def test_static():
# Make an example static method
model = PythonStaticMethodModel.create_model('numpy', 'max', autobatch=False,
function_kwargs={'axis': 0})
model.set_title('Example function')
model.set_name('function')
model.set_inputs('ndarray', 'Matrix', shape=[None, None])
model.set_outputs('ndarray', 'Max of a certain axis', shape=[None])
# Make the servable
servable = PythonStaticMethodServable(**model.to_dict())
# Test it
out, _ = servable.run([[1, 2], [3, 4]])
assert np.isclose([3, 4], out).all()
# Test giving it parameters
assert np.isclose([2, 4], servable.run([[1, 2], [3, 4]], parameters=dict(axis=1))[0]).all()
# Test the autobatch
model.servable.methods['run'].method_details['autobatch'] = True
servable = PythonStaticMethodServable(**model.to_dict())
out, _ = servable.run([[1, 2], [3, 4]])
assert np.isclose([2, 4], out).all()
def test_visibility():
model = PythonStaticMethodModel.create_model('numpy.linalg', 'norm')
model.set_name('1d_norm')
model.set_title('Norm of a 1D Array')
model.set_inputs('ndarray', 'Array to be normed', shape=[None])
model.set_outputs('number', 'Norm of the array')
model.set_visibility(users=['bec215bc-9169-4be9-af49-4872b5e11ef8'
]) # Setting visibility to a user
validate_against_dlhub_schema(model.to_dict(), 'servable')
assert model.dlhub.visible_to[0].startswith('urn:globus:auth:identity:')
model.set_visibility(groups=['fdb38a24-03c1-11e3-86f7-12313809f035'
]) # Setting visibility to a group
validate_against_dlhub_schema(model.to_dict(), 'servable')
assert len(
model.dlhub.visible_to) == 1 # Ensure was replaced, not appended
assert model.dlhub.visible_to[0].startswith('urn:globus:groups:id:')
model.set_visibility(users=['foo']) # Test using a non-UUID for user
with raises(ValidationError):
validate_against_dlhub_schema(model.to_dict(), 'servable')
model.set_visibility() # Default visibility is "public"
validate_against_dlhub_schema(model.to_dict(), 'servable')
assert model.dlhub.visible_to == ['public']
def test_submit(self):
# Make an example function
model = PythonStaticMethodModel.create_model('numpy.linalg', 'norm')
model.set_name('1d_norm')
model.set_title('Norm of a 1D Array')
model.set_inputs('ndarray', 'Array to be normed', shape=[None])
model.set_outputs('number', 'Norm of the array')
# Submit the model
self.dl.publish_servable(model)
def test_loader(self):
# Make an example static method
model = PythonStaticMethodModel.create_model(
'numpy', 'max', autobatch=False, function_kwargs={'axis': 0})
model.set_title('Example function')
model.set_name('function')
model.set_inputs('ndarray', 'Matrix', shape=[None, None])
model.set_outputs('ndarray', 'Max of a certain axis', shape=[None])
# Test the loader
servable = create_servable(model.to_dict())
self.assertIsInstance(servable, PythonStaticMethodServable)
self.assertEqual([0], servable.run([0, -1])[0])
def test_pipeline(self):
"""Make a pipeline composed of two numpy steps"""
# Generate the two steps
step1 = PythonStaticMethodModel.create_model('numpy', 'max', function_kwargs={'axis': 1})\
.set_name('step1')
step2 = PythonStaticMethodModel.create_model('numpy',
'mean').set_name('step2')
# Make the pipeline
pipeline = PipelineModel().set_title(
'Average of Column Maximums').set_name('numpy_test')
pipeline.add_step('username', step1.name, 'Maximum of each column',
{'axis': 0})
pipeline.add_step('username', step2.name, 'Average of the maximums')
# Generate the pipeline metadata
metadata = pipeline.to_dict()
correct_metadata = {
'datacite': {
'creators': [],
'titles': [{
'title': 'Average of Column Maximums'
}],
'publisher': 'DLHub',
'publicationYear': _year,
'identifier': {
'identifier': '10.YET/UNASSIGNED',
'identifierType': 'DOI'
},
'resourceType': {
'resourceTypeGeneral': 'InteractiveResource'
},
"descriptions": [],
"fundingReferences": [],
"relatedIdentifiers": [],
"alternateIdentifiers": [],
"rightsList": []
},
'dlhub': {
'version': __version__,
'domains': [],
'visible_to': ['public'],
'name': 'numpy_test',
'type': 'pipeline',
'files': {}
},
'pipeline': {
'steps': [{
'author': 'username',
'name': step1.name,
'description': 'Maximum of each column',
'parameters': {
'axis': 0
}
}, {
'author': 'username',
'name': step2.name,
'description': 'Average of the maximums'
}]
}
}
self.assertEqual(metadata, correct_metadata)
validate_against_dlhub_schema(metadata, 'pipeline')
from dlhub_sdk.models.servables.python import PythonStaticMethodModel
from dlhub_sdk.utils.schemas import validate_against_dlhub_schema
import json
import os
# Create a model that invokes the "run" function from the
model = PythonStaticMethodModel.create_model('application', 'run')
# Describe the inputs and outputs
model.set_inputs('list',
'Paths to all images in a dataset',
item_type='string')
model.set_outputs('ndarray',
'Accumulated result of decoding all the images',
shape=[208, 208])
# Add provenance information
model.set_title("Deep-Learning Super-resolution Image Reconstruction (DSIR)")
model.set_name('dsir')
model.set_authors(['Duarte, Alex'], ['The Institute of Photonic Sciences'])
model.add_alternate_identifier(
"https://github.com/leaxp/Deep-Learning-Super-Resolution-Image-Reconstruction-DSIR",
'URL')
# Add requirements
model.add_requirement('torch', 'detect')
model.add_requirement('torchvision', 'detect')
# Add app.py, which holds the noop function, to the list of associated files to be submitted
model.add_file("app.py")
from dlhub_sdk.models.servables.python import PythonStaticMethodModel
from dlhub_sdk.utils.schemas import validate_against_dlhub_schema
from dlhub_sdk.utils.types import compose_argument_block
import json
import os
# Create a model that invokes the "run" function from the
model = PythonStaticMethodModel.create_model('app',
'run',
function_kwargs={'relax': False})
# Describe the inputs and outputs
model.set_inputs('string', 'Molecule in XYZ format')
model.set_outputs(
'dict',
'Forces and energies of the molecule',
properties={
'energy':
compose_argument_block('number', 'Energy of the whole system'),
'forces':
compose_argument_block('ndarray',
'Forces acting on each atom in each direction',
shape=[None, 3])
})
# Add provenance information
model.set_title("SchNet C20 Force and Energy Predictor")
model.set_name('schnet_c20')
model.set_domains(['physics'])
model.set_abstract(
"A model based on the SchNet architecture that predicts the energy and forces of a C20 molecule. Useful for molecular dynmaics simulations."