def test_pytorch_load(collect_trial_profiles: Callable[[int], None]) -> None:
config = conf.load_config(
conf.fixtures_path("mnist_pytorch/const-pytorch11.yaml"))
config = conf.set_profiling_enabled(config)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.tutorials_path("mnist_pytorch"), 1)
(Determined(conf.make_master_url()).get_experiment(
experiment_id).top_checkpoint().load(map_location="cpu"))
trial_id = exp.experiment_trials(experiment_id)[0].trial.id
collect_trial_profiles(trial_id)
def test_pytorch_gan_parallel() -> None:
config = conf.load_config(
conf.gan_examples_path("gan_mnist_pytorch/const.yaml"))
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_slots_per_trial(config, 8)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.gan_examples_path("gan_mnist_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
(Determined(conf.make_master_url()).get_trial(
trials[0]["id"]).select_checkpoint(latest=True).load(
map_location="cpu"))
def test_pytorch_cifar10_const() -> None:
config = conf.load_config(
conf.official_examples_path("cifar10_cnn_pytorch/const.yaml"))
config = conf.set_max_steps(config, 2)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.official_examples_path("cifar10_cnn_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
nn = (Determined(conf.make_master_url()).get_trial(
trials[0].id).select_checkpoint(latest=True).load(
map_location=torch.device("cpu")))
assert isinstance(nn, torch.nn.Module)
def test_pytorch_load() -> None:
config = conf.load_config(conf.fixtures_path("mnist_pytorch/const-pytorch11.yaml"))
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.official_examples_path("trial/mnist_pytorch"), 1
)
(
Determined(conf.make_master_url())
.get_experiment(experiment_id)
.top_checkpoint()
.load(map_location="cpu")
)
def test_launch_layer_exit(
collect_trial_profiles: Callable[[int], None]) -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_pytorch/const.yaml"))
config = conf.set_entrypoint(
config, "python3 -m nonexistent_launch_module model_def:CIFARTrial")
experiment_id = exp.run_failure_test_with_temp_config(
config, conf.cv_examples_path("cifar10_pytorch"))
trials = exp.experiment_trials(experiment_id)
Determined(conf.make_master_url()).get_trial(trials[0].trial.id)
collect_trial_profiles(trials[0].trial.id)
assert exp.check_if_string_present_in_trial_logs(
trials[0].trial.id, "container failed with non-zero exit code: 1")
def test_pytorch_cifar10_parallel(
collect_trial_profiles: Callable[[int], None]) -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_pytorch/const.yaml"))
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_slots_per_trial(config, 8)
config = conf.set_profiling_enabled(config)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
(Determined(conf.make_master_url()).get_trial(
trials[0].trial.id).select_checkpoint(latest=True).load(
map_location="cpu"))
collect_trial_profiles(trials[0].trial.id)
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.uuid)
assert model_version.model_version == 1
latest_version = mnist.get_version()
assert latest_version is not None
assert latest_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 test_launch_layer_cifar(
collect_trial_profiles: Callable[[int], None]) -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_pytorch/const.yaml"))
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_slots_per_trial(config, 1)
config = conf.set_profiling_enabled(config)
config = conf.set_entrypoint(
config,
"python3 -m determined.launch.horovod --autohorovod --trial model_def:CIFARTrial"
)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
(Determined(conf.make_master_url()).get_trial(
trials[0].trial.id).select_checkpoint(latest=True).load(
map_location="cpu"))
collect_trial_profiles(trials[0].trial.id)
assert exp.check_if_string_present_in_trial_logs(
trials[0].trial.id,
"container exited successfully with a zero exit code")
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_end_to_end_adaptive() -> None:
exp_id = exp.run_basic_test(
conf.fixtures_path("mnist_pytorch/adaptive_short.yaml"),
conf.tutorials_path("mnist_pytorch"),
None,
)
