Open-source alternatives to Cogitare

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

Super simple fit method for PyTorch Modules

Accelerated deep learning R&D

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

This project is a pretrained model library for PyTorch, providing a collection of convolutional neural network architectures and weights. It serves as a computer vision model zoo for image classification and feature extraction, offering a framework for transfer learning where pretrained networks are adapted for custom image recognition tasks. The library focuses on transforming images into high-level numerical representations and calculating class probability scores. It includes utilities for downloading and initializing standard architectures such as ResNet, Inception, and Xception. Capabil

torchbearer: A model fitting library for PyTorch

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

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

This project is a PyTorch implementation of the Faster R-CNN architecture for object detection. It provides a framework for identifying multiple object classes and their corresponding bounding boxes within images using a deep learning system. The implementation includes a training pipeline for optimizing models on custom datasets and a utility for converting pretrained weights from external formats into a compatible structure for model initialization. The system covers a two-stage detection pipeline comprising a region proposal network and an ROI pooling layer. It incorporates multi-task los

Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and images. It functions as a dataset version control system and a machine learning data streaming engine to support large-scale model training. The system utilizes a serverless PostgreSQL vector store to index high-dimensional embeddings for semantic search. It provides a visual interface for inspecting multimodal datasets and viewing annotations such as bounding boxes and masks. The platform handles cloud-agnostic storage synchronization and implements lazy, compressed data strea

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