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FAQ
Kangas is currently in an open beta. We stress test Kangas heavily and often, and are confident in sharing with the public. That being said, it is a very young project, and there will be bugs and rough edges. Additionally, new features will be added at a fast pace, so if you find a bug or have a request, please do not hesitate to open a ticket or start a discussion.
Kangas can be run as a standalone application on newer version of Windows, MacOS, and most popular Linux distributions. In addition, Kangas can run remotely via Google Colab, or within any Jupyter notebook environment.
Kangas and Pandas are complimentary tools. When you've wrangled your data into a Pandas DataFrame, Kangas can ingest that DataFrame via the DataGrid.read_dataframe()
method, making it easy to visualize and explore your tabular data. Additionally, if your data is too large to process in Pandas or involves multimedia assets, Kangas is a strong alternative.
TensorBoard is one of several tools (including Kangas parent organization, Comet) that specializes in experiment managment. Like Kangas, it provides charting and visualizations out of the box, but is specifically designed for analyzing training workflows. Kangas, in contrast, is designed to analyze any dataset. For example, even if you use a tool like TensorBoard for analyzing training runs, you may still use Kangas before training for exploratory data analysis, or for prediction analysis post-deployment.
Kangas is developed and maintained by the Research team at Comet ML. It began life as a prototype for Comet users who needed to visualize large computer vision datasets, and was later spun out into a standalone open source project. Kangas is and always will be free and open source software, and we are more than happy to accept community contributions.
Kangas has only recently been released, and as such, we don't have much of a formal process for contributions. If you have an idea or would like to make a contribution, we recommend opening a ticket describing your proposed contribution so that we can collaborate directly. We love working with community contributors.
Kangas DataGrid is completely open source; sponsored by Comet ML
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Home
- User Guides
- Installation - installing kangas
- Reading data - importing data
- Constructing DataGrids - building from scratch
- Exploring data - exploration and analysis
- Examples - scripts and notebooks
- Kangas Command-Line Interface
- Kangas Python API
- Integrations - with Hugging Face and Comet
- User Interface
- FAQ - Frequently Asked Questions
- Under the Hood
- Security - issues related to security
- Development - setting up a development environment
- Roadmap - plans and known issues
- User Guides