Search Results for "control"

...The IDE is cross-platform: there are desktop versions for Windows, macOS and Linux, as well as a server version for remote or multi-user deployment via a web browser. In addition to code editing and execution, RStudio offers extensive support for reproducible research via R Markdown, notebooks, and integration with version control systems like Git and SVN. Package development is built in, with tooling for building, checking, and testing R packages, plus integration with documentation tools, CRAN submission workflows, and project templates.

Most software libraries let you compute over the information you own and see inside of machines you control. However, this means that you cannot compute on information without first obtaining (at least partial) ownership of that information. It also means that you cannot compute using machines without first obtaining control over those machines. This is very limiting to human collaboration and systematically drives the centralization of data, because you cannot work with a bunch of data without first putting it all in one (central) place. ...

...DPG offers a solid framework for developing scientific, engineering, gaming, data science and other applications that require fast and interactive interfaces. The Tutorials will provide a great overview and links to each topic in the API Reference for more detailed reading. Complete theme and style control. GPU-based rendering and efficient C/C++ code.

A community‑led open handbook and living documentation project from the Alan Turing Institute, providing best practices and open guidance for reproducible, ethical, collaborative data science and research.

...It brings together all your favourite open-source data science tools and apps into a ready-to-run desktop environment. https://datlinux.com It's based on Lubuntu, so it’s easy to install and use. The custom DAT Linux Control Panel provides a centralised one-stop-shop for running and managing dozens of data science programs. DAT Linux is perfect for students, professionals, academics, or anyone interested in data science who doesn’t want to spend endless hours downloading, installing, configuring, and maintaining applications from a range of sources, each with different technical requirements and set-up challenges.

...It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies easy on-boarding for their team members, freeing them up from complex systems admin and security processes. Administrators control data access and resource provisioning for their users. Notebook Instances are another option. They have the familiar Jupyter and JuypterLab interfaces that work well for single users, or small teams where users are also administrators. Advanced users also use SageMaker solely with the AWS CLI and Python scripts using boto3 and/or the SageMaker Python SDK.