def load(cls, bundle, **kwargs):
"""Load a model from a bundle.
This can be either a local model or a remote, exported model.
:returns a Service implementation
"""
import onnxruntime as ort
# can delegate
if os.path.isdir(bundle):
directory = bundle
# Try and unzip if its a zip file
else:
directory = unzip_files(bundle)
model_basename = find_model_basename(directory)
# model_basename = model_basename.replace(".pyt", "")
model_name = f"{model_basename}.onnx"
vocabs = load_vocabs(directory)
vectorizers = load_vectorizers(directory)
model = ort.InferenceSession(model_name)
return cls(vocabs, vectorizers, model)
def load(cls, bundle, **kwargs):
"""Load a model from a bundle.
This can be either a local model or a remote, exported model.
:returns a Service implementation
"""
# can delegate
if os.path.isdir(bundle):
directory = bundle
else:
directory = unzip_files(bundle)
model_basename = find_model_basename(directory)
vocabs = load_vocabs(directory)
vectorizers = load_vectorizers(directory)
be = normalize_backend(kwargs.get('backend', 'tf'))
remote = kwargs.get("remote", None)
name = kwargs.get("name", None)
if remote:
beam = kwargs.get('beam', 10)
model = Service._create_remote_model(directory, be, remote, name, cls.signature_name(), beam, preproc=kwargs.get('preproc', False))
return cls(vocabs, vectorizers, model)
# Currently nothing to do here
# labels = read_json(os.path.join(directory, model_basename) + '.labels')
import_user_module('baseline.{}.embeddings'.format(be))
import_user_module('baseline.{}.{}'.format(be, cls.task_name()))
model = load_model_for(cls.task_name(), model_basename, **kwargs)
return cls(vocabs, vectorizers, model)
def load_model(self, model_dir):
model_name = find_model_basename(model_dir)
vectorizers = load_vectorizers(model_dir)
model = load_model_for(self.task.task_name(), model_name, device='cpu')
model = model.cpu()
model.eval()
model_name = os.path.basename(model_name)
return model, vectorizers, model_name
def load_model(self, model_dir):
model_name = find_model_basename(model_dir)
vectorizers = load_vectorizers(model_dir)
model = load_model_for(self.task.task_name(), model_name, device='cpu')
model = model.cpu()
model.eval()
model_name = os.path.basename(model_name)
return model, vectorizers, model_name
def load(cls, bundle, **kwargs):
"""Load a model from a bundle.
This can be either a local model or a remote, exported model.
:returns a Service implementation
"""
# can delegate
basehead = None
if os.path.isdir(bundle):
directory = bundle
elif os.path.isfile(bundle):
directory = unzip_files(bundle)
else:
directory = os.path.dirname(bundle)
basehead = os.path.basename(bundle)
model_basename = find_model_basename(directory, basehead)
suffix = model_basename.split('-')[-1] + ".json"
vocabs = load_vocabs(directory, suffix)
be = normalize_backend(kwargs.get('backend', 'tf'))
remote = kwargs.get("remote", None)
name = kwargs.get("name", None)
if remote:
logging.debug("loading remote model")
beam = int(kwargs.get('beam', 30))
model, preproc = Service._create_remote_model(
directory,
be,
remote,
name,
cls.task_name(),
cls.signature_name(),
beam,
preproc=kwargs.get('preproc', 'client'),
version=kwargs.get('version'),
remote_type=kwargs.get('remote_type'),
)
vectorizers = load_vectorizers(directory)
return cls(vocabs, vectorizers, model, preproc)
# Currently nothing to do here
# labels = read_json(os.path.join(directory, model_basename) + '.labels')
import_user_module('baseline.{}.embeddings'.format(be))
try:
import_user_module('baseline.{}.{}'.format(be, cls.task_name()))
except:
pass
model = load_model_for(cls.task_name(), model_basename, **kwargs)
vectorizers = load_vectorizers(directory)
return cls(vocabs, vectorizers, model, 'client')
def load(cls, bundle, **kwargs):
"""Load a model from a bundle.
This can be either a local model or a remote, exported model.
:returns a Service implementation
"""
# can delegate
if os.path.isdir(bundle):
directory = bundle
else:
directory = unzip_files(bundle)
model_basename = find_model_basename(directory)
vocabs = load_vocabs(directory)
vectorizers = load_vectorizers(directory)
be = normalize_backend(kwargs.get('backend', 'tf'))
remote = kwargs.get("remote", None)
name = kwargs.get("name", None)
if remote:
logging.debug("loading remote model")
beam = kwargs.get('beam', 30)
model, preproc = Service._create_remote_model(
directory, be, remote, name, cls.signature_name(), beam,
preproc=kwargs.get('preproc', 'client'),
version=kwargs.get('version')
)
return cls(vocabs, vectorizers, model, preproc)
# Currently nothing to do here
# labels = read_json(os.path.join(directory, model_basename) + '.labels')
import_user_module('baseline.{}.embeddings'.format(be))
try:
import_user_module('baseline.{}.{}'.format(be, cls.task_name()))
except:
pass
model = load_model_for(cls.task_name(), model_basename, **kwargs)
return cls(vocabs, vectorizers, model, 'client')
def load(cls, bundle, **kwargs):
"""Load a model from a bundle.
This can be either a local model or a remote, exported model.
:returns a Service implementation
"""
import onnxruntime as ort
if os.path.isdir(bundle):
directory = bundle
else:
directory = unzip_files(bundle)
model_basename = find_model_basename(directory)
model_name = f"{model_basename}.onnx"
vocabs = load_vocabs(directory)
vectorizers = load_vectorizers(directory)
# Currently nothing to do here
labels = read_json(model_basename + '.labels')
model = ort.InferenceSession(model_name)
return cls(vocabs, vectorizers, model, labels)
