Wallaroo SDK Upload Tutorials: Arbitrary Python

• 1: Wallaroo SDK Upload Arbitrary Python Tutorial: Generate VGG16 Model
• 2: Wallaroo SDK Upload Arbitrary Python Tutorial: Deploy VGG16 Model

vgg_clustering\
    feature_extractor.h5
    kmeans.pkl
    custom_inference.py
    requirements.txt

preds = self.model.predict(data)
preds = preds.numpy()
rows, _ = preds.shape
preds = preds.reshape((rows,))

return {"prediction": preds} # a Dictionary of numpy arrays.

from sklearn.cluster import KMeans

class SampleClass(mac.inference.Inference):
    @property
    def expected_model_types(self) -> Set[Any]:
        return {KMeans}

classDiagram
    class InferenceBuilder {
        +create(config InferenceConfig) * Inference
        -inference()* Any
    }

1 - Wallaroo SDK Upload Arbitrary Python Tutorial: Generate VGG16 Model

This tutorial can be downloaded as part of the Wallaroo Tutorials repository.

Wallaroo SDK Upload Arbitrary Python Tutorial: Generate Model

This tutorial demonstrates how to use arbitrary python as a ML Model in Wallaroo. Arbitrary Python allows organizations to use Python scripts that require specific libraries and artifacts as models in the Wallaroo engine. This allows for highly flexible use of ML models with supporting scripts.

Tutorial Goals

This tutorial is split into two parts:

• Wallaroo SDK Upload Arbitrary Python Tutorial: Generate Model: Train a dummy KMeans model for clustering images using a pre-trained VGG16 model on cifar10 as a feature extractor. The Python entry points used for Wallaroo deployment will be added and described.
  • A copy of the arbitrary Python model models/model-auto-conversion-BYOP-vgg16-clustering.zip is included in this tutorial, so this step can be skipped.
• Arbitrary Python Tutorial Deploy Model in Wallaroo Upload and Deploy: Deploys the KMeans model in an arbitrary Python package in Wallaroo, and perform sample inferences. The file models/model-auto-conversion-BYOP-vgg16-clustering.zip is provided so users can go right to testing deployment.

Arbitrary Python Script Requirements

The entry point of the arbitrary python model is any python script that must include the following.

• class ImageClustering(Inference): The default inference class. This is used to perform the actual inferences. Wallaroo uses the _predict method to receive the inference data and call the appropriate functions for the inference.
  • def __init__: Used to initialize this class and load in any other classes or other required settings.
  • def expected_model_types: Used by Wallaroo to anticipate what model types are used by the script.
  • def model(self, model): Defines the model used for the inference. Accepts the model instance used in the inference.
    • self._raise_error_if_model_is_wrong_type(model): Returns the error if the wrong model type is used. This verifies that only the anticipated model type is used for the inference.
    • self._model = model: Sets the submitted model as the model for this class, provided _raise_error_if_model_is_wrong_type is not raised.
  • def _predict(self, input_data: InferenceData): This is the entry point for Wallaroo to perform the inference. This will receive the inference data, then perform whatever steps and return a dictionary of numpy arrays.
• class ImageClusteringBuilder(InferenceBuilder): Loads the model and prepares it for inferencing.
  • def inference(self) -> ImageClustering: Sets the inference class being used for the inferences.
  • def create(self, config: CustomInferenceConfig) -> ImageClustering: Creates an inference subclass, assigning the model and any attributes required for it to function.

All other methods used for the functioning of these classes are optional, as long as they meet the requirements listed above.

Tutorial Prerequisites

• A Wallaroo version 2023.2.1 or above instance.

