def deploy(project_root_directory, model_key, input_shape, model_uri, host,
port, db, password, socket_timeout, socket_connect_timeout,
socket_keepalive, socket_keepalive_options, connection_pool,
unix_socket_path, encoding, encoding_errors, charset, errors,
decode_responses, retry_on_timeout, ssl, ssl_keyfile, ssl_certfile,
ssl_cert_reqs, ssl_ca_certs, max_connections, backend):
redis_client = redis.StrictRedis(**dict(
host=host,
port=port,
db=db,
password=password,
socket_timeout=socket_timeout,
socket_connect_timeout=socket_connect_timeout,
socket_keepalive=socket_keepalive,
socket_keepalive_options=socket_keepalive_options,
connection_pool=connection_pool,
unix_socket_path=unix_socket_path,
encoding=encoding,
encoding_errors=encoding_errors,
charset=charset,
errors=errors,
decode_responses=decode_responses,
retry_on_timeout=retry_on_timeout,
ssl=ssl,
ssl_keyfile=ssl_keyfile,
ssl_certfile=ssl_certfile,
ssl_cert_reqs=ssl_cert_reqs,
ssl_ca_certs=ssl_ca_certs,
max_connections=max_connections,
))
device = 'cpu' if backend == 'CPU' else 'cuda'
model_path = _download_artifact_from_uri(model_uri)
pyfunc_conf = _get_flavor_configuration(model_path=model_path,
flavor_name=pyfunc.FLAVOR_NAME)
code_subpath = pyfunc_conf.get(pyfunc.CODE)
if code_subpath is not None:
pyfunc_utils._add_code_to_system_path(
code_path=os.path.join(model_path, code_subpath))
pytorch_conf = _get_flavor_configuration(model_path=model_path,
flavor_name=FLAVOR_NAME)
if torch.__version__ != pytorch_conf["pytorch_version"]:
_logger.warning(
"Stored model version '%s' does not match installed PyTorch version '%s'",
pytorch_conf["pytorch_version"], torch.__version__)
sys.path.append(project_root_directory)
model = mlflow.pytorch._load_model(path=model_uri + '/data').to(device)
x = torch.ones(list(map(int, input_shape.split(' ')))).to(device)
traced_net = torch.jit.trace(model, x)
blob_stream = io.BytesIO()
torch.jit.save(traced_net, blob_stream)
blob_stream.seek(0)
set_redismodel(redis_client, blob_stream.read(), model_key, backend)
def load_model(model_uri, **kwargs):
"""
Load a PyTorch model from a local file or a run.
:param model_uri: The location, in URI format, of the MLflow model, for example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs:/<mlflow_run_id>/run-relative/path/to/model``
- ``models:/<model_name>/<model_version>``
- ``models:/<model_name>/<stage>``
For more information about supported URI schemes, see
`Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
artifact-locations>`_.
:param kwargs: kwargs to pass to ``torch.load`` method.
:return: A PyTorch model.
.. code-block:: python
:caption: Example
import torch
import mlflow
import mlflow.pytorch
# Set values
model_path_dir = ...
run_id = "96771d893a5e46159d9f3b49bf9013e2"
pytorch_model = mlflow.pytorch.load_model("runs:/" + run_id + "/" + model_path_dir)
y_pred = pytorch_model(x_new_data)
"""
import torch
local_model_path = _download_artifact_from_uri(artifact_uri=model_uri)
try:
pyfunc_conf = _get_flavor_configuration(
model_path=local_model_path, flavor_name=pyfunc.FLAVOR_NAME
)
except MlflowException:
pyfunc_conf = {}
code_subpath = pyfunc_conf.get(pyfunc.CODE)
if code_subpath is not None:
pyfunc_utils._add_code_to_system_path(
code_path=os.path.join(local_model_path, code_subpath)
)
pytorch_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME)
if torch.__version__ != pytorch_conf["pytorch_version"]:
_logger.warning(
"Stored model version '%s' does not match installed PyTorch version '%s'",
pytorch_conf["pytorch_version"],
torch.__version__,
)
torch_model_artifacts_path = os.path.join(local_model_path, pytorch_conf["model_data"])
return _load_model(path=torch_model_artifacts_path, **kwargs)
def load_model(model_uri, **kwargs):
"""
Load a PyTorch model from a local file (if ``run_id`` is ``None``) or a run.
:param model_uri: The location, in URI format, of the MLflow model, for example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs:/<mlflow_run_id>/run-relative/path/to/model``
For more information about supported URI schemes, see the
`Artifacts Documentation <https://www.mlflow.org/docs/latest/tracking.html#
supported-artifact-stores>`_.
:param kwargs: kwargs to pass to ``torch.load`` method.
:return: A PyTorch model.
>>> import torch
>>> import mlflow
>>> import mlflow.pytorch
>>> # set values
>>> model_path_dir = ...
