# jobs Hugging Face Jobs is a managed compute service for running AI and data workloads on Hugging Face infrastructure. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL DevFeed: [Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL]() Original publisher: [Read original article]() Author: Amine Dirhoussi; Quentin Gallouédec; Kashif Rasul; Sergio Paniego Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [lora](), [grpo](), [jobs](), [vllm](), [NCCL](), [async](), [hugging face]() Tags: [async](), [grpo](), [hub](), [hugging-face](), [jobs](), [llm](), [lora](), [nccl](), [open-source](), [rl](), [storage](), [trl](), [vllm]() ### AI overview This article describes asynchronous GRPO training with a LoRA adapter across separate Hugging Face Jobs. The adapter is synchronized to vLLM replicas through a shared Storage Bucket, while a proxy handles authentication, rollout routing, and adapter-load broadcasts. Five runs reduced the time for 500 steps from 3 hours 27 minutes to 53 minutes. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Training a coding model to paint watercolours with TRL and OpenEnv DevFeed: [Training a coding model to paint watercolours with TRL and OpenEnv]() Original publisher: [Read original article]() Author: Sergio Paniego Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [openenv](), [coding](), [Language models](), [hugging face](), [AI Development](), [jobs](), [dataset]() Tags: [ai-art](), [coding](), [grpo](), [guide](), [hugging-face](), [javascript](), [jobs](), [open-source](), [openenv](), [rl](), [spaces](), [training](), [trl]() ### AI overview A tutorial describing an open reproduction of a reinforcement-learning pipeline that trains a coding model to create watercolor-like paintings by writing JavaScript with p5.brush. It uses TRL and OpenEnv, with datasets, environments, training scripts, models, and other artifacts published on Hugging Face. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code DevFeed: [How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code]() Original publisher: [Read original article]() Author: Niels Rogge Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI search](), [inference-endpoints](), [jobs](), [hugging face](), [Embeddings](), [PostgreSQL](), [Retrieval Augmented Generation (RAG)]() Tags: [database](), [embeddings](), [hugging-face](), [inference](), [inference-endpoints](), [jobs](), [latency](), [rag](), [research](), [search](), [storage]() ### AI overview This article explains how Papers with Code uses hybrid search to find research papers through exact keyword matching and semantic vector search. The production system combines PostgreSQL full-text search, pgvector embeddings, reciprocal rank fusion, and Hugging Face Jobs, Storage Buckets, and Inference Endpoints. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Same Cluster, 33 Points More Utilization: What Changed Was the Order DevFeed: [Same Cluster, 33 Points More Utilization: What Changed Was the Order]() Original publisher: [Read original article]() Author: Gabriel Pimenta de Freitas Cardoso; Breno de Almeida Beleza; Francisco de Almeida Rocha Alves; Bruno Duarte Published: 2026-08-17T19:46:21Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [GPU](), [benchmarking](), [Benchmark](), [jobs](), [Inference](), [model-deployment]() Tags: [batch](), [benchmark](), [comparison](), [gpu](), [inference](), [jobs](), [real-time](), [training]() ### AI overview The article describes a constraint-aware GPU allocator and compares it with a FIFO scheduler across seven benchmark scenarios. On identical hardware and workloads, the allocator increased GPU utilization by up to 33 percentage points and priority-weighted output by up to 105%. It explains how training, batch inference, quantization, and real-time inference impose different scheduling constraints, especially under contention. ### Source excerpt We built a constraint-aware GPU allocator and benchmarked it against a FIFO scheduler across seven benchmark scenarios. On identical hardware, running identical workloads, GPU utilization rose by as much as 33 percentage points, and priority-weighted output rose in every one of them, by as much as 105%. Nothing about the hardware changed. What changed was the order in which allocation decisions get made. One note on measurement before the numbers start. ## OpenAI joins PORTS-Pike project DevFeed: [OpenAI