
地平线【地瓜机器人】算力平台工程师
社招全职5年以上软件序列地点:北京状态:招聘♡ 收藏
工作描述
任职要求 核心技能 1. 5 年以上 AI Infra / 云原生平台 / 机器学习平台 / 后端工程 / 基础架构相关经验,具备 3 年以上 Kubernetes、AI 平台、PaaS 平台、算力调度平台或分布式训练平台建设经验。 2. 熟悉 Kubernetes 基础能力,理解 Pod、Job、CRD、Operator、镜像、存储挂载、资源配额、调度策略等机制,具备容器化任务排查和平台集成经验。 3. 熟悉 AI 算力调度系统,有 Volcano / Slurm / Kueue / Ray / YARN 等一种或多种系统的使用、运维、平台集成或开发经验。 4. 熟悉 GPU 资源管理与调度机制,理解 GPU 资源分配、显存隔离、MIG、GPU 共享、拓扑感知调度、队列调度、Gang Scheduling、弹性调度等常见场景。 5. 熟悉云原生分布式训练平台相关生态,具备 Kubeflow、Volcano、Kueue、Argo Workflows、Ray、Slurm 等一种或多种系统的实际使用或平台集成经验,理解分布式训练任务的提交、编排、调度、监控、重试、弹性伸缩等核心机制。 6. 熟悉 PyTorch 分布式训练机制,理解 DDP、FSDP、Tensor Parallel、Pipeline Parallel、Data Parallel、ZeRO 等常见并行训练策略。 7. 熟悉 NCCL / MPI / Gloo 等通信机制,具备分布式训练通信问题排查经验,能够定位 NCCL 超时、Rank 异常、通信性能差等问题。 8. 具备 Go / Python / Shell 至少一种语言的开发能力,能够独立开发平台后端服务、…
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包括英文材料
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
Kubernetes+
https://kubernetes.io/docs/tutorials/kubernetes-basics/
This tutorial provides a walkthrough of the basics of the Kubernetes cluster orchestration system.
https://kubernetes.io/zh-cn/docs/tutorials/kubernetes-basics/
本教程介绍 Kubernetes 集群编排系统的基础知识。每个模块包含关于 Kubernetes 主要特性和概念的一些背景信息,还包括一个在线教程供你学习。
https://www.youtube.com/watch?v=s_o8dwzRlu4
Hands-On Kubernetes Tutorial | Learn Kubernetes in 1 Hour - Kubernetes Course for Beginners
https://www.youtube.com/watch?v=X48VuDVv0do
Full Kubernetes Tutorial | Kubernetes Course | Hands-on course with a lot of demos
PaaS+
https://www.ibm.com/cn-zh/think/topics/paas
平台即服务 (PaaS) 是一种云计算模型,提供完整的按需云平台(硬件、软件和基础设施),用于开发、运行和管理应用程序。
https://www.ibm.com/think/topics/paas
https://www.youtube.com/watch?v=QAbqJzd0PEE
Volcano+
[英文] Tutorials
https://volcano.sh/en/docs/tutorials/
This section provides guidance to help you quickly get started with Volcano, from deploying a basic Volcano Job/Deployment, to integrating with Volcano Queues
Slurm+
https://researchcomputing.princeton.edu/education/external-online-resources/slurm
All the Research Computing clusters at Princeton rely on a workload manager called SLURM to allocate resources to jobs of different users.
https://www.youtube.com/watch?v=NH_Fb7X6Db0&list=PLZfwi0jHMBxB-Bd0u1lTT5r0C3RHUPLj-
Introduction to the Slurm Resource Manager for users and system administrators.
Ray+
https://github.com/ray-project/ray
Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
https://www.youtube.com/watch?v=FhXfEXUUQp0
In this video, I'll teach you everything you need to know about Apache Ray!
https://www.youtube.com/watch?v=fMiAyj2kgac
Using powerful machine learning algorithms is easy using Ray.io and Python.
https://www.youtube.com/watch?v=q_aTbb7XeL4
Parallel and Distributed computing sounds scary until you try this fantastic Python library.
Yarn+
[英文] Introduction
https://yarnpkg.com/getting-started
Yarn is an established open-source package manager used to manage dependencies in JavaScript projects.
Kubeflow+
https://huggingface.co/blog/turhancan97/building-your-first-kubeflow-pipeline
Kubeflow is an open-source platform designed to be end-to-end, facilitating each step of the Machine Learning (ML) workflow.
https://www.kubeflow.org/docs/started/introduction/
Kubeflow is the foundation of tools for AI Platforms on Kubernetes.
https://www.youtube.com/watch?v=6wWdNg0GMV4
In this walk-through I will show you how I've created a machine learning pipeline with Kubeflow 1.5 using Juypter Notebooks, Kubeflow pipelines, MinIO and Kserve.
Argo+
https://argo-cd.readthedocs.io/en/stable/understand_the_basics/
Before effectively using Argo CD, it is necessary to understand the underlying technology that the platform is built on.
https://www.youtube.com/watch?v=MeU5_k9ssrs
The ArgoCD chapter includes building a pipeline of dynamically updating & building a new application version using GitLab downstream pipeline feature.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具,在灵活性、可读性和性能上都具备相当的优势,近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
FSDP+
https://docs.pytorch.org/tutorials/intermediate/FSDP_tutorial.html
In DistributedDataParallel (DDP) training, each rank owns a model replica and processes a batch of data, finally it uses all-reduce to sync gradients across ranks.
https://www.youtube.com/watch?v=PjEwLgyzuzQ
FSDP provides a comprehensive framework for large model training in PyTorch.
NCCL+
https://developer.nvidia.com/nccl
The NVIDIA Collective Communication Library (NCCL) implements multi-GPU and multi-node communication primitives optimized for NVIDIA GPUs and networking.
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