Matplotlib vs. PyTorch vs. Python vs. Vega-Altair Comparison Chart

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.

The Vega-Altair open-source project is not affiliated with Altair Engineering, Inc. With Vega-Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. The key idea is that you are declaring links between data columns and visual encoding channels, such as the x-axis, y-axis, color, etc. The rest of the plot details are handled automatically. Building on this declarative plotting idea, a surprising range of simple to sophisticated plots and visualizations can be created using relatively concise grammar.