Machine Learning Engineer
I'm Supreeth, a Senior Machine Learning Engineer at GrapheneAI. My work revolves around the intersection of Natural Language Processing, High-Performance Computing, Distributed Computing, and Agentic Systems. With experience in both academia and industry, I bridge the gap between research and practical ML applications. I've led multiple projects in the past, and I'm always looking for new challenges. Beyond tech, I'm passionate about all things food and coffee.
3FS is a fast and scalable filesystem designed for AI workloads. It is a distributed filesystem that is optimized for the needs of AI workloads, such as high throughput, low latency, and high scalability. It is designed to be used with modern storage technologies, such as SSDs and NVMe drives. Delivers higher performance than existing filesystems for AI workloads, such as HDFS and S3.
AlphaChip is groundbreaking work by DeepMind that has transformed the design of computer chips. It is a new approach to chip design that uses reinforcement learning to design chips. The results are impressive, with AlphaChip achieving state-of-the-art performance on a range of benchmarks. To me it closes the loop on the AI-chip-AI-chip-design cycle. It's a great example of how AI can be used to solve complex problems and push the boundaries of what is possible.
This book is a comprehensive guide to scaling training of models. It covers a wide range of topics, from hardware to software to model design, and provides a comprehensive overview of the current state of the art. It's a great read for anyone who wants to understand the challenges and solutions for scaling training of models. Its also got exercises and code examples to help you understand the concepts.
Picrotron provides a barebones implementation of Nvidia's Megatron-LM. It's a great read and a hackable starting point for anyone who wants to build their own language model.
This video is a great overview of the challenges and solutions for scaling AI inference. It covers a wide range of topics, from hardware to software, and provides a comprehensive overview of the current state of the art. Mike Abbot, from Anthropic, talks about the challenges of scaling training and inference, How scaling modern languge models is a fascinating engineering problem
Provides a comprehensive overview of the challenges and solutions for scaling training a model on 10K H100 GPUs. Soumith Chintala, from Meta AI, provides a detailed overview of the challenges and solutions for scaling training a model on 10K H100 GPUs. From the hardware to the software, it's a great read for anyone who wants to understand the challenges and solutions for scaling training a model on a large cluster.