The Unified AI Framework
-
Updated
Jun 2, 2024 - Python
The Unified AI Framework
AI on Hadoop
An Engine-Agnostic Deep Learning Framework in Java
Open standard for machine learning interoperability
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
A library for training and deploying machine learning models on Amazon SageMaker
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
Probabilistic time series modeling in Python
ncnn is a high-performance neural network inference framework optimized for the mobile platform
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Some Data Science examples using Groovy
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Machine Learning Operator & Controller for Kubernetes
Multi Model Server is a tool for serving neural net models for inference
Deep Learning Inference benchmark. Supports OpenVINO™ toolkit, Caffe, TensorFlow, TensorFlow Lite, ONNX Runtime, OpenCV DNN, MXNet, PyTorch, Apache TVM, ncnn, etc.
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Add a description, image, and links to the mxnet topic page so that developers can more easily learn about it.
To associate your repository with the mxnet topic, visit your repo's landing page and select "manage topics."