from __future__ import print_function, with_statement, absolute_import
import shutil
import logging
import os
import json
import sys
from ..version import __version__
from ..exceptions import ClipperException
from .deployer_utils import save_python_function
logger = logging.getLogger(__name__)
MXNET_MODEL_RELATIVE_PATH = "mxnet_model"
[docs]def create_endpoint(clipper_conn,
name,
input_type,
func,
mxnet_model,
mxnet_data_shapes,
default_output="None",
version=1,
slo_micros=3000000,
labels=None,
registry=None,
base_image="default",
num_replicas=1,
batch_size=-1,
pkgs_to_install=None):
"""Registers an app and deploys the provided predict function with MXNet model as
a Clipper model.
Parameters
----------
clipper_conn : :py:meth:`clipper_admin.ClipperConnection`
A ``ClipperConnection`` object connected to a running Clipper cluster.
name : str
The name to be assigned to both the registered application and deployed model.
input_type : str
The input_type to be associated with the registered app and deployed model.
One of "integers", "floats", "doubles", "bytes", or "strings".
func : function
The prediction function. Any state associated with the function will be
captured via closure capture and pickled with Cloudpickle.
mxnet_model : mxnet model object
The MXNet model to save.
the shape of the data used to train the model.
mxnet_data_shapes : list of DataDesc objects
List of DataDesc objects representing the name, shape, type and layout information
of data used for model prediction.
Required because loading serialized MXNet models involves binding, which requires
https://mxnet.incubator.apache.org/api/python/module.html#mxnet.module.BaseModule.bind
default_output : str, optional
The default output for the application. The default output will be returned whenever
an application is unable to receive a response from a model within the specified
query latency SLO (service level objective). The reason the default output was returned
is always provided as part of the prediction response object. Defaults to "None".
version : str, optional
The version to assign this model. Versions must be unique on a per-model
basis, but may be re-used across different models.
slo_micros : int, optional
The query latency objective for the application in microseconds.
This is the processing latency between Clipper receiving a request
and sending a response. It does not account for network latencies
before a request is received or after a response is sent.
If Clipper cannot process a query within the latency objective,
the default output is returned. Therefore, it is recommended that
the SLO not be set aggressively low unless absolutely necessary.
100000 (100ms) is a good starting value, but the optimal latency objective
will vary depending on the application.
labels : list(str), optional
A list of strings annotating the model. These are ignored by Clipper
and used purely for user annotations.
registry : str, optional
The Docker container registry to push the freshly built model to. Note
that if you are running Clipper on Kubernetes, this registry must be accesible
to the Kubernetes cluster in order to fetch the container from the registry.
base_image : str, optional
The base Docker image to build the new model image from. This
image should contain all code necessary to run a Clipper model
container RPC client.
num_replicas : int, optional
The number of replicas of the model to create. The number of replicas
for a model can be changed at any time with
:py:meth:`clipper.ClipperConnection.set_num_replicas`.
batch_size : int, optional
The user-defined query batch size for the model. Replicas of the model will attempt
to process at most `batch_size` queries simultaneously. They may process smaller
batches if `batch_size` queries are not immediately available.
If the default value of -1 is used, Clipper will adaptively calculate the batch size for
individual replicas of this model.
pkgs_to_install : list (of strings), optional
A list of the names of packages to install, using pip, in the container.
The names must be strings.
Note
----
Regarding `mxnet_data_shapes` parameter:
Clipper may provide the model with variable size input batches. Because MXNet can't
handle variable size input batches, we recommend setting batch size for input data
to 1, or dynamically reshaping the model with every prediction based on the current
input batch size.
More information regarding a DataDesc object can be found here:
https://mxnet.incubator.apache.org/versions/0.11.0/api/python/io.html#mxnet.io.DataDesc
"""
clipper_conn.register_application(name, input_type, default_output,
slo_micros)
deploy_mxnet_model(clipper_conn, name, version, input_type, func,
mxnet_model, mxnet_data_shapes, base_image, labels,
registry, num_replicas, batch_size, pkgs_to_install)
clipper_conn.link_model_to_app(name, name)
[docs]def deploy_mxnet_model(clipper_conn,
name,
version,
input_type,
func,
mxnet_model,
mxnet_data_shapes,
base_image="default",
labels=None,
registry=None,
num_replicas=1,
batch_size=-1,
pkgs_to_install=None):
"""Deploy a Python function with a MXNet model.
Parameters
----------
clipper_conn : :py:meth:`clipper_admin.ClipperConnection`
A ``ClipperConnection`` object connected to a running Clipper cluster.
name : str
The name to be assigned to both the registered application and deployed model.
version : str
The version to assign this model. Versions must be unique on a per-model
basis, but may be re-used across different models.
input_type : str
The input_type to be associated with the registered app and deployed model.
One of "integers", "floats", "doubles", "bytes", or "strings".
func : function
The prediction function. Any state associated with the function will be
captured via closure capture and pickled with Cloudpickle.
mxnet_model : mxnet model object
The MXNet model to save.
mxnet_data_shapes : list of DataDesc objects
List of DataDesc objects representing the name, shape, type and layout information
of data used for model prediction.
