list of noarch packages available in Anaconda’s main channel, see https://repo.anaconda.com/pkgs/main/noarch/.
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.
For more information on Anaconda Notebooks, see Anaconda Notebooks.
Package#
Software files and information about the software, such as its name, the specific version, and a description, that are bundled into a file that can be installed and managed by a package manager. While packages are generally used for files, they can also be used for metadata alone. When it is, it is called a metapackage.
Package manager#
A collection of software tools that automates the process of installing, upgdating, configuring, and removing packages. Also known as a package management system.
Anaconda Navigator includes the conda package manager with a GUI overlay for ease of use.
Anaconda.org supports two package managers: 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#
A conda package that installs and runs the R computer language. Examples include R Essentials, a bundle of 210 popular open-source software programs written in the R computer language.
For more information on R packages, see Using R language with Anaconda.
Repository#
Any storage location from which software or software assets, like packages, may be retrieved and installed on a local computer. See also: Anaconda Professional Repository and 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#
An open project, running in an editor or IDE.
Token#
An access control token is a random alphanumeric string that is inserted into a package or channel URL. Tokens can be used with Anaconda.org, the Anaconda Professional Repository, or the AE4 Repository. The token allows you to download a package or add a channel that are restricted. Only those users with the correct access token can access the private file. You can use Anaconda Client in your CLI or your organization in Anaconda.org to generate tokens for various private package and channel purposes.