# LTFB is evaluating on the correct models.
tol = 1e-4
for step in range(_num_epochs - 1):
for trainer in range(num_trainers):
partner = ltfb_partners[trainer][step]
winner = ltfb_winners[trainer][step]
local_val = tournament_metrics[trainer][2 * step]
partner_val = tournament_metrics[trainer][2 * step + 1]
winner_val = validation_metrics[trainer][step + 1]
true_local_val = validation_metrics[trainer][step]
true_partner_val = validation_metrics[partner][step]
true_winner_val = validation_metrics[winner][step]
assert true_local_val-tol < local_val < true_local_val+tol, \
'Incorrect metric value for LTFB local model'
assert true_partner_val-tol < partner_val < true_partner_val+tol, \
'Incorrect metric value for LTFB partner model'
assert true_winner_val-tol < winner_val < true_winner_val+tol, \
'Incorrect metric value for LTFB winner model'
# Return test function from factory function
func.__name__ = test_name
return func
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(setup_experiment,
__file__,
nodes=2,
lbann_args='--procs_per_trainer=2'):
globals()[_test_func.__name__] = augment_test_func(_test_func)
The Python data reader will import the current Python file to
access the sample access functions.
Args:
lbann (module): Module for LBANN Python frontend
"""
# Note: The training data reader should be removed when
# https://github.com/LLNL/lbann/issues/1098 is resolved.
message = lbann.reader_pb2.DataReader()
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'train')
])
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'test')
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
for test in tools.create_tests(setup_experiment, __file__):
globals()[test.__name__] = test
err = 0
err_dirs = ''
fileList = glob.glob('{base}/trainer0/*'.format(base=checkpoint_ckpt))
fileList, tmp_err, tmp_err_str = tools.multidir_diff(
checkpoint_ckpt, restart_ckpt, fileList)
err += tmp_err
err_dirs += tmp_err_str
err_msg = "\nUnmatched checkpoints:\n"
for f in fileList:
err_msg += f + "\n"
assert len(fileList) == 0, \
'Extra checkpoint data in baseline directory: ' + err_msg
assert err == 0, err_dirs
# Return test function from factory function
func.__name__ = test_name
return func
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(
setup_experiment,
__file__,
test_name_base=test_name_base,
nodes=num_nodes,
work_subdir='baseline',
lbann_args=['--disable_cuda',
' --num_epochs=' + str(num_ckpt_epochs)]):
globals()[_test_func.__name__] = create_test_func(_test_func)
def func(cluster, dirname, weekly):
# Run LBANN experiment baseline
print(
'\n################################################################################'
)
print('Running model halfway ')
print(
'################################################################################\n'
)
baseline_test_output = test_func(cluster, dirname)
baseline_training_metrics = tools.collect_metrics_from_log_func(
baseline_test_output['stdout_log_file'],
'training epoch [0-9]+ objective function')
baseline_validation_metrics = tools.collect_metrics_from_log_func(
baseline_test_output['stdout_log_file'],
'validation objective function')
baseline_test_metrics = tools.collect_metrics_from_log_func(
baseline_test_output['stdout_log_file'], 'test objective function')
# Run LBANN model to checkpoint
print(
'\n################################################################################'
)
print('Running model to checkpointed weights')
print(
'################################################################################\n'
)
test_func_checkpoint = tools.create_tests(
setup_experiment,
__file__,
test_name_base=test_name_base,
nodes=num_nodes,
work_subdir='reload_weights_from_checkpoint',
lbann_args=[
'--disable_cuda', '--num_epochs=' + str(num_restart_epochs),
'--load_model_weights_dir=' +
os.path.join(baseline_test_output['work_dir'], checkpoint_dir,
'trainer0')
],
)
checkpoint_test_output = test_func_checkpoint[0](cluster, dirname)
checkpoint_training_metrics = tools.collect_metrics_from_log_func(
checkpoint_test_output['stdout_log_file'],
'training epoch [0-9]+ objective function')
checkpoint_validation_metrics = tools.collect_metrics_from_log_func(
checkpoint_test_output['stdout_log_file'],
'validation objective function')
checkpoint_test_metrics = tools.collect_metrics_from_log_func(
checkpoint_test_output['stdout_log_file'],
'test objective function')