# Check that validation accuracy look sane (more than 93% on MNIST).
trials = exp.experiment_trials(exp_id)
best = None
for trial in trials:
assert len(trial["steps"])
last_step = trial["steps"][-1]
accuracy = last_step["validation"]["metrics"]["validation_metrics"][
"accuracy"]
if not best or accuracy > best:
best = accuracy
assert best is not None
assert best > 0.93
# Check that ExperimentReference returns a sorted order of top checkpoints
# without gaps. The top 2 checkpoints should be the first 2 of the top k
# checkpoints if sorting is stable.
d = Determined(conf.make_master_url())
exp_ref = d.get_experiment(exp_id)
top_2 = exp_ref.top_n_checkpoints(2)
top_k = exp_ref.top_n_checkpoints(len(trials))
top_2_uuids = [c.uuid for c in top_2]
top_k_uuids = [c.uuid for c in top_k]
assert top_2_uuids == top_k_uuids[:2]
# Check that metrics are truly in sorted order.
metrics = [
c.validation["metrics"]["validationMetrics"]["validation_loss"]
for c in top_k
]
assert metrics == sorted(metrics)
# Check that changing smaller is better reverses the checkpoint ordering.
top_k_reversed = exp_ref.top_n_checkpoints(len(trials),
sort_by="validation_loss",
smaller_is_better=False)
top_k_reversed_uuids = [c.uuid for c in top_k_reversed]
assert top_k_uuids == top_k_reversed_uuids[::-1]
checkpoint = top_k[0]
checkpoint.add_metadata({"testing": "metadata"})
db_check = d.get_checkpoint(checkpoint.uuid)
# Make sure the checkpoint metadata is correct and correctly saved to the db.
assert checkpoint.metadata == {"testing": "metadata"}
assert checkpoint.metadata == db_check.metadata
checkpoint.add_metadata({"some_key": "some_value"})
db_check = d.get_checkpoint(checkpoint.uuid)
assert checkpoint.metadata == {
"testing": "metadata",
"some_key": "some_value"
}
assert checkpoint.metadata == db_check.metadata
checkpoint.add_metadata({"testing": "override"})
db_check = d.get_checkpoint(checkpoint.uuid)
assert checkpoint.metadata == {
"testing": "override",
"some_key": "some_value"
}
assert checkpoint.metadata == db_check.metadata
checkpoint.remove_metadata(["some_key"])
db_check = d.get_checkpoint(checkpoint.uuid)
assert checkpoint.metadata == {"testing": "override"}
assert checkpoint.metadata == db_check.metadata
def test_end_to_end_adaptive() -> None:
exp_id = exp.run_basic_test(
conf.fixtures_path("mnist_pytorch/adaptive_short.yaml"),
conf.tutorials_path("mnist_pytorch"),
None,
)
# Check that validation accuracy look sane (more than 93% on MNIST).
trials = exp.experiment_trials(exp_id)
best = None
for trial in trials:
assert len(trial.workloads) > 0
last_validation = exp.workloads_with_validation(trial.workloads)[-1]
accuracy = last_validation.metrics["accuracy"]
if not best or accuracy > best:
best = accuracy
assert best is not None
assert best > 0.93
# Check that ExperimentReference returns a sorted order of top checkpoints
# without gaps. The top 2 checkpoints should be the first 2 of the top k
# checkpoints if sorting is stable.
d = Determined(conf.make_master_url())
exp_ref = d.get_experiment(exp_id)
top_2 = exp_ref.top_n_checkpoints(2)
top_k = exp_ref.top_n_checkpoints(
len(trials), sort_by="validation_loss", smaller_is_better=True
)
top_2_uuids = [c.uuid for c in top_2]
top_k_uuids = [c.uuid for c in top_k]
assert top_2_uuids == top_k_uuids[:2]
# Check that metrics are truly in sorted order.
assert all(c.training is not None for c in top_k)
metrics = [
c.training.validation_metrics["avgMetrics"]["validation_loss"]
for c in top_k
if c.training is not None
]
assert metrics == sorted(metrics)
# Check that changing smaller is better reverses the checkpoint ordering.
top_k_reversed = exp_ref.top_n_checkpoints(
len(trials), sort_by="validation_loss", smaller_is_better=False
)
top_k_reversed_uuids = [c.uuid for c in top_k_reversed]
assert top_k_uuids == top_k_reversed_uuids[::-1]
checkpoint = top_k[0]
checkpoint.add_metadata({"testing": "metadata"})
db_check = d.get_checkpoint(checkpoint.uuid)
