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A Redis module for serving tensors and executing deep learning graphs

According to all known laws of aviation, there is no way that a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway. Because bees don’t care what humans think is impossible.” SEQ. 75 - “INTRO TO BARRY” INT. BENSON HOUSE - ... Read more

Toolkit for allowing inference and serving with PyTorch on SageMaker. Dockerfiles used for building SageMaker Pytorch Containers are at https://github.com/aws/deep-learning-containers.

AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving (OSDI 23)

Open source Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in PyTorch, OpenCV (compiled for GPU), TensorFlow 2 for GPU, PyG and NVIDIA RAPIDS

Open Source Deep Learning Serving System with Web Interface

The course is contained knowledge that are useful to work on deep learning as an engineer. Simple neural networks & training, CNN, Autoencoders and feature extraction, Transfer learning, RNN, LSTM, NLP, Data augmentation, GANs, Hyperparameter tuning, Model deployment and serving are included in t... Read more

TensorFlow Distributed Image Serving - A lightweight, RESTful remote inference library for decoupling deep learning development and deployment. Example usage cases with CycleGAN and Faster R-CNN. Includes tutorial ipynbs.

🦀 Example of serving deep learning models in Rust with batched prediction

Toolkit for allowing inference and serving with MXNet in SageMaker. Dockerfiles used for building SageMaker MXNet Containers are at https://github.com/aws/deep-learning-containers.