Required because loading serialized MXNet models involves binding, which requires
the shape of the data used to train the model.
https://mxnet.incubator.apache.org/api/python/module.html#mxnet.module.BaseModule.bind
base_image : str, optional
The base Docker image to build the new model image from. This
image should contain all code necessary to run a Clipper model
container RPC client.
labels : list(str), optional
A list of strings annotating the model. These are ignored by Clipper
and used purely for user annotations.
registry : str, optional
The Docker container registry to push the freshly built model to. Note
that if you are running Clipper on Kubernetes, this registry must be accesible
to the Kubernetes cluster in order to fetch the container from the registry.
num_replicas : int, optional
The number of replicas of the model to create. The number of replicas
for a model can be changed at any time with
:py:meth:`clipper.ClipperConnection.set_num_replicas`.
batch_size : int, optional
The user-defined query batch size for the model. Replicas of the model will attempt
to process at most `batch_size` queries simultaneously. They may process smaller
batches if `batch_size` queries are not immediately available.
If the default value of -1 is used, Clipper will adaptively calculate the batch size for
individual replicas of this model.
pkgs_to_install : list (of strings), optional
A list of the names of packages to install, using pip, in the container.
The names must be strings.
Note
----
Regarding `mxnet_data_shapes` parameter:
Clipper may provide the model with variable size input batches. Because MXNet can't
handle variable size input batches, we recommend setting batch size for input data
to 1, or dynamically reshaping the model with every prediction based on the current
input batch size.
More information regarding a DataDesc object can be found here:
https://mxnet.incubator.apache.org/versions/0.11.0/api/python/io.html#mxnet.io.DataDesc
Example
-------
Create a MXNet model and then deploy it::
from clipper_admin import ClipperConnection, DockerContainerManager
from clipper_admin.deployers.mxnet import deploy_mxnet_model
import mxnet as mx
clipper_conn = ClipperConnection(DockerContainerManager())
# Connect to an already-running Clipper cluster
clipper_conn.connect()
# Create a MXNet model
# Configure a two layer neuralnetwork
data = mx.symbol.Variable('data')
fc1 = mx.symbol.FullyConnected(data, name='fc1', num_hidden=128)
act1 = mx.symbol.Activation(fc1, name='relu1', act_type='relu')
fc2 = mx.symbol.FullyConnected(act1, name='fc2', num_hidden=10)
softmax = mx.symbol.SoftmaxOutput(fc2, name='softmax')
# Load some training data
data_iter = mx.io.CSVIter(
data_csv="/path/to/train_data.csv", data_shape=(785, ), batch_size=1)
# Initialize the module and fit it
mxnet_model = mx.mod.Module(softmax)
mxnet_model.fit(data_iter, num_epoch=1)
data_shape = data_iter.provide_data
deploy_mxnet_model(
clipper_conn,
name="example",
version = 1,
input_type="doubles",
func=predict,
mxnet_model=model,
mxnet_data_shapes=data_shape)
"""
serialization_dir = save_python_function(name, func)
# save MXNet model
mxnet_model_save_loc = os.path.join(serialization_dir,
MXNET_MODEL_RELATIVE_PATH)
try:
# Saves model in two files: <serialization_dir>/mxnet_model.json will be saved for symbol,
# <serialization_dir>/mxnet_model.params will be saved for parameters.
mxnet_model.save_checkpoint(prefix=mxnet_model_save_loc, epoch=0)
# Saves data_shapes to mxnet_model_metadata.json
with open(
os.path.join(serialization_dir, "mxnet_model_metadata.json"),
"w") as f:
json.dump({"data_shapes": mxnet_data_shapes}, f)
logger.info("MXNet model saved")
py_minor_version = (sys.version_info.major, sys.version_info.minor)
# Check if Python 2 or Python 3 image
if base_image == "default":
if py_minor_version < (3, 0):
logger.info("Using Python 2 base image")
base_image = "clipper/mxnet-container:{}".format(__version__)
elif py_minor_version == (3, 5):
logger.info("Using Python 3.5 base image")
base_image = "clipper/mxnet35-container:{}".format(__version__)
elif py_minor_version == (3, 6):
logger.info("Using Python 3.6 base image")
base_image = "clipper/mxnet36-container:{}".format(__version__)
else:
msg = (
"MXNet deployer only supports Python 2.7, 3.5, and 3.6. "
"Detected {major}.{minor}").format(
major=sys.version_info.major,
minor=sys.version_info.minor)
logger.error(msg)
# Remove temp files
shutil.rmtree(serialization_dir)
raise ClipperException(msg)
# Deploy model
clipper_conn.build_and_deploy_model(
name, version, input_type, serialization_dir, base_image, labels,
registry, num_replicas, batch_size, pkgs_to_install)
except Exception as e:
logger.error("Error saving MXNet model: %s" % e)
raise e
# Remove temp files
shutil.rmtree(serialization_dir)