This repository contains the code for Building a simple Keras + deep learning REST API, published on the Keras.io blog.
Principalele funcționalități ale jrosebr1/simple-keras-rest-api sunt: Model Serving and Deployment, Model Serving.
Alternativele open-source pentru jrosebr1/simple-keras-rest-api includ: pytorch/serve — This project is a PyTorch model serving framework designed to deploy and scale machine learning models in production… triton-inference-server/server — Triton Inference Server is a high-performance server designed to deploy machine learning models from multiple… l1aoxingyu/code-of-learn-deep-learning-with-pytorch — This repository serves as a structured educational resource for learning to build, train, and deploy neural networks…
This project is a PyTorch model serving framework designed to deploy and scale machine learning models in production via scalable network endpoints. It functions as a high-performance inference server, optimizer, and model lifecycle manager that handles model loading, request batching, and hardware acceleration. The system distinguishes itself through advanced orchestration and optimization capabilities, such as chaining multiple models into sequential workflows using execution graphs and employing dynamic batching to improve throughput and latency. It provides specialized support for generat
Triton Inference Server is a high-performance server designed to deploy machine learning models from multiple frameworks across GPUs and CPUs. It functions as a hardware-accelerated inference engine and a gRPC inference gateway, providing a standardized communication layer for transmitting binary tensor data with low latency. The system acts as a multi-framework model orchestrator, allowing users to link multiple AI models into ensembles and scripts to create complex inference pipelines. It also serves as a model lifecycle manager, providing controls to load, unload, and monitor the performan
This repository serves as a structured educational resource for learning to build, train, and deploy neural networks using the PyTorch framework. It provides a collection of practical code examples and tutorials designed to guide practitioners through the implementation of deep learning models. The project covers a broad range of machine learning domains, including computer vision, natural language processing, generative modeling, and reinforcement learning. By utilizing modular components and automated gradient computation, the materials demonstrate how to construct complex architectures and