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Back to graal-research/pytoune

Projects sharing features with Pytoune

30 open-source projects similar to graal-research/pytoune, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.

  • microsoft/tensorwatchmicrosoft avatar

    microsoft/tensorwatch

    3,468View on GitHub↗

    Debugging, monitoring and visualization for Python Machine Learning and Data Science

    Jupyter Notebook
    View on GitHub↗3,468
  • ecs-vlc/torchbearerecs-vlc avatar

    ecs-vlc/torchbearer

    641View on GitHub↗

    torchbearer: A model fitting library for PyTorch

    Python
    View on GitHub↗641
  • catalyst-team/catalystcatalyst-team avatar

    catalyst-team/catalyst

    3,376View on GitHub↗

    Accelerated deep learning R&D

    Python
    View on GitHub↗3,376
  • pytorch/ignitepytorch avatar

    pytorch/ignite

    4,770View on GitHub↗

    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
    View on GitHub↗4,770
  • dnouri/skorchdnouri avatar

    dnouri/skorch

    6,166View on GitHub↗

    Skorch is a deep learning workflow manager and tensor-based model interface. It provides a consistent API for training and predicting with neural networks within standard machine learning workflows, acting as a hyperparameter optimizer for finding optimal network configurations. The library specializes in wrapping PyTorch neural networks in a scikit-learn compatible interface. This allows tensor-based models to be used within traditional machine learning pipelines and grid search tools, including the mapping of parameter grids to model configurations. The framework covers training lifecycle

    Jupyter Notebook
    View on GitHub↗6,166

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  • pytorch/extension-cpppytorch avatar

    pytorch/extension-cpp

    1,192View on GitHub↗

    C++ extensions in PyTorch

    Python
    View on GitHub↗1,192
  • nasimrahaman/infernoN

    nasimrahaman/inferno

    0View on GitHub↗
    View on GitHub↗0
  • pytorch/hubpytorch avatar

    pytorch/hub

    1,434View on GitHub↗

    Submission to https://pytorch.org/hub/

    Python
    View on GitHub↗1,434
  • facebook/axfacebook avatar

    facebook/Ax

    2,768View on GitHub↗

    Adaptive Experimentation Platform

    Python
    View on GitHub↗2,768
  • dmarnerides/pydltD

    dmarnerides/pydlt

    0View on GitHub↗
    View on GitHub↗0
  • iamaziz/pytorch-docsetI

    iamaziz/PyTorch-docset

    0View on GitHub↗
    View on GitHub↗0
  • mrdrozdov/pytorch-extrasM

    mrdrozdov/pytorch-extras

    0View on GitHub↗
    View on GitHub↗0
  • oval-group/loggerO

    oval-group/logger

    0View on GitHub↗
    View on GitHub↗0
  • pytorch/contribP

    pytorch/contrib

    0View on GitHub↗
    View on GitHub↗0
  • fastai/fastaifastai avatar

    fastai/fastai

    27,862View on GitHub↗

    Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the entire machine learning lifecycle. It functions as a comprehensive training toolkit, abstracting hardware management and automating complex training loops to simplify the construction and execution of neural network models. The framework is distinguished by its notebook-centric development environment and a type-dispatching data pipeline that automatically applies transformations based on input data formats. It emphasizes transfer learning through discriminative layer-wise optimiza

    Jupyter Notebookcolabdeep-learningfastai
    View on GitHub↗27,862
  • blue-season/pywarmB

    blue-season/pywarm

    0View on GitHub↗
    View on GitHub↗0
  • bloodaxe/pytorch-toolbeltBloodAxe avatar

    BloodAxe/pytorch-toolbelt

    1,572View on GitHub↗

    PyTorch extensions for fast R&D prototyping and Kaggle farming

    Pythonaugmentationdeep-learningfocal-loss
    View on GitHub↗1,572
  • awwong1/torchprofawwong1 avatar

    awwong1/torchprof

    605View on GitHub↗

    PyTorch layer-by-layer model profiler

    Python
    View on GitHub↗605
  • asappresearch/flambeA

    asappresearch/flambe

    0View on GitHub↗
    View on GitHub↗0
  • dnouri/infernoD

    dnouri/inferno

    0View on GitHub↗
    View on GitHub↗0
  • backprop-ai/backpropbackprop-ai avatar

    backprop-ai/backprop

    240View on GitHub↗

    Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

    Python
    View on GitHub↗240
  • ekami/torchliteE

    EKami/Torchlite

    0View on GitHub↗
    View on GitHub↗0
  • henryre/pytorch-fitmodulehenryre avatar

    henryre/pytorch-fitmodule

    102View on GitHub↗

    Super simple fit method for PyTorch Modules

    Python
    View on GitHub↗102
  • huggingface/acceleratehuggingface avatar

    huggingface/accelerate

    9,725View on GitHub↗

    Accelerate is a PyTorch distributed training library that abstracts the boilerplate required to run models across multiple GPUs, TPUs, and CPUs. It functions as a deep learning model scaler and distributed hardware orchestrator, allowing the same training script to run on different hardware backends without modifying the core logic. The project provides a distributed training command line interface for configuring compute environments and launching jobs across single or multi-node clusters. It includes a mixed precision training framework to implement FP16 and BF16 precision, reducing memory

    Python
    View on GitHub↗9,725
  • lanpa/tensorboard-pytorchlanpa avatar

    lanpa/tensorboard-pytorch

    7,983View on GitHub↗

    This project is a machine learning experiment tracker and event file generator that enables the recording of scalars, images, and histograms to monitor model performance. It functions as an integration bridge that allows training metrics from PyTorch to be logged into files compatible with the TensorBoard dashboard. The system includes a remote log synchronizer designed to stream experiment data to cloud services. This allows for the remote management and analysis of training results and the comparison of datasets across different training runs. The utility covers a broad range of monitoring

    Python
    View on GitHub↗7,983
  • cogitare-ai/cogitarecogitare-ai avatar

    cogitare-ai/cogitare

    77View on GitHub↗

    🔥 Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python

    Python
    View on GitHub↗77
  • ncullen93/torchsamplencullen93 avatar

    ncullen93/torchsample

    1,878View on GitHub↗

    Train AI models efficiently on medical images using any framework

    Python
    View on GitHub↗1,878
  • nearai/pytorch-toolsN

    nearai/pytorch-tools

    0View on GitHub↗
    View on GitHub↗0
  • pistony/torch-toolboxP

    PistonY/torch-toolbox

    0View on GitHub↗
    View on GitHub↗0
  • graal-research/poutyneGRAAL-Research avatar

    GRAAL-Research/poutyne

    578View on GitHub↗

    A simplified framework and utilities for PyTorch

    Python
    View on GitHub↗578