## IMDB Tutorial

The IMDB Tutorial demonstrates how to use Wallaroo to determine if reviews are positive or negative.

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

## IMDB Sample

The following example demonstrates how to use Wallaroo with chained models. In this example, we will be using information from the IMDB (Internet Movie DataBase) with a sentiment model to detect whether a given review is positive or negative. Imagine using this to automatically scan Tweets regarding your product and finding either customers who need help or have nice things to say about your product.

Note that this example is considered a “toy” model - only the first 100 words in the review were tokenized, and the embedding is very small.

The following example is based on the Large Movie Review Dataset, and sample data can be downloaded from the aclIMDB dataset.

## Open a Connection to Wallaroo

The first step is to 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.

import wallaroo
from wallaroo.object import EntityNotFoundError
from IPython.display import display

# used to display dataframe information without truncating
import pandas as pd
pd.set_option('display.max_colwidth', None)

print(wallaroo.__version__)

2023.1.0rc1


### Arrow Support

As of the 2023.1 release, Wallaroo provides support for dataframe and Arrow for inference inputs. This tutorial allows users to adjust their experience based on whether they have enabled Arrow support in their Wallaroo instance or not.

If Arrow support has been enabled, arrowEnabled=True. If disabled or you’re not sure, set it to arrowEnabled=False

The examples below will be shown in an arrow enabled environment.

import os
# Only set the below to make the OS environment ARROW_ENABLED to TRUE.  Otherwise, leave as is.
# os.environ["ARROW_ENABLED"]="True"

if "ARROW_ENABLED" not in os.environ or os.environ["ARROW_ENABLED"].casefold() == "False".casefold():
arrowEnabled = False
else:
arrowEnabled = True
print(arrowEnabled)

True

# Login through local Wallaroo instance

wl = wallaroo.Client()

# wl = wallaroo.Client(api_endpoint=f"https://{wallarooPrefix}.api.{wallarooSuffix}",
#                     auth_endpoint=f"https://{wallarooPrefix}.keycloak.{wallarooSuffix}",
#                     auth_type="sso")


To test this model, we will perform the following:

• Create a workspace for our models.
• embedder: Takes pre-tokenized text documents (model input: 100 integers/datum; output 800 numbers/datum) and creates an embedding from them.
• sentiment: The second model classifies the resulting embeddings from 0 to 1, which 0 being an unfavorable review, 1 being a favorable review.
• Create a pipeline that will take incoming data and pass it to the embedder, which will pass the output to the sentiment model, and then export the final result.
• To test it, we will use information that has already been tokenized and submit it to our pipeline and gauge the results.

Just for the sake of this tutorial, we’ll use the SDK below to create our workspace , assign as our current workspace, then display all of the workspaces we have at the moment. We’ll also set up for our models and pipelines down the road, so we have one spot to change names to whatever fits your organization’s standards best.

To allow this tutorial to be run multiple times or by multiple users in the same Wallaroo instance, a random 4 character prefix will be added to the workspace, pipeline, and model.

When we create our new workspace, we’ll save it in the Python variable workspace so we can refer to it as needed.

First we’ll create a workspace for our environment, and call it imdbworkspace. We’ll also set up our pipeline so it’s ready for our models.

import string
import random

# make a random 4 character prefix
prefix= ''.join(random.choice(string.ascii_lowercase) for i in range(4))
workspace_name = f'{prefix}imdbworkspace'
pipeline_name = f'{prefix}imdbpipeline'

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(pipeline_name)[0]
except EntityNotFoundError:
pipeline = wl.build_pipeline(pipeline_name)
return pipeline

workspace = get_workspace(workspace_name)

wl.set_current_workspace(workspace)

imdb_pipeline = get_pipeline(pipeline_name)
imdb_pipeline

name vzfximdbpipeline 2023-02-27 18:54:24.344787+00:00 2023-02-27 18:54:24.344787+00:00 (none) 1f3f3e66-56ec-401b-b550-5f06655f4770

Just to make sure, let’s list our current workspace. If everything is going right, it will show us we’re in the imdb-workspace.

wl.get_current_workspace()

{'name': 'vzfximdbworkspace', 'id': 18, 'archived': False, 'created_by': '435da905-31e2-4e74-b423-45c38edb5889', 'created_at': '2023-02-27T18:54:23.351035+00:00', 'models': [], 'pipelines': [{'name': 'vzfximdbpipeline', 'create_time': datetime.datetime(2023, 2, 27, 18, 54, 24, 344787, tzinfo=tzutc()), 'definition': '[]'}]}


Now we’ll upload our two models:

• embedder.onnx: This will be used to embed the tokenized documents for evaluation.
• sentiment_model.onnx: This will be used to analyze the review and determine if it is a positive or negative review. The closer to 0, the more likely it is a negative review, while the closer to 1 the more likely it is to be a positive review.
embedder = wl.upload_model(f'{prefix}embedder-o', './embedder.onnx').configure()


With our models uploaded, now we’ll create our pipeline that will contain two steps:

• First, it runs the data through the embedder.
• Second, it applies it to our sentiment model.
# now make a pipeline

name vzfximdbpipeline 2023-02-27 18:54:24.344787+00:00 2023-02-27 18:54:24.344787+00:00 (none) 1f3f3e66-56ec-401b-b550-5f06655f4770

Now that we have our pipeline set up with the steps, we can deploy the pipeline.

