LSDPO - R244

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Open Source Projects

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 Candidates for Open Source Project Study

The list is not exhausted. If you take anything other than the one in the list, please discuss with me. The purpose of this assignment is to understand the prototype of the proposed architecture, algorithms, and systems through running an actual prototype and present/explain to the other people how the prototype runs, any additional work you have done including your own applications and setup process of the prototype. This experience will give you better understanding of the project. These Open Source Projects come with a set of published papers and you should be able to examine your interests in the paper through running the prototype. Some projects are rather large and may require extensive environment and time; make sure you are able to complete this assignment.

Project title in red colour is suggested but choose what you like. Some projects are more framework like, while others are more specific approaches. The project 27 and beyond are added this year.

1. Ciel: https://github.com/mrry/skywriting, https://www.cl.cam.ac.uk/netos/ciel/

2. Apache Hadoop: https://hadoop.apache.org/

3. DryadLINQ: https://research.microsoft.com/en-us/projects/dryadlinq/

4. MapReduce Online: https://code.google.com/p/hop/

5. Naiad: data-parallel dataflow computation https://research.microsoft.com/en-us/projects/naiad/, and https://github.com/frankmcsherry/timely-dataflow (Rust version)

6. Apache Giraph: Graph processing based on BSP https://incubator.apache.org/giraph/

7. Spark: Fast Cluter Computing https://spark-project.org/

8. X-Stream: https://labos.epfl.ch/x-stream

9. Storm: https://storm-project.net/

10. GraphX: https://github.com/amplab/graphx

11. Tensorflow: https://www.tensorflow.org/

12. Chaos: https://github.com/epfl-labos/chaos

13. PyTorch: https://pytorch.org/

14. CNTK: https://docs.microsoft.com/en-us/cognitive-toolkit/  https://github.com/Microsoft/CNTK

15. Ray (+RLLib): https://github.com/ray-project/ray, https://github.com/ray-project/ray/tree/master/python/ray/rllib

16. RLgraph: https://github.com/rlgraph/rlgraph

17. BoTorch: hhttps://github.com/pytorch/botorch

18. BOAT: https://github.com/VDalibard/BOAT

19. Saber: https://github.com/lsds/Saber

20. Snorkel / FlyingSquid: https://www.snorkel.org/ https://github.com/HazyResearch/flyingsquid

21. Park: https://github.com/park-project/park (https://openreview.net/pdf?id=BkgfRbEPsE)

22. Pyro: https://pyro.ai/

23. Apache Flink: https://flink.apache.org/

24. Jax: https://github.com/google/jax

25. Numpyro - building pyro on jax: https://github.com/pyro-ppl/numpyro

26. Emukit: https://emukit.github.io

27. PGM: https://github.com/gvinciguerra/PGM-index

28. ALEX: https://github.com/microsoft/ALEX

29. OtterTune: https://github.com/cmu-db/ottertune

30. CDBTune: https://github.com/ZhengtongYan/CDBTune

31. Bao: https://github.com/learnedsystems/baoforpostgresql

32. NAS-MCTS (LA-MCTS): https://github.com/facebookresearch/LaMCTS

33. World-Model: https://worldmodels.github.io/

34. DRL-Pytorch: https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

35. Inverse RL-GAIL: https://github.com/hcnoh/gail-pytorch

36. MARL: https://www.david-albert.fr/marl/html/index.html

37. Meta-RL: https://github.com/rlworkgroup/metaworld

38. SMAC3: https://github.com/automl/SMAC3

39. EGG: https://egraphs-good.github.io/

40. TVM: https://github.com/apache/tvm

41. Ray-Tune: https://github.com/ray-project/ray/tree/master/python/ray/tune