8000 Add ability to install packages + xgboost model deployer + example by RehanSD · Pull Request #431 · ucbrise/clipper · GitHub
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Merged
merged 20 commits into from
Mar 26, 2018
Merged

Add ability to install packages + xgboost model deployer + example #431

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aa1e646
Implement pip install packages in clipper_con object
RehanSD Mar 8, 2018
1402acd
Begin integration test
RehanSD Mar 8, 2018
25edfd5
Continue work on integration tests
RehanSD Mar 9, 2018
198bfb9
Add feature to install packages using pip and add integration test fo…
RehanSD Mar 9, 2018
b7f5516
Delete old imports
RehanSD Mar 9, 2018
6d04d0b
Remove extraneous testing code
RehanSD Mar 9, 2018
f259775
Add example for XGBoost
RehanSD Mar 9, 2018
d8ef8db
Fix typo in walkthrough
RehanSD Mar 9, 2018
3439cdf
Remove Tutorial from this PR
RehanSD Mar 12, 2018
ca8af14
Add bin/yapf
RehanSD Mar 12, 2018
51aa646
Fix formatting
RehanSD Mar 12, 2018
01ac7f8
Add the ability to install packages to all deployers
RehanSD Mar 12, 2018
24727c4
Use Python Closure Container deployer
RehanSD Mar 12, 2018
0102f12
Fix typo from python_model to python_closure
RehanSD Mar 12, 2018
14a27e7
Remove unused import
RehanSD Mar 12, 2018
75e5d85
Add the ability to install packages to all create_endpoint functions
RehanSD Mar 13, 2018
2d74499
Take care of unused imports
RehanSD Mar 17, 2018
6dd7b75
Merge branch 'develop' into pip_install
Corey-Zumar Mar 17, 2018
2dd22c1
updated yapf
dcrankshaw Mar 17, 2018
cd81a42
format code
dcrankshaw Mar 17, 2018
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26 changes: 21 additions & 5 deletions clipper_admin/clipper_admin/clipper_admin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -240,7 +240,8 @@ def build_and_deploy_model(self,
labels=None,
container_registry=None,
num_replicas=1,
batch_size=-1):
batch_size=-1,
pkgs_to_install=None):
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Add this pkgs_to_install command arg to the all of the model deployers in clipper_admin/deployers/.

"""Build a new model container Docker image with the provided data and deploy it as
a model to Clipper.

Expand Down Expand Up @@ -289,6 +290,9 @@ def build_and_deploy_model(self,
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.
Raises
------
:py:exc:`clipper.UnconnectedException`
Expand All @@ -298,7 +302,7 @@ def build_and_deploy_model(self,
if not self.connected:
raise UnconnectedException()
image = self.build_model(name, version, model_data_path, base_image,
container_registry)
container_registry, pkgs_to_install)
self.deploy_model(name, version, input_type, image, labels,
num_replicas, batch_size)

Expand All @@ -307,7 +311,8 @@ def build_model(self,
version,
model_data_path,
base_image,
container_registry=None):
container_registry=None,
pkgs_to_install=None):
"""Build a new model container Docker image with the provided data"

This method builds a new Docker image from the provided base image with the local directory specified by
Expand Down Expand Up @@ -341,6 +346,9 @@ def build_model(self,
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.
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.
Returns
-------
str :
Expand All @@ -363,6 +371,11 @@ def build_model(self,

_validate_versioned_model_name(name, version)

run_cmd = ''
if pkgs_to_install:
run_as_lst = 'RUN apt-get -y install build-essential && pip install'.split(
' ')
run_cmd = ' '.join(run_as_lst + pkgs_to_install)
with tempfile.NamedTemporaryFile(
mode="w+b", suffix="tar") as context_file:
# Create build context tarfile
Expand All @@ -371,8 +384,11 @@ def build_model(self,
context_tar.add(model_data_path)
# From https://stackoverflow.com/a/740854/814642
df_contents = six.StringIO(
"FROM {container_name}\nCOPY {data_path} /model/\n".format(
container_name=base_image, data_path=model_data_path))
"FROM {container_name}\nCOPY {data_path} /model/\n{run_command}\n".
format(
container_name=base_image,
data_path=model_data_path,
run_command=run_cmd))
df_tarinfo = tarfile.TarInfo('Dockerfile')
df_contents.seek(0, os.SEEK_END)
df_tarinfo.size = df_contents.tell()
Expand Down
20 changes: 14 additions & 6 deletions clipper_admin/clipper_admin/deployers/mxnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,8 @@ def create_endpoint(
labels=None,
registry=None,
base_image="clipper/mxnet-container:{}".format(__version__),
num_replicas=1):
num_replicas=1,
pkgs_to_install=None):
"""Registers an app and deploys the provided predict function with MXNet model as
a Clipper model.
Parameters
Expand Down Expand Up @@ -83,6 +84,9 @@ def create_endpoint(
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`.
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
----
Expand All @@ -97,7 +101,7 @@ def create_endpoint(
slo_micros)
deploy_mxnet_model(clipper_conn, name, version, input_type, func,
mxnet_model, mxnet_data_shapes, base_image, labels,
registry, num_replicas)
registry, num_replicas, pkgs_to_install)

