State of Ray (Robert Nishihara, Anyscale)

Robert Nishihara, co-founder and CEO, Anyscale, kicks off Ray Summit 2021 with a look back at the past year, celebrating the growth of the Ray project, ecosystem, and community.

Transcript

1. State of Ray Robert Nishihara CEO and Co-Founder, Anyscale
2. An Amazing Year for Ray Vibrant Community Richer Ecosystem Inspiring User Stories
3. 2017 2018 2019 2020 2021 480+ contributors 487 200 400 600 Community
4. Vibrant Community Richer Ecosystem Inspiring User Stories An Amazing Year for Ray
5. Native Libraries 3rd Party Libraries Your app here! Ecosystem - the big picture! Universal framework for distributed computing Run anywhere Library + app ecosystem
6. New and improved integrations Ecosystem and more ...
7. 5X speed up with Ray XGBoost and more ...
8. “Ray will play an increasingly important role in bringing much needed common infrastructure and standardization to the production machine learning ecosystem, both within Uber and the industry at large.” Horovod and more ...
9. 13X speed up with Ray Dask and more ...
10. Ray is the tool of choice for scaling libraries Ecosystem and more ...
11. Vibrant Community Richer Ecosystem Inspiring User Stories An Amazing Year for Ray
12. Diverse users … with diverse use cases
13. Making Boats Fly with AI Mckinsey | QuantumBlack Australia
14. Scaling Ecosystem Restoration Dendra Systems
15. Large Scale ML Platforms Uber, Shopify, Robinhood, and more
16. Vibrant Community Richer Ecosystem Inspiring User Stories An Amazing Year for Ray
17. What’s new? A lookback at the last six months
18. Object spilling Improved memory management Scalability testing Robustness & Scale NEW
19. Driver Driver Ray cluster import tensorflow import pandas @ray.remote def f(): # app logic if __name__ == ‘__main__’: main() import tensorflow import pandas @ray.remote def f(): # app logic if __name__ == ‘__main__’: main() ray.client() Streamlined Workflows NEW
20. Driver import tensorflow import pandas @ray.remote def f(): # app logic if __name__ == ‘__main__’: main() Ray cluster ray.client() Streamlined Workflows NEW
21. ray.client().env( ) ray.client() Environments Streamlined Workflows
22. ray.client().env( ) ray.client().env( { 'pip': ['tensorflow', 'pandas'], } ) Environments Streamlined Workflows
23. ray.client().env( { 'pip': ['tensorflow', 'pandas'], 'working_dir': '/home/my-project' } ) ray.client().env( { 'pip': ['tensorflow', 'pandas'], } ) Environments Streamlined Workflows
24. Revamped API Refactored architecture Production Serving NEW
25. What’s next?
26. Expand your knowledge 50+ breakout sessions Go deeper Tutorials (day 3) Connect Slack and Gather.town Welcome to Ray Summit!
27. Gather Slack Connect with other attendees Office hours with Ray developers
28. Thank you!
29. Get Involved Meet Ray users and developers (today after the break) Keynote: Ion Stoica Find the talks online youtube.com/anyscale Thank you!
30. Backup
31. Ray Summit 2021
32. Ray Summit 2021 Ray Core • Ray internals: object management (today) • Deep dive into Ray’s scheduling (tomorrow) Ecosystem • Patterns of ML models in production (today) • Distributed XGBoost on Ray (today) • A bridge for preprocessing and training (today) • Data processing on Ray (today) • The ML ecosystem (tomorrow)
33. An incredible year Community Ecosystem User Stories
34. Ecosystem • Airflow
35. Ecosystem • Airflow • XGBoost
36. Ecosystem • Airflow • XGBoost • PyTorch
37. Ecosystem • Airflow • XGBoost • PyTorch • Horovod We believe that Ray will continue to play an increasingly important role in bringing much needed common infrastructure and standardization to the production machine learning ecosystem, both within Uber and the industry at large.
38. Ecosystem • Airflow • XGBoost • PyTorch • Horovod • Hugging Face
39. Ecosystem • Airflow • XGBoost • PyTorch • Horovod • Hugging Face • MLflow
40. Ecosystem • Airflow • XGBoost • PyTorch • Horovod • Hugging Face • MLflow • Scikit-learn
41. Ecosystem • Airflow • XGBoost • PyTorch • Horovod • Hugging Face • MLflow • Scikit-learn • Dask
