Validate a PyTorch Lightning model with given model configuration and optional trainer settings. Ensure model correctness before full training.
Apache 2.0
519,862 tools. Updated 2026-09-06 08:15
"PyTorch" matching MCP tools:
- Train PyTorch Lightning models by providing explicit model and trainer configurations.Apache 2.0
- Test a PyTorch Lightning model by specifying model and optional trainer configurations.Apache 2.0
- Find entities in the knowledge graph by meaning rather than exact text matching, enabling retrieval of related concepts even without keyword overlap.MIT
- Retrieve complete module details including description, notebooks with GitHub and Colab links, tags, and prerequisites for any course topic.Apache 2.0
- Inspect a connected ComfyUI server without mutating it: read GPU/VRAM stats, runtime logs, and a health report to confirm it is ready before you dispatch work.MIT
Matching MCP Servers
- FlicenseNot gradedqualityDmaintenanceA full-featured MCP server for PyTorch documentation workflows, providing tools for search, symbol lookup, code examples, troubleshooting, and question-answering using local docs.-
- AlicenseBqualityDmaintenanceA minimal integration layer exposing PyTorch Lightning via a structured, machine-readable API for tools, agents, and orchestration systems.65Apache 2.0
- Check GPU availability and performance in conda environments for PyTorch or TensorFlow, verifying Metal acceleration setup and providing benchmark comparisons.MIT
- Check which quantization backends (GGUF, GPTQ, AWQ) are installed, verify PyTorch and transformers availability, and view GPU and RAM details. No arguments required.MIT
- Preserve the current sandbox system state including installed packages as a reusable image. Restore later to replicate the exact environment.MIT
- Retrieve ComfyUI server health metrics including version details, memory usage, and device information to monitor system status and resource utilization.MIT
- Retrieve a list of available templates, with options to include official RunPod templates, community public templates, or endpoint-bound templates.Apache 2.0
- Look up an error message in deadends.dev to get known dead ends, workarounds, and next steps. Avoid failed approaches before spending time on fixes across 51 domains.MIT
- Search shared notes or dialog through a parent process when embeddings aren't loaded, using hybrid, semantic, or full-text modes. Get top results without loading heavy models.MIT
- Detect AI system usage in a project directory and generate an EU AI Act compliance report that classifies risk level and identifies compliance gaps.MIT
- Provision a GPU or CPU pod on RunPod. Configure image, GPU type, storage, ports, and environment variables for compute workloads.Apache 2.0
- Retrieve details of a specific Kaggle model instance by providing owner, model slug, framework, and instance slug.MIT
- List all versions of a model instance on Kaggle by providing the owner, model slug, framework, and instance slug.MIT
- Run inference by providing a model configuration with _target_ and kwargs. Optionally specify trainer settings for prediction.Apache 2.0
- Save, load, or list PyTorch Lightning model checkpoints via a structured API.Apache 2.0
- Create a new instance for a Kaggle model by specifying owner, model slug, framework, and instance slug. Configure overview, usage, license, and privacy settings.MIT
- Create a new version for a Kaggle model instance by specifying owner, model slug, framework, instance slug, and optional version notes.MIT