joins PORTS-Pike project]() Original publisher: [Read original article]() Published: 2026-08-17T05:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [datacenter](), [OpenAI](), [Nvidia](), [jobs]() Tags: [data-center](), [energy](), [global-affairs](), [infrastructure](), [jobs](), [local](), [nvidia](), [openai](), [partnership]() ### AI overview OpenAI has agreed to secure approximately 8 gigawatts-IT at the PORTS-Pike Technology Campus in Ohio in partnership with SB Energy, NVIDIA, and the U.S. Department of Energy. The project is expected to create construction and long-term operating jobs, fund community priorities, and support local infrastructure. Its data center will pay its energy and infrastructure costs and use closed-loop, air-cooled cooling systems to reduce ongoing water demand. ### Source excerpt OpenAI joins PORTS-Pike project, expanding community investment and supporting thousands of Southern Ohio jobs ## Rerun Only the Jobs That Failed DevFeed: [Rerun Only the Jobs That Failed]() Original publisher: [Read original article]() Author: Pete Miloravac Published: 2026-08-12T10:31:39Z Content type: release Language: en Sources: [Semaphore Engineering]() Topics: [ci](), [jobs](), [Development]() Tags: [2026](), [ci](), [cost](), [flag](), [jobs](), [pipeline](), [product-news](), [rebuilds](), [run](), [workflow]() ### AI overview Semaphore now reruns only failed jobs in a pipeline, carrying over successful jobs and preserving the workflow topology. This reduces rerun time and billable usage, while a YAML flag preserves the previous full-block rebuild behavior. ### Source excerpt Your pipeline fails on one job, and until now you had to rebuild the entire block to recover. Not anymore. Semaphore now reruns only the jobs that actually failed, so you get feedback faster and pay less to get it. What Shipped: Job Rerun When a pipeline failed, the old behavior rebuilt every job inside [...] The post Rerun Only the Jobs That Failed appeared first on Semaphore. ## Fair by design: orchestrating background jobs in Ruby DevFeed: [Fair by design: orchestrating background jobs in Ruby]() Original publisher: [Read original article]() Author: Travis Turner (richardturner@evilmartians.com) Published: 2026-08-11T00:00:00Z Content type: tutorial Language: en Sources: [Evil Martians]() Topics: [Ruby](), [jobs](), [Latency](), [autoscaling]() Tags: [autoscaling](), [how-to](), [jobs](), [latency](), [performance](), [rails](), [redis](), [ruby](), [sidekiq]() ### AI overview This tutorial examines fairness in Ruby background-job processing. It explains how queue latency affects quality of service, why adding workers or autoscaling may be limited by shared resources and operational cost, and introduces background-job prioritization as a way to address bottlenecks. ### Source excerpt Are you treating your users fairly? They could be stuck in the queue while a greedy user monopolizes resources. And you might not even know it! In this post, you'll see if it's time for you to take background job prioritization seriously, and how to make it fair for all users. ## How HeyGen runs millions of AI workflows on Temporal DevFeed: [How HeyGen runs millions of AI workflows on Temporal]() Original publisher: [Read original article]() Author: Jiajun Zhao Published: 2026-08-06T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [jobs](), [GPU](), [Cloud]() Tags: [cloud](), [gpu](), [jobs](), [orchestration](), [scale](), [temporal-voices]() ### AI overview HeyGen replaced an internal job-queue system with Temporal to coordinate video-generation workflows across many services, task queues, worker deployments, and a multi-cloud GPU fleet. The article explains the operational problems in the former system and the platform capabilities used to scale workflow execution. ### Source excerpt HeyGen replaced a brittle job-queue system with Temporal, coordinating millions of daily workflow runs across a multi-cloud GPU fleet. ## Millions of Batch Jobs Migrated in Four Weeks DevFeed: [Millions of Batch Jobs Migrated in Four Weeks]() Original publisher: [Read original article]() Author: Byte-Sized Design Published: 2026-07-13T17:15:37Z Content type: article Language: en Sources: [Byte-Sized Design]() Topics: [jobs]() Tags: [batch](), [jobs]() ### AI overview The article describes migrating millions of batch jobs in four weeks without users noticing. ### Source excerpt Millions