Sematic + Ray: The Best of Orchestration and Distributed Compute at your Fingertips

This page summarizes the projects mentioned and recommended in the original post on dev.to

Scout Monitoring - Free Django app performance insights with Scout Monitoring
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InfluxDB - Power Real-Time Data Analytics at Scale
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  • examples

    TensorFlow examples (by tensorflow)

    Perform distributed Hyperparameter tuning of a TensorFlow natural language model using Ray Tune.

  • Scout Monitoring

    Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.

    Scout Monitoring logo
  • Pytorch

    Tensors and Dynamic neural networks in Python with strong GPU acceleration

    Do quick and efficient distributed training on a PyTorch image classifier using PyTorch Lightning and Ray.

  • Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

    Sometimes, two tools seem to “just fit” together, and you forget that you’re even working with multiple tools as the lines blur into a coherent experience. One example that every ML Engineer or Data Scientist is familiar with is numpy and pandas. Numpy enables fast and powerful mathematical computations with arrays/matrices in Python. Pandas provides higher-level data structures for manipulating tabular data. While you can of course use one without (explicitly) using the other, they complement each other so well that they are often used together. Pandas works as a usability layer, while numpy supercharges it with compute efficiency.

  • NumPy

    The fundamental package for scientific computing with Python.

    Sometimes, two tools seem to “just fit” together, and you forget that you’re even working with multiple tools as the lines blur into a coherent experience. One example that every ML Engineer or Data Scientist is familiar with is numpy and pandas. Numpy enables fast and powerful mathematical computations with arrays/matrices in Python. Pandas provides higher-level data structures for manipulating tabular data. While you can of course use one without (explicitly) using the other, they complement each other so well that they are often used together. Pandas works as a usability layer, while numpy supercharges it with compute efficiency.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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