NVIDIA 開源 OSMO:一個 YAML 即可編排物理 AI 訓練、模擬與機器人測試

2026年9月14日 08:04
站內 AI 整理稿

Robot developers do not have one compute problem.They have 3.A policy is trained on GB200 or H100 clusters, tested in Isaac Sim on RTX GPUs, then validated on a Jetson mounted inside a real robot.Each tier has its own cluster, its own scheduler, and its own glue scripts.

NVIDIA OSMO is NVIDIA’s answer to that fragmentation: an open-source, Kubernetes-native workflow orchestrator that lets a team describe the whole pipeline in a single YAML file and run it across every tier without touching infrastructure code.Deployable?Yes.OSMO is Apache-2.

0 licensed, ships Helm charts and containers on NGC, and has a local quickstart that runs the full control plane on a workstation with KIND.The Three Computer Problem NVIDIA frames physical AI as a three computer problem.Training happens on data-center GPUs.

Simulation, physics, and sensor rendering happen on workstation-class RTX hardware.Deployment and hardware-in-the-loop (HIL) testing happen on edge devices such as Jetson AGX Thor, usually on premises.Each tier usually gets its own tooling, and the handoffs are where custom scripts accumulate.

OSMO treats all 3 as backends of one control plane.Each backend is a Kubernetes cluster registered through the CLI.Workflows never name a cluster.They name a platform (for example gb200, rtx-pro-6000, or jetson-agx-thor) and OSMO routes the task to a pool that offers it.

What a Workflow Looks Liake The Repo’s canonical example is 3 tasks cAhained by data:A simulation runs an Isaac Sim container on rtx-pro-6000 train-policy runs a PyTorch container on gb200 with 8 GPUs, taking the simulation task’s output as input evaluate-thor runs a ROS app on jetson-agx-thor, consuming the trained policy and writing results to a named dataset Dependencies come from inputs, persistence from outputs, and placement from platform.

The user guide covers serial and parallel task groups, Jinja templating for parameterized workflows, retry policies, and HIGH/NORMAL/LOW priorities with preemption and GPU borrowing across pools.

Key Capabilities Portability: The same YAML runs on a laptop (Docker/KIND) or on EKS, AKS, GKE, on-premise, or air-gapped clusters.The 6.3.0 release added a multi-provider deploy-k8s.

sh that provisions OSMO on Azure AKS, AWS EKS, microk8s, or any existing cluster, with storage wiring for MinIO, Azure Blob, AWS S3, or bring-your-own S3.

Interactive development: Developers can launch VS Code, Jupyter, or SSH sessions on a remote GPU node, exec into running tasks, port-forward services, and rsync files in both directions.Version 6.3.0 added osmo workflow rsync download with a live progress bar.

Scheduling: OSMO uses the NVIDIA KAI Scheduler by default.Release 6.2.8 added NVLink topology-aware placement for multi-GPU tasks.Release 6.3.0 made exectimeout and queuetimeout per group, so a stalled simulation group no longer kills sibling training groups.

Data: The project describes content-addressable datasets with deduplication that it claims can cut storage by 10 to 100x.Note that the standalone osmo dataset CLI and /datasets API were deprecated in 6.3.0 and are slated for removal in 6.4, with workflow-managed dataset outputs as the replacement.

Security and identity: Since 6.2.8 OSMO ships an RBAC authorization sidecar, OAuth2 proxy integration with device-code login, and identity-provider user mapping.Release 6.3.

0 added TLS termination at the Envoy gateway and cloud workload identity (Azure Workload Identity, AWS IRSA/Pod Identity) so services no longer mount storage keys as Kubernetes Secrets.Release 6.3.1 tightened the default osmo-user role to the default pool.

Agent integration: The repository ships an AGENTS.md, a skills directory, and an MCP deployment guide.At GTC 2026 NVIDIA said OSMO integrates with Claude Code, OpenAI Codex, and Cursor so coding agents can submit, monitor, and debug pipelines.

Interactive Explainer: Watch OSMO Route One Workflow Across 3 Compute Tiers Press Run workflow to see how OSMO schedules the README example task by task.Click any tier or step number to inspect what happens there.(function(){ var f=document.getElementById('osmo-explainer-frame'); window.

addEventListener('message',function(e){ if(e.data&&e.data.type==='osmo-explainer-height'&&f){f.style.height=e.data.height+'px';} }); })(); Key Takeaways OSMO orchestrates training, simulation, and edge HIL tasks from one YAML across heterogeneous Kubernetes clusters Apache-2.

