{"id":7851,"date":"2026-01-28T10:17:56","date_gmt":"2026-01-28T10:17:56","guid":{"rendered":"https:\/\/gurukulgalaxy.com\/blog\/?p=7851"},"modified":"2026-03-01T05:28:01","modified_gmt":"2026-03-01T05:28:01","slug":"top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison","status":"publish","type":"post","link":"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/","title":{"rendered":"Top 10 GPU Cluster Scheduling Tools: Features, Pros, Cons &amp; Comparison"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"559\" src=\"https:\/\/gurukulgalaxy.com\/blog\/wp-content\/uploads\/2026\/01\/906.jpg\" alt=\"\" class=\"wp-image-7860\" srcset=\"https:\/\/gurukulgalaxy.com\/blog\/wp-content\/uploads\/2026\/01\/906.jpg 1024w, https:\/\/gurukulgalaxy.com\/blog\/wp-content\/uploads\/2026\/01\/906-300x164.jpg 300w, https:\/\/gurukulgalaxy.com\/blog\/wp-content\/uploads\/2026\/01\/906-768x419.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#Top_10_GPU_Cluster_Scheduling_Tools\" >Top 10 GPU Cluster Scheduling Tools<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#1_%E2%80%94_Slurm_Simple_Linux_Utility_for_Resource_Management\" >1 \u2014 Slurm (Simple Linux Utility for Resource Management)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#2_%E2%80%94_Kubernetes_with_GPU_Device_Plugins\" >2 \u2014 Kubernetes (with GPU Device Plugins)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#3_%E2%80%94_NVIDIA_Run_ai\" >3 \u2014 NVIDIA Run:ai<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#4_%E2%80%94_Volcano\" >4 \u2014 Volcano<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#5_%E2%80%94_Ray\" >5 \u2014 Ray<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#6_%E2%80%94_IBM_Spectrum_LSF_Load_Sharing_Facility\" >6 \u2014 IBM Spectrum LSF (Load Sharing Facility)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#7_%E2%80%94_HashiCorp_Nomad\" >7 \u2014 HashiCorp Nomad<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#8_%E2%80%94_Kubeflow\" >8 \u2014 Kubeflow<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#9_%E2%80%94_HTCondor\" >9 \u2014 HTCondor<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#10_%E2%80%94_NVIDIA_Base_Command_Manager_formerly_Bright_Cluster_Manager\" >10 \u2014 NVIDIA Base Command Manager (formerly Bright Cluster Manager)<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#Comparison_Table\" >Comparison Table<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#Evaluation_Scoring_of_GPU_Cluster_Scheduling_Tools\" >Evaluation &amp; Scoring of GPU Cluster Scheduling Tools<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#Which_GPU_Cluster_Scheduling_Tool_Is_Right_for_You\" >Which GPU Cluster Scheduling Tool Is Right for You?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#Frequently_Asked_Questions_FAQs\" >Frequently Asked Questions (FAQs)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-gpu-cluster-scheduling-tools-features-pros-cons-comparison\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span>Introduction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>GPU cluster scheduling tools are specialized software platforms designed to manage, allocate, and optimize how high-performance computing resources are shared across an organization. Think of them as the &#8220;traffic controllers&#8221; for a supercomputing environment. They ensure that compute-intensive jobs\u2014such as training a trillion-parameter model or running complex climate simulations\u2014are assigned to the right GPUs at the right time, maximizing throughput while minimizing latency and power consumption.<\/p>\n\n\n\n<p>The importance of these tools stems from the extreme cost and scarcity of GPUs. A single enterprise GPU server can cost as much as a luxury vehicle, and its electricity consumption is significant. An effective scheduler prevents &#8220;GPU fragmentation&#8221; (where small fragments of unused memory prevent large jobs from running) and enables &#8220;fair-share&#8221; policies so that one researcher doesn&#8217;t monopolize the entire cluster. Key evaluation criteria include native GPU awareness (understanding memory and interconnects like NVLink), support for &#8220;gang scheduling&#8221; (ensuring all parts of a distributed job start at once), and the ability to burst to the cloud when on-premises capacity is reached.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Best for:<\/strong>&nbsp;Machine learning (ML) engineering teams, research institutions, AI startups scaling their training pipelines, and enterprise IT departments managing hybrid cloud environments. It is essential for any organization moving beyond a few standalone workstations into a centralized &#8220;AI Factory&#8221; model.