Does this skill support PyTorch 2.0 features?

Yes, it includes specific implementation patterns for PyTorch 2.0+, such as torch.compile, FSDP optimization, and inference mode.

Can I use this for distributed training?

Absolutely. The skill covers setup and cleanup for DistributedDataParallel (DDP) and FullyShardedDataParallel (FSDP) for multi-GPU scaling.

Does it include model deployment patterns?

Yes, the skill includes standardized methods for exporting models via TorchScript and ONNX for production deployment.

How does it handle GPU memory issues?

It provides best practices for memory optimization, including activation checkpointing, gradient accumulation, and monitoring memory allocation.

PyTorch Deep Learning

Name: PyTorch Deep Learning
Author: itsmostafa

byitsmostafa

•

데이터 과학 및 ML

Build, train, and optimize high-performance neural networks using the PyTorch deep learning framework.

소개

This skill provides Claude with expert-level guidance for implementing sophisticated deep learning architectures and training pipelines in PyTorch. It covers everything from basic tensor operations and custom nn.Module construction to advanced performance optimizations like torch.compile, mixed-precision training, and distributed data parallelism (DDP). Whether you are building a simple MLP or a complex transformer-based model, this skill ensures best practices for memory management, GPU acceleration, and production-ready model deployment.

주요 기능

Modern performance optimization techniques including torch.compile for PyTorch 2.0+
Efficient data pipeline construction using custom Datasets and DataLoaders
Advanced neural network architecture design using the nn.Module pattern
High-performance training loop implementation with mixed precision and gradient accumulation
10 GitHub stars
Scalable distributed training configurations using DDP and FSDP

사용 사례

Developing production-ready data loaders for large-scale, variable-length datasets
Implementing custom computer vision or NLP model architectures from scratch
Optimizing training scripts for better GPU utilization and faster convergence

소개

주요 기능

Modern performance optimization techniques including torch.compile for PyTorch 2.0+
Efficient data pipeline construction using custom Datasets and DataLoaders
Advanced neural network architecture design using the nn.Module pattern
High-performance training loop implementation with mixed precision and gradient accumulation
10 GitHub stars
Scalable distributed training configurations using DDP and FSDP

사용 사례

Developing production-ready data loaders for large-scale, variable-length datasets
Implementing custom computer vision or NLP model architectures from scratch
Optimizing training scripts for better GPU utilization and faster convergence