Concepts

Project

A project is a folder that contains an anaconda-project.yml configuration file together with scripts, notebooks and other files.

You can make any folder into a project by adding a configuration file named anaconda-project.yml to the folder. The configuration file can include the following sections:

• commands
• variables
• services
• downloads
• packages or dependencies
• channels
• env_specs

Data scientists use projects to encapsulate data science projects and make them easily portable. A project is usually compressed into a .tar.bz2 file for sharing and storing.

Anaconda Project automates setup steps, so that data scientists that you share projects with can run your project with a single command—anaconda-project run.

Configuration files

Projects are affected by 3 configuration files:

• anaconda-project.yml—Contains information about a project to be shared across users and machines. If you use source control, put anaconda-project.yml into your system.
• anaconda-project-local.yml—Contains your local configuration state, which you do not want to share with others. Put this file into .gitignore, .svnignore or the equivalent in your source control system.
• anaconda-project-lock.yml—Optional. Contains information needed to lock your package dependencies at specific versions. Put this file into source control along with anaconda-project.yml. For more information on anaconda-project-lock.yml, see Reference.

To modify these files, use Project commands, Anaconda Navigator, or any text editor.

Environment variables

Anything in the “variables” section of an anaconda-project.yml file is considered to be an environment variable needed by your project.

EXAMPLE: The variables section of an anaconda-project.yml file that specifies 2 variables:

variables:
  - AMAZON_EC2_USERNAME
  - AMAZON_EC2_PASSWORD

When a user runs your project, Project asks them for values to assign to these variables.

In your script, you can use os.getenv() to obtain these variables. This is a much better option than hardcoding passwords into your script, which can be a security risk.

Comparing Project to conda env and environment.yml

Project has similar functionality to the conda env command and the environment.yml file, but it may be more convenient. The advantage of Project for environment handling is that it performs conda operations and records them in a configuration file for reproducibility, all in one step.

EXAMPLE: The following command uses conda to install Bokeh and adds bokeh=0.11 to an environment spec in anaconda-project.yml:

anaconda-project add-packages bokeh=0.11

The effect is comparable to adding the environment spec to environment.yml. In this way, the state of your current conda environment and your configuration to be shared with others will not get out of sync.

Project also automatically sets up environments for other users when they type anaconda-project run on their machines. They do not have to separately create, update or activate environments before they run the code. This may be especially useful when you change the required dependencies. With conda env, users may forget to rerun it and update their packages, while anaconda-project run automatically adds missing packages every time.

In addition to creating environments, Project can perform other kinds of setup, such as adding data files and running a database server. In that sense, it is a superset of conda env.