Inference URL Tutorials

• 1: Wallaroo SDK Inferencing with Pipeline Inference URL Tutorial
• 2: Wallaroo MLOps API Inferencing with Pipeline Inference URL Tutorial

1 - Wallaroo SDK Inferencing with Pipeline Inference URL Tutorial

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

Wallaroo SDK Inference Tutorial

Wallaroo provides the ability to perform inferences through deployed pipelines via the Wallaroo SDK and the Wallaroo MLOps API. This tutorial demonstrates performing inferences using the Wallaroo SDK.

This tutorial provides the following:

• ccfraud.onnx: A pre-trained credit card fraud detection model.
• data/cc_data_1k.arrow, data/cc_data_10k.arrow: Sample testing data in Apache Arrow format with 1,000 and 10,000 records respectively.
• wallaroo-model-endpoints-sdk.py: A code-only version of this tutorial as a Python script.

This tutorial and sample data comes from the Machine Learning Group’s demonstration on Credit Card Fraud detection.

Prerequisites

The following is required for this tutorial:

• A deployed Wallaroo instance with Model Endpoints Enabled
• The following Python libraries:
  • pandas
  • pyarrow
  • wallaroo (Installed in the Wallaroo JupyterHub service by default).

Tutorial Goals

This demonstration provides a quick tutorial on performing inferences using the Wallaroo SDK using the Pipeline infer and infer_from_file methods. This following steps will be performed:

• Connect to a Wallaroo instance using environmental variables. This bypasses the browser link confirmation for a seamless login. For more information, see the Wallaroo SDK Essentials Guide: Client Connection.
• Create a workspace for our models and pipelines.
• Upload the ccfraud model.
• Create a pipeline and add the ccfraud model as a pipeline step.
• Run a sample inference through SDK Pipeline infer method.
• Run a batch inference through SDK Pipeline infer_from_file method.
• Run a DataFrame and Arrow based inference through the pipeline Inference URL.

Open a Connection to Wallaroo

The first step is to connect to Wallaroo through the Wallaroo client. This example will store the user’s credentials into the file ./creds.json which contains the following:

{
    "username": "{Connecting User's Username}", 
    "password": "{Connecting User's Password}", 
    "email": "{Connecting User's Email Address}"
}

Replace the username, password, and email fields with the user account connecting to the Wallaroo instance. This allows a seamless connection to the Wallaroo instance and bypasses the standard browser based confirmation link. For more information, see the Wallaroo SDK Essentials Guide: Client Connection.

If running this example within the internal Wallaroo JupyterHub service, use the wallaroo.Client(auth_type="user_password") method. If connecting externally via the Wallaroo SDK, use the following to specify the URL of the Wallaroo instance as defined in the Wallaroo DNS Integration Guide, replacing wallarooPrefix. and wallarooSuffix with your Wallaroo instance’s DNS prefix and suffix.

Note the . is part of the prefix. If there is no prefix, then wallarooPrefix = ""

import wallaroo
from wallaroo.object import EntityNotFoundError
import pandas as pd
import os
import pyarrow as pa

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

import requests

# Used to create unique workspace and pipeline names
import string
import random

# make a random 4 character suffix to prevent workspace and pipeline name clobbering
suffix= ''.join(random.choice(string.ascii_lowercase) for i in range(4))

# Retrieve the login credentials.
os.environ["WALLAROO_SDK_CREDENTIALS"] = './creds.json'

# Client connection from local Wallaroo instance

wl = wallaroo.Client(auth_type="user_password")

Create the Workspace

We will create a workspace to work in and call it the sdkinferenceexampleworkspace, then set it as current workspace environment. We’ll also create our pipeline in advance as sdkinferenceexamplepipeline.

The model to be uploaded and used for inference will be labeled as ccfraud.

workspace_name = f'sdkinferenceexampleworkspace{suffix}'
pipeline_name = f'sdkinferenceexamplepipeline{suffix}'
model_name = f'ccfraud{suffix}'
model_file_name = './ccfraud.onnx'

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

workspace = get_workspace(workspace_name)

wl.set_current_workspace(workspace)

{'name': 'sdkinferenceexampleworkspacesrsw', 'id': 47, 'archived': False, 'created_by': 'fec5b97a-934b-487f-b95b-ade7f3b81f9c', 'created_at': '2023-05-19T15:14:02.432103+00:00', 'models': [], 'pipelines': []}

Build Pipeline

In a production environment, the pipeline would already be set up with the model and pipeline steps. We would then select it and use it to perform our inferences.

