How do I install the Pytorch Ml skill?

Run npx skillmds@latest add dreamlab-ai/pytorch-ml in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Pytorch Ml safe to use?

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Which AI agents work with Pytorch Ml?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Pytorch Ml free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Pytorch Ml?

dreamlab-ai (@dreamlab-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Pytorch Ml by dreamlab-ai — Agent Skill

Name: Pytorch Ml
Author: dreamlab-ai

Pytorch Ml

Train and fine-tune deep learning models in PyTorch with CUDA GPU acceleration — nn.Module definition, training loops, DataLoaders, checkpointing, mixed-precision (AMP), and transfer learning with torchvision/torchaudio pretrained models. Use when writing PyTorch model code, building a training loop or custom Dataset/DataLoader, moving tensors/models to a GPU device, fine-tuning a pretrained CNN/ResNet/Transformer, adding autocast/GradScaler mixed precision, or saving/loading checkpoints. Triggers: 'train a PyTorch model', 'nn.Module', 'DataLoader', '.to(device)', 'fine-tune ResNet', 'CUDA out of memory during training', 'mixed precision'. Not for custom CUDA kernels (cuda skill), Stable Diffusion/FLUX image generation (comfyui), notebook data exploration (jupyter-notebooks), or cloud-sandbox distributed training (flow-nexus-neural).

dreamlab-ai a2b4d44 5 files · 8.9 KB Updated Sep 17, 2026

File contents

PyTorch ML Skill

Complete machine learning environment with PyTorch, CUDA, and the data science stack for deep learning research and production. Use it for building models, training loops, custom datasets, transfer learning, checkpointing, and mixed-precision GPU training.

When Not To Use

Custom CUDA kernel development and GPU profiling — use the cuda skill instead
Distributed neural network training in cloud sandboxes — use flow-nexus-neural instead
AgentDB reinforcement learning plugins — use agentdb-advanced (RL Plugins section) instead
Interactive data exploration in notebooks — use jupyter-notebooks instead
AI image generation (Stable Diffusion, FLUX) — use comfyui instead

Quick start

import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}, CUDA {torch.version.cuda}, {torch.cuda.device_count()} GPU(s)")

Always move both model and data to the same device, batch with a DataLoader, and enable torch.backends.cudnn.benchmark = True for fixed-shape workloads.

References

Load the reference for the task at hand:

references/cookbook.md — core code: environment check, nn.Module definition, training loop, custom Dataset/DataLoader, pretrained-model fine-tuning, and checkpoint save/load.
references/mixed-precision-and-cuda.md — AMP mixed-precision training with the current torch.amp API, cache management, and GPU-properties introspection.
references/architectures.md — ready-made module definitions: image CNN and Transformer encoder.
references/environment-and-best-practices.md — installed packages, best-practice checklist, CUDA/multi-GPU environment notes, capability list, and related skills.

---name: pytorch-mldescription: Train and fine-tune deep learning models in PyTorch with CUDA GPU acceleration — nn.Module definition, training loops, DataLoaders, checkpointing, mixed-precision (AMP), and transfer learning with torchvision/torchaudio pretrained models. Use when writing PyTorch model code, building a training loop or custom Dataset/DataLoader, moving tensors/models to a GPU device, fine-tuning a pretrained CNN/ResNet/Transformer, adding autocast/GradScaler mixed precision, or saving/loading checkpoints. Triggers: 'train a PyTorch model', 'nn.Module', 'DataLoader', '.to(device)', 'fine-tune ResNet', 'CUDA out of memory during training', 'mixed precision'. Not for custom CUDA kernels (cuda skill), Stable Diffusion/FLUX image generation (comfyui), notebook data exploration (jupyter-notebooks), or cloud-sandbox distributed training (flow-nexus-neural).---# PyTorch ML SkillComplete machine learning environment with PyTorch, CUDA, and the data science stack for deep learning research and production. Use it for building models, training loops, custom datasets, transfer learning, checkpointing, and mixed-precision GPU training.## When Not To Use- Custom CUDA kernel development and GPU profiling — use the **cuda** skill instead- Distributed neural network training in cloud sandboxes — use **flow-nexus-neural** instead- AgentDB reinforcement learning plugins — use **agentdb-advanced** (RL Plugins section) instead- Interactive data exploration in notebooks — use **jupyter-notebooks** instead- AI image generation (Stable Diffusion, FLUX) — use **comfyui** instead## Quick start```pythonimport torchdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")print(f"Using device: {device}, CUDA {torch.version.cuda}, {torch.cuda.device_count()} GPU(s)")```Always move both model and data to the same device, batch with a `DataLoader`, andenable `torch.backends.cudnn.benchmark = True` for fixed-shape workloads.## ReferencesLoad the reference for the task at hand:- [references/cookbook.md](references/cookbook.md) — core code: environment check, `nn.Module` definition, training loop, custom `Dataset`/`DataLoader`, pretrained-model fine-tuning, and checkpoint save/load.- [references/mixed-precision-and-cuda.md](references/mixed-precision-and-cuda.md) — AMP mixed-precision training with the current `torch.amp` API, cache management, and GPU-properties introspection.- [references/architectures.md](references/architectures.md) — ready-made module definitions: image CNN and Transformer encoder.- [references/environment-and-best-practices.md](references/environment-and-best-practices.md) — installed packages, best-practice checklist, CUDA/multi-GPU environment notes, capability list, and related skills.

Frequently asked questions