2 مستودعات
Awesome GitHub RepositoriesModel Deployment Workflows
Processes and strategies for packaging and hosting trained neural networks in production environments.
Distinct from Deep Learning Model Converters: The candidates provided were either too generic (awesome lists) or too specific (converters), whereas this describes the overarching deployment capability.
Explore 2 awesome GitHub repositories matching devops & infrastructure · Model Deployment Workflows. Refine with filters or upvote what's useful.
Awesome Model Deployment Workflows GitHub Repositories
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- udacity/deep-learning-v2-pytorch
udacity/deep-learning-v2-pytorch
5,505عرض على GitHubThis project is a collection of PyTorch deep learning courseware consisting of practical projects and programming exercises. It focuses on implementing neural network architectures and model training to solve complex data problems. The repository includes a computer vision project suite for building image classifiers, autoencoders, and style transfer applications. It features a generative adversarial network lab for creating synthetic images and specific implementations for transfer learning to adapt pre-trained weights to new tasks. The codebase covers sequential data analysis for natural l
Provides guidance on packaging and hosting trained neural networks on cloud infrastructure for production use.
Jupyter Notebookconvolutional-networksdeep-learningneural-network - higgsfield-ai/higgsfield
higgsfield-ai/higgsfield
3,866عرض على GitHubHiggsfield is a distributed machine learning training framework and GPU cluster orchestrator designed for scaling neural networks with billions of parameters. It functions as a large model sharding system and a containerized deployment tool to manage computational workflows across heterogeneous compute resources. The platform provides a centralized interface for experiment management, enabling the monitoring of real-time telemetry, performance metrics, and logs. It ensures reproducible results by using container isolation to standardize dependencies across different computing environments. T
Provides automated workflows for pushing code to compute nodes and managing training runs and checkpoints.
Jupyter Notebookcluster-managementdeep-learningdistributed