References

• Wallaroo SDK Essentials Guide: Model Uploads and Registrations: Arbitrary Python

VGG16 Model Training Steps

This process will train a dummy KMeans model for clustering images using a pre-trained VGG16 model on cifar10 as a feature extractor. This model consists of the following elements:

• All elements are stored in the folder models/vgg16_clustering. This will be converted to the zip file model-auto-conversion-BYOP-vgg16-clustering.zip.
• models/vgg16_clustering will contain the following:
  • All necessary model artifacts
  • One or multiple Python files implementing the classes Inference and InferenceBuilder. The implemented classes can have any naming they desire as long as they inherit from the appropriate base classes.
  • a requirements.txt file with all necessary pip requirements to successfully run the inference

Import Libraries

The first step is to import the libraries we’ll be using. These are included by default in the Wallaroo instance’s JupyterHub service.

import numpy as np
import pandas as pd
import json
import os
import pickle
import pyarrow as pa
import tensorflow as tf
import warnings
warnings.filterwarnings('ignore')

from sklearn.cluster import KMeans
from tensorflow.keras.datasets import cifar10
from tensorflow.keras import Model
from tensorflow.keras.layers import Flatten

2023-07-07 16:16:26.511340: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2023-07-07 16:16:26.511369: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.

Variables

We’ll use these variables in later steps rather than hard code them in. In this case, the directory where we’ll store our artifacts.

model_directory = './models/vgg16_clustering'

Load Data Set

In this section, we will load our sample data and shape it.

# Load and preprocess the CIFAR-10 dataset
(X_train, y_train), (X_test, y_test) = cifar10.load_data()

# Normalize the pixel values to be between 0 and 1
X_train = X_train / 255.0
X_test = X_test / 255.0

X_train.shape

(50000, 32, 32, 3)

Train KMeans with VGG16 as feature extractor

Now we will train our model.

pretrained_model = tf.keras.applications.VGG16(include_top=False, 
                                               weights='imagenet', 
                                               input_shape=(32, 32, 3)
                                               )
embedding_model = Model(inputs=pretrained_model.input, 
                        outputs=Flatten()(pretrained_model.output))

2023-07-07 16:16:30.207936: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2023-07-07 16:16:30.207966: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2023-07-07 16:16:30.207987: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (jupyter-john-2ehummel-40wallaroo-2eai): /proc/driver/nvidia/version does not exist
2023-07-07 16:16:30.208169: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

X_train_embeddings = embedding_model.predict(X_train[:100])
X_test_embeddings = embedding_model.predict(X_test[:100])

kmeans = KMeans(n_clusters=2, random_state=0).fit(X_train_embeddings)

Save Models

Let’s first create the directory where the model artifacts will be saved:

os.makedirs(model_directory, exist_ok=True)

And now save the two models:

with  open(f'{model_directory}/kmeans.pkl', 'wb') as fp:
    pickle.dump(kmeans, fp)

embedding_model.save(f'{model_directory}/feature_extractor.h5')

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.

All needed model artifacts have been now saved under our model directory.

Sample Arbitrary Python Script

The following shows an example of extending the Inference and InferenceBuilder classes for our specific model. This script is located in our model directory under ./models/vgg16_clustering.

"""This module features an example implementation of a custom Inference and its
corresponding InferenceBuilder."""

import pathlib
import pickle
from typing import Any, Set

import tensorflow as tf
from mac.config.inference import CustomInferenceConfig
from mac.inference import Inference
from mac.inference.creation import InferenceBuilder
from mac.types import InferenceData
from sklearn.cluster import KMeans

class ImageClustering(Inference):
    """Inference class for image clustering, that uses
    a pre-trained VGG16 model on cifar10 as a feature extractor
    and performs clustering on a trained KMeans model.

    Attributes:
        - feature_extractor: The embedding model we will use
        as a feature extractor (i.e. a trained VGG16).
        - expected_model_types: A set of model instance types that are expected by this inference.
        - model: The model on which the inference is calculated.
    """

    def __init__(self, feature_extractor: tf.keras.Model):
        self.feature_extractor = feature_extractor
        super().__init__()

    @property
    def expected_model_types(self) -> Set[Any]:
        return {KMeans}

    @Inference.model.setter  # type: ignore
    def model(self, model) -> None:
        """Sets the model on which the inference is calculated.

        :param model: A model instance on which the inference is calculated.

        :raises TypeError: If the model is not an instance of expected_model_types
            (i.e. KMeans).
        """
        self._raise_error_if_model_is_wrong_type(model) # this will make sure an error will be raised if the model is of wrong type
        self._model = model

    def _predict(self, input_data: InferenceData) -> InferenceData:
        """Calculates the inference on the given input data.
        This is the core function that each subclass needs to implement
        in order to calculate the inference.