of batch jobs, migrated in 4 weeks, and nobody noticed ## Profiling in PyTorch (Part 3): Attention is all you profile DevFeed: [Profiling in PyTorch (Part 3): Attention is all you profile]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Sergio Paniego; Sayak Paul; Rémi Ouazan Reboul Published: 2026-07-10T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [jobs](), [cpu]() Tags: [artificial-intelligence](), [cudnn](), [flash](), [gpu](), [jobs](), [kernel](), [profile](), [pytorch](), [torch](), [traces](), [xformers]() ### AI overview A PyTorch profiling tutorial examines naive attention, identifying its primitive operations and the CPU and GPU kernels they launch. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Run a vLLM Server on HF Jobs in One Command DevFeed: [Run a vLLM Server on HF Jobs in One Command]() Original publisher: [Read original article]() Author: Quentin Gallouédec Published: 2026-06-26T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [jobs](), [vllm](), [API](), [inference-endpoints](), [model-deployment](), [cURL](), [GPU](), [Python]() Tags: [api](), [gpu](), [guide](), [inference](), [inference-endpoints](), [infrastructure](), [jobs](), [json](), [model](), [models](), [python](), [vllm]() ### AI overview A practical guide to launching a vLLM model server on Hugging Face Jobs with a single command, querying it through the OpenAI-compatible API, authenticating requests with an HF token, managing costs, and scaling to larger multi-GPU models. It also contrasts ephemeral Jobs with managed Inference Endpoints. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Inspect Volcano workloads faster with Headlamp DevFeed: [Inspect Volcano workloads faster with Headlamp]() Original publisher: [Read original article]() Author: Mahmoud Magdy Published: 2026-06-25T20:00:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [jobs](), [High-Performance Computing](), [Machine learning](), [Web](), [Command-line interface]() Tags: [ai-ml](), [cli](), [high-performance-computing](), [jobs](), [kubernetes](), [quotas](), [ui](), [workflow]() ### AI overview This article introduces the Volcano plugin for Headlamp, a Kubernetes web UI. The plugin brings Volcano jobs, queues, PodGroups, Pods, and events into a unified interface, helping teams inspect batch workloads, scheduling behavior, quotas, priorities, and gang scheduling for Kubernetes, AI/ML, and high-performance computing workloads. ### Source excerpt Volcano is a cloud native batch scheduler for Kubernetes, built for high-performance computing, AI/ML, and other batch workloads. Headlamp is an extensible Kubernetes web UI. With its plugin system, Headlamp can surface APIs and workflows beyond the built-in Kubernetes resources. The Volcano plugin brings core Volcano resources into Headlamp so you can inspect workload state, queue behavior, and gang scheduling details in one place. Kubernetes was originally designed around long-running services, where applications are expected to start and remain available over time. Batch, AI/ML, and HPC workloads often behave differently: jobs arrive dynamically, compete for limited resources, and may need multiple workers to start together before useful work can begin. Volcano extends Kubernetes with concepts such as queues, priorities, quotas, and gang scheduling. Instead of treating every Pod independently, Volcano schedules workloads with awareness of the job as a whole and the resources it needs to make progress. To make these workloads easier to operate and troubleshoot, the Volcano plugin brings that scheduling context directly into Headlamp. Watch this short walkthrough to see the Volcano plugin in Headlamp: Visual context helps teams understand Volcano jobs, queues, and PodGroups faster Working with Volcano often means moving across several related resources while trying to understand a batch workload. You might start with a Job, then look at the related PodGroup, inspect the Pods behind it, check the Queue, and finally return to the Job again. All of that is possible with CLI tools like kubectl and the Volcano CLI, but it can become fragmented very quickly. The Volcano plugin for Headlamp makes that workflow easier by bringing the key resources together in a single UI. Instead of reconstructing relationships manually, you can move directly between Jobs, Queues, PodGroups, Pods, and events from the same interface. Volcano introduces