0, Helm charts on NGC, latest release 6.3.

1 (June 2026), local KIND quickstart available KAI Scheduler default, NVLink-aware placement, per-group timeouts, RBAC and OAuth2 built in Battle-tested on GR00T, Isaac Lab, Isaac Sim, and Isaac ROS; Azure and Nebius integrations exist Dataset CLI is deprecated in 6.3 and removed in 6.

4; plan migrations accordingly Check out the GitHub, Documentation, Releases, Cookbook and NVIDIA OSMO page.All credit goes to the researcher of this project.Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter.Wait!

are you on telegram?now you can join us on telegram as well.Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.?

Connect with us The post NVIDIA Open-Sources OSMO: One YAML Orchestrates Physical AI Training, Simulation, and Robot Testing appeared first on MarkTechPost.

Related

相關文章

IT之家AI Agent

長安汽車首席專家譚歡:未來要用機器人造車、賣車,讓機器人上車、造機器人

作者:清源 責編:清源 評論: 9 月 18 日消息,在今天(18 日)的第 22 屆中國汽車產業發展(泰達)國際論壇“新賽道生態專場:具身智能新賽道”活動中,長安汽車首席專家、長安天樞智能機器人公司總經理譚歡在演講中指出,AI 正推動以物理具身智能為核心的基礎設施革新,汽車未來形態是“汽車機器人”—— 自學習、自組織、自進化的組合智能體。

2 小時前
量子位AI Agent

具身智能技術路線尚未定型,基礎設施卻先收斂

具身智能技術路線尚未成形,但基礎設施需求已開始收斂,重點從製造機器人轉向持續迭代機器人能力。百度集團沈抖指出,智能體能力邊界快速擴展,進入規模化部署階段,但機器人學習新任務與跨環境適應性仍待突破。

3 小時前

88小時抵一個人思考4000年,OpenAI核心研究員:除了自我進化,更可怕的是AI正學會“隱藏自己”

AI正在把4000年的人類認知勞動壓縮進88小時,OpenAI研究員Noam Brown坦言連他自己也被進展速度持續震驚。AI正在把過去需要數千年完成的認知勞動壓縮到數天。真正的問題已經不只是模型能否變得更聰明,而是實驗能否跟上、人類能否在模型繼續自我改進前確認它仍然安全。

4 小時前
智東西AI Agent

Agent辦事、花式P圖、動嘴玩電腦……實測Wildcat Lake輕薄本玩AI有多爽

作者 | ZeR0 編輯 | 漠影 桂林依山傍水,連城市的輪廓,都是一座座山勾勒出來的。抬眼一望,便是翰墨丹青般的自然光景,既沉靜婉約,又意境悠遠。這種乾淨的留白之美,早已被古人融入山水畫藝中,幾筆山石,一帶煙雲,餘下的留給水色,也留給看畫的人。 淨,並非空無一物,而是通過剋制的取捨,讓真正重要的東西凸顯出來。這與今年推出的第三代英特爾酷睿處理器(代號Wildcat Lake)的設計理念不謀而合。

9 小時前
AIbaseAI Agent

吳恩達回應AI末日論:別被科幻敘事帶偏,應解決現實工程問題

吳恩達曾參與創辦Google Brain和Coursera。吳恩達稱,科技行業早期曾放大AI潛在災難性風險,以獲取關注並影響監管方向;近兩週相關討論再次升溫,也可能存在類似動機。他認為AI確實存在現實風險,尤其包括網絡安全等領域,但不認同將人類滅絕風險作為當前AI發展的核心判斷依據。

10 小時前
IT之家AI Agent

智譜 GLM-5.3-FlashX 模型上線,更快、更流暢

作者:汪淼 責編:汪淼 評論: 感謝網友 Agent 的線索投遞!9 月 18 日消息,智譜今日宣佈推出 GLM-5.3-FlashX(最高 200 tokens/s),為企業與開發者帶來更快、更流暢的模型體驗。智譜官方表示,GLM-5.3-Flash 此前以“Ox Alpha”之名與全球開發者見面,獲得海內外開發者的廣泛認可,調用量持續攀升。

12 小時前