For this example we will create the pipeline and add the ccfraud model as a pipeline step and deploy it. Deploying a pipeline allocates resources from the Kubernetes cluster hosting the Wallaroo instance and prepares it for performing inferences.

If this process was already completed, it can be commented out and skipped for the next step Select Pipeline.

Then we will list the pipelines and select the one we will be using for the inference demonstrations.

# Create or select the current pipeline

ccfraudpipeline = get_pipeline(pipeline_name)

# Add ccfraud model as the pipeline step

ccfraud_model = (wl.upload_model(model_name, 
                                 model_file_name, 
                                 framework=wallaroo.framework.Framework.ONNX)
                                 .configure(tensor_fields=["tensor"])
                )

ccfraudpipeline.add_model_step(ccfraud_model).deploy()

Select Pipeline

This step assumes that the pipeline is prepared with ccfraud as the current step. The method pipelines_by_name(name) returns an array of pipelines with names matching the pipeline_name field. This example assumes only one pipeline is assigned the name sdkinferenceexamplepipeline.

# List the pipelines by name in the current workspace - just the first several to save space.

display(wl.list_pipelines()[:5])

# Set the `pipeline` variable to our sample pipeline.

pipeline = wl.pipelines_by_name(pipeline_name)[0]
display(pipeline)

[{'name': 'sdkinferenceexamplepipelinesrsw', 'create_time': datetime.datetime(2023, 5, 19, 15, 14, 3, 916503, tzinfo=tzutc()), 'definition': '[]'},
 {'name': 'ccshadoweonn', 'create_time': datetime.datetime(2023, 5, 19, 15, 13, 48, 963815, tzinfo=tzutc()), 'definition': '[]'},
 {'name': 'ccshadowgozg', 'create_time': datetime.datetime(2023, 5, 19, 15, 8, 23, 58929, tzinfo=tzutc()), 'definition': '[]'}]

Interferences via SDK

Once a pipeline has been deployed, an inference can be run. This will submit data to the pipeline, where it is processed through each of the pipeline’s steps with the output of the previous step providing the input for the next step. The final step will then output the result of all of the pipeline’s steps.

• Inputs are either sent one of the following:
  • pandas.DataFrame. The return value will be a pandas.DataFrame.
  • Apache Arrow. The return value will be an Apache Arrow table.
  • Custom JSON. The return value will be a Wallaroo InferenceResult object.

Inferences are performed through the Wallaroo SDK via the Pipeline infer and infer_from_file methods.

infer Method

Now that the pipeline is deployed we’ll perform an inference using the Pipeline infer method, and submit a pandas DataFrame as our input data. This will return a pandas DataFrame as the inference output.

For more information, see the Wallaroo SDK Essentials Guide: Inferencing: Run Inference through Local Variable.

smoke_test = pd.DataFrame.from_records([
    {
        "tensor":[
            1.0678324729,
            0.2177810266,
            -1.7115145262,
            0.682285721,
            1.0138553067,
            -0.4335000013,
            0.7395859437,
            -0.2882839595,
            -0.447262688,
            0.5146124988,
            0.3791316964,
            0.5190619748,
            -0.4904593222,
            1.1656456469,
            -0.9776307444,
            -0.6322198963,
            -0.6891477694,
            0.1783317857,
            0.1397992467,
            -0.3554220649,
            0.4394217877,
            1.4588397512,
            -0.3886829615,
            0.4353492889,
            1.7420053483,
            -0.4434654615,
            -0.1515747891,
            -0.2668451725,
            -1.4549617756
        ]
    }
])
result = pipeline.infer(smoke_test)
display(result)

infer_from_file Method

This example uses the Pipeline method infer_from_file to submit 10,000 records as a batch using an Apache Arrow table. The method will return an Apache Arrow table. For more information, see the Wallaroo SDK Essentials Guide: Inferencing: Run Inference From A File

The results will be converted into a pandas.DataFrame. The results will be filtered by transactions likely to be credit card fraud.