clipper_conn.link_model_to_app(name, name)

Expand All @@ -113,7 +117,8 @@ def deploy_mxnet_model(
base_image="clipper/mxnet-container:{}".format(__version__),
labels=None,
registry=None,
num_replicas=1):
num_replicas=1,
pkgs_to_install=None):
"""Deploy a Python function with a MXNet model.
Parameters
----------
Expand Down Expand Up @@ -152,6 +157,9 @@ def deploy_mxnet_model(
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`.
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
----
Expand Down Expand Up @@ -216,9 +224,9 @@ def deploy_mxnet_model(
json.dump({"data_shapes": mxnet_data_shapes}, f)

# Deploy model
clipper_conn.build_and_deploy_model(name, version, input_type,
serialization_dir, base_image,
labels, registry, num_replicas)
clipper_conn.build_and_deploy_model(
name, version, input_type, serialization_dir, base_image, labels,
registry, num_replicas, pkgs_to_install)

except Exception as e:
logger.error("Error saving MXNet model: %s" % e)
Expand Down
16 changes: 12 additions & 4 deletions clipper_admin/clipper_admin/deployers/onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,8 @@ def create_pytorch_endpoint(clipper_conn,
base_image=None,
num_replicas=1,
onnx_backend="caffe2",
batch_size=-1):
batch_size=-1,
pkgs_to_install=None):
"""This function deploys the prediction function with a PyTorch model.
It serializes the PyTorch model in Onnx format and creates a container that loads it as a Caffe2 model.
Parameters
Expand Down Expand Up @@ -87,13 +88,16 @@ def create_pytorch_endpoint(clipper_conn,
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.
"""

clipper_conn.register_application(name, input_type, default_output,
slo_micros)
deploy_pytorch_model(clipper_conn, name, version, input_type, inputs, func,
pytorch_model, base_image, labels, registry,
num_replicas, onnx_backend)
num_replicas, onnx_backend, pkgs_to_install)

clipper_conn.link_model_to_app(name, name)

Expand All @@ -110,7 +114,8 @@ def deploy_pytorch_model(clipper_conn,
registry=None,
num_replicas=1,
onnx_backend="caffe2",
batch_size=-1):
batch_size=-1,
pkgs_to_install=None):
"""This function deploys the prediction function with a PyTorch model.
It serializes the PyTorch model in Onnx format and creates a container that loads it as a Caffe2 model.
Parameters
Expand Down Expand Up @@ -155,6 +160,9 @@ def deploy_pytorch_model(clipper_conn,
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.
"""
if base_image is None:
if onnx_backend is "caffe2":
Expand All @@ -172,7 +180,7 @@ def deploy_pytorch_model(clipper_conn,
# Deploy model
clipper_conn.build_and_deploy_model(
name, version, input_type, serialization_dir, base_image, labels,
registry, num_replicas, batch_size)
registry, num_replicas, batch_size, pkgs_to_install)

except Exception as e:
logger.error(
Expand Down
20 changes: 14 additions & 6 deletions clipper_admin/clipper_admin/deployers/pyspark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,8 @@ def create_endpoint(
registry=None,
base_image="clipper/pyspark-container:{}".format(__version__),
num_replicas=1,
batch_size=-1):
batch_size=-1,
pkgs_to_install=None):
"""Registers an app and deploys the provided predict function with PySpark model as
a Clipper model.

Expand Down Expand Up @@ -86,13 +87,16 @@ def create_endpoint(
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.
"""

clipper_conn.register_application(name, input_type, default_output,
slo_micros)
deploy_pyspark_model(clipper_conn, name, version, input_type, func,
pyspark_model, sc, base_image, labels, registry,
num_replicas, batch_size)
num_replicas, batch_size, pkgs_to_install)

clipper_conn.link_model_to_app(name, name)

Expand All @@ -109,7 +113,8 @@ def deploy_pyspark_model(
labels=None,
registry=None,
num_replicas=1,
batch_size=-1):
batch_size=-1,
pkgs_to_install=None):
"""Deploy a Python function with a PySpark model.

