‘Zero-configuration’ getting started experience for machine learning users
With this update, we’ve released a new version of the default image that enables a seamless getting started experience for machine learning scenarios:
- The default image now has Jupyter pre-installed by default so that you can immediately run
jupyter notebook or
jupyter lab from the terminal to get started with your data science project.
- The default image also has the following commonly used machine learning specific Python libraries pre-installed by default:
- For numeric computing: numpy, pandas, scipy
- For data visualizations: matplotlib, seaborn
- For model building: scikit-learn, tensorflow, keras, torch
- For data fetching: requests
- Lastly, you can include any additional project-dependent Python packages in the
requirements.txt file, and those will be automatically installed at a user scope when you create your codespace.
- Alongside these improvements, the default image has been updated with the following tools to optimize for speed and costs associated with default codespaces. We’ve also updated the major version of the image to 2 since the removal of these toolsets introduces breaking changes. While some of the tools are not included by default anymore, you can always add these by creating your own devcontainer configuration.
- Node 14
- Node 16
- Python 3.9
- Python 3.10
- Conda 4.12
- Java 11
- Maven 3.8
- Ruby 3.0
- Ruby 3.1
- PHP 8.0
- PHP 8.1
- PHP Composer 2.3
- Hugo 0.96
- Node 12
- Python 3.8
- Conda 4.8
- .NET 5.0
- Ruby 2.7
- PHP 7.2
- PHP 7.3
- PHP Composer 2.0
- Hugo 0.90
- Maven 3.6
- Az CLI
- Lastly, as a part of this update, for tools that have LTS support (e.g. node, python), the default image will include the latest two LTS releases going forward.
To learn more, check out the latest Codespaces default image configuration.