def test_dmlscheduler_arbitrary_scheduling(scheduler):
"""
Manually schedule events and check that all jobs are completed.
"""
model_json = make_model_json()
first = make_initialize_job(model_json)
second = make_initialize_job(model_json)
scheduler.add_job(first)
scheduler.add_job(second)
while len(scheduler.processed) == 0:
scheduler.runners_run_next_jobs()
third = make_initialize_job(model_json)
fourth = make_initialize_job(model_json)
scheduler.add_job(third)
scheduler.add_job(fourth)
while len(scheduler.processed) < 4:
scheduler.runners_run_next_jobs()
fifth = make_initialize_job(model_json)
scheduler.add_job(fifth)
while len(scheduler.processed) < 5:
scheduler.runners_run_next_jobs()
assert len(scheduler.processed) == 5, \
"Jobs {} failed/not completed in time!".format([
result.job.job_type for result in scheduler.processed])
while scheduler.processed:
output = scheduler.processed.pop(0)
initial_weights = output.results['weights']
assert type(initial_weights) == list
assert type(initial_weights[0]) == np.ndarray
def test_communication_manager_can_inform_new_job_to_the_optimizer(
config_manager, ipfs_client):
"""
Ensures that Communication Manager can tell the optimizer of something,
and that the job will transfer correctly.
"""
communication_manager = CommunicationManager()
scheduler = DMLScheduler(config_manager)
communication_manager.configure(scheduler)
scheduler.configure(communication_manager, ipfs_client)
true_job = make_initialize_job(make_model_json())
true_job.hyperparams['epochs'] = 10
true_job.hyperparams['batch_size'] = 128
true_job.hyperparams['split'] = .05
serialized_job = serialize_job(true_job)
new_session_event = {
TxEnum.KEY.name: None,
TxEnum.CONTENT.name: {
"optimizer_params": {},
"serialized_job": serialized_job
}
}
communication_manager.inform(MessageEventTypes.NEW_SESSION.name,
new_session_event)
optimizer_job = communication_manager.optimizer.job
assert optimizer_job.weights == true_job.weights
assert optimizer_job.serialized_model == true_job.serialized_model
assert optimizer_job.framework_type == true_job.framework_type
assert optimizer_job.hyperparams == true_job.hyperparams
assert optimizer_job.label_column_name == true_job.label_column_name
def split_dmlresult_obj(config_manager, mnist_filepath):
model_json = make_model_json()
runner = DMLRunner(config_manager)
split_job = make_split_job(
model_json,
mnist_filepath,
)
split_job.hyperparams['split'] = 0.75
job_results = runner.run_job(split_job)
return job_results
def train_dmlresult_obj(config_manager, split_dmlresult_obj,
init_dmlresult_obj):
runner = DMLRunner(config_manager)
initial_weights = init_dmlresult_obj.results['weights']
session_filepath = split_dmlresult_obj.results['session_filepath']
datapoint_count = split_dmlresult_obj.results['datapoint_count']
train_job = make_train_job(make_model_json(), initial_weights,
make_hyperparams(split=1), session_filepath,
datapoint_count)
result = runner.run_job(train_job)
return result
def split_dmlresult_obj(config_manager, mnist_uuid, dataset_manager):
model_json = make_model_json()
runner = DMLRunner(config_manager)
mnist_filepath = dataset_manager.get_mappings()[mnist_uuid]
split_job = make_split_job(
model_json,
mnist_filepath,
)
split_job.hyperparams['split'] = 0.75
job_results = runner.run_job(split_job)
print(job_results)
return job_results
def test_dmlscheduler_sanity(scheduler):
"""
Check that the scheduling/running functionality is maintained.
