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TBD (Training Benchmark for DNNs) Training Suite


This repository contains the benchmark suite for DNN training.

The benchmarks was categorized according to what their application and model (e.g. MachineTranslation-Seq2Seq, MachineTranslation-Transformer, ...). Each category contains a that points to related resources (e.g. papers, tutorials, ...).

Each benchmark contains the following:

  • source: Source files of the DNN benchmarks, with the suffix indicating its version (e.g. git branch or tag name). Each source folder includes requirements.gpu-cu80.txt that contains all the packages needed on a CUDA 8.0 machine. Please note that benchmark source files might be changed in order for them to run properly or make fair comparisons. All the changes will be highlighted using the following format:
# <EcoSys> A brief description of what the changes are.
Changes go here ...
# </EcoSys>
  • dataset: Scripts that download the dataset, and follows the naming convention

  • scripts: Scripts that run the benchmark on certain dataset, and follows the naming convention If no arguments are given, the script will help you run the benchmark. If command argument --profile is given, the script will do profiling on CUDA kernels. If command argument --profile-fp32 is given, the script will do profiling on utilization on floating point units. (Please note that you will need to input those .nvvp files to NVidia Visual Profiler to see the profiling results.)

  • An introduction on what the benchmark is, and contains changelog that records changes that were made to the benchmark. If the benchmark was published as a paper, then proper bibtex will also be included.


  • Setup Virtual Environment: To avoid conflicts between different benchmarks, we strongly recommend that you setup a Python virtual environment for each benchmark separately. Please note that many benchmarks have a restriction on the Python version used.
virtualenv --system-site-packages -p python2 <virtual-env-name> # python 2.x
virtualenv --system-site-packages -p python3 <virtual-env-name> # python 3.x

source <virtual-env-name>/bin/activate # Activate the virtual environment.
  • Install the benchmark prerequisite:
cd <benchmark-name>/source/; pip install -r requirements.gpu-cu80.txt
  • Download the benchmark dataset:
cd <benchmark-name>/dataset/
chmod 700 ./download-<dataset_name>.sh
  • Use the script files to run or profile the benchmark:
cd <benchmark-name>/scripts/
chmod 700 ./<benchmark_name>-<dataset-name>.sh

Known Issues

  • The hyperparameters have been tuned specifically to fit into the GPU that has 8 GB memory. If you receive an error message that informs you of Out-Of-Memory error, please carefully check nvidia-smi and make sure that no one else is using the machine.