def test_mnist_estimator_warm_start(tf2: bool) -> None:
config = conf.load_config(
conf.fixtures_path("mnist_estimator/single.yaml"))
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
experiment_id1 = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mnist_estimator"), 1)
trials = exp.experiment_trials(experiment_id1)
assert len(trials) == 1
first_trial = trials[0]
first_trial_id = first_trial["id"]
assert len(first_trial["steps"]) == 1
first_checkpoint_id = first_trial["steps"][0]["checkpoint"]["id"]
config_obj = conf.load_config(
conf.fixtures_path("mnist_estimator/single.yaml"))
config_obj["searcher"]["source_trial_id"] = first_trial_id
config_obj = conf.set_tf2_image(config_obj) if tf2 else conf.set_tf1_image(
config_obj)
experiment_id2 = exp.run_basic_test_with_temp_config(
config_obj, conf.cv_examples_path("mnist_estimator"), 1)
trials = exp.experiment_trials(experiment_id2)
assert len(trials) == 1
assert trials[0]["warm_start_checkpoint_id"] == first_checkpoint_id
def test_mnist_estimator_warm_start(tf2: bool) -> None:
config = conf.load_config(
conf.fixtures_path("mnist_estimator/single.yaml"))
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
experiment_id1 = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mnist_estimator"), 1)
trials = exp.experiment_trials(experiment_id1)
assert len(trials) == 1
first_trial = trials[0]
first_trial_id = first_trial.trial.id
assert len(first_trial.workloads) == 3
checkpoint_workloads = exp.workloads_with_checkpoint(first_trial.workloads)
first_checkpoint_uuid = checkpoint_workloads[0].uuid
config_obj = conf.load_config(
conf.fixtures_path("mnist_estimator/single.yaml"))
config_obj["searcher"]["source_trial_id"] = first_trial_id
config_obj = conf.set_tf2_image(config_obj) if tf2 else conf.set_tf1_image(
config_obj)
experiment_id2 = exp.run_basic_test_with_temp_config(
config_obj, conf.cv_examples_path("mnist_estimator"), 1)
trials = exp.experiment_trials(experiment_id2)
assert len(trials) == 1
assert trials[0].trial.warmStartCheckpointUuid == first_checkpoint_uuid
def test_tf_keras_const_warm_start(tf2: bool) -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_tf_keras/const.yaml"))
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_min_validation_period(config, {"batches": 1000})
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
experiment_id1 = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1)
trials = exp.experiment_trials(experiment_id1)
assert len(trials) == 1
first_trial = trials[0]
first_trial_id = first_trial["id"]
assert len(first_trial["steps"]) == 2
first_checkpoint_id = first_trial["steps"][1]["checkpoint"]["id"]
# Add a source trial ID to warm start from.
config["searcher"]["source_trial_id"] = first_trial_id
experiment_id2 = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1)