"""
model_json = make_model_json()
initialize_job = make_initialize_job(model_json)
scheduler.add_job(initialize_job)
scheduler.runners_run_next_jobs()
timeout = time.time
while not scheduler.processed:
time.sleep(0.1)
scheduler.runners_run_next_jobs()
output = scheduler.processed.pop(0)
initial_weights = output.results['weights']
assert type(initial_weights) == list
assert type(initial_weights[0]) == np.ndarray
def test_dmlrunner_transform_and_split( \
config_manager, small_filepath):
model_json = make_model_json()
runner = DMLRunner(config_manager)
split_job = make_split_job(model_json, small_filepath)
split_job.hyperparams['split'] = 0.75
job_results = runner.run_job(split_job)
session_filepath = job_results.results['session_filepath']
assert os.path.isdir(session_filepath), \
"Session folder does not exist!"
train_filepath = os.path.join(session_filepath, 'train.csv')
test_filepath = os.path.join(session_filepath, 'test.csv')
assert os.path.isfile(train_filepath) and os.path.isfile(test_filepath), \
"Training and test set not created!"
train = pd.read_csv(train_filepath)
test = pd.read_csv(test_filepath)
assert len(train) == 6 and len(test) == 2, \
"Train test split was not performed correctly."
def test_dmlscheduler_cron(scheduler):
"""
Test that the scheduler's cron works.
"""
model_json = make_model_json()
m = 2
for _ in range(m):
initialize_job = make_initialize_job(model_json)
scheduler.add_job(initialize_job)
scheduler.start_cron(period_in_mins=0.01)
timeout = time.time() + 6
while time.time() < timeout and len(scheduler.processed) != m:
time.sleep(1)
scheduler.stop_cron()
assert len(scheduler.processed) == m
while scheduler.processed:
output = scheduler.processed.pop(0)
initial_weights = output.results['weights']
assert type(initial_weights) == list
assert type(initial_weights[0]) == np.ndarray
def test_dmlrunner_validate_job_returns_stats( \
config_manager, mnist_filepath, train_dmlresult_obj):
model_json = make_model_json()
hyperparams = make_hyperparams()
runner = DMLRunner(config_manager)
job_results = train_dmlresult_obj
session_filepath = job_results.job.session_filepath
datapoint_count = job_results.job.datapoint_count
result = train_dmlresult_obj
assert result.status == 'successful'
results = result.results
new_weights = results['weights']
omega = results['omega']
train_stats = results['train_stats']
hyperparams['split'] = 1 - hyperparams['split']
validate_job = make_validate_job(model_json, new_weights, hyperparams,
session_filepath, datapoint_count)
result = runner.run_job(validate_job)
assert result.status == 'successful'
results = result.results
val_stats = results['val_stats']
assert result.job.job_type is JobTypes.JOB_VAL.name
assert type(val_stats) == dict
def test_dmlrunner_same_train_job_with_split_1( \
config_manager, mnist_filepath):
model_json = make_model_json()
hyperparams = make_hyperparams(split=1)
runner = DMLRunner(config_manager)
initialize_job = make_initialize_job(model_json)
initial_weights = runner.run_job(initialize_job).results['weights']
split_job = make_split_job(model_json, mnist_filepath)
job_results = runner.run_job(split_job)
session_filepath = job_results.results['session_filepath']
datapoint_count = job_results.results['datapoint_count']
train_job = make_train_job(model_json, initial_weights, hyperparams,
session_filepath, datapoint_count)
result = runner.run_job(train_job)
assert result.status == 'successful'
results = result.results
new_weights = results['weights']
omega = results['omega']
train_stats = results['train_stats']
assert result.job.job_type is JobTypes.JOB_TRAIN.name
assert type(new_weights) == list
assert type(new_weights[0]) == np.ndarray
assert type(omega) == int or type(omega) == float
assert type(train_stats) == dict
def init_dmlresult_obj(config_manager, small_filepath):
runner = DMLRunner(config_manager)
initialize_job = make_initialize_job(make_model_json(), small_filepath)
result = runner.run_job(initialize_job)
return result
def init_dmlresult_obj(config_manager, small_uuid, dataset_manager):
runner = DMLRunner(config_manager)
small_filepath = dataset_manager.get_mappings()[small_uuid]
initialize_job = make_initialize_job(make_model_json(), small_filepath)
result = runner.run_job(initialize_job)
return result
