Package to support simplified application of machine learning models to datasets in materials science
Project description
foundry
Foundry data environment repository for NSF CSSI jointly with UW-Madison
Installation
Foundry can be installed via pip with:
pip install foundry-ml
Example Usage
The Foundry client can be used to access datasets using a source_id, e.g. here "_test_foundry_fashion_mnist_v1.1":
from foundry import Foundry
f = Foundry()
f = f.load("_test_foundry_fashion_mnist_v1.1")
This will remotely load the necessary metadata as well as download the data to local storage if it is not already present. To ensure successful data download, have a Globus endpoint setup on your machine. Once the data is accessible locally, load the data into the client:
X, y = f.load_data()
The data is then usable:
n_cols = 6
display_shape = (28,28)
fig, ax = plt.subplots(1,n_cols)
for i in range(0, n_cols):
ax[i].imshow(X[i].reshape(display_shape), cmap='gray')
This example can be found in examples/fashion-mnist/.
Other uses
To just download the data without loading the additional metadata:
f = Foundry().download("_test_foundry_fashion_mnist_v1.1")
While it is strongly recommended to load metadata remotely, it can be done locally with a foundry_metadata.json file:
f = Foundry().from_file()
Primary Support
This work was supported by the National Science Foundation under NSF Award Number: 1931306 "Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure".
Other Support
Foundry brings together many components in the materials data ecosystem. Including MAST-ML, the Data and Learning Hub for Science (DLHub), and The Materials Data Facility (MDF).
MAST-ML
This work was supported by the National Science Foundation (NSF) SI2 award No. 1148011 and DMREF award number DMR-1332851
The Data and Learning Hub for Science (DLHub)
This material is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. https://www.dlhub.org
The Materials Data Facility
This work was performed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Material Design (CHiMaD). This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). This work was also supported by the National Science Foundation as part of the Midwest Big Data Hub under NSF Award Number: 1636950 "BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate". https://www.materialsdatafacility.org
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file foundry_ml-0.0.5.tar.gz.
File metadata
- Download URL: foundry_ml-0.0.5.tar.gz
- Upload date:
- Size: 15.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | bff01587c01b8db0aeccf4307e2ef81e32b3c49d9fc9203108d7474c34397f5a |
|
| MD5 | 69aa10317492adcc365656362d90481a |
|
| BLAKE2b-256 | d0c9bb864f33c5a36119fa680c02611b3778d8a0da5197138018fcd2055c0783 |
File details
Details for the file foundry_ml-0.0.5-py3-none-any.whl.
File metadata
- Download URL: foundry_ml-0.0.5-py3-none-any.whl
- Upload date:
- Size: 15.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d07467658ba50bf1ed8814dd055c853f4b0021dd12ce8b547610403fc0fdb14d |
|
| MD5 | d62063821464ac265037b577426f49ad |
|
| BLAKE2b-256 | 17922a42495ce556fdbea770ae14cc941538924fd09415b903a1ad46c887097e |
