dnouri/skorch

अक्सर पूछे जाने वाले प्रश्न

• pytorch/ignite

  pytorch/ignite

  4,770GitHub पर देखें↗

  Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep learning lifecycle manager. It provides a structured system for organizing and automating training and evaluation loops, managing data iterators and triggering event handlers at specific milestones during the model training process. The project distinguishes itself through a comprehensive suite of tools for distributed training and model evaluation. It includes utilities for synchronizing gradients and coordinating collective communication across multiple GPUs or nodes, as well as a

  Python

  GitHub पर देखें↗4,770
• skorch-dev/skorch

  skorch-dev/skorch

  6,166GitHub पर देखें↗

  Skorch is a library that wraps PyTorch neural networks in a scikit-learn compatible interface, allowing deep learning models to be used within standard machine learning pipelines and hyperparameter optimization tools. It functions as a data adapter, training manager, and optimization tool that bridges the gap between deep learning modules and conventional machine learning workflows. The project distinguishes itself by providing a toolkit for automating the PyTorch training lifecycle, including integrated checkpointing, early stopping, and learning rate scheduling. It further enables transfer

  Jupyter Notebook

  GitHub पर देखें↗6,166
• pytorchlightning/pytorch-lightning

  PyTorchLightning/pytorch-lightning

  31,189GitHub पर देखें↗

  PyTorch Lightning is a high-level deep learning framework for PyTorch that automates training loops and removes repetitive engineering boilerplate. It functions as a structured pipeline for managing machine learning experiments, providing a distributed training orchestrator and tools for mixed-precision training. The framework decouples scientific model architecture from the engineering required for infrastructure and scaling. This separation allows the same model code to execute across CPUs, GPUs, or TPUs through a hardware-agnostic execution engine and a centralized trainer that manages the

  Python

  GitHub पर देखें↗31,189
• aws/amazon-sagemaker-examples

  aws/amazon-sagemaker-examples

  10,958GitHub पर देखें↗

  This repository is a collection of Jupyter notebooks providing reference implementations and templates for building, training, and deploying machine learning models using Amazon SageMaker. It serves as an example library for implementing model architectures and automating the machine learning lifecycle. The library provides practical patterns for machine learning training, data engineering, and model deployment. It includes implementation guides for MLOps, including workflows for model monitoring, lineage tracking, and hyperparameter tuning. The examples cover a broad range of capabilities i

  Jupyter Notebookawsdata-sciencedeep-learning

  GitHub पर देखें↗10,958