        :param input_data: The input data on which the inference is calculated.
        It is of type InferenceData, meaning it comes as a dictionary of numpy
        arrays.

        :raises InferenceDataValidationError: If the input data is not valid.
        Ideally, every subclass should raise this error if the input data is not valid.

        :return: The output of the model, that is a dictionary of numpy arrays.
        """

        # input_data maps to the input_schema we have defined
        # with PyArrow, coming as a dictionary of numpy arrays
        inputs = input_data["images"]

        # Forward inputs to the models
        embeddings = self.feature_extractor(inputs)
        predictions = self.model.predict(embeddings.numpy())

        # Return predictions as dictionary of numpy arrays
        return {"predictions": predictions}

class ImageClusteringBuilder(InferenceBuilder):
    """InferenceBuilder subclass for ImageClustering, that loads
    a pre-trained VGG16 model on cifar10 as a feature extractor
    and a trained KMeans model, and creates an ImageClustering object."""

    @property
    def inference(self) -> ImageClustering:
        return ImageClustering

    def create(self, config: CustomInferenceConfig) -> ImageClustering:
        """Creates an Inference subclass and assigns a model and additionally
        needed attributes to it.

        :param config: Custom inference configuration. In particular, we're
        interested in `config.model_path` that is a pathlib.Path object
        pointing to the folder where the model artifacts are saved.
        Every artifact we need to load from this folder has to be
        relative to `config.model_path`.

        :return: A custom Inference instance.
        """
        feature_extractor = self._load_feature_extractor(
            config.model_path / "feature_extractor.h5"
        )
        inference = self.inference(feature_extractor)
        model = self._load_model(config.model_path / "kmeans.pkl")
        inference.model = model

        return inference

    def _load_feature_extractor(
        self, file_path: pathlib.Path
    ) -> tf.keras.Model:
        return tf.keras.models.load_model(file_path)

    def _load_model(self, file_path: pathlib.Path) -> KMeans:
        with open(file_path.as_posix(), "rb") as fp:
            model = pickle.load(fp)
        return model

Create Requirements File

As a last step we need to create a requirements.txt file and save it under our vgg_clustering/. The file should contain all the necessary pip requirements needed to run the inference. It will have this data inside.

• IMPORTANT NOTE: Verify that the library versions match the required model versions and libraries for Wallaroo. Otherwise the deployed models

tensorflow==2.8.0
scikit-learn==1.2.2

Zip model folder

Assuming we have stored the following files inside out model directory models/vgg_clustering/:

1. feature_extractor.h5
2. kmeans.pkl
3. custom_inference.py
4. requirements.txt

Now we will zip the file. This is performed with the zip command and the -r option to zip the contents of the entire directory.

zip -r model-auto-conversion-BYOP-vgg16-clustering.zip vgg16_clustering/

The arbitrary Python custom model can now be uploaded to the Wallaroo instance.

2 - Wallaroo SDK Upload Arbitrary Python Tutorial: Deploy VGG16 Model

This tutorial can be downloaded as part of the Wallaroo Tutorials repository.

Arbitrary Python Tutorial Deploy Model in Wallaroo Upload and Deploy

This tutorial demonstrates how to use arbitrary python as a ML Model in Wallaroo. Arbitrary Python allows organizations to use Python scripts that require specific libraries and artifacts as models in the Wallaroo engine. This allows for highly flexible use of ML models with supporting scripts.

Tutorial Goals

This tutorial is split into two parts:

• Wallaroo SDK Upload Arbitrary Python Tutorial: Generate Model: Train a dummy KMeans model for clustering images using a pre-trained VGG16 model on cifar10 as a feature extractor. The Python entry points used for Wallaroo deployment will be added and described.
  • A copy of the arbitrary Python model models/model-auto-conversion-BYOP-vgg16-clustering.zip is included in this tutorial, so this step can be skipped.
• Arbitrary Python Tutorial Deploy Model in Wallaroo Upload and Deploy: Deploys the KMeans model in an arbitrary Python package in Wallaroo, and perform sample inferences. The file models/model-auto-conversion-BYOP-vgg16-clustering.zip is provided so users can go right to testing deployment.