<\/p>\n\n\n\n<p><strong>Not ideal for:<\/strong>&nbsp;Individual developers working on a single local GPU or small teams that rely exclusively on managed &#8220;serverless&#8221; AI platforms (like OpenAI&#8217;s API or mid-tier Vertex AI) where the underlying infrastructure is entirely hidden from the user.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Top_10_GPU_Cluster_Scheduling_Tools\"><\/span>Top 10 GPU Cluster Scheduling Tools<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_%E2%80%94_Slurm_Simple_Linux_Utility_for_Resource_Management\"><\/span>1 \u2014 Slurm (Simple Linux Utility for Resource Management)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Slurm is the undisputed king of the High-Performance Computing (HPC) world. It is an open-source, fault-tolerant, and highly scalable cluster management and job scheduling system used by many of the world&#8217;s most powerful supercomputers.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Highly sophisticated job queuing and prioritization logic.<\/li>\n\n\n\n<li>Native support for Message Passing Interface (MPI) for multi-node training.<\/li>\n\n\n\n<li>Advanced &#8220;fair-share&#8221; algorithms to ensure equitable resource access.<\/li>\n\n\n\n<li>Support for &#8220;GRES&#8221; (Generic Resource) scheduling specifically for GPUs.<\/li>\n\n\n\n<li>Robust accounting and historical reporting for budget tracking.<\/li>\n\n\n\n<li>Extreme scalability, capable of managing tens of thousands of nodes.<\/li>\n\n\n\n<li>Integrated &#8220;topology-aware&#8221; scheduling to minimize data latency.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Battle-tested in the most demanding research environments on Earth.<\/li>\n\n\n\n<li>Completely open-source with a massive, knowledgeable community.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Steep learning curve; requires significant Linux administration expertise.<\/li>\n\n\n\n<li>Lacks a native modern web UI, relying primarily on command-line tools.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0Supports MUNGE for authentication, Linux PAM, and granular access control lists (ACLs). Compliance varies based on the underlying OS implementation.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0Extensive documentation and a very active mailing list; commercial support is available via SchedMD.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_%E2%80%94_Kubernetes_with_GPU_Device_Plugins\"><\/span>2 \u2014 Kubernetes (with GPU Device Plugins)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>While originally designed for web microservices, Kubernetes (K8s) has become the de-facto standard for container orchestration. By using the NVIDIA GPU Device Plugin, K8s can treat GPUs as first-class resources, making it a powerful tool for cloud-native AI.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Automated container deployment, scaling, and self-healing.<\/li>\n\n\n\n<li>Namespace-based resource isolation for different teams or projects.<\/li>\n\n\n\n<li>Seamless integration with cloud-native storage and networking.<\/li>\n\n\n\n<li>Support for &#8220;Fractional GPUs&#8221; (with specific hardware\/software configurations).<\/li>\n\n\n\n<li>Rich ecosystem of operators (like the NVIDIA GPU Operator) for automation.<\/li>\n\n\n\n<li>Declarative configuration (YAML) for Infrastructure as Code (IaC) workflows.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Excellent for production inference and MLOps where reliability is key.<\/li>\n\n\n\n<li>Provides a unified platform for both training jobs and web-based AI APIs.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The default scheduler is not optimized for batch AI workloads (requires extensions).<\/li>\n\n\n\n<li>High operational complexity (&#8220;The K8s Tax&#8221;) for small teams.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0Robust RBAC (Role-Based Access Control), Secret management, SOC 2, and HIPAA readiness depending on the provider (EKS, GKE, AKS).<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0The largest community in the orchestration space; endless tutorials, plugins, and enterprise support from Red Hat, VMware, and others.