Training AI To Code Using The Largest Code Dataset (Project CodeNet)
summary
Project CodeNet provides 14 million submissions across 4,000 problems and 55 languages (over 500 million lines of code) with per-submission CPU/memory and verdict labels; the repo supplies tokenizers, AST and dataflow graph generators, and notebooks that train a language classifier which in one demo misclassifies 1 of 100 samples. The pipeline uses Kubeflow pipelines, papermill notebooks and PVC-mounted caches, and deploys models via kserve serving as containerized Flask endpoints with Swagger on localhost:5000.
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chapters (19)
- 0:00Start
- 7:32Project CodeNet: Polyglot of languages
- 7:48The Data
- 9:03Metadata - Dataset Level
- 9:24Metadata - Problem Level
- 9:58Metadata - Submission status codes
- 10:14Tools and Examples
- 11:27Real-World Applications
- 13:43The Machine Learning eXchange (MLX)
- 16:26Datasets -Integration with Datashim
- 18:51Watson Studio Pipelines Open Beta and CPD Rele
- 19:28Speeding up the Al Lifecycle!
- 21:00Pillars of Al Lifecycle - Datasets, Models..
- 21:41and Pipelines
- 23:03View, download, and execute Pipelines
- 23:54View, download, and execute Pipeline Components
- 24:46Library of prepackaged models. Register your own models, run with Pipe
- 25:25Register and deploy Datasets
- 26:36Library of prepackaged notebooks. Register your own notebooks
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