def test_model_registry() -> None:
exp_id = exp.run_basic_test(
conf.fixtures_path("mnist_pytorch/const-pytorch11.yaml"),
conf.official_examples_path("trial/mnist_pytorch"),
None,
)
d = Determined(conf.make_master_url())
mnist = d.create_model("mnist", "simple computer vision model")
assert mnist.metadata == {}
mnist.add_metadata({"testing": "metadata"})
assert mnist.metadata == {"testing": "metadata"}
mnist.add_metadata({"some_key": "some_value"})
assert mnist.metadata == {"testing": "metadata", "some_key": "some_value"}
mnist.add_metadata({"testing": "override"})
assert mnist.metadata == {"testing": "override", "some_key": "some_value"}
mnist.remove_metadata(["some_key"])
assert mnist.metadata == {"testing": "override"}
checkpoint = d.get_experiment(exp_id).top_checkpoint()
model_version = mnist.register_version(checkpoint)
assert model_version == 1
assert mnist.get_version().uuid == checkpoint.uuid
d.create_model("transformer", "all you need is attention")
d.create_model("object-detection", "a bounding box model")
models = d.get_models(sort_by=ModelSortBy.NAME)
assert [m.name
for m in models] == ["mnist", "object-detection", "transformer"]
def register(detmaster: str, experiment_id: int, model_name: str) -> bool:
# Submit determined experiment via CLI
from determined.experimental import Determined
import os
os.environ['DET_MASTER'] = detmaster
def get_validation_metric(checkpoint):
metrics = checkpoint.validation['metrics']
config = checkpoint.experiment_config
searcher = config['searcher']
smaller_is_better = bool(searcher['smaller_is_better'])
metric_name = searcher['metric']
metric = metrics['validationMetrics'][metric_name]
return (metric, smaller_is_better)
def is_better(c1, c2):
m1, smaller_is_better = get_validation_metric(c1)
m2, _ = get_validation_metric(c2)
if smaller_is_better and m1 < m2:
return True
return False
d = Determined()
checkpoint = d.get_experiment(experiment_id).top_checkpoint()
try:
model = d.get_model(model_name)
except: # Model not yet in registry
print(f'Registering new Model: {model_name}')
model = d.create_model(model_name)
print(f'Registering new version: {model_name}')
model.register_version(checkpoint.uuid)
return True
def test_model_registry() -> None:
d = Determined(conf.make_master_url())
mnist = d.create_model("mnist", "simple computer vision model")
assert mnist.metadata == {}
mnist.add_metadata({"testing": "metadata"})
assert mnist.metadata == {"testing": "metadata"}
mnist.add_metadata({"some_key": "some_value"})
assert mnist.metadata == {"testing": "metadata", "some_key": "some_value"}
mnist.add_metadata({"testing": "override"})
assert mnist.metadata == {"testing": "override", "some_key": "some_value"}
mnist.remove_metadata(["some_key"])
assert mnist.metadata == {"testing": "override"}
d.create_model("transformer", "all you need is attention")
d.create_model("object-detection", "a bounding box model")
models = d.get_models(sort_by=ModelSortBy.NAME)
assert [m.name
for m in models] == ["mnist", "object-detection", "transformer"]
def test_model_registry() -> None:
exp_id = exp.run_basic_test(
conf.fixtures_path("mnist_pytorch/const-pytorch11.yaml"),
conf.tutorials_path("mnist_pytorch"),
None,
)
d = Determined(conf.make_master_url())
mnist = None
objectdetect = None
tform = None
try:
# Create a model and validate twiddling the metadata.
mnist = d.create_model("mnist",
"simple computer vision model",
labels=["a", "b"])
assert mnist.metadata == {}
mnist.add_metadata({"testing": "metadata"})
db_model = d.get_model(mnist.name)
# Make sure the model metadata is correct and correctly saved to the db.
assert mnist.metadata == db_model.metadata
assert mnist.metadata == {"testing": "metadata"}
# Confirm we can look up a model by its ID
db_model = d.get_model_by_id(mnist.model_id)
assert db_model.name == "mnist"
db_model = d.get_model(mnist.model_id)
assert db_model.name == "mnist"
# Confirm DB assigned username
assert db_model.username == "determined"
mnist.add_metadata({"some_key": "some_value"})
db_model = d.get_model(mnist.name)
assert mnist.metadata == db_model.metadata
assert mnist.metadata == {
"testing": "metadata",
"some_key": "some_value"
}
mnist.add_metadata({"testing": "override"})
db_model = d.get_model(mnist.name)
assert mnist.metadata == db_model.metadata
assert mnist.metadata == {
"testing": "override",
"some_key": "some_value"
}
mnist.remove_metadata(["some_key"])
db_model = d.get_model(mnist.name)
assert mnist.metadata == db_model.metadata
assert mnist.metadata == {"testing": "override"}
mnist.set_labels(["hello", "world"])
db_model = d.get_model(mnist.name)
assert mnist.labels == db_model.labels
assert db_model.labels == ["hello", "world"]
# confirm patch does not overwrite other fields
mnist.set_description("abcde")
db_model = d.get_model(mnist.name)
assert db_model.metadata == {"testing": "override"}
assert db_model.labels == ["hello", "world"]
# overwrite labels to empty list
mnist.set_labels([])
db_model = d.get_model(mnist.name)
assert db_model.labels == []
# archive and unarchive
assert mnist.archived is False
mnist.archive()
db_model = d.get_model(mnist.name)
assert db_model.archived is True
mnist.unarchive()
db_model = d.get_model(mnist.name)
