3 个仓库
Awesome GitHub RepositoriesTraining Data Ingestion
Mechanisms for importing and preprocessing raw data for efficient consumption by neural network models.
Distinct from Data Ingestion: Distinct from database ingestion; focuses specifically on preparing data for ML model training pipelines.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Training Data Ingestion. Refine with filters or upvote what's useful.
Awesome Training Data Ingestion GitHub Repositories
用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
- flashlight/flashlight
flashlight/flashlight
5,443在 GitHub 上查看Flashlight is a standalone C++ machine learning library and tensor library used for building and training neural networks. It functions as a comprehensive neural network framework and automatic differentiation engine, providing the tools to construct computation graphs and calculate gradients via backpropagation. The project serves as a distributed training framework, utilizing all-reduce operations to synchronize gradients and parameters across multiple compute nodes and devices. It distinguishes itself through deep integration of high-performance tensor manipulation, native device memory in
Provides built-in utilities to ingest and preprocess data for efficient delivery to neural network models.
C++ - open-mmlab/mmtracking
open-mmlab/mmtracking
3,881在 GitHub 上查看mmtracking is a PyTorch video perception framework designed for training and deploying computer vision models that analyze sequential image data. It provides specialized tools for multi-object tracking, video instance segmentation, and a configuration-driven system for managing deep learning models. The project utilizes a deep learning model registry and a configuration-driven pipeline to swap model backbones and detectors without modifying the core codebase. This modular approach allows for the development of custom perception architectures by combining various components and configurations.
Provides mechanisms for importing and streaming large-scale video datasets into neural network training pipelines.
Pythonmulti-object-trackingsingle-object-trackingtracking - yahoo/tensorflowonspark
yahoo/TensorFlowOnSpark
3,850在 GitHub 上查看TensorFlowOnSpark is a distributed framework for running TensorFlow machine learning workloads and model training across Apache Spark clusters. It functions as a cluster computing orchestrator that manages worker processes and resource allocation to scale deep learning tasks across multiple computing nodes. The platform enables distributed deep learning training and large-scale model inference, allowing users to execute tasks across a cluster of servers to handle datasets that exceed the memory of a single machine. It integrates deep learning workloads with Spark data processing to create end
Provides mechanisms for importing and preprocessing raw training data from HDFS or Spark for neural network consumption.
Python
