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At https://hal9.com https://hal9.com, we built components for data science in native JavaScript to avoid the waiting times and download overhead if Pyodide. We
by javierluraschi 4y ago
At https://hal9.com https://hal9.com, we built components for data science in native JavaScript to avoid the waiting times and download overhead if Pyodide. We found out the best tools for doing data science in the browser are a combination of Arquero and D3 and TensorFlow.js. At least for now.
We wrote our findings of this and many other libraries here: https://news.hal9.com/posts/data-science-with-javascript https://news.hal9.com/posts/data-science-with-javascript
- throwaway894345 4y agoI’m curious what are the primitives for the scientific computing stack? I’m vaguely aware that libfortran or gdpr tran or something live at the bottom, but I’m not sure if there are other base libraries or what immediately depends on them. Also, did you need to reimplement these in native JS? How does the performance compare?
- javierluraschi 4y agoWe are not using libfortran not gdpr, we are basically using whatever libraries are available for the web. Since most data scientists don't want to use JS per se, you can build the apps as blocks in the Hal9 site or using a soon-to-be-released Python/R package, see https://notebooks.hal9.com https://notebooks.hal9.com Feel free to check out our repo as well, all the "primitives" / blocks code is in the scripts folder: https://github.com/hal9ai/hal9ai https://github.com/hal9ai/hal9ai