result = pipeline.infer_from_file('./data/cc_data_10k.arrow')

display(result)

    pyarrow.Table
    time: timestamp[ms]
    in.tensor: list<item: float> not null
      child 0, item: float
    out.dense_1: list<inner: float not null> not null
      child 0, inner: float not null
    check_failures: int8
    ----
    time: [[2023-05-19 15:14:22.851,2023-05-19 15:14:22.851,2023-05-19 15:14:22.851,2023-05-19 15:14:22.851,2023-05-19 15:14:22.851,...,2023-05-19 15:14:22.851,2023-05-19 15:14:22.851,2023-05-19 15:14:22.851,2023-05-19 15:14:22.851,2023-05-19 15:14:22.851]]
    in.tensor: [[[-1.0603298,2.3544967,-3.5638788,5.138735,-1.2308457,...,0.038412016,1.0993439,1.2603409,-0.14662448,-1.4463212],[-1.0603298,2.3544967,-3.5638788,5.138735,-1.2308457,...,0.038412016,1.0993439,1.2603409,-0.14662448,-1.4463212],...,[-2.1694233,-3.1647356,1.2038506,-0.2649221,0.0899006,...,1.8174038,-0.19327773,0.94089776,0.825025,1.6242892],[-0.12405868,0.73698884,1.0311689,0.59917533,0.11831961,...,-0.36567155,-0.87004745,0.41288367,0.49470216,-0.6710689]]]
    out.dense_1: [[[0.99300325],[0.99300325],...,[0.00024175644],[0.0010648072]]]
    check_failures: [[0,0,0,0,0,...,0,0,0,0,0]]

# use pyarrow to convert results to a pandas DataFrame and display only the results with > 0.75

list = [0.75]

outputs =  result.to_pandas()
# display(outputs)
filter = [elt[0] > 0.75 for elt in outputs['out.dense_1']]
outputs = outputs.loc[filter]
display(outputs)

Inferences via HTTP POST

Each pipeline has its own Inference URL that allows HTTP/S POST submissions of inference requests. Full details are available from the Inferencing via the Wallaroo MLOps API.

This example will demonstrate performing inferences with a DataFrame input and an Apache Arrow input.

Request JWT Token

There are two ways to retrieve the JWT token used to authenticate to the Wallaroo MLOps API.

• Wallaroo SDK. This method requires a Wallaroo based user.
• API Clent Secret. This is the recommended method as it is user independent. It allows any valid user to make an inference request.

This tutorial will use the Wallaroo SDK method Wallaroo Client wl.auth.auth_header() method, extracting the Authentication header from the response.

Reference: MLOps API Retrieve Token Through Wallaroo SDK

headers = wl.auth.auth_header()
display(headers)