# Make sure the checkpoint metadata is correct and correctly saved to the db.
# Beginning with 0.18 the system contributes a few items to the dict
assert checkpoint.metadata.get("testing") == "metadata"
assert checkpoint.metadata.keys() == {"format", "framework", "steps_completed", "testing"}
assert checkpoint.metadata == db_check.metadata
checkpoint.add_metadata({"some_key": "some_value"})
db_check = d.get_checkpoint(checkpoint.uuid)
assert checkpoint.metadata.items() > {"testing": "metadata", "some_key": "some_value"}.items()
assert checkpoint.metadata.keys() == {
"format",
"framework",
"steps_completed",
"testing",
"some_key",
}
assert checkpoint.metadata == db_check.metadata
checkpoint.add_metadata({"testing": "override"})
db_check = d.get_checkpoint(checkpoint.uuid)
assert checkpoint.metadata.items() > {"testing": "override", "some_key": "some_value"}.items()
assert checkpoint.metadata == db_check.metadata
checkpoint.remove_metadata(["some_key"])
db_check = d.get_checkpoint(checkpoint.uuid)
assert "some_key" not in checkpoint.metadata
assert checkpoint.metadata["testing"] == "override"
assert checkpoint.metadata == db_check.metadata
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"]
def main():
parser = argparse.ArgumentParser(description='Run Determined Example')
parser.add_argument('experiment_id',
type=str,
help='path to context directory')
parser.add_argument('model_name',
type=str,
help='path to context directory')
args = parser.parse_args()
checkpoint = Determined().get_experiment(
args.experiment_id).top_checkpoint()
metric, smaller_is_better = get_validation_metric(checkpoint)
models = Determined().get_models(name=args.model_name)
model = None
for m in models:
if m.name == args.model_name:
model = m
break
if not model:
print(f'Registering new Model: {args.model_name}')
model = Determined().create_model(args.model_name)
model.register_version(checkpoint.uuid)
better = True
else:
latest_version = model.get_version()
if latest_version is None:
print(f'Registering new version: {args.model_name}')
model.register_version(checkpoint.uuid)
better = True
else:
old_metric, _ = get_validation_metric(latest_version)
if smaller_is_better:
if metric < old_metric:
print(f'Registering new version: {args.model_name}')
model.register_version(checkpoint.uuid)
better = True
else:
better = False
else:
if metric > old_metric:
print(f'Registering new version: {args.model_name}')
model.register_version(checkpoint.uuid)
better = True
else:
better = False
if not better:
print('Previous model version was better, logging...')
# Write experiment id to output file
with open('/tmp/decision.txt', 'w') as f:
if better:
f.write('yes')
else:
f.write('no')
def decide(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']
if 'validation_metrics' in metrics:
metric = metrics['validation_metrics'][metric_name]
else:
metric = metrics['validationMetrics'][metric_name]
return (metric, smaller_is_better)
d = Determined()
checkpoint = d.get_experiment(experiment_id).top_checkpoint()
metric, smaller_is_better = get_validation_metric(checkpoint)
models = d.get_models(name=model_name)
model = None
for m in models:
if m.name == model_name:
model = m
break
if not model:
print(f'Registering new Model: {model_name}')
model = Determined().create_model(model_name)
model.register_version(checkpoint.uuid)
better = True
else:
latest_version = model.get_version()
if latest_version is None:
print(f'Registering new version: {model_name}')
model.register_version(checkpoint.uuid)
better = True
else:
old_metric, _ = get_validation_metric(latest_version)
if smaller_is_better:
if metric < old_metric:
print(f'Registering new version: {model_name}')
model.register_version(checkpoint.uuid)
better = True
else:
better = False
else:
if metric > old_metric:
print(f'Registering new version: {model_name}')
model.register_version(checkpoint.uuid)
better = True
else:
better = False
if not better:
print('Previous model version was better, logging...')
return better