Roshani Nagmote

Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.

NLP Rule based question answering system

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.

A library for training and deploying machine learning models on Amazon SageMaker

Convert TensorFlow models to ONNX

Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker

Utilities and samples for building on CodeBuild

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

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Multi Model Server is a tool for serving neural net models for inference

Deep Learning Examples

Docker container for running PyTorch scripts to train and host PyTorch models on SageMaker

Train and Deploy Machine Learning Models on Kubernetes using Amazon EKS

Kubernetes Operator for Allreduce-style Distributed Training

Dynamic training with Apache MXNet reduces cost and time for training deep neural networks by leveraging AWS cloud elasticity and scale. The system reduces training cost and time by dynamically updating the training cluster size during training, with minimal impact on model training accuracy.

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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This is mirror of deep-learning-benchmark

Notebooks for MXNet

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Tutorials for using ONNX

Tensorflow Backend for ONNX

ONNX model format support for Apache MXNet

Caffe2 implementation of Open Neural Network Exchange (ONNX)

Open Neural Network Exchange

CFN cluster for DeepLearning AMIs.

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

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Matrix Factorization ALS Algorithm

Java Interview questions practice