>>> run_id="96771d893a5e46159d9f3b49bf9013e2"
>>> pytorch_model = mlflow.pytorch.load_model(model_path_dir, run_id)
>>> y_pred = pytorch_model(x_new_data)
"""
import torch
local_model_path = _download_artifact_from_uri(artifact_uri=model_uri)
try:
pyfunc_conf = _get_flavor_configuration(model_path=local_model_path,
flavor_name=pyfunc.FLAVOR_NAME)
except MlflowException:
pyfunc_conf = {}
code_subpath = pyfunc_conf.get(pyfunc.CODE)
if code_subpath is not None:
pyfunc_utils._add_code_to_system_path(
code_path=os.path.join(local_model_path, code_subpath))
pytorch_conf = _get_flavor_configuration(model_path=local_model_path,
flavor_name=FLAVOR_NAME)
if torch.__version__ != pytorch_conf["pytorch_version"]:
_logger.warning(
"Stored model version '%s' does not match installed PyTorch version '%s'",
pytorch_conf["pytorch_version"], torch.__version__)
torch_model_artifacts_path = os.path.join(local_model_path,
pytorch_conf['model_data'])
return _load_model(path=torch_model_artifacts_path, **kwargs)
def load_model(path, run_id=None, **kwargs):
"""
Load a PyTorch model from a local file (if ``run_id`` is ``None``) or a run.
:param path: Local filesystem path or run-relative artifact path to the model saved
by :py:func:`mlflow.pytorch.log_model`.
:param run_id: Run ID. If provided, combined with ``path`` to identify the model.
:param kwargs: kwargs to pass to ``torch.load`` method.
>>> import torch
>>> import mlflow
>>> import mlflow.pytorch
>>> # set values
>>> model_path_dir = ...
>>> run_id="96771d893a5e46159d9f3b49bf9013e2"
>>> pytorch_model = mlflow.pytorch.load_model(model_path_dir, run_id)
>>> y_pred = pytorch_model(x_new_data)
"""
if run_id is not None:
path = mlflow.tracking.utils._get_model_log_dir(model_name=path,
run_id=run_id)
path = os.path.abspath(path)
try:
pyfunc_conf = _get_flavor_configuration(model_path=path,
flavor_name=pyfunc.FLAVOR_NAME)
except MlflowException:
pyfunc_conf = {}
code_subpath = pyfunc_conf.get(pyfunc.CODE)
if code_subpath is not None:
pyfunc_utils._add_code_to_system_path(
code_path=os.path.join(path, code_subpath))
pytorch_conf = _get_flavor_configuration(model_path=path,
flavor_name=FLAVOR_NAME)
if torch.__version__ != pytorch_conf["pytorch_version"]:
_logger.warning(
"Stored model version '%s' does not match installed PyTorch version '%s'",
pytorch_conf["pytorch_version"], torch.__version__)
torch_model_artifacts_path = os.path.join(path, pytorch_conf['model_data'])
return _load_model(path=torch_model_artifacts_path, **kwargs)
def load_model(model_uri, dst_path=None, **kwargs):
"""
Load a PyTorch model from a local file or a run.
:param model_uri: The location, in URI format, of the MLflow model, for example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs:/<mlflow_run_id>/run-relative/path/to/model``
- ``models:/<model_name>/<model_version>``
- ``models:/<model_name>/<stage>``
For more information about supported URI schemes, see
`Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
artifact-locations>`_.
:param dst_path: The local filesystem path to which to download the model artifact.
This directory must already exist. If unspecified, a local output
path will be created.
:param kwargs: kwargs to pass to ``torch.load`` method.
:return: A PyTorch model.
.. code-block:: python
:caption: Example
import torch
import mlflow.pytorch
# Class defined here
class LinearNNModel(torch.nn.Module):
...
# Initialize our model, criterion and optimizer
...
# Training loop
...
# Log the model
with mlflow.start_run() as run:
mlflow.pytorch.log_model(model, "model")
# Inference after loading the logged model
model_uri = "runs:/{}/model".format(run.info.run_id)
loaded_model = mlflow.pytorch.load_model(model_uri)
for x in [4.0, 6.0, 30.0]:
X = torch.Tensor([[x]])
y_pred = loaded_model(X)
print("predict X: {}, y_pred: {:.2f}".format(x, y_pred.data.item()))
.. code-block:: text
:caption: Output
predict X: 4.0, y_pred: 7.57
predict X: 6.0, y_pred: 11.64
predict X: 30.0, y_pred: 60.48
"""
import torch
local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path)
try:
pyfunc_conf = _get_flavor_configuration(
model_path=local_model_path, flavor_name=pyfunc.FLAVOR_NAME
)
except MlflowException:
pyfunc_conf = {}
code_subpath = pyfunc_conf.get(pyfunc.CODE)
if code_subpath is not None:
pyfunc_utils._add_code_to_system_path(
code_path=os.path.join(local_model_path, code_subpath)
)
pytorch_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME)
if torch.__version__ != pytorch_conf["pytorch_version"]:
_logger.warning(
"Stored model version '%s' does not match installed PyTorch version '%s'",
pytorch_conf["pytorch_version"],
torch.__version__,
)
torch_model_artifacts_path = os.path.join(local_model_path, pytorch_conf["model_data"])
return _load_model(path=torch_model_artifacts_path, **kwargs)
