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PyTorch

Client Challenge Client Challenge PyTorch 2.13 Release Blog – PyTorch Bringing PyTorch Monarch to AMD GPUs: Single-Controller Distributed Training on ROCm – PyTorch Understanding PyTorch’s Test Infrastructure – PyTorch Building the Future of On-Device AI at the ExecuTorch Hackathon – PyTorch Shopify Joins the PyTorch Foundation as a Platinum Member – PyTorch A PyTorch-Native Stack for Large-Scale LLM RL Post-Training – PyTorch Scalable CI for PyTorch’s Out-of-Tree Backends – PyTorch Portable APIs and High-Performance Kernels for Multi-Silicon LLM Inference – PyTorch Serving DeepSeek-V4 on GB300 with SGLang: 5x Higher Throughput at the Same Interactivity Since Day-0 – PyTorch Client Challenge Client Challenge Nominations Open for the 2026 PyTorch Foundation Contributor Awards – PyTorch A milestone in APAC – PyTorch Portable vLLM Model Inference Kernels in Helion – PyTorch Using Muon Optimizer with DeepSpeed – PyTorch How LinkedIn Uses PyTorch to Solve Extreme-Scale Optimization Problems – PyTorch Why Is PyTorch Compile So Fast: Kernel Fusion – PyTorch Up to 580tps! New Speed Record of Qwen3.5-397B-A17B on GPU for Agentic Workloads with TokenSpeed – PyTorch Alibaba Cloud Joins the PyTorch Foundation as a Platinum Member – PyTorch A Warp-Specialized Blackwell Kernel for Fixed-Block Sparse Self-Attention – PyTorch Join the PyTorch Foundation Ambassador Program: A Global Network of Community Leaders – PyTorch PyTorch Docathon 2026 Results in 150+ Merged Pull Requests – PyTorch Client Challenge Running PyTorch Models on Apple Silicon GPUs with the ExecuTorch MLX Delegate – PyTorch PyTorch 2.12 Release Blog – PyTorch Efficient Edge AI on Arm CPUs and NPUs: Understanding ExecuTorch through Practical Labs – PyTorch Co-Designing Kernels for RecSys Inference – PyTorch The Case for Disaggregating CPU from GPU in LLM Serving – PyTorch IBM Research uses vLLM at the heart of its RITS Platform – PyTorch Client Challenge
JUST LAUNCHED! PyTorch Certified Associate (PTCA) – PyTorch
By PyTorch Foundation · 2026-06-18 · via PyTorch

PTCA

Prove You Can Work with the Technology Behind Modern AI

Linux Foundation Education and PyTorch Foundation have launched the PyTorch Certified Associate (PTCA), a new certification designed for early-stage practitioners looking to build credibility in AI and machine learning. PTCA validates your ability to work with PyTorch, demonstrating that you understand how models are designed, trained, and used in real-world environments.
AI adoption is accelerating across industries, and organizations are under pressure to turn ideas into working systems. PyTorch sits at the center of that shift, widely used by engineers and researchers to build and experiment with machine learning models. As teams expand their AI capabilities, they need professionals who can contribute with a shared understanding of core concepts, workflows, and tools.

Earning PTCA helps you stand out for AI and machine learning roles by proving you can work within real PyTorch workflows. With PTCA, you can:

  • Stand out for AI and machine learning roles
  • Prove you can work with PyTorch in real-world AI workflows
  • Give employers confidence in your PyTorch skills

The certification is designed for individuals with some Python and machine learning experience who are beginning to work with PyTorch. The exam is multiple choice, includes a free retake, and provides 12 months of eligibility, giving you flexibility as you prepare. Once earned, the certification is valid for two years and demonstrates your foundational competency in one of the most widely used technologies in modern AI.

Prove Your PyTorch Skills for an AI-Driven Market

Enroll Today!

“Last year at PyTorch Conference, the PyTorch Foundation launched a beta of the PyTorch Certified Associate training, and the response from the community was extraordinary. The event sold out, the course was incredibly well received, and we heard clearly from practitioners, educators, and organizations that formal PyTorch certification was something they had wanted for quite some time.

The launch of the PyTorch Certified Associate is an important milestone for the community. It gives learners a clear path to onboard to PyTorch, understand core workflows, and use the framework effectively and optimally in real-world AI and machine learning environments. It also gives organizations a trusted signal: when someone earns the PTCA credential, employers know what foundational skills and practical PyTorch knowledge they can expect.

This is just the beginning. Following the PTCA launch, we are already looking ahead to a more advanced PyTorch Certified Developer course, designed to help practitioners continue building deeper expertise as PyTorch remains central to modern AI development.”

— Matt White, CTO, PyTorch Foundation