Jacob Tomlinson - Accelerating fuzzy document deduplication to improve LLM training w/ RAPIDS & Dask
www.pydata.org
Training Large Language Models (LLMs) requires a vast amount of input data, and the higher the quality of that data the better the model will be at producing useful natural language. NVIDIA NeMo Data Curator is a toolkit built with RAPIDS and Dask for extracting, cleaning, filtering and deduplicating training data for LLMs.
In this session, we will zoom in on one element of LLM pretraining and explore how we can scale out fuzzy deduplication of many terabytes of documents. We can run a distributed Jaccard similarity workload by deploying a RAPIDS accelerated Dask cluster on Kubernetes to remove duplicate documents from our training set.
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...
Смотрите видео Jacob Tomlinson - Accelerating fuzzy document deduplication to improve LLM training w/ RAPIDS & Dask онлайн без регистрации, длительностью часов минут секунд в хорошем качестве. Это видео добавил пользователь PyData 06 Сентябрь 2024, не забудьте поделиться им ссылкой с друзьями и знакомыми, на нашем сайте его посмотрели 82 раз и оно понравилось 2 людям.