# The new trials should have a warm start checkpoint ID.
trials = exp.experiment_trials(experiment_id2)
assert len(trials) == 1
for trial in trials:
assert trial["warm_start_checkpoint_id"] == first_checkpoint_id
def test_cifar10_pytorch_distributed() -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_pytorch/distributed.yaml"))
config = conf.set_max_length(config, {"batches": 200})
exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_pytorch"), 1)
def test_unets_tf_keras_distributed() -> None:
config = conf.load_config(
conf.cv_examples_path("unets_tf_keras/distributed.yaml"))
config = conf.set_max_length(config, {"batches": 200})
exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("unets_tf_keras"), 1)
def test_mnist_estimator_distributed() -> None:
config = conf.load_config(
conf.cv_examples_path("mnist_estimator/distributed.yaml"))
config = conf.set_max_length(config, {"batches": 200})
exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mnist_estimator"), 1)
def test_mnist_pytorch_multi_output() -> None:
config = conf.load_config(
conf.cv_examples_path("mnist_multi_output_pytorch/const.yaml"))
config = conf.set_max_length(config, {"batches": 200})
exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mnist_multi_output_pytorch"), 1)
def test_pl_mnist() -> None:
exp_dir = "mnist_pl"
config = conf.load_config(conf.cv_examples_path(exp_dir + "/const.yaml"))
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_tf2_image(config)
exp.run_basic_test_with_temp_config(config, conf.cv_examples_path(exp_dir), 1)
def test_tf_keras_const_warm_start(
tf2: bool, collect_trial_profiles: Callable[[int], None]) -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_tf_keras/const.yaml"))
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_min_validation_period(config, {"batches": 1000})
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
config = conf.set_profiling_enabled(config)
experiment_id1 = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1)
trials = exp.experiment_trials(experiment_id1)
assert len(trials) == 1
first_trial = trials[0]
first_trial_id = first_trial.trial.id
assert len(first_trial.workloads) == 4
checkpoints = exp.workloads_with_checkpoint(first_trial.workloads)
first_checkpoint_uuid = checkpoints[0].uuid
# Add a source trial ID to warm start from.
config["searcher"]["source_trial_id"] = first_trial_id
experiment_id2 = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1)
# The new trials should have a warm start checkpoint ID.
trials = exp.experiment_trials(experiment_id2)
assert len(trials) == 1
for t in trials:
assert t.trial.warmStartCheckpointUuid != ""
assert t.trial.warmStartCheckpointUuid == first_checkpoint_uuid
trial_id = trials[0].trial.id
collect_trial_profiles(trial_id)
def test_deformabledetr_coco_pytorch_const() -> None:
config = conf.load_config(
conf.cv_examples_path("deformabledetr_coco_pytorch/const_fake.yaml"))
config = conf.set_max_length(config, {"batches": 200})
exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("deformabledetr_coco_pytorch"), 1)
def test_cifar10_byol_pytorch_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("byol_pytorch/const-cifar10.yaml"))
# Limit convergence time, since was running over 30 minute limit.
config["searcher"]["max_length"]["epochs"] = 20
config["hyperparameters"]["classifier"]["train_epochs"] = 1
config = conf.set_random_seed(config, 1591280374)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("byol_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0].trial.id)
validation_accuracies = [
step["validation"]["metrics"]["validation_metrics"]["test_accuracy"]
for step in trial_metrics["steps"] if step.get("validation")
]
# Accuracy reachable within limited convergence time -- goes higher given full training.
target_accuracy = 0.40
assert max(validation_accuracies) > target_accuracy, (
"cifar10_byol_pytorch did not reach minimum target accuracy {} in {} steps."
" full validation accuracy history: {}".format(
target_accuracy, len(trial_metrics["steps"]),
validation_accuracies))
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,
"allocation stopped after resources exited successfully with a zero exit code",
)
def test_mmdetection_pytorch_const() -> None:
config = conf.load_config(
conf.cv_examples_path("mmdetection_pytorch/const_fake_data.yaml"))
config = conf.set_max_length(config, {"batches": 200})
exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mmdetection_pytorch"), 1)
def test_detr_coco_pytorch_distributed() -> None:
config = conf.load_config(
conf.cv_examples_path("detr_coco_pytorch/const_fake.yaml"))
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_slots_per_trial(config, 2)
exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("detr_coco_pytorch"), 1)
def test_tf_keras_single_gpu(tf2: bool) -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_tf_keras/const.yaml"))
config = conf.set_slots_per_trial(config, 1)
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1)
trials = exp.experiment_trials(experiment_id)
assert len(trials) == 1
def test_pytorch_cifar10_parallel() -> 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)
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]["id"]).select_checkpoint(latest=True).load(