its own resources on top of core Kuber ## Is it agentic enough? Benchmarking open models on your own tooling DevFeed: [Is it agentic enough? Benchmarking open models on your own tooling]() Original publisher: [Read original article]() Author: Lysandre; Nathan Habib; Pedro Cuenca Published: 2026-06-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [benchmarking](), [Tooling](), [LLM evaluation / benchmarking](), [AI-assisted coding](), [jobs](), [hugging face](), [Open Source]() Tags: [agents](), [benchmark](), [benchmarking](), [cli](), [code](), [coding](), [developers](), [development](), [evaluation](), [hugging-face](), [jobs](), [open-source](), [tooling](), [transformers]() ### AI overview A benchmark for evaluating how effectively open models use developer tooling, measuring the work required to reach an answer across models, library revisions, and tasks. The article presents an agent-focused harness running on open models with workloads distributed through Hugging Face Jobs. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP DevFeed: [Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Rémi Ouazan Reboul; Sergio Paniego; Pedro Cuenca; Sayak Paul Published: 2026-06-11T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [cpu](), [jobs]() Tags: [cpu](), [cuda](), [deep-learning](), [gpu](), [jobs](), [kernels](), [liger](), [mlp](), [open-source](), [profile](), [pytorch](), [torch](), [traces]() ### AI overview A PyTorch profiling tutorial that moves from nn.Linear to a fused multilayer perceptron, explaining GPU kernels, CPU launch overhead, and tensor-transpose behavior in profiler traces. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Migrating Your GitHub CI to Hugging Face Jobs DevFeed: [Migrating Your GitHub CI to Hugging Face Jobs]() Original publisher: [Read original article]() Author: Abubakar Abid Published: 2026-06-09T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [jobs](), [ci](), [GitHub Actions](), [hugging face](), [GPU](), [Docker](), [GitHub]() Tags: [ci](), [docker](), [github](), [github-actions](), [gpu](), [guide](), [how-to](), [hub](), [hugging-face](), [jobs](), [open-source](), [self-hosted]() ### AI overview This tutorial explains how to migrate GitHub Actions CI to Hugging Face Jobs. It combines GitHub-managed workflows with ephemeral self-hosted runners on selectable CPU or GPU hardware, provides real-time logs, and reports about a 30% reduction in CPU CI time for Trackio. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing the OpenAI Economic Research Exchange DevFeed: [Introducing the OpenAI Economic Research Exchange]() Original publisher: [Read original article]() Published: 2026-06-08T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [data-governance](), [jobs](), [data]() Tags: [ai](), [company](), [data](), [data-governance](), [economics](), [external](), [jobs](), [openai](), [research]() ### AI overview OpenAI is launching the Economic Research Exchange, a platform for structured collaborations with external researchers studying AI's economic effects on workers, firms, institutions, and the broader economy. Selected projects may use privacy-protected OpenAI tools and datasets under defined milestones, data governance, and review processes. ### Source excerpt OpenAI launches the Economic Research Exchange to study AI's impact on jobs, productivity, and the economy. Applications are now open for selected research projects. ## What's new in Astro - May 2026 DevFeed: [What's new in Astro - May 2026]() Original publisher: [Read original article]() Author: Sarah Rainsberger Published: 2026-05-31T00:00:00Z Content type: article Language: en Sources: [The Astro Blog]() Topics: [Astro](), [jobs](), [Content Management System](), [releases](), [Hono](), [Routing (disambiguation)](), [Rust](), [Vite](), [Cloudflare](), [Markdown](), [Express](), [stripe]() Tags: [astro](), [cloudflare](), [developer](), [features](), [integration](), [jobs](), [learn](), [markdown](), [real-time](), [releases](), [routing](), [rust](), [stripe](), [web]() ### AI overview Astro's May 2026 ecosystem update covers the new Astro Jobs board, TinaCMS adopting Astro by default, an ImageKit integration, the Astro 7 alpha, recent Astro and Starlight releases, community adoption, showcase sites, team news, and selected community content. ### Source excerpt May 2026 - A new Astro jobs board, TinaCMS makes Astro