This repository is the wrapper for the open-source implementation of the tool perf4sight proposed in the paper perf4sight: A toolflow to model CNN training performance on Edge GPUs published at the Workshop on "Embeeded and Real-World Computer Vision in Autonomous Driving" at ICCV 2021. If you reference this work in a publication, please cite it through the following citation:

@misc{rajagopal2021perf4sight,
      title={perf4sight: A toolflow to model CNN training performance on Edge GPUs}, 
      author={Aditya Rajagopal and Christos-Savvas Bouganis},
      year={2021},
      eprint={2108.05580},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

git submodule update --init --recursive

This will pull the submodules perf4sight and perf4sight/pruners which are required to use the tool.

This repo is just a wrapper around the perf4sight repo which implements the actual tool.

• main.py implements the basic interface that reads the configurations from the config.ini file and calls the relevant functions within perf4sight.
• models/imagenet contains various PyTorch model description files
• models/pruned contains the pruned versions of the models that were found in models/imagenet
• profiling_csvs/ contains csvs with profiled latency and memory consumption for various networks on the Tx2
• fingerprinting/ stores the various performance models that are developed by the tool
• non_tx2_env.yaml is the miniconda3 environment required to run the files on this project on a non-arm core (Tx2) device
• tx2_requirements.txt is the virtual env requirements (to be installed through pip) for running the code on an ARM core device. This requires a special installation of PyTorch, but the rest of the packages can be installed through pip.

This prunes a all the networks specified in the create_network_dataset section of config.ini and stores them in [model_path]/imagenet. [model_desc_dir] is the location on your machine where the model description files are stored. Refer to the README in perf4sight/pruners on how to prune a custom model. Decorators need to be placed on the model description file in order to do so.

This performs the profiling on the target device and is controlled by the parameters in the profile section of the config.ini. The create_config part of the section can be run on any machine and creates a config file in a format understood by the profilers present in perf4sight/profiling/tx2/ and perf4sight/profiling/2080ti. The config files are created in perf4sight/profiling/profiling_configs. The [device_model_loc] parameter is a path to the model description files that are found in [local_model_loc] but on the target device.

Following this step, the model description files need to be copied over to the location in [device_model_loc] on the target device, and the generated test config should be copied over to the location specified in [test_config] on the target device.

The run_profiling section has to be run on the target device and the resulting data will be stored in [log_dir] on the target device. The [till_first_fail] option causes the profiler to stop profiling after the first network configuration that can"t be run on that device without running out of memory.

IMPORTANT: In order to run profiling, the environment variables CUDA_VISIBLE_DEVICES and PROFILING needs to be set.

CUDA_VISIBLE_DEVICES="0,1" PROFILING=1 python main.py --config-file config.ini

This performs the modelling using decision trees and is controlled by the parameters in fingerprint in config.ini. The csvs with profiled data that were generated on the target device should be copied to the paths specified in [memory_logs] and [latency_logs]. This repo has profiling csvs for the training stage for a range of networks on the NVIDIA Jetson TX2. When training, data from all the networks present in the list of [train_nets] is used and like wise with the evaluation. The [eval_memory_logs] section is only relevant if the location of the profiling csvs used to train the decision tree is different from those used to test it.