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Reproduce experimental results

Before running the experiments, download FOSI's source code:

git clone https://github.com/hsivan/fosi

Let fosi_root be the root folder of the project on your local computer. Make sure to add fosi_root to PYTHONPATH:

export PYTHONPATH=$PYTHONPATH:<fosi_root>

Download external dataset

Download the external dataset used in the AC (MobileNetV1 on AudioSet data) experiment:

  1. Change directory into the AudioSet dataset folder: cd <fosi_root>/experiments/audioset_dataset
  2. Download the train_wav folder, class_labels_indices.csv file, and train.csv file from https://www.kaggle.com/datasets/zfturbo/audioset
  3. Download the valid_wav folder and valid.csv file from https://www.kaggle.com/datasets/zfturbo/audioset-valid
  4. Run the script that converts the wav files to melspectogram images: python convert_to_melspectogram.py

The script runs for ~1 hour and creates two folders, train_jpg and valid_jpg, with melspectogram images.

Installation

Running the experiments could be done using a Docker container or directly. If using Docker container, ignore the following installation instructions and see instructions for Docker. Otherwise, CUDA toolkit and other packages must be installed manually.

CUDA toolkit

To run FOSI with GPU, CUDA toolkit must be installed. If using conda environment, the installation command is:

conda install -c "nvidia/label/cuda-11.8.0" cuda

Otherwise, a global installation is required:

sudo apt-get install cuda-11-8

After installing CUDA toolkit, follow NVIDIA's environment setup instructions to set the environment variables PATH and LD_LIBRARY_PATH. To find the lib/bin folders in case of conda environment use find ~ -name 'libcusolver.so.11' and in case of a global installation with apt-get find /usr/ -name 'libcusolver.so.11' and use the containing folder.

Note: CUDA toolkit installation is not required when using the Docker container to run the experiments, or if running on the CPU.

Experiment requirements

To install the requirements for the experiments run:

cd <fosi_root>
pip install -r experiments/experiments_requirements.txt -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Quadratic functions

In these experiments we minimize quadratic functions. Run these experiments from within the experiments/quadratic folder:

cd <fosi_root>/experiments/quadratic

Run the following three scripts:

python quadratic_jax_random_ortho_basis_gd.py
python quadratic_jax_random_ortho_basis.py
python quadratic_jax_kappa_zeta.py

After running all the experiments, run the following to generate the figures:

cd <fosi_root>/experiments/visualization
python plot_quadratic.py

The figures can be found under <fosi_root>/experiments/visualization/figures.

Deep neural networks

In these experiments we train DNNs with standard datasets. Run these experiments from within the experiments/dnn folder:

cd <fosi_root>/experiments/dnn

Run the following three scripts (can run in parallel to save time):

python logistic_regression_mnist.py
python transfer_learning_cifar10.py
python autoencoder_cifar10.py
python rnn_shakespeare.py
python mobilenet_audioset.py

python logistic_regression_mnist_kfac_lbfgs.py
python transfer_learning_cifar10_kfac_lbfgs.py
python autoencoder_cifar10_kfac_lbfgs.py
python mobilenet_audioset_kfac_lbfgs.py

After running all the experiments, run the following to generate the figures:

cd <fosi_root>/experiments/visualization
python plot_dnn.py

The figures can be found under <fosi_root>/experiments/visualization/figures.

To generate loss and accuracy summary tables run:

cd <fosi_root>/experiments/visualization
python generate_result_summary.py

The script generates *_summary.csv files with the relevant information under <fosi_root>/experiments/visualization/result_summary.

Run as a docker container

We provide Dockerfile to support building the project as a docker image. To build the docker image you must first install docker engine and docker cli, and set up the Nvidia container-toolkit. After installing these, run the command to build the docker image from within fosi_root:

cd <fosi_root>
sudo docker build -f experiments/experiments.Dockerfile -t fosi_experiment .

This docker image could be used to run the different experiments.

Quadratic functions

The docker supports running the experiments quadratic_jax_random_ortho_basis_gd, quadratic_jax_random_ortho_basis, and quadratic_jax_kappa_zeta. For example, to run the quadratic_jax_random_ortho_basis_gd execute the following commands

cd <fosi_root>
export experiment=quadratic_jax_random_ortho_basis_gd
export local_result_dir=$(pwd)"/experiments/quadratic/test_results"
export docker_result_dir="/app/experiments/test_results"
sudo docker run --gpus all -v ${local_result_dir}:${docker_result_dir} --rm fosi_experiment python3 /app/experiments/quadratic/${experiment}.py

The result folders and files can be found in the same location as running the experiments without Docker, under <fosi_root>/experiments/quadratic.

Deep neural networks

The docker supports running the experiments logistic_regression_mnist, transfer_learning_cifar10, autoencoder_cifar10, rnn_shakespeare, mobilenet_audioset, logistic_regression_mnist_kfac, transfer_learning_cifar10_kfac, autoencoder_cifar10_kfac, and mobilenet_audioset_kfac. For example, to run the logistic_regression_mnist execute the following commands

cd <fosi_root>
export experiment=logistic_regression_mnist
export local_result_dir=$(pwd)"/experiments/dnn/test_results_"${experiment}
export docker_result_dir="/app/experiments/test_results_"${experiment}
sudo docker run --gpus all -v ${local_result_dir}:${docker_result_dir} --rm fosi_experiment python3 /app/experiments/dnn/${experiment}.py

The result folders and files can be found in the same location as running the experiments without Docker, under <fosi_root>/experiments/dnn.

Figures and summary tables

To generate figures run:

export local_result_dir=$(pwd)"/experiments/visualization/figures"
export docker_result_dir="/app/experiments/figures"
sudo docker run -v ${local_result_dir}:${docker_result_dir} --rm fosi_experiment python3 /app/experiments/visualization/plot_dnn.py /app/experiments/dnn
sudo docker run -v ${local_result_dir}:${docker_result_dir} --rm fosi_experiment python3 /app/experiments/visualization/plot_quadratic.py /app/experiments/quadratic

The figures can be found under <fosi_root>/experiments/visualization/figures.

To generate loss and accuracy summary tables run:

export local_result_dir=$(pwd)"/experiments/visualization/result_summary"
export docker_result_dir="/app/experiments/result_summary"
sudo docker run -v ${local_result_dir}:${docker_result_dir} --rm fosi_experiment python3 /app/experiments/visualization/generate_result_summary.py /app/experiments/dnn

The summary tables *_summary.csv can be found under <fosi_root>/experiments/visualization/result_summary.