print(
'\n################################################################################'
)
print('Running model from save_model weights')
print(
'################################################################################\n'
)
test_func_restart = tools.create_tests(
setup_experiment,
__file__,
test_name_base=test_name_base,
nodes=num_nodes,
work_subdir='reload_weights_from_save_model_cb',
lbann_args=[
'--disable_cuda', '--num_epochs=' + str(num_restart_epochs),
'--load_model_weights_dir=' +
os.path.join(baseline_test_output['work_dir'], save_model_dir,
'trainer0', 'model0/'),
'--load_model_weights_dir_is_complete'
],
)
# Restart LBANN model and run to completion
restart_test_output = test_func_restart[0](cluster, dirname)
restart_training_metrics = tools.collect_metrics_from_log_func(
restart_test_output['stdout_log_file'],
'training epoch [0-9]+ objective function')
restart_validation_metrics = tools.collect_metrics_from_log_func(
restart_test_output['stdout_log_file'],
'validation objective function')
restart_test_metrics = tools.collect_metrics_from_log_func(
restart_test_output['stdout_log_file'], 'test objective function')
print(
'\n################################################################################'
)
print('Comparing results of models')
print(
'################################################################################\n'
)
# Check if metrics are same in baseline and test experiments
# Note: "Print statistics" callback will print up to 6 digits
# of metric values.
# Comparing training objective functions
tools.compare_metrics(checkpoint_training_metrics,
restart_training_metrics)
# Comparing validation objective functions
tools.compare_metrics(checkpoint_validation_metrics,
restart_validation_metrics)
# Comparing test objective functions
tools.compare_metrics(checkpoint_test_metrics, restart_test_metrics)
baseline_ckpt = os.path.join(baseline_test_output['work_dir'],
checkpoint_dir)
checkpoint_ckpt = os.path.join(checkpoint_test_output['work_dir'],
checkpoint_dir)
restart_ckpt = os.path.join(restart_test_output['work_dir'],
checkpoint_dir)
err = 0
err_dirs = ''
fileList = glob.glob('{base}/trainer0/*'.format(base=checkpoint_ckpt))
fileList, tmp_err, tmp_err_str = tools.multidir_diff(
checkpoint_ckpt, restart_ckpt, fileList)
err += tmp_err
err_dirs += tmp_err_str
err_msg = "\nUnmatched checkpoints:\n"
for f in fileList:
err_msg += f + "\n"
assert len(fileList) == 0, \
'Extra checkpoint data in baseline directory: ' + err_msg
assert err == 0, err_dirs
#Only tested on Ray. Skip if mini-batch test on another cluster. Change this when mini-batch values are available for other clusters
# Check if mini-batch time is within expected range
# Note: Skip first epoch since its runtime is usually an outlier
mini_batch_times = mini_batch_times[1:]
mini_batch_time = sum(mini_batch_times) / len(mini_batch_times)
assert (0.75 * expected_mini_batch_times[cluster]
< mini_batch_time
< 1.25 * expected_mini_batch_times[cluster]), \
'average mini-batch time is outside expected range'
# Check for GPU usage and memory leaks
# Note: Skip first epoch
gpu_usages = gpu_usages[1:]
gpu_usage = sum(gpu_usages)/len(gpu_usages)
assert (0.75 * expected_gpu_usage[cluster]
< gpu_usage
< 1.25 * expected_gpu_usage[cluster]),\
'average gpu usage is outside expected range'
# Return test function from factory function
func.__name__ = test_name
return func
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(setup_experiment,
__file__,
lbann_args=['--num_io_threads=1'],
nodes=compute_nodes):
globals()[_test_func.__name__] = augment_test_func(_test_func)
Args:
lbann (module): Module for LBANN Python frontend
"""
# Note: The training data reader should be removed when
# https://github.com/LLNL/lbann/issues/1098 is resolved.
message = lbann.reader_pb2.DataReader()
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'train')
])
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'test')
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
# Note: Create test name by removing ".py" from file name
_test_name = os.path.splitext(os.path.basename(current_file))[0]
for test in tools.create_tests(setup_experiment,
_test_name,
environment=tools.get_distconv_environment()):
globals()[test.__name__] = test
The Python data reader will import the current Python file to
access the sample access functions.
