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AI Hypercomputer developer experience enhancements from Q1 25: build faster, scale bigger
- Google Cloud has been enhancing its AI Hypercomputer system to make AI training easier and more productive for developers, focusing on building faster and scaling bigger.
- The recent enhancements include Pathways on Cloud, an orchestration system for scaling AI workloads from a notebook to thousands of accelerators, boosting interactive supercomputing capabilities.
- Xprofiler library provides deep performance analysis on Google Cloud accelerators, helping optimize code execution by profiling and tracing operations to identify bottlenecks and improve efficiency.
- Pre-built container images for popular AI frameworks like PyTorch and JAX streamline setup and configuration, offering optimized environments for quick deployment and avoiding compatibility issues.
- AI Hypercomputer provides proven recipes and techniques for maximizing GPU training efficiency, including asynchronous checkpointing and the ML Goodput recipe in partnership with NVIDIA.
- MaxText offers advanced training capabilities for JAX Language Model Models (LLMs) on Google Cloud with support for fine-tuning techniques and resilient training, enhancing performance across NVIDIA GPUs.
- PyTorch/XLA 2.7 and torchprime enable PyTorch users on TPUs to call JAX functions directly and leverage high-performance model training on TPUs with reference implementation for vLLM.
- RecML is an optimized recommender system library for training deep-learning models on TPUs, offering performance advantages with SparseCore for processing massive embedding tables efficiently.
- Google Cloud continues to improve the AI developer experience by introducing these enhancements to the AI Hypercomputer system, encouraging developers to experiment, innovate, and contribute back to open-source projects.
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