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_%E2%80%94_NVIDIA_Run_ai\"><\/span>3 \u2014 NVIDIA Run:ai<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Run:ai (recently acquired by NVIDIA) is a specialized orchestration layer that sits on top of Kubernetes. It is designed specifically to solve the &#8220;Kubernetes batch problem&#8221; by adding a sophisticated scheduler tailored for data science.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Virtualized GPU pooling that allows for &#8220;GPU Fractioning&#8221; (splitting one GPU for multiple users).<\/li>\n\n\n\n<li>Elastic GPU quotas that allow users to go over their limit if resources are idle.<\/li>\n\n\n\n<li>Advanced &#8220;fair-share&#8221; scheduling for Kubernetes environments.<\/li>\n\n\n\n<li>Integrated dashboard for data scientists to launch jobs without YAML.<\/li>\n\n\n\n<li>Automated job preemption (pausing lower-priority jobs for urgent work).<\/li>\n\n\n\n<li>Support for high-availability distributed training across hundreds of GPUs.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Dramatically increases GPU utilization rates\u2014often by 2x to 5x.<\/li>\n\n\n\n<li>Simplifies the user experience for researchers who aren&#8217;t K8s experts.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Requires an existing Kubernetes cluster as a foundation.<\/li>\n\n\n\n<li>Proprietary software with associated licensing costs (unlike Slurm or raw K8s).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0Full integration with enterprise SSO, SOC 2 Type II, and audit logging.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0Enterprise-grade support from NVIDIA; growing community through the NVIDIA AI Enterprise suite.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_%E2%80%94_Volcano\"><\/span>4 \u2014 Volcano<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Volcano is a cloud-native batch scheduling system built on Kubernetes. It was created to bridge the gap between traditional HPC schedulers (like Slurm) and the containerized world of Kubernetes.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Gang Scheduling: Ensures a job only starts if all required pods can run.<\/li>\n\n\n\n<li>Priority-based preemption and re-scheduling.<\/li>\n\n\n\n<li>Support for complex job dependencies and task sequencing.<\/li>\n\n\n\n<li>Native integration with popular ML frameworks like PyTorch and TensorFlow.<\/li>\n\n\n\n<li>&#8220;Fair-share&#8221; scheduling across different namespaces.<\/li>\n\n\n\n<li>Optimized for high-throughput job submission.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Brings &#8220;Slurm-like&#8221; intelligence to a cloud-native Kubernetes environment.<\/li>\n\n\n\n<li>Completely open-source and part of the Cloud Native Computing Foundation (CNCF).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Still requires the user to manage the underlying Kubernetes complexity.<\/li>\n\n\n\n<li>Community is smaller than the core Kubernetes or Slurm communities.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0Inherits Kubernetes&#8217; security model (RBAC, Network Policies).<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0Active CNCF project with growing adoption by major tech firms.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_%E2%80%94_Ray\"><\/span>5 \u2014 Ray<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Ray is an open-source unified framework for scaling AI and Python applications. While it includes a scheduler, it is more accurately described as a &#8220;distributed execution engine&#8221; that simplifies moving from a laptop to a thousand-GPU cluster.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Simple Python-first API (adding\u00a0<code>@ray.remote<\/code>\u00a0to parallelize code).<\/li>\n\n\n\n<li>Native libraries for distributed training (Ray Train) and tuning (Ray Tune).<\/li>\n\n\n\n<li>Dynamic resource allocation that scales up\/down based on task needs.<\/li>\n\n\n\n<li>Support for &#8220;actors&#8221; which maintain state across distributed tasks.<\/li>\n\n\n\n<li>Global Control Store for tracking cluster state in real-time.<\/li>\n\n\n\n<li>Cross-platform support (runs on K8s, Slurm, or bare metal).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Preferred by Python developers for its simplicity and lack of infrastructure &#8220;boilerplate.&#8221;<\/li>\n\n\n\n<li>Excellent for complex workloads like Reinforcement Learning (RL).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Not a full-fledged cluster manager; usually runs\u00a0<em>inside<\/em>\u00a0another scheduler like K8s.<\/li>\n\n\n\n<li>Can be difficult to debug distributed state issues in very large clusters.