Args:
lbann (module): Module for LBANN Python frontend
"""
# Note: The training data reader should be removed when
# https://github.com/LLNL/lbann/issues/1098 is resolved.
message = lbann.reader_pb2.DataReader()
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'train')
])
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'test')
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
for test in tools.create_tests(setup_experiment, __file__, procs_per_node=4):
globals()[test.__name__] = test
The Python data reader will import the current Python file to
access the sample access functions.
Args:
lbann (module): Module for LBANN Python frontend
"""
# Note: The training data reader should be removed when
# https://github.com/LLNL/lbann/issues/1098 is resolved.
message = lbann.reader_pb2.DataReader()
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'train')
])
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'test')
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(setup_experiment, __file__):
globals()[_test_func.__name__] = _test_func
fileList = glob.glob('{base}/trainer0/*'.format(base=baseline_ckpt))
fileList, tmp_err, tmp_err_str = tools.multidir_diff(
baseline_ckpt, restart_ckpt, fileList)
err += tmp_err
err_dirs += tmp_err_str
fileList, tmp_err, tmp_err_str = tools.multidir_diff(
baseline_ckpt, checkpoint_ckpt, fileList)
err += tmp_err
err_dirs += tmp_err_str
err_msg = "\nUnmatched checkpoints:\n"
for f in fileList:
err_msg += f + "\n"
assert len(fileList) == 0, \
'Extra checkpoint data in baseline directory: ' + err_msg
assert err == 0, err_dirs
# Return test function from factory function
func.__name__ = test_name
return func
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(setup_experiment,
__file__,
test_name_base=test_name_base,
nodes=num_nodes,
work_subdir='baseline',
lbann_args=['--disable_cuda=True']):
globals()[_test_func.__name__] = create_test_func(_test_func)
'train reconstruction error is outside expected range'
# Check if testing reconstruction is within expected range
assert (expected_test_pc_range[0]
< test_pc
< expected_test_pc_range[1]), \
'test reconstruction error is outside expected range'
# Check if mini-batch time is within expected range
# Note: Skip first epoch since its runtime is usually an outlier
mini_batch_times = mini_batch_times[1:]
mini_batch_time = sum(mini_batch_times) / len(mini_batch_times)
assert (0.75 * expected_mini_batch_times[cluster]
< mini_batch_time
< 1.25 * expected_mini_batch_times[cluster]), \
'average mini-batch time is outside expected range'
# Return test function from factory function
func.__name__ = test_name
return func
m_lbann_args = f"--use_data_store --preload_data_store --metadata={metadata_prototext}"
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(setup_experiment,
__file__,
lbann_args=[m_lbann_args],
procs_per_node=procs_per_node,
nodes=num_nodes):
globals()[_test_func.__name__] = augment_test_func(_test_func)
access the sample access functions.
Args:
lbann (module): Module for LBANN Python frontend
"""
# Note: The training data reader should be removed when
# https://github.com/LLNL/lbann/issues/1098 is resolved.
message = lbann.reader_pb2.DataReader()
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'train')
])
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'test')
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
# Note: Create test name by removing ".py" from file name
_test_name = os.path.splitext(os.path.basename(current_file))[0]
for _test_func in tools.create_tests(setup_experiment, _test_name):
globals()[_test_func.__name__] = _test_func
Args:
lbann (module): Module for LBANN Python frontend
"""