{'Authorization': 'Bearer eyJhbGciOiJSUzI1NiIsInR5cCIgOiAiSldUIiwia2lkIiA6ICJhSFpPS1RacGhxT1JQVkw4Y19JV25qUDNMU29iSnNZNXBtNE5EQTA1NVZNIn0.eyJleHAiOjE2ODQ1MDkzMDgsImlhdCI6MTY4NDUwOTI0OCwiYXV0aF90aW1lIjoxNjg0NTA4ODUwLCJqdGkiOiJkZmU3ZTIyMS02ODMyLTRiOGItYjJiMS1hYzFkYWY2YjVmMWYiLCJpc3MiOiJodHRwczovL3NwYXJrbHktYXBwbGUtMzAyNi5rZXljbG9hay53YWxsYXJvby5jb21tdW5pdHkvYXV0aC9yZWFsbXMvbWFzdGVyIiwiYXVkIjpbIm1hc3Rlci1yZWFsbSIsImFjY291bnQiXSwic3ViIjoiZmVjNWI5N2EtOTM0Yi00ODdmLWI5NWItYWRlN2YzYjgxZjljIiwidHlwIjoiQmVhcmVyIiwiYXpwIjoic2RrLWNsaWVudCIsInNlc3Npb25fc3RhdGUiOiI5ZTQ0YjNmNC0xYzg2LTRiZmQtOGE3My0yYjc0MjY5ZmNiNGMiLCJhY3IiOiIwIiwicmVhbG1fYWNjZXNzIjp7InJvbGVzIjpbImNyZWF0ZS1yZWFsbSIsImRlZmF1bHQtcm9sZXMtbWFzdGVyIiwib2ZmbGluZV9hY2Nlc3MiLCJhZG1pbiIsInVtYV9hdXRob3JpemF0aW9uIl19LCJyZXNvdXJjZV9hY2Nlc3MiOnsibWFzdGVyLXJlYWxtIjp7InJvbGVzIjpbInZpZXctaWRlbnRpdHktcHJvdmlkZXJzIiwidmlldy1yZWFsbSIsIm1hbmFnZS1pZGVudGl0eS1wcm92aWRlcnMiLCJpbXBlcnNvbmF0aW9uIiwiY3JlYXRlLWNsaWVudCIsIm1hbmFnZS11c2VycyIsInF1ZXJ5LXJlYWxtcyIsInZpZXctYXV0aG9yaXphdGlvbiIsInF1ZXJ5LWNsaWVudHMiLCJxdWVyeS11c2VycyIsIm1hbmFnZS1ldmVudHMiLCJtYW5hZ2UtcmVhbG0iLCJ2aWV3LWV2ZW50cyIsInZpZXctdXNlcnMiLCJ2aWV3LWNsaWVudHMiLCJtYW5hZ2UtYXV0aG9yaXphdGlvbiIsIm1hbmFnZS1jbGllbnRzIiwicXVlcnktZ3JvdXBzIl19LCJhY2NvdW50Ijp7InJvbGVzIjpbIm1hbmFnZS1hY2NvdW50IiwibWFuYWdlLWFjY291bnQtbGlua3MiLCJ2aWV3LXByb2ZpbGUiXX19LCJzY29wZSI6ImVtYWlsIHByb2ZpbGUiLCJzaWQiOiI5ZTQ0YjNmNC0xYzg2LTRiZmQtOGE3My0yYjc0MjY5ZmNiNGMiLCJlbWFpbF92ZXJpZmllZCI6dHJ1ZSwiaHR0cHM6Ly9oYXN1cmEuaW8vand0L2NsYWltcyI6eyJ4LWhhc3VyYS11c2VyLWlkIjoiZmVjNWI5N2EtOTM0Yi00ODdmLWI5NWItYWRlN2YzYjgxZjljIiwieC1oYXN1cmEtZGVmYXVsdC1yb2xlIjoidXNlciIsIngtaGFzdXJhLWFsbG93ZWQtcm9sZXMiOlsidXNlciJdLCJ4LWhhc3VyYS11c2VyLWdyb3VwcyI6Int9In0sInByZWZlcnJlZF91c2VybmFtZSI6ImpvaG4uaHVtbWVsQHdhbGxhcm9vLmFpIiwiZW1haWwiOiJqb2huLmh1bW1lbEB3YWxsYXJvby5haSJ9.YksrXBWIxMHz2Mh0dhM8GVvFUQJH5sCVTfA5qYiMIquME5vROVjqlm72k2FwdHQmRdwbwKGU1fGfuw6ijAfvVvd50lMdhYrT6TInhdaXX6UZ0pqsuuXyC1HxaTfC5JA7yOQo7SGQ3rjVvsSo_tHhf08HW6gmg2FO9Sdsbo3y2cPEqG7xR_vbB93s_lmQHjN6T8lAdq_io2jkDFUlKtAapAQ3Z5d68-Na5behVqtGeYRb6UKJTUoH-dso7zRwZ1RcqX5_3kT2xEL-dfkAndkvzRCfjOz-OJQEjo2j9iJFWpVaNjsUA45FCUhSNfuG1-zYtAOWcSmq8DyxAt6hY-fgaA'}

Retrieve the Pipeline Inference URL

The Pipeline Inference URL is retrieved via the Wallaroo SDK with the Pipeline ._deployment._url() method.

• IMPORTANT NOTE: The _deployment._url() method will return an internal URL when using Python commands from within the Wallaroo instance - for example, the Wallaroo JupyterHub service. When connecting via an external connection, _deployment._url() returns an external URL.
  • External URL connections requires the authentication be included in the HTTP request, and Model Endpoints are enabled in the Wallaroo configuration options.

deploy_url = pipeline._deployment._url()
print(deploy_url)

https://sparkly-apple-3026.api.wallaroo.community/v1/api/pipelines/infer/sdkinferenceexamplepipelinesrsw-28/sdkinferenceexamplepipelinesrsw

HTTP Inference with DataFrame Input

The following example performs a HTTP Inference request with a DataFrame input. The request will be made with first a Python requests method, then using curl.

