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Py torch

AI Apps in a Flash: Ship to GPUs Without Docker — Dean Quiñanola, Runpod

AI Apps in a Flash: Ship to GPUs Without Docker — Dean Quiñanola, Runpod

Dean Quiñanola, a staff engineer at Runpod, presents Runpod Flash, a way to run Python on cloud GPUs as if they were local, with no Dockerfiles, image builds, or registry pushes. The talk covers serverless and load-balanced endpoints, endpoint-to-endpoint calls, network volumes with warm caching, and live serverless iteration. A live demo moves from GPU detection to text and image generation and then deploys to production with a single command.

Why Reflection AI Is Making the Open Intelligence Bet - and What It Means for Deploying Models

Why Reflection AI Is Making the Open Intelligence Bet - and What It Means for Deploying Models

Joe Spisak of Reflection discusses why open models and open intelligence matter, covering RL-driven customization, environments as the new dataset, the OpenEnv project, PyTorch and Llama lessons, inference scaling, and the case for ecosystem control and open safety.

Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs

Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs

A practical guide to the engineering principles and trade-offs involved in training a small language model from scratch on a local machine, based on a workshop by Angelos Perivolaropoulos from ElevenLabs.

Performance Optimization and Software/Hardware Co-design across PyTorch, CUDA, and NVIDIA GPUs

Performance Optimization and Software/Hardware Co-design across PyTorch, CUDA, and NVIDIA GPUs

Chris Fregly discusses his new book, "AI Systems Performance Engineering", covering the co-design and optimization of hardware, software, and algorithms across PyTorch, CUDA, and NVIDIA GPUs. The talk explores GPU architecture, system-level reliability challenges, and the use of modern coding agents for low-level kernel optimization.

919: Hopes and Fears of AGI, with All-Time Bestselling ML Author Aurélien Géron

919: Hopes and Fears of AGI, with All-Time Bestselling ML Author Aurélien Géron

Bestselling author Aurélien Géron discusses the next version of his book, "Hands-On Machine Learning," which will shift from TensorFlow to PyTorch. He shares his revised 5-10 year timeline for AGI, citing a temporary plateau in LLM capabilities and the need for better world models. Géron also expresses significant concerns about AI alignment, highlighting recent experiments showing deceptive behavior in models and calling for urgent research into controlling emergent sub-goals like self-preservation.