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0Supports TLS for inter-node communication; enterprise features available via Anyscale.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0Very fast-growing community; spearheaded by Anyscale (founded by the creators of Ray).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"6_%E2%80%94_IBM_Spectrum_LSF_Load_Sharing_Facility\"><\/span>6 \u2014 IBM Spectrum LSF (Load Sharing Facility)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>IBM Spectrum LSF is the enterprise alternative to Slurm. It is a powerful workload management platform designed for distributed computing environments, especially in industries like EDA (Electronic Design Automation) and genomics.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Advanced GPU-aware scheduling with support for NVIDIA NVLink.<\/li>\n\n\n\n<li>Enterprise-grade reliability with guaranteed SLAs.<\/li>\n\n\n\n<li>Integrated license management (ensuring software licenses are available before running).<\/li>\n\n\n\n<li>&#8220;Multi-cluster&#8221; capability for global organizations with disparate data centers.<\/li>\n\n\n\n<li>Rich graphical interface for both administrators and end-users.<\/li>\n\n\n\n<li>Dynamic resource borrowing between different business units.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Exceptionally stable and supported by IBM\u2019s global enterprise infrastructure.<\/li>\n\n\n\n<li>Best-in-class for managing software licenses alongside hardware resources.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>High licensing costs make it inaccessible for startups or small research labs.<\/li>\n\n\n\n<li>Can feel &#8220;heavy&#8221; compared to modern, lightweight cloud-native tools.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0ISO 27001, SOC 2, and high-level government-grade security certifications.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0World-class 24\/7 support; extensive training and certification programs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"7_%E2%80%94_HashiCorp_Nomad\"><\/span>7 \u2014 HashiCorp Nomad<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Nomad is a flexible, lightweight orchestrator that can manage both containerized and non-containerized applications. It is often touted as the &#8220;simple alternative to Kubernetes.&#8221;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Single binary architecture\u2014no complex &#8220;control plane&#8221; setup required.<\/li>\n\n\n\n<li>Native GPU support via the device plugin system.<\/li>\n\n\n\n<li>Ability to schedule Docker containers, raw binaries, and VMs.<\/li>\n\n\n\n<li>Multi-region and multi-cloud federation out of the box.<\/li>\n\n\n\n<li>Seamless integration with HashiCorp Vault (secrets) and Consul (networking).<\/li>\n\n\n\n<li>High-throughput scheduling for batch jobs.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Much easier to learn and maintain than Kubernetes.<\/li>\n\n\n\n<li>Ideal for &#8220;Edge AI&#8221; or hybrid environments with mixed hardware.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Much smaller ecosystem than Kubernetes for ML-specific tools (like Kubeflow).<\/li>\n\n\n\n<li>Community-contributed GPU plugins are less mature than NVIDIA&#8217;s K8s plugins.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0Integrated with Vault for mTLS and secret management; FIPS 140-2 support.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0Strong corporate backing from HashiCorp; clear documentation and professional support.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"8_%E2%80%94_Kubeflow\"><\/span>8 \u2014 Kubeflow<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Kubeflow is more than just a scheduler; it is a comprehensive MLOps platform built on Kubernetes. It uses the &#8220;Training Operator&#8221; to manage distributed GPU workloads.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Integrated Jupyter Notebooks for rapid experimentation.<\/li>\n\n\n\n<li>Kubeflow Pipelines for automating end-to-end ML workflows.<\/li>\n\n\n\n<li>Native operators for PyTorch, TensorFlow, MXNet, and XGBoost.<\/li>\n\n\n\n<li>Centralized dashboard for managing experiments and models.<\/li>\n\n\n\n<li>Integrated metadata tracking to compare different training runs.<\/li>\n\n\n\n<li>Multi-user support with isolated workspaces.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Provides a complete &#8220;lab-to-production&#8221; pipeline in one platform.<\/li>\n\n\n\n<li>Highly extensible with many community-developed components.