DAIBench

DAIBench (DiDi Cloud AI Benchmark) aims to provide a set of GPU evaluation sets for AI production environments, spanning different types of GPU servers and cloud environments, to provide users with effective and credible test results for future hardware selection , Software and library optimization, business model improvement, link stress testing and other stages to lay a solid data foundation and technical reference.

Supported Features

• Evaluation from Hardware to Application
• Foreign BENCHMARK adaptation
• Impact of virtualized cloud environment scenarios and business pertinence

General Structure

DAIBench comprehensively considers the existing GPU performance testing tools, and divides the indicators into hardware layer, framework (operator) layer, and algorithm layer.

For each level, DAIBench currently supports the following tests:

Layer	Supported Test
Hardware layer	Focusing on the indicators of the hardware itself, such as peak computing throughput (TFLOPS/TOPS) calculation indicators and memory access bandwidth, PCIe communication bandwidth and other I/O indicators.
Frame/operator layer	Evaluating the computing power of commonly used operators (convolution, Softmax, matrix multiplication, etc.) based on mainstream AI frameworks.
Model layer	Performing end-to-end evaluation by selecting models in a series of production tasks.

Getting started

Hardware Layer

cd <test_folder>
bash install.sh
bash run.sh

For GPU test, please install suitable nvidia-driver and cuda first.

Operator Layer

Current operator layer is using DeepBench

cd operator
bash install.sh # download source code & prepare nccl

To run GEMM, convolution, recurrent op and sparse GEMM benchmarks:

bin/gemm_bench <inference|train> <int8|float|half>

To execute the NCCL single All-Reduce benchmark:

bin/nccl_single_all_reduce <num_gpus>

The NCCL MPI All-Reduce benchmark can be run using mpirun as shown below:

mpirun -np <num_ranks> bin/nccl_mpi_all_reduce

num_ranks cannot be greater than the number of GPUs in the system.

Model layer

docker and nvidia-docker is required for model testing. To run specific model, please read Readme.md in the folder.

General test procedure:

• Download dataset
• Preprocess dataset (if needed)
• Build docker
• Launch benchmark
• Get result

Developer guide

See wiki for guidelines.

Contributing

Welcome to contribute by creating issues or sending pull requests. See Contributing Guide for guidelines.

License

DAIBench is licensed under the Apache License 2.0.