assert db_model.archived is False
# Register a version for the model and validate the latest.
checkpoint = d.get_experiment(exp_id).top_checkpoint()
model_version = mnist.register_version(checkpoint.uuid)
assert model_version.model_version == 1
latest_version = mnist.get_version()
assert latest_version is not None
assert latest_version.checkpoint.uuid == checkpoint.uuid
latest_version.set_name("Test 2021")
db_version = mnist.get_version()
assert db_version is not None
assert db_version.name == "Test 2021"
latest_version.set_notes("# Hello Markdown")
db_version = mnist.get_version()
assert db_version is not None
assert db_version.notes == "# Hello Markdown"
# Run another basic test and register its checkpoint as a version as well.
# Validate the latest has been updated.
exp_id = exp.run_basic_test(
conf.fixtures_path("mnist_pytorch/const-pytorch11.yaml"),
conf.tutorials_path("mnist_pytorch"),
None,
)
checkpoint = d.get_experiment(exp_id).top_checkpoint()
model_version = mnist.register_version(checkpoint.uuid)
assert model_version.model_version == 2
latest_version = mnist.get_version()
assert latest_version is not None
assert latest_version.checkpoint.uuid == checkpoint.uuid
# Ensure the correct number of versions are present.
all_versions = mnist.get_versions()
assert len(all_versions) == 2
# Test deletion of model version
latest_version.delete()
all_versions = mnist.get_versions()
assert len(all_versions) == 1
# Create some more models and validate listing models.
tform = d.create_model("transformer", "all you need is attention")
objectdetect = d.create_model("ac - Dc", "a test name model")
models = d.get_models(sort_by=ModelSortBy.NAME)
assert [m.name for m in models] == ["ac - Dc", "mnist", "transformer"]
# Test model labels combined
mnist.set_labels(["hello", "world"])
tform.set_labels(["world", "test", "zebra"])
labels = d.get_model_labels()
assert labels == ["world", "hello", "test", "zebra"]
# Test deletion of model
tform.delete()
tform = None
models = d.get_models(sort_by=ModelSortBy.NAME)
assert [m.name for m in models] == ["ac - Dc", "mnist"]
finally:
# Clean model registry of test models
for model in [mnist, objectdetect, tform]:
if model is not None:
model.delete()
def test_model_registry() -> None:
exp_id = exp.run_basic_test(
conf.fixtures_path("mnist_pytorch/const-pytorch11.yaml"),
conf.tutorials_path("mnist_pytorch"),
None,
)
d = Determined(conf.make_master_url())
# Create a model and validate twiddling the metadata.
mnist = d.create_model("mnist", "simple computer vision model")
assert mnist.metadata == {}
mnist.add_metadata({"testing": "metadata"})
db_model = d.get_model("mnist")
# Make sure the model metadata is correct and correctly saved to the db.
assert mnist.metadata == db_model.metadata
assert mnist.metadata == {"testing": "metadata"}
mnist.add_metadata({"some_key": "some_value"})
db_model = d.get_model("mnist")
assert mnist.metadata == db_model.metadata
assert mnist.metadata == {"testing": "metadata", "some_key": "some_value"}
mnist.add_metadata({"testing": "override"})
db_model = d.get_model("mnist")
assert mnist.metadata == db_model.metadata
assert mnist.metadata == {"testing": "override", "some_key": "some_value"}
mnist.remove_metadata(["some_key"])
db_model = d.get_model("mnist")
assert mnist.metadata == db_model.metadata
assert mnist.metadata == {"testing": "override"}
# Register a version for the model and validate the latest.
checkpoint = d.get_experiment(exp_id).top_checkpoint()
model_version = mnist.register_version(checkpoint.uuid)
assert model_version.model_version == 1
latest_version = mnist.get_version()
assert latest_version is not None
assert latest_version.uuid == checkpoint.uuid
# Run another basic test and register its checkpoint as a version as well.
# Validate the latest has been updated.
exp_id = exp.run_basic_test(
conf.fixtures_path("mnist_pytorch/const-pytorch11.yaml"),
conf.tutorials_path("mnist_pytorch"),
None,
)
checkpoint = d.get_experiment(exp_id).top_checkpoint()
model_version = mnist.register_version(checkpoint.uuid)
assert model_version.model_version == 2
latest_version = mnist.get_version()
assert latest_version is not None
assert latest_version.uuid == checkpoint.uuid
# Ensure the correct number of versions are present.
all_versions = mnist.get_versions()
assert len(all_versions) == 2
# Create some more models and validate listing models.
d.create_model("transformer", "all you need is attention")
d.create_model("object-detection", "a bounding box model")
models = d.get_models(sort_by=ModelSortBy.NAME)
assert [m.name
for m in models] == ["mnist", "object-detection", "transformer"]
