PicoProbeDataFlow

Data flow infrastructure for Argonne National Laboratory PicoProbe.

Preprint: https://arxiv.org/abs/2308.13701

Dataset

• The datasets used in this study can be downloaded here: https://zenodo.org/record/8284219
• The machine learning nanoparticle prediciton dataset: https://app.roboflow.com/picoprobe/nanoparticle-detection/1

For convenience, here is a command to download the zenodo dataset. It is a 1.32 GB tarball:

wget https://zenodo.org/record/8284219/files/data.tar.gz

Installation

On macOS, Linux:

python3 -m venv env
source env/bin/activate
pip3 install -U pip setuptools wheel
pip3 install -r requirements/dev.txt
pip3 install -r requirements/requirements.txt
pip3 install -e .

On Windows, see docs/windows_setup.md.

Globus Transfer setup

For running locally, install Globus Personal Connect.

Globus Compute setup

Configure a Globus Compute Endpoint locally:

globus-compute-endpoint list
globus-compute-endpoint start AlexsMacBookPro-06-2023

Note: Replace AlexsMacBookPro-06-2023 with the name of your endpoint.

Note: To setup Globus Compute on Polaris@ALCF, see docs/polaris_setup.md.

Globus Search Index setup

PicoProbe uses Globus Search to index data. To configure Globus Search, run:

globus-search index --help
globus-search login
globus-search index list
globus-search index create picoprobe_test picoprobe_testing
globus-search index show [search-index-uuid]

Now give Globus group members permission to write to the search index:

globus-search index role create --type group <search-index-uuid> writer <group-uuid>

Usage

You will need to collect the UUIDs for the Globus tools (Transfer, Compute, Search) and add them to a configuration YAML file. The Transfer tool also requires the the absolute path (and Globus-relative) of the Globus endpoint you are using. For an example configuration, please see:

• For the Hyperspectral flow: examples/hyperspectral_flow/config/macbook_test.yaml
• For the Spatiotemporal flow: examples/spatiotemporal_flow/config/macbook_to_polaris_compute.yaml

For detailed usage, please follow the instructions in docs/windows_setup.md.

Note: The Windows-specific commands can be ignored if you are running on a different system.

We also provide instructions to generate flow runtime statistics in docs/windows_setup.md.

Training the Machine Learning Model

The easiest way to reproduce the nanoparticle detection model is to make an accont on Roboflow and then run our notebook in Google Colab:

Note: Make sure to add your Roboflow API key on the code cell with the YOUR_API_KEY placeholder.

If you are interested in buidling the dataset manually, please see examples/xloop2023/machine_learning/build_yolov8_dataset.ipynb for how to convert the EMD data files to PNG images for YOLOv8 compatibilty.

Django Globus Portal Framework (DGPF) Data Portal

We provide an interactive visualization of our experimental results using the source code at this repository.

Citation

If you use our work in your research, please cite this paper:

@inproceedings{10.1145/3624062.3624614,
author = {Brace, Alexander and Vescovi, Rafael and Chard, Ryan and Saint, Nickolaus D. and Ramanathan, Arvind and Zaluzec, Nestor J. and Foster, Ian},
title = {Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers},
year = {2023},
isbn = {9798400707858},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3624062.3624614},
doi = {10.1145/3624062.3624614},
abstract = {The Dynamic PicoProbe at Argonne National Laboratory is undergoing upgrades that will enable it to produce up to 100s of GB of data per day. While this data is highly important for both fundamental science and industrial applications, there is currently limited on-site infrastructure to handle these high-volume data streams. We address this problem by providing a software architecture capable of supporting large-scale data transfers to the neighboring supercomputers at the Argonne Leadership Computing Facility. To prepare for future scientific workflows, we implement two instructive use cases for hyperspectral and spatiotemporal datasets, which include: (i) off-site data transfer, (ii) machine learning/artificial intelligence and traditional data analysis approaches, and (iii) automatic metadata extraction and cataloging of experimental results. This infrastructure supports expected workloads and also provides domain scientists the ability to reinterrogate data from past experiments to yield additional scientific value and derive new insights.},
booktitle = {Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis},
pages = {2140–2146},
numpages = {7},
keywords = {data flow, AI, ML, automated science, HPC},
location = {Denver, CO, USA},
series = {SC-W '23}
}