How to deploy a Google dataflow worker with a file loaded into memory? - streaming

Question

I am trying to deploy Google Dataflow streaming for use in my machine learning streaming pipeline, but cannot seem to deploy the worker with a file already loaded into memory. Currently, I have setup the job to pull a pickle file from a GCS bucket, load it into memory, and use it for model prediction. But this is executed on every cycle of the job, i.e. pull from GCS every time a new object enters the dataflow pipeline - meaning that the current execution of the pipeline is much slower than it needs to be.
What I really need, is a way to allocate a variable within the worker nodes on setup of each worker. Then use that variable within the pipeline, without having to re-load on every execution of the pipeline.
Is there a way to do this step before the job is deployed, something like
with open('model.pkl', 'rb') as file:
pickle_model = pickle.load(file)
But within my setup.py file?
##### based on - https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/complete/juliaset/setup.py
"""Setup.py module for the workflow's worker utilities.
All the workflow related code is gathered in a package that will be built as a
source distribution, staged in the staging area for the workflow being run and
then installed in the workers when they start running.
This behavior is triggered by specifying the --setup_file command line option
when running the workflow for remote execution.
"""
# pytype: skip-file
from __future__ import absolute_import
from __future__ import print_function
import subprocess
from distutils.command.build import build as _build # type: ignore
import setuptools
# This class handles the pip install mechanism.
class build(_build): # pylint: disable=invalid-name
"""A build command class that will be invoked during package install.
The package built using the current setup.py will be staged and later
installed in the worker using `pip install package'. This class will be
instantiated during install for this specific scenario and will trigger
running the custom commands specified.
"""
sub_commands = _build.sub_commands + [('CustomCommands', None)]
CUSTOM_COMMANDS = [['pip', 'install', 'scikit-learn==0.23.1']]
CUSTOM_COMMANDS = [['pip', 'install', 'google-cloud-storage']]
CUSTOM_COMMANDS = [['pip', 'install', 'mlxtend']]
class CustomCommands(setuptools.Command):
"""A setuptools Command class able to run arbitrary commands."""
def initialize_options(self):
pass
def finalize_options(self):
pass
def RunCustomCommand(self, command_list):
print('Running command: %s' % command_list)
p = subprocess.Popen(
command_list,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
# Can use communicate(input='y\n'.encode()) if the command run requires
# some confirmation.
stdout_data, _ = p.communicate()
print('Command output: %s' % stdout_data)
if p.returncode != 0:
raise RuntimeError(
'Command %s failed: exit code: %s' % (command_list, p.returncode))
def run(self):
for command in CUSTOM_COMMANDS:
self.RunCustomCommand(command)
REQUIRED_PACKAGES = [
'google-cloud-storage',
'mlxtend',
'scikit-learn==0.23.1',
]
setuptools.setup(
name='ML pipeline',
version='0.0.1',
description='ML set workflow package.',
install_requires=REQUIRED_PACKAGES,
packages=setuptools.find_packages(),
cmdclass={
'build': build,
'CustomCommands': CustomCommands,
})
Snippet of current ML load mechanism:
class MlModel(beam.DoFn):
def __init__(self):
self._model = None
from google.cloud import storage
import pandas as pd
import pickle as pkl
self._storage = storage
self._pkl = pkl
self._pd = pd
def process(self,element):
if self._model is None:
bucket = self._storage.Client().get_bucket(myBucket)
blob = bucket.get_blob(myBlob)
self._model = self._pkl.loads(blob.download_as_string())
new_df = self._pd.read_json(element, orient='records').iloc[:, 3:-1]
predict = self._model.predict(new_df)
df = self._pd.DataFrame(data=predict, columns=["A", "B"])
A = df.iloc[0]['A']
B = df.iloc[0]['B']
d = {'A':A, 'B':B}
return [d]

Answer 1

You can use the #Setup method in your MlModel DoFn method where you can load your model and then use it in your #Process method. The #Setup method is called once per worker initialization.
I had written a similar answer here
HTH