def upload_checkpoint(return_page: str):
"""
Uploads a checkpoint .pt file.
:param return_page: The name of the page to render after uploading the checkpoint file.
"""
warnings, errors = [], []
current_user = request.cookies.get('currentUser')
if not current_user:
# Use DEFAULT as current user if the client's cookie is not set.
current_user = app.config['DEFAULT_USER_ID']
ckpt = request.files['checkpoint']
ckpt_name = request.form['checkpointName']
# Create temporary file to get ckpt_args without losing data.
with NamedTemporaryFile() as temp_file:
ckpt.save(temp_file.name)
ckpt_args = load_args(temp_file)
ckpt_id, new_ckpt_name = db.insert_ckpt(ckpt_name, current_user,
ckpt_args.dataset_type,
ckpt_args.epochs, 1,
ckpt_args.train_data_size)
model_id = db.insert_model(ckpt_id)
model_path = os.path.join(app.config['CHECKPOINT_FOLDER'],
f'{model_id}.pt')
if ckpt_name != new_ckpt_name:
warnings.append(
name_already_exists_message('Checkpoint', ckpt_name,
new_ckpt_name))
shutil.copy(temp_file.name, model_path)
warnings, errors = json.dumps(warnings), json.dumps(errors)
return redirect(
url_for(return_page,
checkpoint_upload_warnings=warnings,
checkpoint_upload_errors=errors))
def train():
"""Renders the train page and performs training if request method is POST."""
global PROGRESS, TRAINING
warnings, errors = [], []
if request.method == 'GET':
return render_train()
# Get arguments
data_name, epochs, ensemble_size, checkpoint_name = \
request.form['dataName'], int(request.form['epochs']), \
int(request.form['ensembleSize']), request.form['checkpointName']
gpu = request.form.get('gpu')
data_path = os.path.join(app.config['DATA_FOLDER'], f'{data_name}.csv')
dataset_type = request.form.get('datasetType', 'regression')
# Create and modify args
parser = ArgumentParser()
add_train_args(parser)
args = parser.parse_args([])
args.data_path = data_path
args.dataset_type = dataset_type
args.epochs = epochs
args.ensemble_size = ensemble_size
# Check if regression/classification selection matches data
data = get_data(path=data_path)
targets = data.targets()
unique_targets = {target for row in targets for target in row if target is not None}
if dataset_type == 'classification' and len(unique_targets - {0, 1}) > 0:
errors.append('Selected classification dataset but not all labels are 0 or 1. Select regression instead.')
return render_train(warnings=warnings, errors=errors)
if dataset_type == 'regression' and unique_targets <= {0, 1}:
errors.append('Selected regression dataset but all labels are 0 or 1. Select classification instead.')
return render_train(warnings=warnings, errors=errors)
if gpu is not None:
if gpu == 'None':
args.no_cuda = True
else:
args.gpu = int(gpu)
current_user = request.cookies.get('currentUser')
if not current_user:
# Use DEFAULT as current user if the client's cookie is not set.
current_user = app.config['DEFAULT_USER_ID']
ckpt_id, ckpt_name = db.insert_ckpt(checkpoint_name,
current_user,
args.dataset_type,
args.epochs,
args.ensemble_size,
len(targets))
with TemporaryDirectory() as temp_dir:
args.save_dir = temp_dir
modify_train_args(args)
process = mp.Process(target=progress_bar, args=(args, PROGRESS))
process.start()
TRAINING = 1
# Run training
logger = create_logger(name='train', save_dir=args.save_dir, quiet=args.quiet)
task_scores = run_training(args, logger)
process.join()
# Reset globals
TRAINING = 0
PROGRESS = mp.Value('d', 0.0)
# Check if name overlap
if checkpoint_name != ckpt_name:
warnings.append(name_already_exists_message('Checkpoint', checkpoint_name, ckpt_name))
# Move models
for root, _, files in os.walk(args.save_dir):
for fname in files:
if fname.endswith('.pt'):
model_id = db.insert_model(ckpt_id)
save_path = os.path.join(app.config['CHECKPOINT_FOLDER'], f'{model_id}.pt')
shutil.move(os.path.join(args.save_dir, root, fname), save_path)
return render_train(trained=True,
metric=args.metric,
num_tasks=len(args.task_names),
task_names=args.task_names,
task_scores=format_float_list(task_scores),
mean_score=format_float(np.mean(task_scores)),
warnings=warnings,
errors=errors)
def upload_checkpoint(return_page: str):
"""
Uploads a checkpoint .pt file.
:param return_page: The name of the page to render after uploading the checkpoint file.
"""
warnings, errors = [], []
current_user = request.cookies.get('currentUser')
if not current_user:
# Use DEFAULT as current user if the client's cookie is not set.
current_user = app.config['DEFAULT_USER_ID']
ckpt = request.files['checkpoint']
ckpt_name = request.form['checkpointName']
ckpt_ext = os.path.splitext(ckpt.filename)[1]
# Collect paths to all uploaded checkpoints (and unzip if necessary)
temp_dir = TemporaryDirectory()
ckpt_paths = []
if ckpt_ext.endswith('.pt'):
ckpt_path = os.path.join(temp_dir.name, 'model.pt')
ckpt.save(ckpt_path)
ckpt_paths = [ckpt_path]
elif ckpt_ext.endswith('.zip'):
ckpt_dir = os.path.join(temp_dir.name, 'models')
zip_path = os.path.join(temp_dir.name, 'models.zip')
ckpt.save(zip_path)
with zipfile.ZipFile(zip_path, mode='r') as z:
z.extractall(ckpt_dir)
for root, _, fnames in os.walk(ckpt_dir):
ckpt_paths += [
os.path.join(root, fname) for fname in fnames
if fname.endswith('.pt')
]
else:
errors.append(
f'Uploaded checkpoint(s) file must be either .pt or .zip but got {ckpt_ext}'
)
# Insert checkpoints into database
if len(ckpt_paths) > 0:
ckpt_args = load_args(ckpt_paths[0])
ckpt_id, new_ckpt_name = db.insert_ckpt(ckpt_name, current_user,
ckpt_args.dataset_type,
ckpt_args.epochs,
len(ckpt_paths),
ckpt_args.train_data_size)
for ckpt_path in ckpt_paths:
model_id = db.insert_model(ckpt_id)
model_path = os.path.join(app.config['CHECKPOINT_FOLDER'],
f'{model_id}.pt')
if ckpt_name != new_ckpt_name:
warnings.append(
name_already_exists_message('Checkpoint', ckpt_name,
new_ckpt_name))
shutil.copy(ckpt_path, model_path)
temp_dir.cleanup()
warnings, errors = json.dumps(warnings), json.dumps(errors)
return redirect(
url_for(return_page,
checkpoint_upload_warnings=warnings,
checkpoint_upload_errors=errors))