map_location="cpu"))
def test_tf_keras_parallel(aggregation_frequency: int, tf2: bool) -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_tf_keras/const.yaml"))
config = conf.set_slots_per_trial(config, 8)
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_aggregation_frequency(config, aggregation_frequency)
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1)
trials = exp.experiment_trials(experiment_id)
assert len(trials) == 1
def test_unets_tf_keras_distributed() -> None:
config = conf.load_config(conf.cv_examples_path("unets_tf_keras/distributed.yaml"))
config = conf.set_max_length(config, {"batches": 200})
download_dir = "/tmp/data"
url = "https://s3-us-west-2.amazonaws.com/determined-ai-datasets/oxford_iiit_pet/oxford_iiit_pet.tar.gz" # noqa
with tempfile.TemporaryDirectory() as tmpdir:
copy_destination = os.path.join(tmpdir, "example")
shutil.copytree(conf.cv_examples_path("unets_tf_keras"), copy_destination)
with open(os.path.join(copy_destination, "startup-hook.sh"), "a") as f:
f.write("\n")
f.write(f"wget -O /tmp/data.tar.gz {url}\n")
f.write(f"mkdir {download_dir}\n")
f.write(f"tar -xzvf /tmp/data.tar.gz -C {download_dir}\n")
exp.run_basic_test_with_temp_config(config, copy_destination, 1)
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_tf_keras_single_gpu(
tf2: bool, collect_trial_profiles: Callable[[int], None]) -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_tf_keras/const.yaml"))
config = conf.set_slots_per_trial(config, 1)
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
config = conf.set_profiling_enabled(config)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1)
trials = exp.experiment_trials(experiment_id)
assert len(trials) == 1
# Test exporting a checkpoint.
export_and_load_model(experiment_id)
collect_trial_profiles(trials[0].trial.id)
def test_mnist_tf_layers_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("mnist_tf_layers/const.yaml"))
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mnist_tf_layers"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_errors = [
step["validation"]["metrics"]["validation_metrics"]["error"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_error = 0.04
assert min(validation_errors) < target_error, (
"mnist_estimator did not reach minimum target error {} in {} steps."
" full validation error history: {}".format(
target_error, len(trial_metrics["steps"]), validation_errors))
def test_mnist_estimator_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("mnist_estimator/const.yaml"))
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mnist_estimator"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_accuracies = [
step["validation"]["metrics"]["validation_metrics"]["accuracy"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_accuracy = 0.95
assert max(validation_accuracies) > target_accuracy, (
"mnist_estimator did not reach minimum target accuracy {} in {} steps."
" full validation accuracy history: {}".format(
target_accuracy, len(trial_metrics["steps"]),
validation_accuracies))
def test_fasterrcnn_coco_pytorch_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("fasterrcnn_coco_pytorch/const.yaml"))
config = conf.set_random_seed(config, 1590497309)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("fasterrcnn_coco_pytorch"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_iou = [
step["validation"]["metrics"]["validation_metrics"]["val_avg_iou"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_iou = 0.42
assert max(validation_iou) > target_iou, (
"fasterrcnn_coco_pytorch did not reach minimum target accuracy {} in {} steps."
" full validation avg_iou history: {}".format(
target_iou, len(trial_metrics["steps"]), validation_iou))
def test_mnist_estimator_const_parallel(tf2: bool) -> None:
config = conf.load_config(
conf.fixtures_path("mnist_estimator/single-multi-slot.yaml"))
config = conf.set_slots_per_trial(config, 8)
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
config = conf.set_perform_initial_validation(config, True)
exp_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mnist_estimator"), 1)
exp.assert_performed_initial_validation(exp_id)
def test_mnist_estimator_load() -> None:
config = conf.load_config(
conf.fixtures_path("mnist_estimator/single.yaml"))
config = conf.set_tf1_image(config)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("mnist_estimator"), 1)
trials = exp.experiment_trials(experiment_id)
model = Determined(conf.make_master_url()).get_trial(
trials[0]["id"]).top_checkpoint().load()
assert isinstance(model, AutoTrackable)
def test_unets_tf_keras_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("unets_tf_keras/const.yaml"))
config = conf.set_random_seed(config, 1591280374)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("unets_tf_keras"), 1)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0]["id"])
validation_accuracies = [
step["validation"]["metrics"]["validation_metrics"]["val_accuracy"]
for step in trial_metrics["steps"] if step.get("validation")
]
target_accuracy = 0.85
assert max(validation_accuracies) > target_accuracy, (
"unets_tf_keras did not reach minimum target accuracy {} in {} steps."
