An Open Source Deep Learning/Machine Learning Stack on Kubernetes
AI control plane to schedule, run, debug, observe, and automate AI/ML workloads, agents, and sandboxes on your infra.
An Open Source Deep Learning/Machine Learning Stack on Kubernetes launched on February 7, 2018 with 12 votes, #921 of 2,635 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 8 years on.
In plain words
This open source platform provides an AI control plane for managing machine learning workloads on Kubernetes infrastructure. It enables users to schedule, run, debug, observe, and automate AI/ML jobs, agents, and sandboxes across their own clusters and clouds. The system keeps data within controlled infrastructure while offering unified orchestration, result comparison, asset registration, and governance for training, services, and sandbox environments across various accelerators and storage backends.
written from the facts on this page · September 2026
From the sources
Run everything from GPU training to agent sessions on your own clusters — scheduled, tracked, and governed in one place. Self-hosted deployments keep your data inside the infrastructure you control. Orchestrate runs, observe systems, compare results, and register assets. Agents and queues translate product intent into cluster work. Polyaxon runs across your clouds, clusters, accelerators, and storage. Training, services, and sandboxes share the same specs, queues, governance, and data connections — on your own Kubernetes and storage backends. Train new models, post-train open-source ones, and fine-tune with LoRA — from a single GPU to multi-node runs. Sweeps, DAGs, batch jobs, and agent…from polyaxon.com
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