ARcode: HPC Application Recognition Through Image-encoded Monitoring Data

This tar file contains the docker image for building the ARcode model and baseline models for application recognition for the SC22 paper with the same title. The files/folders in this image contains: notebooks: The notebooks for models and experiment results.-- ARcode.ipynb: The interactive Jupyter Notebook for the ARcode model.-- ARcode_unknown.ipynb: The interactive Jupyter Notebook for the ARcode model for detecting unknown applications.-- ARcode_partial.ipynb: The interactive Jupyter Notebook for the ARcode model on partial job signatures.-- ARcode_channel.ipynb: The interactive Jupyter Notebook for the ARcode model on one channel of job signatures.-- baselines.ipynb: The interactive Jupyter Notebook for the baseline models. These models are Random Forest, LinearSVC and SVC; all of them are implemented through Taxonomist(https://doi.org/10.6084/m9.figshare.6384248.v1).-- baselines_unknown.ipynb: The interactive Jupyter Notebook for the baseline models for detecting unknown applications. dataset: The dataset for training the models mentioned above.-- ARcode_labels.npy: A numpy array of the signatures' labels.-- ARcode_signatures.npy: A numpy array of the generated signatures.-- baseline_labels.npy: A numpy array of the labels for the baseline dataset.-- baseline_features.npy: A numpy array of the statistic features generated from the raw monitoring data.-- knl_app_code.json: Mapping of IDs to application names. This mapping is used when creating the dataset. models: The saved models.-- arcode.h5: An HDF5 file containing the serialized weights for the ARcode model.-- arcode.json: A JSON file describing the ARcode model. results: The saved experiment results. Following these steps to start Jupyter Notebook in the image: 1. Load the image into Docker on your local machine:docker load < archive-arcode.tar 2. Start the Jupyter notebook in the docker image:docker run --init --user root -p 8888:8888 artlands/arcode 3. Copy the URL shown in your terminal and paste in a brower: http://127.0.0.1:8888/?token=your_token Acknowledgement: This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231