mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
54 lines
4.4 KiB
Markdown
54 lines
4.4 KiB
Markdown
# Learn More
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Here are some talks, papers, and press coverage involving Ray and its libraries.
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Please raise an issue if any of the below links are broken, or if you'd like to add your own talk!
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## Blog and Press
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- [Modern Parallel and Distributed Python: A Quick Tutorial on Ray](https://towardsdatascience.com/modern-parallel-and-distributed-python-a-quick-tutorial-on-ray-99f8d70369b8)
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- [Why Every Python Developer Will Love Ray](https://www.datanami.com/2019/11/05/why-every-python-developer-will-love-ray/)
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- [Ray: A Distributed System for AI (BAIR)](http://bair.berkeley.edu/blog/2018/01/09/ray/)
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- [10x Faster Parallel Python Without Python Multiprocessing](https://towardsdatascience.com/10x-faster-parallel-python-without-python-multiprocessing-e5017c93cce1)
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- [Implementing A Parameter Server in 15 Lines of Python with Ray](https://ray-project.github.io/2018/07/15/parameter-server-in-fifteen-lines.html)
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- [Ray Distributed AI Framework Curriculum](https://rise.cs.berkeley.edu/blog/ray-intel-curriculum/)
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- [RayOnSpark: Running Emerging AI Applications on Big Data Clusters with Ray and Analytics Zoo](https://medium.com/riselab/rayonspark-running-emerging-ai-applications-on-big-data-clusters-with-ray-and-analytics-zoo-923e0136ed6a)
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- [First user tips for Ray](https://rise.cs.berkeley.edu/blog/ray-tips-for-first-time-users/)
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- [Tune: a Python library for fast hyperparameter tuning at any scale](https://towardsdatascience.com/fast-hyperparameter-tuning-at-scale-d428223b081c)
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- [Cutting edge hyperparameter tuning with Ray Tune](https://medium.com/riselab/cutting-edge-hyperparameter-tuning-with-ray-tune-be6c0447afdf)
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- [New Library Targets High Speed Reinforcement Learning](https://www.datanami.com/2018/02/01/rays-new-library-targets-high-speed-reinforcement-learning/)
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- [Scaling Multi Agent Reinforcement Learning](http://bair.berkeley.edu/blog/2018/12/12/rllib/)
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- [Functional RL with Keras and Tensorflow Eager](https://bair.berkeley.edu/blog/2019/10/14/functional-rl/)
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- [How to Speed up Pandas by 4x with one line of code](https://www.kdnuggets.com/2019/11/speed-up-pandas-4x.html)
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- [Quick Tip -- Speed up Pandas using Modin](https://pythondata.com/quick-tip-speed-up-pandas-using-modin/)
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- [Ray Blog](https://medium.com/distributed-computing-with-ray)
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## Talks (Videos)
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- [Unifying Large Scale Data Preprocessing and Machine Learning Pipelines with Ray Datasets \| PyData 2021](https://zoom.us/rec/share/0cjbk_YdCTbiTm7gNhzSeNxxTCCEy1pCDUkkjfBjtvOsKGA8XmDOx82jflHdQCUP.fsjQkj5PWSYplOTz?startTime=1635456658000) [(slides)](https://docs.google.com/presentation/d/19F_wxkpo1JAROPxULmJHYZd3sKryapkbMd0ib3ndMiU/edit?usp=sharing)
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- [Programming at any Scale with Ray \| SF Python Meetup Sept 2019](https://www.youtube.com/watch?v=LfpHyIXBhlE)
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- [Ray for Reinforcement Learning \| Data Council 2019](https://www.youtube.com/watch?v=Ayc0ca150HI)
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- [Scaling Interactive Pandas Workflows with Modin](https://www.youtube.com/watch?v=-HjLd_3ahCw)
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- [Ray: A Distributed Execution Framework for AI \| SciPy 2018](https://www.youtube.com/watch?v=D_oz7E4v-U0)
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- [Ray: A Cluster Computing Engine for Reinforcement Learning Applications \| Spark Summit](https://www.youtube.com/watch?v=xadZRRB_TeI)
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- [RLlib: Ray Reinforcement Learning Library \| RISECamp 2018](https://www.youtube.com/watch?v=eeRGORQthaQ)
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- [Enabling Composition in Distributed Reinforcement Learning \| Spark Summit 2018](https://www.youtube.com/watch?v=jAEPqjkjth4)
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- [Tune: Distributed Hyperparameter Search \| RISECamp 2018](https://www.youtube.com/watch?v=38Yd_dXW51Q)
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## Slides
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- [Talk given at UC Berkeley DS100](https://docs.google.com/presentation/d/1sF5T_ePR9R6fAi2R6uxehHzXuieme63O2n_5i9m7mVE/edit?usp=sharing)
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- [Talk given in October 2019](https://docs.google.com/presentation/d/13K0JsogYQX3gUCGhmQ1PQ8HILwEDFysnq0cI2b88XbU/edit?usp=sharing)
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- [Talk given at RISECamp 2019](https://docs.google.com/presentation/d/1v3IldXWrFNMK-vuONlSdEuM82fuGTrNUDuwtfx4axsQ/edit?usp=sharing)
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## Papers
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- [Ray 1.0 Architecture whitepaper](https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview)
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- [Ray Design Patterns](https://docs.google.com/document/d/167rnnDFIVRhHhK4mznEIemOtj63IOhtIPvSYaPgI4Fg/edit)
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- [RLlib paper](https://arxiv.org/abs/1712.09381)
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- [RLlib flow paper](https://arxiv.org/abs/2011.12719)
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- [Tune paper](https://arxiv.org/abs/1807.05118)
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- [Ray paper (old)](https://arxiv.org/abs/1712.05889)
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- [Ray HotOS paper (old)](https://arxiv.org/abs/1703.03924)
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