Trainy
Trainy, formerly known as AB Labs, is a B2B company specializing in optimizing ML infrastructure for training large models. Based in San Francisco with a fully remote operation, Trainy offers a managed Kubernetes platform, performance tuning, and detailed profiling to enhance training efficiency and speed.
Company Overview
Trainy, previously known as AB Labs, operates in the B2B industry with a focus on developer tools, machine learning, SaaS, and open source. Based in San Francisco, CA, and providing services remotely, Trainy aims to optimize ML infrastructure for large-scale model training. Part of Y Combinator's S23 batch, the company supports regions including the United States and Canada.
Products
Trainy offers a managed Kubernetes platform tailored for launching training jobs across any cloud or on-premises environment. Their platform includes a cluster management dashboard that provides resource allocation insights and highlights training efficiency. Additionally, Trainy's software features interpretable visualizations and a profiler in action to help ML engineers identify and eliminate performance bottlenecks.
Services
Trainy provides performance tuning consultations, aiding customers in achieving up to 10x speedups for billion parameter-scale model trainings. Other services include a quick start guide for users to install packages with pip and visualize logs on TensorBoard in minutes. The platform also offers access to low-level NCCL communication and CUDA computation timings, helping users enhance compute utilization and streamline distributed training efforts.
Technological Integration
Trainy utilizes SkyPilot as the job submission frontend for its managed Kubernetes platform. The platform is designed to optimize ML infrastructure by eliminating performance bottlenecks and enhancing compute usage. Users can quickly access profiling information during large distributed trainings, thanks to Trainy's comprehensive interface.
Online Presence and Community
Trainy engages with the tech community through various platforms such as GitHub, LinkedIn, Discord, and Twitter. They publish blog posts on topics such as efficient training on a single GPU, fine-tuning Mistral 7B, and the impact of data parallelism and hardware on training speed, contributing to the broader discussion around machine learning and its optimization.