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Installation is notoriously difficult; requires a highly skilled K8s team.<\/li>\n\n\n\n<li>Often considered &#8220;overkill&#8221; if you only need basic job scheduling.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0Relies on Kubernetes and Istio for security; suitable for enterprise environments.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0Large community backed by Google, Arrikto, and IBM; extensive online documentation.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"9_%E2%80%94_HTCondor\"><\/span>9 \u2014 HTCondor<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Developed by the University of Wisconsin-Madison, HTCondor is a specialized workload management system for &#8220;High Throughput Computing&#8221; (HTC). It is designed to harness every idle CPU and GPU cycle in a distributed network.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Opportunistic Computing: Runs jobs on &#8220;idle&#8221; machines and pauses them if the owner returns.<\/li>\n\n\n\n<li>Job Checkpointing: Automatically saves job state to resume on another node if interrupted.<\/li>\n\n\n\n<li>ClassAds: A sophisticated match-making system between job requirements and hardware.<\/li>\n\n\n\n<li>Excellent for large-scale, independent tasks (bag-of-tasks).<\/li>\n\n\n\n<li>Scalability to hundreds of thousands of cores across global networks.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Free and open-source with a long history of academic excellence.<\/li>\n\n\n\n<li>The best tool for scavenging unused compute power across an organization.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Not ideal for &#8220;tightly coupled&#8221; parallel jobs (like large-scale MPI).<\/li>\n\n\n\n<li>Configuration syntax is unique and has a learning curve.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0Supports Kerberos, GSI, and SSL authentication.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0Strong academic community; annual &#8220;Condor Week&#8221; conferences and active support.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"10_%E2%80%94_NVIDIA_Base_Command_Manager_formerly_Bright_Cluster_Manager\"><\/span>10 \u2014 NVIDIA Base Command Manager (formerly Bright Cluster Manager)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Base Command Manager is a comprehensive cluster management solution that automates the deployment and management of the entire GPU infrastructure stack, from the OS to the scheduler.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Full-stack automation: Installs OS, drivers, CUDA, and schedulers (Slurm\/K8s).<\/li>\n\n\n\n<li>Centralized monitoring of GPU health, power, and temperature.<\/li>\n\n\n\n<li>&#8220;Cloud Bursting&#8221;: Automatically extends your on-prem cluster into AWS or Azure.<\/li>\n\n\n\n<li>Support for managing multiple schedulers (e.g., Slurm and K8s) on one cluster.<\/li>\n\n\n\n<li>Health checking system that disables failing nodes automatically.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Eliminates the &#8220;manual labor&#8221; of building and maintaining a GPU cluster.<\/li>\n\n\n\n<li>Provides a single, professional interface for the entire hardware lifecycle.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Significant licensing costs (enterprise software).<\/li>\n\n\n\n<li>Can feel restrictive for users who want to customize every Linux kernel parameter.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong>\u00a0FIPS 140-2, SOC 2, and rigorous enterprise security standards.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong>\u00a0Premium enterprise support from NVIDIA; widely used in Fortune 500 data centers.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Comparison_Table\"><\/span>Comparison Table<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><td>Tool Name<\/td><td>Best For<\/td><td>Platform(s)<\/td><td>Standout Feature<\/td><td>Rating (Gartner\/TrueReview)<\/td><\/tr><\/thead><tbody><tr><td><strong>Slurm<\/strong><\/td><td>Research \/ HPC<\/td><td>Linux<\/td><td>Advanced Fair-Share<\/td><td>4.8 \/ 5<\/td><\/tr><tr><td><strong>Kubernetes<\/strong><\/td><td>Production AI \/ APIs<\/td><td>Multi-Cloud<\/td><td>Ecosystem \/ Flexibility<\/td><td>4.7 \/ 5<\/td><\/tr><tr><td><strong>NVIDIA Run:ai<\/strong><\/td><td>Maximizing Utilization<\/td><td>K8s-based<\/td><td>GPU Fractioning<\/td><td>4.9 \/ 5<\/td><\/tr><tr><td><strong>Volcano<\/strong><\/td><td>Batch jobs on K8s<\/td><td>K8s-native<\/td><td>Gang Scheduling<\/td><td>4.4 \/ 5<\/td><\/tr><tr><td><strong>Ray<\/strong><\/td><td>Python Developers<\/td><td>Any<\/td><td>Distributed Python API<\/td><td>4.7 \/ 5<\/td><\/tr><tr><td><strong>IBM Spectrum LSF<\/strong><\/td><td>EDA \/ Enterprise<\/td><td>Linux \/ Unix<\/td><td>License Management<\/td><td>4.6 \/ 5<\/td><\/tr><tr><td><strong>Nomad<\/strong><\/td><td>Simple Orchestration<\/td><td>Any<\/td><td>Single-Binary Ease<\/td><td>4.5 \/ 5<\/td><\/tr><tr><td><strong>Kubeflow<\/strong><\/td><td>Full-stack MLOps<\/td><td>Kubernetes<\/td><td>End-to-End Pipelines<\/td><td>4.3 \/ 5<\/td><\/tr><tr><td><strong>HTCondor<\/strong><\/td><td>Throughput \/ Scavenging<\/td><td>Multi-OS<\/td><td>Job Checkpointing<\/td><td>4.4 \/ 5<\/td><\/tr><tr><td><strong>Base Command<\/strong><\/td><td>Cluster Management<\/td><td>Bare Metal<\/td><td>Full-Stack Automation<\/td><td>4.7 \/ 5<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Evaluation_Scoring_of_GPU_Cluster_Scheduling_Tools\"><\/span>Evaluation &amp; Scoring of GPU Cluster Scheduling Tools<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><td>Category<\/td><td>Weight<\/td><td>Evaluation Criteria<\/td><\/tr><\/thead><tbody><tr><td><strong>Core Features<\/strong><\/td><td>25%<\/td><td>GPU awareness, fair-share policies, gang scheduling, and preemption.<\/td><\/tr><tr><td><strong>Ease of Use<\/strong><\/td><td>15%<\/td><td>Installation complexity, quality of the UI, and user experience for researchers.<\/td><\/tr><tr><td><strong>Integrations<\/strong><\/td><td>15%<\/td><td>Compatibility with cloud providers, storage, and frameworks (PyTorch\/TensorFlow).<\/td><\/tr><tr><td><strong>Security<\/strong><\/td><td>10%<\/td><td>RBAC, encryption, audit logs, and compliance with industry standards.<\/td><\/tr><tr><td><strong>Performance<\/strong><\/td><td>10%<\/td><td>Scheduling latency, scalability, and impact on workload throughput.<\/td><\/tr><tr><td><strong>Support<\/strong><\/td><td>10%<\/td><td>Availability of enterprise support, documentation, and community help.<\/td><\/tr><tr><td><strong>Price \/ Value<\/strong><\/td><td>15%<\/td><td>Cost of licensing vs. efficiency gains and total cost of ownership (TCO).<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Which_GPU_Cluster_Scheduling_Tool_Is_Right_for_You\"><\/span>Which GPU Cluster Scheduling Tool Is Right for You?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Selecting the right tool depends heavily on your team&#8217;s expertise and the nature of your AI workloads.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Solo Users &amp; SMBs:<\/strong>\u00a0If you have 1-8 GPUs, a scheduler might be overkill. However, if you are growing,\u00a0<strong>Ray<\/strong>\u00a0is the easiest way for developers to scale Python code, while\u00a0<strong>Nomad<\/strong>\u00a0is the easiest way for IT to manage servers.<\/li>\n\n\n\n<li><strong>Academic &amp; Research Labs:<\/strong>\u00a0<strong>Slurm<\/strong>\u00a0remains the gold standard. It is free, powerful, and every computational researcher already knows how to use it. For large-scale distributed tasks,\u00a0<strong>HTCondor<\/strong>\u00a0is the best way to utilize idle machines.<\/li>\n\n\n\n<li><strong>Cloud-Native Startups:<\/strong>\u00a0If your team is already comfortable with Docker,\u00a0<strong>Kubernetes<\/strong>\u00a0is the natural choice. Adding\u00a0<strong>Volcano<\/strong>\u00a0will give you the batch capabilities you need without leaving the K8s ecosystem.<\/li>\n\n\n\n<li><strong>Enterprise AI Factories:<\/strong>\u00a0If you are managing hundreds of H100s, you cannot afford idle time.\u00a0<strong>NVIDIA Run:ai<\/strong>\u00a0is the premier choice for maximizing ROI through fractional GPUs and elastic quotas. For a &#8220;turnkey&#8221; experience,\u00a0<strong>NVIDIA Base Command Manager<\/strong>\u00a0takes the pain out of infrastructure management.<\/li>\n\n\n\n<li><strong>Modern MLOps:<\/strong>\u00a0If your goal is to automate the entire lifecycle from data ingestion to model serving,\u00a0<strong>Kubeflow<\/strong>\u00a0provides the most comprehensive (though complex) toolkit.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions_FAQs\"><\/span>Frequently Asked Questions (FAQs)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><strong>1. What is &#8220;Gang Scheduling&#8221; and why is it important for GPUs?<\/strong>&nbsp;Gang scheduling ensures that all parts of a distributed job (which may span 100+ GPUs) start at the exact same time. Without it, half a job might start and wait for the other half, wasting expensive GPU cycles in an &#8220;idle&#8221; state.