Arbitrary Python Script Requirements

The entry point of the arbitrary python model is any python script that must include the following.

• class ImageClustering(Inference): The default inference class. This is used to perform the actual inferences. Wallaroo uses the _predict method to receive the inference data and call the appropriate functions for the inference.
  • def __init__: Used to initialize this class and load in any other classes or other required settings.
  • def expected_model_types: Used by Wallaroo to anticipate what model types are used by the script.
  • def model(self, model): Defines the model used for the inference. Accepts the model instance used in the inference.
    • self._raise_error_if_model_is_wrong_type(model): Returns the error if the wrong model type is used. This verifies that only the anticipated model type is used for the inference.
    • self._model = model: Sets the submitted model as the model for this class, provided _raise_error_if_model_is_wrong_type is not raised.
  • def _predict(self, input_data: InferenceData): This is the entry point for Wallaroo to perform the inference. This will receive the inference data, then perform whatever steps and return a dictionary of numpy arrays.
• class ImageClusteringBuilder(InferenceBuilder): Loads the model and prepares it for inferencing.
  • def inference(self) -> ImageClustering: Sets the inference class being used for the inferences.
  • def create(self, config: CustomInferenceConfig) -> ImageClustering: Creates an inference subclass, assigning the model and any attributes required for it to function.

All other methods used for the functioning of these classes are optional, as long as they meet the requirements listed above.

Tutorial Prerequisites

• A Wallaroo version 2023.2.1 or above instance.

References

• Wallaroo SDK Essentials Guide: Model Uploads and Registrations: Arbitrary Python

Tutorial Steps

Import Libraries

The first step is to import the libraries we’ll be using. These are included by default in the Wallaroo instance’s JupyterHub service.

import numpy as np
import pandas as pd
import json
import os
import pickle
import pyarrow as pa
import tensorflow as tf
import wallaroo

from sklearn.cluster import KMeans
from tensorflow.keras.datasets import cifar10
from tensorflow.keras import Model
from tensorflow.keras.layers import Flatten
from wallaroo.pipeline   import Pipeline
from wallaroo.deployment_config import DeploymentConfigBuilder
from wallaroo.framework import Framework
from wallaroo.object import EntityNotFoundError

Open a Connection to Wallaroo

The next step is connect to Wallaroo through the Wallaroo client. The Python library is included in the Wallaroo install and available through the Jupyter Hub interface provided with your Wallaroo environment.

This is accomplished using the wallaroo.Client() command, which provides a URL to grant the SDK permission to your specific Wallaroo environment. When displayed, enter the URL into a browser and confirm permissions. Store the connection into a variable that can be referenced later.

If logging into the Wallaroo instance through the internal JupyterHub service, use wl = wallaroo.Client(). If logging in externally, update the wallarooPrefix and wallarooSuffix variables with the proper DNS information. For more information on Wallaroo DNS settings, see the Wallaroo DNS Integration Guide.

wl = wallaroo.Client()

Set Variables and Helper Functions

We’ll set the name of our workspace, pipeline, models and files. Workspace names must be unique across the Wallaroo workspace. For this, we’ll add in a randomly generated 4 characters to the workspace name to prevent collisions with other users’ workspaces. If running this tutorial, we recommend hard coding the workspace name so it will function in the same workspace each time it’s run.

We’ll set up some helper functions that will either use existing workspaces and pipelines, or create them if they do not already exist.

# import string
# import random

# # make a random 4 character suffix to prevent overwriting other user's workspaces
# suffix= ''.join(random.choice(string.ascii_lowercase) for i in range(4))

suffix=''

workspace_name = f'vgg16-clustering-workspace{suffix}'
pipeline_name = f'vgg16-clustering-pipeline'

model_name = 'vgg16-clustering'
model_file_name = './models/model-auto-conversion-BYOP-vgg16-clustering.zip'

def get_workspace(name):
    workspace = None
    for ws in wl.list_workspaces():
        if ws.name() == name:
            workspace= ws
    if(workspace == None):
        workspace = wl.create_workspace(name)
    return workspace

def get_pipeline(name):
    try:
        pipeline = wl.pipelines_by_name(name)[0]
    except EntityNotFoundError:
        pipeline = wl.build_pipeline(name)
    return pipeline

