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Beginner's Guide to Profiling in PyTorch
Hugging Face Bloghigh confidence
Why it matters
- →Provides a beginner-friendly introduction to profiling in PyTorch, a crucial skill for optimization.
- →Helps developers understand and identify performance bottlenecks in neural networks.
- →Aims to make profiling more accessible, encouraging better optimization practices in AI development.
Hugging Face has introduced a new series focused on profiling in PyTorch, beginning with a guide for beginners on using torch.profiler. The initial post explains how to profile a basic matrix multiplication and addition operation, offering insights into reading profiler traces and understanding the interaction between CPU and GPU. The series aims to help developers optimize their PyTorch code by gradually increasing complexity, eventually covering large language models. This educational effort is designed to make profiling more accessible and useful for developers seeking to improve neural network performance.
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Claude Code v2.1.157 Update Released
The latest update to Claude Code, version 2.1.157, introduces several enhancements and fixes that streamline plugin management and improve user experience. Notably, plugins in the .claude/skills directories are now automatically loaded, eliminating the need for a marketplace. The update also adds autocomplete for plugin arguments and honors theagent field in settings.json for dispatched sessions. Additionally, various bug fixes enhance stability, such as resolving issues with unprocessable images and improving performance in long conversations. These changes make Claude Code more efficient and user-friendly for developers.
Claude Code Releases
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Claude Code v2.1.158 Update Released
Claude Code's latest update, v2.1.158, introduces Auto mode for Bedrock, Vertex, and Foundry platforms, specifically for Opus 4.7 and 4.8. This feature can be activated by setting the environment variable CLAUDE_CODE_ENABLE_AUTO_MODE to 1. The addition of Auto mode aims to streamline processes and enhance user experience across these platforms. While the update doesn't introduce groundbreaking changes, it offers a practical enhancement for developers working with these specific versions.
Claude Code Releases
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GitHub Copilot API Adds AI Adoption Cohorts
GitHub has enhanced its Copilot usage metrics API by introducing AI adoption phases, allowing enterprises to better understand how users engage with Copilot. This new feature classifies users into four phases based on their interaction with Copilot over a 28-day period, providing insights into their adoption journey. The API now includes a field for ai_adoption_phase, offering detailed metrics at both user and organizational levels. This development enables enterprises to tailor training and support efforts, focusing on phases with the most potential for growth. By moving beyond simple active-user counts, organizations can now track user progression and optimize Copilot adoption strategies.
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