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Infrastructure for managing GPU clusters for training/serving.
Goodbye Slurm, Hello Konduktor. Trainy Konduktor is a software platform for AI teams to schedule workloads with priority, control resource allocation, and improve GPU reliability. With Konduktor, teams submit jobs to a healthy pool of GPUs, assign job priority with a simple user interface, and never worry about hardware faults again.
Trainy provides on-demand infrastructure to run large-scale GPU workloads across multiple clouds, handling networking, scaling, and fault tolerance with YAML-driven deployment. It enables rapid multi-node GPU setups, on-demand vs reserved pricing, and real-time visibility into usage and costs. The platform emphasizes zero-code changes to ML workflows and cloud-agnostic deployment via simple YAML configuration.
Users write a simple YAML file specifying nodes, priority, and GPU types, then deploy with a single CLI command. Trainy automatically handles multi-node setups across clouds, networking, scheduling, and fault recovery. It offers on-demand and reserved (hybrid) GPU usage, real-time monitoring of training progress, preemptive queuing with high-priority jobs, multi-framework support, health monitoring, and comprehensive resource management. The platform focuses on reducing idle GPU time, enabling scalable training across thousands of GPUs with high-bandwidth networking and cloud-provider agility.
Who it’s for: AI/ML teams and infrastructure engineers at organizations running large-scale GPU training and inference across multiple clouds or on-prem, seeking scalable, on-demand GPU management with simplified YAML-based workflows.
Customer testimonials/logos not listed; mentions book a demo and deployment in minutes, ongoing docs/blogs, and multi-cloud migrations, indicating product maturity and early traction.
Studied CS & Mathematics at UC Santa Cruz. Led Audio team at Hive AI, where we trained and deployed 500M parameter-scale models to production.
Co-founder and CTO at Trainy building a training platform to make deep learning go faster. Previously a lead Machine Learning Engineer for Hive AI's object detection products. I completed my Physics Ph.D. UC Berkeley '22 where my thesis focused on applying computer vision and deep learning on nanoscience. Physics & Computer Science B.A. UC Berkeley '17.
Dashboards to help ML engineers training large models isolate performance bottlenecks and boost training speed.
Trainy builds a dashboard that aggregates profiling data across many GPUs to help ML engineers training large models identify performance bottlenecks, isolate slow GPUs, and optimize training speed by balancing computation, communication, and memory operations.
From the original launch (Jun 2023) — may be outdated.
Formerly “AB Labs”
Vercel For GPUs
Fast, reliable, reproducible AI with GPU live migration
