Dynolog: Open-Source System Observability
Dynolog is an open-source observability tool for optimizing AI applications on distributed CPU-GPU systems. It offers continuous monitoring of performance metrics, integrates with PyTorch Profiler and Kineto CUDA profiling library, and supports GPU monitoring for NVIDIA GPUs and CPU events for Intel and AMD CPUs. Developed in Rust, Dynolog focuses on Linux platforms to enhance AI model observability in cloud environments.
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Dynolog is an open-source system observability tool designed to optimize AI applications running on distributed CPU-GPU systems like Meta AI research supercluster, AWS Sagemaker, and Azure ML. It offers continuous monitoring of CPU, storage, network, and GPU metrics, providing insights into performance bottlenecks and resource utilization. Dynolog integrates with PyTorch Profiler and Kineto CUDA profiling library to enable on-demand profiling of AI applications without code changes, enhancing developers' ability to debug and improve performance. The tool supports GPU performance monitoring for NVIDIA GPUs and CPU micro-architecture specific performance events for Intel and AMD CPUs. Additionally, Dynolog includes a generic Logger class for customizable data logging to various stores. By focusing on Linux platforms and collaborating with PyTorch Profiler, Dynolog aims to enhance observability for AI models in cloud environments. The project is developed using Rust and follows an open-source first approach to ensure community accessibility and integration with related projects like PyTorch Kineto Profiler and LLVM.
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