" full validation accuracy history: {}".format(
target_accuracy, len(trial_metrics["steps"]),
validation_accuracies))
def test_cifar10_tf_keras_accuracy() -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_tf_keras/const.yaml"))
config = conf.set_random_seed(config, 1591110586)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1, None, 6000)
trials = exp.experiment_trials(experiment_id)
trial_metrics = exp.trial_metrics(trials[0].trial.id)
validation_accuracies = [
step["validation"]["metrics"]["validation_metrics"]
["val_categorical_accuracy"] for step in trial_metrics["steps"]
if step.get("validation")
]
target_accuracy = 0.73
assert max(validation_accuracies) > target_accuracy, (
"cifar10_pytorch did not reach minimum target accuracy {} in {} steps."
" full validation accuracy history: {}".format(
target_accuracy, len(trial_metrics["steps"]),
validation_accuracies))
def test_tf_keras_parallel(
aggregation_frequency: int, tf2: bool,
collect_trial_profiles: Callable[[int], None]) -> None:
config = conf.load_config(
conf.cv_examples_path("cifar10_tf_keras/const.yaml"))
config = conf.set_slots_per_trial(config, 8)
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_aggregation_frequency(config, aggregation_frequency)
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
config = conf.set_profiling_enabled(config)
experiment_id = exp.run_basic_test_with_temp_config(
config, conf.cv_examples_path("cifar10_tf_keras"), 1)
trials = exp.experiment_trials(experiment_id)
assert len(trials) == 1
# Test exporting a checkpoint.
export_and_load_model(experiment_id)
collect_trial_profiles(trials[0].trial.id)
# Check on record/batch counts we emitted in logs.
validation_size = 10000
global_batch_size = config["hyperparameters"]["global_batch_size"]
num_workers = config.get("resources", {}).get("slots_per_trial", 1)
global_batch_size = config["hyperparameters"]["global_batch_size"]
scheduling_unit = config.get("scheduling_unit", 100)
per_slot_batch_size = global_batch_size // num_workers
exp_val_batches = (validation_size +
(per_slot_batch_size - 1)) // per_slot_batch_size
patterns = [
# Expect two copies of matching training reports.
f"trained: {scheduling_unit * global_batch_size} records.*in {scheduling_unit} batches",
f"trained: {scheduling_unit * global_batch_size} records.*in {scheduling_unit} batches",
f"validated: {validation_size} records.*in {exp_val_batches} batches",
]
exp.assert_patterns_in_trial_logs(trials[0].trial.id, patterns)
def test_mnist_estimmator_const_parallel(native_parallel: bool,
tf2: bool) -> None:
if tf2 and native_parallel:
pytest.skip("TF2 native parallel training is not currently supported.")
config = conf.load_config(
conf.fixtures_path("mnist_estimator/single-multi-slot.yaml"))
config = conf.set_slots_per_trial(config, 8)
config = conf.set_native_parallel(config, native_parallel)
config = conf.set_max_length(config, {"batches": 200})
config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
config = conf.set_perform_initial_validation(config, True)
exp_id = exp.run_basic_test_with_temp_config(
config,
conf.cv_examples_path("mnist_estimator"),
1,
has_zeroth_step=True)
exp.assert_performed_initial_validation(exp_id)
def test_invalid_experiment() -> None:
completed_process = exp.maybe_create_experiment(
conf.fixtures_path("invalid_experiment/const.yaml"),
conf.cv_examples_path("mnist_tf"))
assert completed_process.returncode != 0
