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PyTorch Tutorial 09 - Dataset and DataLoader - Batch Training
Grain: Data Processing for JAX models
JAX DevLab 2025 - Grain: Feeding JAX Models
PyTorch DataLoader Explained: How to make Basic and Custom Datasets
JAX Quickstart (Usage, JIT, Derivatives, and Vectorization)
Getting started with JAX on NVIDIA GPUs
Customizing JAX stack with NVIDIA's kernel DSLs
Build and optimize JAX training loops
Automatic Vectorization in JAX (100X Your Batch Processing)
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Last Updated: September 29, 2026
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Summary
Accelerators are getting faster, but is your data loading keeping up? In this video, we explore the Younghee Kwon from Google introduces how to build efficient, deterministic, and preemption-resilient data processing pipelines ... See my updated PyTorch Data loading video: youtu.be/Jo8lCFLMpCQ -- Learn how data flows in PyTorch: Dataset ... Learn how to get started with Google's powerful Access the full course and codelabs → g.dev/cloud/nvidia-jaxgpus-learningpathway Join the Google Cloud & NVIDIA ... Michael Goldfarb from NVIDIA introduces how to author and execute high-performance tile-based GPU kernels in Discover how to supercharge your machine learning operations with Welcome to Lecture 19 of the PyTorch for Beginners series! In this lecture, we learn how PyTorch handles and prepares data ... In this video we present an overview of the
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