their default template, experimental advanced routing, and more! ## Kubernetes v1.36: Mutable Pod Resources for Suspended Jobs (beta) DevFeed: [Kubernetes v1.36: Mutable Pod Resources for Suspended Jobs (beta)]() Original publisher: [Read original article]() Author: Kevin Hannon Published: 2026-04-27T18:35:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [jobs](), [kueue](), [GPU](), [cpu](), [Availability](), [Machine learning]() Tags: [availability](), [batch](), [cpu](), [gpu](), [jobs](), [kubernetes](), [kueue](), [machine-learning]() ### AI overview Kubernetes v1.36 promotes mutable CPU, memory, GPU, and extended-resource requests and limits for suspended Jobs to beta. Queue controllers such as Kueue can adjust resources before a Job resumes, avoiding deletion and recreation when cluster capacity or workload requirements change. ### Source excerpt Kubernetes v1.36 promotes the ability to modify container resource requests and limits in the pod template of a suspended Job to beta. First introduced as alpha in v1.35, this feature allows queue controllers and cluster administrators to adjust CPU, memory, GPU, and extended resource specifications on a Job while it is suspended, before it starts or resumes running. Why mutable pod resources for suspended Jobs? Batch and machine learning workloads often have resource requirements that are not precisely known at Job creation time. The optimal resource allocation depends on current cluster capacity, queue priorities, and the availability of specialized hardware like GPUs. Before this feature, resource requirements in a Job's pod template were immutable once set. If a queue controller like Kueue determined that a suspended Job should run with different resources, the only option was to delete and recreate the Job, losing any associated metadata, status, or history. This feature also provides a way to let a specific Job instance for a CronJob progress slowly with reduced resources, rather than outright failing to run if the cluster is heavily loaded. Consider a machine learning training Job initially requesting 4 GPUs: apiVersion: batch/v1 kind: Job metadata: name: training-job-example-abcd123 labels: app.kubernetes.io/name: trainer spec: suspend: true template: metadata: annotations: kubernetes.io/description: "ML training, ID abcd123" spec: containers: - name: trainer image: example-registry.example.com/training:2026-04-23T150405.678 resources: requests: cpu: "8" memory: "32Gi" example-hardware-vendor.com/gpu: "4" limits: cpu: "8" memory: "32Gi" example-hardware-vendor.com/gpu: "4" restartPolicy: Never A queue controller managing cluster resources might determine that only 2 GPUs are available. With this feature, the controller can update the Job's resource requests before resuming it: apiVersion: batch/v1 kind: Job metadata: name: training-job-example-abcd123 labels ## Modeling an AI jobs transition DevFeed: [Modeling an AI jobs transition]() Original publisher: [Read original article]() Published: 2026-04-25T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [jobs](), [OpenAI](), [Automation]() Tags: [ai](), [automation](), [global-affairs](), [jobs](), [openai](), [work]() ### AI overview OpenAI's AI Jobs Transition Framework examines how artificial intelligence may reshape employment across 921 occupations and approximately 148 million U.S. jobs. It distinguishes between automation risk, job reorganization, growth with AI, and less immediate change, emphasizing that technical capability alone does not determine displacement. ### Source excerpt A new framework analyzes 921 occupations and 148 million U.S. jobs to identify which roles face automation risk, reorganization, growth, or minimal AI disruption. ## Train AI models with Unsloth and Hugging Face Jobs for FREE DevFeed: [Train AI models with Unsloth and Hugging Face Jobs for FREE]() Original publisher: [Read original article]() Author: ben burtenshaw; Daniel (Unsloth); Michael Han; Maxime Labonne; Daniel van Strien; shaun smith Published: 2026-02-20T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [jobs](), [Fine-tuning](), [Training AI Models](), [hugging face](), [AI Models](), [Command-line interface](), [Open Source]() Tags: [agents](), [ai](), [ai-models](), [claude](), [cli](), [codex](), [documentation](), [fine-tuning](), [hub](), [hugging-face](), [jobs](), [llm](), [models](), [monitoring](), [open-source](), [pricing](), [train](), [training]() ### AI overview This article explains how to fine-tune the small LFM2.5-1.2B-Instruct language model using Unsloth and Hugging Face Jobs. It covers free credits, prerequisites, CLI-based job submission, coding-agent skills, managed cloud GPU training, monitoring, and pushing the trained model to the Hugging Face Hub. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## We Got Claude to Fine-Tune an Open Source LLM DevFeed: [We Got Claude to Fine-Tune an Open Source LLM]() Original publisher: [Read original article]() Author: ben burtenshaw; shaun smith Published: 2025-12-04T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Claude Code](), [Fine-tuning](), [hugging face](), [jobs](), [GPU](), [lora](), [rlvr](), [codex]() Tags: [agents](), [claude](), [claude-code](), [codex](), [community](), [fine-tuning](), [gemini](), [gpu](), [hugging-face](), [llm](), [lora](), [open-source](), [reinforcement-learning](), [training]() ### AI overview This tutorial explains how Hugging Face Skills enable Claude Code to fine-tune language models by validating datasets, selecting GPUs, configuring authentication, submitting cloud training jobs, monitoring progress, and publishing finished models to the Hugging Face Hub. It covers LoRA, full fine-tuning, supervised fine-tuning, direct preference optimization, reinforcement learning with verifiable rewards, GGUF conversion, and multi-stage training pipelines. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Say hello to \`hf\`: a faster, friendlier Hugging Face CLI ✨ DevFeed: [Say hello to \`hf\`: a faster, friendlier Hugging Face CLI ✨]() Original publisher: [Read original article]() Author: Lucain Pouget; Célina Hanouti; Julien Chaumond Published: 2025-07-25T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Command-line interface](), [hugging face](), [jobs](), [Docker]() Tags: [cli](), [docker](), [hugging-face](), [huggingface-hub](), [jobs](), [python]() ### AI overview This tutorial introduces the renamed `hf` Hugging Face CLI, explaining its reorganized resource-based command structure, installation and testing, authentication and local profile commands, and compatibility with the legacy CLI. It also presents Hugging Face Jobs, which runs scripts or Docker images on Hugging Face Infrastructure with pay-as-you-go billing. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Scaling ML Training on Kubernetes with JobSet DevFeed: [Scaling ML Training on Kubernetes with JobSet]() Original publisher: [Read original article]() Author: Abhimanyu Saharan Published: 2025-05-05T00:00:00Z Content type: article Language: en Sources: [Abhimanyu Saharan]() Topics: [Kubernetes](), [Machine learning](), [API](), [jobs]() Tags: [api](), [distributed](), [hpc](), [jobs](), [kubernetes](), [ml](), [training]() ### AI overview The article introduces JobSet, a Kubernetes-native API for managing distributed machine learning and high-performance computing jobs. It highlights multi-role pods, topology-aware placement, and scaling capabilities. ### Source excerpt JobSet is a Kubernetes-native API for managing distributed ML and HPC jobs with support for multi-role pods, topology-aware placement, and scaling. ## NLNOG Presentation trilogy on technical jobs and careers DevFeed: [NLNOG Presentation trilogy on technical jobs and careers]() Original publisher: [Read original article]() Published: 2025-01-02T07:00:00Z Content type: article Language: en Sources: [Bert Hubert's writings]() Topics: [jobs](), [meetings]() Tags: [careers](), [events](), [personal-development](), [presentation](), [technical](), [videos]() ### AI overview This page brings together a four-part series of NLNOG presentations about the working life and career of technical people. The talks discuss communication between technical staff and management, career improvement, and changing or improving jobs, while noting that the perspective is primarily Dutch and western European. ### Source excerpt Over the past eight years I've held a trilogy (in four parts) of presentations over at NLNOG covering the working life and career of technical people. This is around two hours of video and slides (with notes), but I've heard from people who binge-watched all of it in one go. The presentations have been online for ages, but you had to know where to look for them. On this page I bring them all together, with some context. [Next page]()