Skills tagged: GPU

• modal-serverless-gpu - Run ML workloads on Modal serverless GPUs

  InfrastructureServerlessGPUCloud

  ★ 0 · Updated 2026-09-20

  Provides guidance for running machine learning workloads, deploying web APIs, and scheduling batch jobs on Modal serverless GPUs.

  ⚙ Configure serverless GPU resources⚙ Deploy ML web endpoints⚙ Define custom container images
• modal-serverless-gpu - Serverless GPU Cloud Platform for Machine Learning

  InfrastructureServerlessGPUCloud

  ★ 5 · Updated 2026-09-20

  Provides serverless GPU compute for running machine learning workloads, deploying auto-scaling APIs, and executing batch jobs.

  ⚙ Deploy ML models as APIs⚙ Execute serverless GPU batch jobs⚙ Define infrastructure using Python code
• metal-kernel - PyTorch Metal Kernels for Apple Silicon

  MetalPyTorchApple SiliconGPU

  ★ 0 · Updated 2026-06-30

  Implement Metal/MPS kernels for PyTorch operators using native c10/metal infrastructure

  ⚙ write Metal kernel⚙ update native_functions.yaml⚙ implement host-side stub
• skypilot-multi-cloud-orchestration - SkyPilot Multi-Cloud Orchestration for ML

  InfrastructureMulti-CloudOrchestrationGPU

  ★ 602 · Updated 2026-06-15

  Run ML workloads across multiple clouds with cost optimization

  ⚙ launch cluster⚙ execute task⚙ run distributed training
• mlx-jax-splitmix - MLX JAX SplitMix64 GPU Accelerated

  MLrandomGPUcolor

  ★ 18 · Updated 2026-06-15

  Implements JAX-style SplitMix64 PRNG on Apple Silicon using MLX with GPU acceleration for deterministic color generation

  ⚙ generate random seeds⚙ split random keys⚙ derive colors
• get-available-resources - Detect System Resources for Computing

  systemresource-detectionhardwareGPU

  ★ 97 · Updated 2026-06-15

  Detect CPU, GPU, memory, disk resources and provide computing strategy recommendations

  ⚙ detect CPU cores and architecture⚙ detect NVIDIA GPUs via nvidia-smi⚙ detect AMD GPUs via rocm-smi
• pytorch-guide - PyTorch Training Best Practices Guide

  PyTorchdeep learningmodel trainingGPU

  ★ 4,344 · Updated 2026-06-15

  Catalog common PyTorch mistakes and provide battle-tested training patterns and optimization techniques

  ⚙ detect common PyTorch mistakes⚙ provide training templates⚙ fix evaluation mode issues
• profile-isaac-sim - Profile Isaac Sim Performance

  performanceprofilingbenchmarkIsaac Sim

  ★ 3,468 · Updated 2026-06-15

  Run benchmarks, capture Tracy profiles, and optimize Isaac Sim workloads

  ⚙ run benchmark⚙ capture Tracy profile⚙ export to CSV
• forge-shader-grid - Procedural Anti-Aliased Grid for SDL3

  SDL3GPUshadergrid

  ★ 0 · Updated 2026-06-15

  Add procedural anti-aliased grid floor to SDL3 GPU scenes using fragment shader

  ⚙ create grid pipeline⚙ bind graphics pipeline⚙ upload grid geometry
• gpu - Ollama GPU Monitoring

  GPUmonitoringOllamaperformance

  ★ 18 · Updated 2026-05-28

  Monitor GPU status and performance metrics for Ollama inference

  ⚙ check GPU status⚙ monitor VRAM usage⚙ view loaded models
• teoria-engine - Production-grade self-hosted LLM inference stack

  LLMself-hostedinferenceOpenAI API

  ★ 0 · Updated 2026-05-09

  Starts and manages a local LLM inference stack with OpenAI-compatible API for Linux and macOS

  ⚙ start inference engine⚙ stop inference engine⚙ check engine status
• llm-inference-scaling - Auto-scale LLM inference clusters on Kubernetes

  kubernetesautoscalingLLMGPU

  ★ 602 · Updated 2026-03-26

  Scale LLM inference horizontally on Kubernetes with GPU-aware autoscaling, request queuing, and spot instance strategies.

  ⚙ Deploy vLLM inference pods⚙ Configure KEDA autoscaling⚙ Set up queue-based scaling
• unsloth-finetuning - Fine-tune LLMs with Unsloth

  LLMfine-tuning4-bit quantizationLoRA

  ★ 602 · Updated 2026-03-26

  Fine-tune LLMs with Unsloth using 4-bit quantization and LoRA

  ⚙ load 4-bit quantized models⚙ configure LoRA parameters⚙ train on datasets
• ml-pipeline-orchestration - ML Pipeline Orchestration for GPU Workflows

  MLOpsML pipelinesorchestrationGPU

  ★ 18 · Updated 2026-03-24

  Design and orchestrate ML pipelines with GPU steps and artifact tracking

  ⚙ select orchestrator⚙ design pipeline flow⚙ configure GPU resources
• Vram-GPU-OOM - GPU VRAM Management for Multi-Service Sharing

  GPUmemory managementmodel loadingservice coordination

  ★ 18 · Updated 2026-03-23

  Manage GPU VRAM sharing across services with OOM retry and auto-unload

  ⚙ load model⚙ catch OOM error⚙ retry model load
• Vram-GPU-OOM - GPU VRAM Sharing and OOM Management

  GPUmemory managementOOMmodel loading

  ★ 602 · Updated 2026-03-22

  Manage GPU VRAM sharing with OOM retry and auto-unload for multiple services

  ⚙ catch OOM errors⚙ retry model loading⚙ wait between retries
• forge-mesh-loading - 3D OBJ Model Loading & Textured Rendering

  3DOBJmodel loadingtextures

  ★ 0 · Updated 2026-03-22

  Load 3D models from OBJ files and render textured meshes with mipmaps

  ⚙ parse OBJ file⚙ create GPU buffer⚙ upload vertices
• forge-mesh-loading - Load and Render 3D OBJ Models with SDL3 GPU

  3DOBJmeshtexture

  ★ 18 · Updated 2026-03-22

  Load 3D models from OBJ files, create textured meshes with mipmaps, and render with a fly-around camera in SDL3 GPU.

  ⚙ load OBJ file⚙ parse OBJ model⚙ create mesh texture
• remote-run-ssh - Run CVlization Examples on Remote GPU via SSH

  SSHGPUDockerRemote Training

  ★ 18 · Updated 2026-03-20

  Run CVlization examples on remote GPU host by syncing minimal workspace and launching Docker scripts

  ⚙ Sync example directory to remote host⚙ Build Docker image on remote host⚙ Run training script with GPU access
• remote-run-ssh - Execute CVlization on Remote GPU via SSH

  sshremote executionGPUDocker

  ★ 602 · Updated 2026-03-20

  Sync CVlization examples and library to remote GPU host via SSH and launch Docker-based training scripts.

  ⚙ sync files to remote host⚙ build Docker image⚙ check GPU availability

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