rai

mlmodelscope

MLModelScope is an open source, extensible, and customizable platform to facilitate evaluation and measurement of ML models within AI pipelines.

go-tensorrt

Go binding to TensorRT C API to do inference with pre-trained model in Go

tensorflow-go-examples

Examples of using TensorFlow Go API for different Deep Learning models

go-pytorch

go-mxnet

Go binding to MXNet C API to do inference with pre-trained model in Go

go-cupti

Use NVIDIA CUPTI from within GO

mxnet

MXNet agent for MLModelScope

pytorch

tensorflow

TensorFlow agent for MLModelScope

dlframework

Common code for all deep learning predictors used by MLModelScope

config

Configuration entry point for both RAI and CarML

dockerfile-builder

The aim of D4P is to enrich the PowerPC container ecosystem by providing both a platform for developers to create docker containers, and for PowerPC community to find docker images. We have already built and published over 200 docker images using this platform, and they are available both in the D4P's image catalog or accessible through the C3SR center's Docker Hub link.

evaluation

Evaluation tools for model performance / accuracy for MLModelScope

tensorrt

TensorRT agent for MLModelScope

dldataset

Builtin deep learning datasets used by MLModelScope

scratch

tracer

Common tracer abstraction

trims_mxnet

run using https://github.com/rai-project/trims-tools

aresdb

A GPU-powered real-time analytics storage and query engine.

cudnn_scope

Benchmark cuDNN for different Deep Learning layers.

dlperf

Déjà vu: Modeling DNN Performance by Recalling History

go-mkl-dnn

grpc

lib_log_parser

menoh

onnx_examples

ONNX model inference using different backend frameworks

pytorch_dlperf_opt

Tensors and Dynamic neural networks in Python with strong GPU acceleration

service

synthetic_benchmark

tensorrt_samples

Samples from TensorRT installation