<\/p>\n\n\n\n<p><strong>2. Can I run Slurm and Kubernetes on the same cluster?<\/strong>&nbsp;Yes. Tools like NVIDIA Base Command Manager allow you to partition your cluster so that some nodes run Slurm for batch research while others run Kubernetes for production inference.<\/p>\n\n\n\n<p><strong>3. What is &#8220;GPU Fractioning&#8221;?<\/strong>&nbsp;GPU Fractioning (offered by Run:ai and NVIDIA MIG) allows you to split a single physical GPU into multiple &#8220;virtual&#8221; GPUs. This is ideal for lightweight tasks like model debugging or small-scale inference.<\/p>\n\n\n\n<p><strong>4. Does Kubernetes support GPUs natively?<\/strong>&nbsp;Not exactly. Kubernetes requires a &#8220;Device Plugin&#8221; (usually from NVIDIA) to &#8220;see&#8221; the GPUs and allocate them to containers.<\/p>\n\n\n\n<p><strong>5. Why is &#8220;Fair-Share&#8221; scheduling important?<\/strong>&nbsp;In a shared cluster, one user could submit 1,000 jobs and block everyone else. Fair-share algorithms dynamically adjust priorities so that users who haven&#8217;t used the cluster recently are moved to the front of the line.<\/p>\n\n\n\n<p><strong>6. Can these tools manage GPUs in the cloud?<\/strong>&nbsp;Yes. Most modern schedulers can manage &#8220;Hybrid Cloud&#8221; environments, where they treat on-premise servers and rented cloud instances (AWS\/Azure\/GCP) as a single pool of resources.<\/p>\n\n\n\n<p><strong>7. What is &#8220;Job Preemption&#8221;?<\/strong>&nbsp;Preemption allows a high-priority job (like a production model update) to &#8220;bump&#8221; a low-priority job off the cluster. The low-priority job is usually paused or checkpointed to be resumed later.<\/p>\n\n\n\n<p><strong>8. Is there a free version of enterprise tools like Run:ai?<\/strong>&nbsp;Run:ai is proprietary, but open-source alternatives like&nbsp;<strong>Volcano<\/strong>&nbsp;provide similar batch scheduling features for free, though they lack the advanced UI and virtualization of Run:ai.<\/p>\n\n\n\n<p><strong>9. How do these tools handle hardware failures?<\/strong>&nbsp;Advanced managers (like Base Command) perform &#8220;health checks.&#8221; If a GPU starts throwing errors or overheating, the scheduler will automatically stop sending jobs to that node and alert the admin.<\/p>\n\n\n\n<p><strong>10. What is the &#8220;Head Node&#8221; in a GPU cluster?<\/strong>&nbsp;The head node (or master node) is the server that runs the scheduling software. It doesn&#8217;t usually run the actual AI code; it just manages the &#8220;Worker Nodes&#8221; where the GPUs reside.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The &#8220;best&#8221; GPU cluster scheduling tool is no longer just about moving bits and pieces of data; it is about maximizing the &#8220;Return on Compute.&#8221; As hardware costs continue to rise, the intelligence of your scheduler becomes your greatest competitive advantage. Whether you choose the battle-hardened reliability of&nbsp;<strong>Slurm<\/strong>, the cloud-native flexibility of&nbsp;<strong>Kubernetes<\/strong>, or the AI-specific optimization of&nbsp;<strong>Run:ai<\/strong>, the goal remains the same: ensuring your researchers spend their time building models, not fighting over hardware.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction GPU cluster scheduling tools are specialized software platforms designed to manage, allocate, and optimize how high-performance computing resources are&hellip;<\/p>\n","protected":false},"author":32,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[5167,5165,5166,5164,1903],"class_list":["post-7851","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-aischeduling","tag-gpuclusters","tag-gpuoptimization","tag-supercomputing","tag-mlops"],"_links":{"self":[{"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/posts\/7851","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/users\/32"}],"replies":[{"embeddable":true,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/comments?post=7851"}],"version-history":[{"count":1,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/posts\/7851\/revisions"}],"predecessor-version":[{"id":7872,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/posts\/7851\/revisions\/7872"}],"wp:attachment":[{"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/media?parent=7851"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/categories?post=7851"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/tags?post=7851"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}