The function must take 3 arguments (in order): a SparkSession, the PySpark model, and a list of
Expand Down Expand Up @@ -155,6 +160,9 @@ def deploy_pyspark_model(
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.

Example
-------
Expand Down Expand Up @@ -225,9 +233,9 @@ def predict(spark, model, inputs):
logger.info("Spark model saved")

# Deploy model
clipper_conn.build_and_deploy_model(name, version, input_type,
serialization_dir, base_image, labels,
registry, num_replicas, batch_size)
clipper_conn.build_and_deploy_model(
name, version, input_type, serialization_dir, base_image, labels,
registry, num_replicas, batch_size, pkgs_to_install)

# Remove temp files
shutil.rmtree(serialization_dir)
20 changes: 14 additions & 6 deletions clipper_admin/clipper_admin/deployers/python.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,8 @@ def create_endpoint(
registry=None,
base_image="clipper/python-closure-container:{}".format(__version__),
num_replicas=1,
batch_size=-1):
batch_size=-1,
pkgs_to_install=None):
"""Registers an application and deploys the provided predict function as a model.

Parameters
Expand Down Expand Up @@ -77,13 +78,16 @@ def create_endpoint(
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.
"""

clipper_conn.register_application(name, input_type, default_output,
slo_micros)
deploy_python_closure(clipper_conn, name, version, input_type, func,
base_image, labels, registry, num_replicas,
batch_size)
batch_size, pkgs_to_install)

clipper_conn.link_model_to_app(name, name)

Expand All @@ -98,7 +102,8 @@ def deploy_python_closure(
labels=None,
registry=None,
num_replicas=1,
batch_size=-1):
batch_size=-1,
pkgs_to_install=None):
"""Deploy an arbitrary Python function to Clipper.

The function should take a list of inputs of the type specified by `input_type` and
Expand Down Expand Up @@ -140,6 +145,9 @@ def deploy_python_closure(
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.

Example
-------
Expand Down Expand Up @@ -183,8 +191,8 @@ def centered_predict(inputs):
serialization_dir = posixpath.join(*os.path.split(serialization_dir))
logger.info("Python closure saved")
# Deploy function
clipper_conn.build_and_deploy_model(name, version, input_type,
serialization_dir, base_image, labels,
registry, num_replicas, batch_size)
clipper_conn.build_and_deploy_model(
name, version, input_type, serialization_dir, base_image, labels,
registry, num_replicas, batch_size, pkgs_to_install)
# Remove temp files
shutil.rmtree(serialization_dir)
20 changes: 14 additions & 6 deletions clipper_admin/clipper_admin/deployers/pytorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,8 @@ def create_endpoint(
registry=None,
base_image="clipper/pytorch-container:{}".format(__version__),
num_replicas=1,
batch_size=-1):
batch_size=-1,
pkgs_to_install=None):
"""Registers an app and deploys the provided predict function with CEB7 PyTorch model as
a Clipper model.
Parameters
Expand Down Expand Up @@ -85,13 +86,16 @@ def create_endpoint(
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.
"""

clipper_conn.register_application(name, input_type, default_output,
slo_micros)
deploy_pytorch_model(clipper_conn, name, version, input_type, func,
pytorch_model, base_image, labels, registry,
num_replicas, batch_size)
num_replicas, batch_size, pkgs_to_install)

clipper_conn.link_model_to_app(name, name)

Expand All @@ -107,7 +111,8 @@ def deploy_pytorch_model(
labels=None,
registry=None,
num_replicas=1,
batch_size=-1):
batch_size=-1,
pkgs_to_install=None):
"""Deploy a Python function with a PyTorch model.
Parameters
----------
Expand Down Expand Up @@ -147,6 +152,9 @@ def deploy_pytorch_model(
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.

Example
-------
Expand Down Expand Up @@ -198,9 +206,9 @@ def predict(model, inputs):
logger.info("Torch model saved")

# Deploy model
clipper_conn.build_and_deploy_model(name, version, input_type,
serialization_dir, base_image, labels,
registry, num_replicas, batch_size)
clipper_conn.build_and_deploy_model(
name, version, input_type, serialization_dir, base_image, labels,
registry, num_replicas, batch_size, pkgs_to_install)

# Remove temp files
shutil.rmtree(serialization_dir)
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