A machine learning model-sharing specification based on STAC MLM and Safetensors.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for ContrerasNK99 from gravatar.com ContrerasNK99 Avatar for csaybar from gravatar.com csaybar Avatar for ingridAybar from gravatar.com ingridAybar

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers
Report project as malware

Project description

MLSTAC: Machine Learning with STAC

PyPI version Python Versions License Documentation

We take advantage of the new mlm STAC extension to provide a unified interface for working with machine learning models. Experimental

Installation

pip install mlstac

Quick Start

import mlstac
import matplotlib.pyplot as plt


# Download model
file="https://huggingface.co/tacofoundation/supers2/resolve/main/CNN_Light_SR/mlm.json"
output_dir="models2/CNN_Light_SR"
mlstac.download(file, output_dir)

# Create a mlstac object
mlstac_object = mlstac.load(output_dir)
device = "cpu" # "cpu"

# Load model
#srmodel = mlstac_object.trainable_model() # for fine-tuning
srmodel = mlstac_object.compiled_model(device=device) # for benchmarking

# Load Demo Data
lr, hr = mlstac_object.example_data()

# Inference
sr = srmodel(lr.to(device))


# Plot
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
ax[0].imshow(lr[0, 0:3].permute(1, 2, 0)*3)
ax[0].set_title("Low Resolution")
ax[1].imshow(hr[0, 0:3].permute(1, 2, 0)*3)
ax[1].set_title("High Resolution")
ax[2].imshow(sr[0, 0:3].permute(1, 2, 0)*3)
ax[2].set_title("Super Resolution")
plt.show()


# Fast plot
fig = mlstac_object.display_results()
plt.show()

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page