# Note: The training data reader should be removed when
# https://github.com/LLNL/lbann/issues/1098 is resolved.
message = lbann.reader_pb2.DataReader()
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'train')
])
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'test')
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
### @todo Run on >1 proc when https://github.com/LLNL/lbann/issues/1548 is resolved
for _test_func in tools.create_tests(setup_experiment,
__file__,
procs_per_node=1,
nodes=1):
globals()[_test_func.__name__] = _test_func
tools.create_python_data_reader(
lbann,
current_file,
'get_sample',
'num_samples',
'sample_dims',
'train'
)
])
message.reader.extend([
tools.create_python_data_reader(
lbann,
current_file,
'get_sample',
'num_samples',
'sample_dims',
'test'
)
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
# Note: Create test name by removing ".py" from file name
_test_name = os.path.splitext(os.path.basename(current_file))[0]
for test in tools.create_tests(setup_experiment, _test_name, procs_per_node=4):
globals()[test.__name__] = test
if (cluster == 'ray'):
# Check if mini-batch time is within expected range
# Note: Skip first epoch since its runtime is usually an outlier
mini_batch_times = mini_batch_times[1:]
mini_batch_time = sum(mini_batch_times) / len(mini_batch_times)
assert (0.75 * expected_mini_batch_times[cluster]
< mini_batch_time
< 1.25 * expected_mini_batch_times[cluster]), \
'average mini-batch time is outside expected range'
# Check for GPU usage and memory leaks
# Note: Skip first epoch
gpu_usages = gpu_usages[1:]
gpu_usage = sum(gpu_usages) / len(gpu_usages)
assert (0.75 * expected_gpu_usage[cluster]
< gpu_usage
< 1.25 * expected_gpu_usage[cluster]),\
'average gpu usage is outside expected range'
# Return test function from factory function
func.__name__ = test_name
return func
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(setup_experiment,
__file__,
nodes=num_nodes):
globals()[_test_func.__name__] = augment_test_func(_test_func)
access the sample access functions.
Args:
lbann (module): Module for LBANN Python frontend
"""
# Note: The training data reader should be removed when
# https://github.com/LLNL/lbann/issues/1098 is resolved.
message = lbann.reader_pb2.DataReader()
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'train')
])
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'test')
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
# Note: Create test name by removing ".py" from file name
_test_name = os.path.splitext(os.path.basename(current_file))[0]
for test in tools.create_tests(setup_experiment, _test_name):
globals()[test.__name__] = test
< expected_train_accuracy_range[1]), \
'train accuracy is outside expected range'
# Check if testing accuracy is within expected range
assert (expected_test_accuracy_range[0]
< test_accuracy
< expected_test_accuracy_range[1]), \
'test accuracy is outside expected range'
# Check if mini-batch time is within expected range
# Note: Skip first epoch since its runtime is usually an outlier
mini_batch_times = mini_batch_times[1:]
mini_batch_time = sum(mini_batch_times) / len(mini_batch_times)
assert (0.75 * expected_mini_batch_times[cluster]
< mini_batch_time
< 1.25 * expected_mini_batch_times[cluster]), \
'average mini-batch time is outside expected range'
# Return test function from factory function
func.__name__ = test_name
return func
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(
setup_experiment,
__file__,
nodes=num_nodes,
lbann_args=['--load_full_sample_list_once']):
globals()[_test_func.__name__] = augment_test_func(_test_func)
Args:
lbann (module): Module for LBANN Python frontend
"""
# Note: The training data reader should be removed when
# https://github.com/LLNL/lbann/issues/1098 is resolved.
message = lbann.reader_pb2.DataReader()
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'train')
])
message.reader.extend([
tools.create_python_data_reader(lbann, current_file, 'get_sample',
'num_samples', 'sample_dims', 'test')
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(
setup_experiment,
__file__,
environment={"LBANN_KEEP_ERROR_SIGNALS": 1},
):
globals()[_test_func.__name__] = _test_func
message.reader.extend([
tools.create_python_data_reader(
lbann,
current_file,
'get_sample',
'num_samples',
'sample_dims',
'train'
)
])
message.reader.extend([
tools.create_python_data_reader(
lbann,
current_file,
'get_sample',
'num_samples',
'sample_dims',
'test'
)
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(setup_experiment, __file__,
environment=tools.get_distconv_environment()):
globals()[_test_func.__name__] = _test_func
message = lbann.reader_pb2.DataReader()
message.reader.extend([
tools.create_python_data_reader(
lbann,
current_file,
'get_sample',
'num_samples',
'sample_dims',
'train'
)
])
message.reader.extend([
tools.create_python_data_reader(
lbann,
current_file,
'get_sample',
'num_samples',
'sample_dims',
'test'
)
])
return message
# ==============================================
# Setup PyTest
# ==============================================
# Create test functions that can interact with PyTest
for _test_func in tools.create_tests(setup_experiment, __file__, skip_clusters=["corona"]):
globals()[_test_func.__name__] = _test_func
