Learn how to interact with this file using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Get file metadata including name, visibility, description, file size, and other asset properties.
Get a URL to download or embed the file. For private assets, the URL is temporary and will expire after 1 hour.
Update file metadata (name, description, visibility, etc.) and optionally replace the file data with a new file. Requires write or admin permission.
Permanently delete a file from the platform. Requires admin permission. This action cannot be undone.
import os
from ouro import Ouro
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
file_id = "97c3797e-4e07-465b-87ae-4ea8c80a75a8"
# Retrieve file metadata
file = ouro.files.retrieve(file_id)
print(file.name, file.visibility)
print(file.metadata)# Get signed URL to download the file
file_data = file.read_data()
print(file_data.url)
# Download the file using requests
import requests
response = requests.get(file_data.url)
with open('downloaded_file', 'wb') as output_file:
output_file.write(response.content)# Update file metadata
updated = ouro.files.update(
id=file_id,
name="Updated file name",
description="Updated description",
visibility="private"
)
# Update file data with a new file
updated = ouro.files.update(
id=file_id,
file_path="./new_file.txt"
)# Delete a file (requires admin permission)
ouro.files.delete(id=file_id)Speed + max GPU memory allocated on an NVIDIA H200 for the computation of energies, forces and stress. The batch size is fixed to 1, but authors vary the number of atoms across the subplots. Relative times are computed with respect to the fastest model: orb-v3 Direct (20 neighbors). Times include both model inference and graph construction, with the latter marked by hatched lines. The graph construction method for Orb is a function of the number of atoms, as described in Appendix D. A key takeaway from this figure is that extreme scalability requires a confluence of i) efficient graph construction ii) Finite max neighbors iii) Non-conservative direct predictions. For the baselines, the authors use mace-medium-mpa-0 (v0.3.10, cuequivariance-torch v0.1.0), mattersim-v1.0.0-5m (v1.1.2), 7net-mf-ompa (v0.11.0). All models are benchmarked using PyTorch v2.6.0+cu124.
explains how to pick from eight Orb-v3 models that balance accuracy, speed, and memory for atomistic simulations. The post breaks down model names (orb-v3-X-Y-Z), where X is how forces are computed, Y is neighbor limits, and Z is the training dataset (omat or mpa). It compares conservative vs direct force calculations, unlimited vs limited neighbors, and AIMD-based -omat versus MPTraj/Alexandria-based -mpa models. Readers gain practical guidance for phonon calculations, geometry optimization, and molecular dynamics, including which models excel at energy conservation, speed, or large-scale simulations. The piece also covers workflow tips, performance at scale, and licensing (Apache 2.0). Use this guide to choose the right Orb-v3 model for your system size and research goals.