def bundle(self):
"""Save a model bundle with whatever is needed to make predictions.
The model bundle is a zip file and it is used by the Predictor and
predict CLI subcommand.
"""
with tempfile.TemporaryDirectory(dir=self.tmp_dir) as tmp_dir:
bundle_dir = join(tmp_dir, 'bundle')
make_dir(bundle_dir)
for fn in self.config.backend.get_bundle_filenames():
path = download_if_needed(join(self.config.train_uri, fn),
tmp_dir)
shutil.copy(path, join(bundle_dir, fn))
for a in self.config.analyzers:
for fn in a.get_bundle_filenames():
path = download_if_needed(
join(self.config.analyze_uri, fn), tmp_dir)
shutil.copy(path, join(bundle_dir, fn))
path = download_if_needed(self.config.get_config_uri(), tmp_dir)
shutil.copy(path, join(bundle_dir, 'pipeline-config.json'))
model_bundle_uri = self.config.get_model_bundle_uri()
model_bundle_path = get_local_path(model_bundle_uri, self.tmp_dir)
zipdir(bundle_dir, model_bundle_path)
upload_or_copy(model_bundle_path, model_bundle_uri)
def get_file(uri, cache_dir):
path = get_local_path(uri, cache_dir)
if file_exists(path):
print(f'Using cached file in {path}')
else:
path = download_if_needed(uri, cache_dir)
return path
def open_zip_file(zip_uri, cache_dir):
zip_path = get_file(zip_uri, cache_dir)
zip_dir = splitext(get_local_path(zip_uri, cache_dir))[0]
if isdir(zip_dir):
print(f'Using cached data in {zip_dir}')
else:
unzip(zip_path, zip_dir)
return zip_dir
def main(args, extra_args):
make_dir(args.tmp_root)
make_dir(args.cache_dir)
with tempfile.TemporaryDirectory(dir=args.tmp_root) as tmp_dir:
output_uri = get_local_path(args.output_uri, tmp_dir)
pretrained_uri = (
get_file(args.pretrained_uri, args.cache_dir)
if args.pretrained_uri else None)
dataset_uri = open_zip_file(args.dataset_uri, args.cache_dir)
try:
run_vissl(
args.config, dataset_uri, output_uri, extra_args,
pretrained_path=pretrained_uri)
extract_backbone(
join(output_uri, 'checkpoint.torch'),
join(output_uri, 'backbone.torch'))
finally:
sync_to_dir(output_uri, args.output_uri)