imdb_pipeline.deploy()

name vzfximdbpipeline 2023-02-27 18:54:24.344787+00:00 2023-02-27 18:54:30.220884+00:00 True bc2644e2-d780-4515-ab6a-044f4e6fd27e, 1f3f3e66-56ec-401b-b550-5f06655f4770 vzfxembedder-o

We’ll check the pipeline status to verify it’s deployed and the models are ready.

imdb_pipeline.status()

{'status': 'Running',
'details': [],
'engines': [{'ip': '10.244.0.45',
'name': 'engine-57d4bf6d5d-j77n8',
'status': 'Running',
'reason': None,
'details': [],
'pipeline_statuses': {'pipelines': [{'id': 'vzfximdbpipeline',
'status': 'Running'}]},
'model_statuses': {'models': [{'name': 'vzfxsmodel-o',
'version': '69f78f5e-633d-49d8-b46e-d57563669e7b',
'sha': '3473ea8700fbf1a1a8bfb112554a0dde8aab36758030dcde94a9357a83fd5650',
'status': 'Running'},
{'name': 'vzfxembedder-o',
'version': 'b50749b8-af50-4404-9f95-ffd084ee9416',
'status': 'Running'}]}}],
'engine_lbs': [{'ip': '10.244.2.18',
'name': 'engine-lb-ddd995646-njpqc',
'status': 'Running',
'reason': None,
'details': []}],
'sidekicks': []}

print(arrowEnabled)

True


To test this out, we’ll start with a single piece of information from our data directory.

if arrowEnabled is True:
# using Dataframe JSON
results = results = imdb_pipeline.infer_from_file('./data/singleton.df.json')
display(results)
else:
# pre-arrow Wallaroo JSON
results = imdb_pipeline.infer_from_file('./data/singleton.json')
display(results[0].data())

time in.tensor out.dense_1 check_failures
0 2023-02-27 18:54:47.498 [1607.0, 2635.0, 5749.0, 199.0, 49.0, 351.0, 16.0, 2919.0, 159.0, 5092.0, 2457.0, 8.0, 11.0, 1252.0, 507.0, 42.0, 287.0, 316.0, 15.0, 65.0, 136.0, 2.0, 133.0, 16.0, 4311.0, 131.0, 286.0, 153.0, 5.0, 2826.0, 175.0, 54.0, 548.0, 48.0, 1.0, 17.0, 9.0, 183.0, 1.0, 111.0, 15.0, 1.0, 17.0, 284.0, 982.0, 18.0, 28.0, 211.0, 1.0, 1382.0, 8.0, 146.0, 1.0, 19.0, 12.0, 9.0, 13.0, 21.0, 1898.0, 122.0, 14.0, 70.0, 14.0, 9.0, 97.0, 25.0, 74.0, 1.0, 189.0, 12.0, 9.0, 6.0, 31.0, 3.0, 244.0, 2497.0, 3659.0, 2.0, 665.0, 2497.0, 63.0, 180.0, 1.0, 17.0, 6.0, 287.0, 3.0, 646.0, 44.0, 15.0, 161.0, 50.0, 71.0, 438.0, 351.0, 31.0, 5749.0, 2.0, 0.0, 0.0] [0.37142318] 0

Since that works, let’s load up all 50 rows and do a full inference on each of them. Note that Jupyter Hub has a size limitation, so for production systems the outputs should be piped out to a different output.

if arrowEnabled is True:
# using Dataframe JSON
results = imdb_pipeline.infer_from_file('./data/test_data.df.json')
# just display the first row for space
display(results.loc[0,:])
else:
# pre-arrow Wallaroo JSON
results = imdb_pipeline.infer_from_file('./data/test_data.json')
# just display the first row for space
display(results[0].data())

time                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                2023-02-27 18:54:48.032000
in.tensor         [1607.0, 2635.0, 5749.0, 199.0, 49.0, 351.0, 16.0, 2919.0, 159.0, 5092.0, 2457.0, 8.0, 11.0, 1252.0, 507.0, 42.0, 287.0, 316.0, 15.0, 65.0, 136.0, 2.0, 133.0, 16.0, 4311.0, 131.0, 286.0, 153.0, 5.0, 2826.0, 175.0, 54.0, 548.0, 48.0, 1.0, 17.0, 9.0, 183.0, 1.0, 111.0, 15.0, 1.0, 17.0, 284.0, 982.0, 18.0, 28.0, 211.0, 1.0, 1382.0, 8.0, 146.0, 1.0, 19.0, 12.0, 9.0, 13.0, 21.0, 1898.0, 122.0, 14.0, 70.0, 14.0, 9.0, 97.0, 25.0, 74.0, 1.0, 189.0, 12.0, 9.0, 6.0, 31.0, 3.0, 244.0, 2497.0, 3659.0, 2.0, 665.0, 2497.0, 63.0, 180.0, 1.0, 17.0, 6.0, 287.0, 3.0, 646.0, 44.0, 15.0, 161.0, 50.0, 71.0, 438.0, 351.0, 31.0, 5749.0, 2.0, 0.0, 0.0]
out.dense_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       [0.37142318]
check_failures                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               0
Name: 0, dtype: object


## Undeploy

With our pipeline’s work done, we’ll undeploy it and give our Kubernetes environment back its resources.

imdb_pipeline.undeploy()

name vzfximdbpipeline 2023-02-27 18:54:24.344787+00:00 2023-02-27 18:54:30.220884+00:00 False bc2644e2-d780-4515-ab6a-044f4e6fd27e, 1f3f3e66-56ec-401b-b550-5f06655f4770 vzfxembedder-o

And there is our example. Please feel free to contact us at Wallaroo for if you have any questions.