Create Workspace and Pipeline

We will now create the Wallaroo workspace to store our model and set it as the current workspace. Future commands will default to this workspace for pipeline creation, model uploads, etc. We’ll create our Wallaroo pipeline that is used to deploy our arbitrary Python model.

workspace = get_workspace(workspace_name)
wl.set_current_workspace(workspace)

pipeline = get_pipeline(pipeline_name)

Upload Arbitrary Python Model

Arbitrary Python models are uploaded to Wallaroo through the Wallaroo Client upload_model method.

Upload Arbitrary Python Model Parameters

The following parameters are required for Arbitrary Python models. Note that while some fields are considered as optional for the upload_model method, they are required for proper uploading of a Arbitrary Python model to Wallaroo.

Once the upload process starts, the model is containerized by the Wallaroo instance. This process may take up to 10 minutes.

Upload Arbitrary Python Model Return

The following is returned with a successful model upload and conversion.

For our example, we’ll start with setting the input_schema and output_schema that is expected by our ImageClustering._predict() method.

input_schema = pa.schema([
    pa.field('images', pa.list_(
        pa.list_(
            pa.list_(
                pa.int64(),
                list_size=3
            ),
            list_size=32
        ),
        list_size=32
    )),
])

output_schema = pa.schema([
    pa.field('predictions', pa.int64()),
])

Upload Model

Now we’ll upload our model. The framework is Framework.CUSTOM for arbitrary Python models, and we’ll specify the input and output schemas for the upload.

model = wl.upload_model(model_name, 
                        model_file_name, 
                        framework=Framework.CUSTOM, 
                        input_schema=input_schema, 
                        output_schema=output_schema, 
                        convert_wait=True)
model

Waiting for model loading - this will take up to 10.0min.
Model is pending loading to a container runtime..
Model is attempting loading to a container runtime..........................successful
Ready

model.config().runtime()

'flight'

Deploy Pipeline

The model is uploaded and ready for use. We’ll add it as a step in our pipeline, then deploy the pipeline. For this example we’re allocated 0.25 cpu and 4 Gi RAM to the pipeline through the pipeline’s deployment configuration.

pipeline.add_model_step(model)

deployment_config = DeploymentConfigBuilder() \
    .cpus(0.25).memory('4Gi') \
    .build()

pipeline.deploy(deployment_config=deployment_config)
pipeline.status()

pipeline.status()

{'status': 'Error',
 'details': [],
 'engines': [{'ip': '10.244.3.130',
   'name': 'engine-6fcbccd45c-wrrfq',
   'status': 'Running',
   'reason': None,
   'details': [],
   'pipeline_statuses': {'pipelines': [{'id': 'vgg16-clustering-pipeline',
      'status': 'Running'}]},
   'model_statuses': {'models': [{'name': 'vgg16-clustering',
      'version': '49e8e007-27ae-49da-8a69-6ed5c4aec890',
      'sha': '7bb3362b1768c92ea7e593451b2b8913d3b7616c19fd8d25b73fb6990f9283e0',
      'status': 'Running'}]}}],
 'engine_lbs': [{'ip': '10.244.2.188',
   'name': 'engine-lb-584f54c899-ssbjw',
   'status': 'Running',
   'reason': None,
   'details': []}],
 'sidekicks': [{'ip': '10.244.3.129',
   'name': 'engine-sidekick-vgg16-clustering-55-7bcb767b67-ljl6k',
   'status': 'Running',
   'reason': None,
   'details': [],
   'statuses': None}]}

Run inference

Everything is in place - we’ll now run a sample inference with some toy data. In this case we’re randomly generating some values in the data shape the model expects, then submitting an inference request through our deployed pipeline.

input_data = {
        "images": [np.random.randint(0, 256, (32, 32, 3), dtype=np.uint8)] * 2,
}
dataframe = pd.DataFrame(input_data)
dataframe

pipeline.infer(dataframe, timeout=10000)

Undeploy Pipelines

The inference is successful, so we will undeploy the pipeline and return the resources back to the cluster.

pipeline.undeploy()