42. Ecosystem • Airflow • XGBoost • PyTorch • Horovod • Hugging Face • MLflow • Scikit-learn • Dask • And more… Ray is the tool of choice for scaling libraries
43. Native Libraries 3rd Party Libraries Your app here! Ecosystem universal framework for distributed computing run anywhere library + app ecosystem
44. What’s New - Robustness & Scale Last Ray Summit
45. What’s New - Robustness & Scale • 13x throughput (1.4 PB/hour) • 90% cost reduction
46. What’s New - Robustness & Scale • Deploying Ray on 200K cores
47. What’s New - Dev & Prod Improved workflows for development & production • Ray client • Environments
48. What’s New - Dev & Prod Ray Client Driver Driver Ray cluster import tensorflow import pandas @ray.remote def f(): # app logic if __name__ == ‘__main__’: main() import tensorflow import pandas @ray.remote def f(): # app logic if __name__ == ‘__main__’: main()
49. What’s New - Dev & Prod Ray Client Driver import tensorflow import pandas @ray.remote def f(): # app logic if __name__ == ‘__main__’: main() Ray cluster
50. What’s New - Dev & Prod ray.client() Environments ray.client().env( )
51. What’s New - Dev & Prod Environments ray.client().env( ) Environments ray.client().env( { 'pip': ['tensorflow', 'pandas'], } )
52. What’s New - Dev & Prod Environments ray.client().env( { 'pip': ['tensorflow', 'pandas'], } ) Environments ray.client().env( { 'pip': ['tensorflow', 'pandas'], 'working_dir': '/home/my-project' } )
53. What’s New - Serving
54. User Stories
55. User Stories - Uber
56. User Stories - Uber Ray will play an increasingly important role in bringing much needed common infrastructure and standardization to the production machine learning ecosystem, both within Uber and the industry at large.
57. User Stories - Uber
58. User Stories - Ant Group
59. User Stories - McKinsey
60. Conclusion Talk about call to actions Also, call out exciting upcoming talks
61. Asawari’s Version
62. State of Ray Robert Nishihara CEO and Co-Founder, Anyscale
63. Welcome TO Ray Summit 2021
64. An Amazing Year! Vibrant Community Richer Ecosystem Inspiring User Stories
65. 2017 2018 2019 2020 2021 560+ contributors from XXX companies 560 200 400 600 COMMUNITY
66. New and improved integrations ECOSYSTEM and more ...
67. 5X speed up with Ray XGBoost and more ...
68. We believe that Ray will continue to play an increasingly important role in bringing much needed common infrastructure and standardization to the production machine learning ecosystem, both within Uber and the industry at large. Horovod and more ...
69. Ray is the tool of choice for scaling libraries ECOSYSTEM and more ...
70. Native Libraries 3rd Party Libraries Your app here! Ecosystem - the big picture! Universal framework for distributed computing Run anywhere Library + app ecosystem
71. Diverse users …
72. … with diverse use cases
73. Making Boats Fly with AI Mckinsey | QuantumBlack Australia
74. Scaling Ecosystem Restoration Dendra Systems
75. Large Scale ML Platforms Uber, Spotify, Robinhood, and more
76. An Amazing Year! Vibrant Community Richer Ecosystem Inspiring User Stories
77. Thank You We wouldn’t be here without YOU.
78. What’s new? A lookback at last year
79. Object spilling Improved memory management Scalability envelope Proof point (Optional) Robustness & Scale NEW
80. ray.connect() One line benefit statement Streamlined Workflows NEW
81. Environments One line benefit statement Streamlined Workflows NEW
82. Feature / Improvement Feature / improvement Serving Improvement NEW
83. What’s next? A peek into the future
84. Feature / Improvement Feature / improvement bucket ROADMAP
85. Thank You
86. Backup
88. 480+ contributors from XXX companies
89. What’s New - Robustness & Scale • Data Processing on Ray ◦ SangBin Cho • Ray Internals: Object Management with the Ownership Model ◦ Stephanie Wang, Yi Cheng • A Deep Dive into Ray’s Scheduling Policy ◦ Alex Wu • MLDataset: A Ray Bridge for Data Preprocessing and Distributed Training ◦ Clark Zinzow • Building High Availability and Scalability Online Computing Applications on Ray ◦ Tengwei Cai • Improving Ray for Large-scale Applications ◦ Hao Chen
90. What’s New - Robustness & Scale Enables 100TB shuffle