# get authorization header
headers = wl.auth.auth_header()

## Inference through external URL using dataframe

# retrieve the json data to submit
data = pd.DataFrame.from_records([
    {
        "tensor":[
            1.0678324729,
            0.2177810266,
            -1.7115145262,
            0.682285721,
            1.0138553067,
            -0.4335000013,
            0.7395859437,
            -0.2882839595,
            -0.447262688,
            0.5146124988,
            0.3791316964,
            0.5190619748,
            -0.4904593222,
            1.1656456469,
            -0.9776307444,
            -0.6322198963,
            -0.6891477694,
            0.1783317857,
            0.1397992467,
            -0.3554220649,
            0.4394217877,
            1.4588397512,
            -0.3886829615,
            0.4353492889,
            1.7420053483,
            -0.4434654615,
            -0.1515747891,
            -0.2668451725,
            -1.4549617756
        ]
    }
])

# set the content type for pandas records
headers['Content-Type']= 'application/json; format=pandas-records'

# set accept as pandas-records
headers['Accept']='application/json; format=pandas-records'

# submit the request via POST, import as pandas DataFrame
response = pd.DataFrame.from_records(
                requests.post(
                    deploy_url, 
                    data=data.to_json(orient="records"), 
                    headers=headers)
                .json()
            )
display(response.loc[:,["time", "out"]])

!curl -X POST {deploy_url} -H "Authorization: {headers['Authorization']}" -H "Content-Type:{headers['Content-Type']}" -H "Accept:{headers['Accept']}" --data '{data.to_json(orient="records")}'

[{"time":1684509264292,"in":{"tensor":[1.0678324729,0.2177810266,-1.7115145262,0.682285721,1.0138553067,-0.4335000013,0.7395859437,-0.2882839595,-0.447262688,0.5146124988,0.3791316964,0.5190619748,-0.4904593222,1.1656456469,-0.9776307444,-0.6322198963,-0.6891477694,0.1783317857,0.1397992467,-0.3554220649,0.4394217877,1.4588397512,-0.3886829615,0.4353492889,1.7420053483,-0.4434654615,-0.1515747891,-0.2668451725,-1.4549617756]},"out":{"dense_1":[0.0014974177]},"check_failures":[],"metadata":{"last_model":"{\"model_name\":\"ccfraudsrsw\",\"model_sha\":\"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507\"}","pipeline_version":"81840bdb-a1bc-48b9-8df0-4c7a196fa79a","elapsed":[62451,212744]}}]

HTTP Inference with Arrow Input

The following example performs a HTTP Inference request with an Apache Arrow input. The request will be made with first a Python requests method, then using curl.

Only the first 5 rows will be displayed for space purposes.

# get authorization header
headers = wl.auth.auth_header()

# Submit arrow file
dataFile="./data/cc_data_10k.arrow"

data = open(dataFile,'rb').read()

# set the content type for Arrow table
headers['Content-Type']= "application/vnd.apache.arrow.file"

# set accept as Apache Arrow
headers['Accept']="application/vnd.apache.arrow.file"

response = requests.post(
                    deploy_url, 
                    headers=headers, 
                    data=data, 
                    verify=True
                )

# Arrow table is retrieved 
with pa.ipc.open_file(response.content) as reader:
    arrow_table = reader.read_all()

# convert to Polars DataFrame and display the first 5 rows
display(arrow_table.to_pandas().head(5).loc[:,["time", "out"]])

!curl -X POST {deploy_url} -H "Authorization: {headers['Authorization']}" -H "Content-Type:{headers['Content-Type']}" -H "Accept:{headers['Accept']}" --data-binary @{dataFile} > curl_response.arrow

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 4200k  100 3037k  100 1162k  1980k   757k  0:00:01  0:00:01 --:--:-- 2766k

Undeploy Pipeline

When finished with our tests, we will undeploy the pipeline so we have the Kubernetes resources back for other tasks.

pipeline.undeploy()

2 - Wallaroo MLOps API Inferencing with Pipeline Inference URL Tutorial

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

Wallaroo API Inference Tutorial

Wallaroo provides the ability to perform inferences through deployed pipelines via the Wallaroo SDK and the Wallaroo MLOps API. This tutorial demonstrates performing inferences using the Wallaroo MLOps API.

This tutorial provides the following:

• ccfraud.onnx: A pre-trained credit card fraud detection model.
• data/cc_data_1k.arrow, data/cc_data_10k.arrow: Sample testing data in Apache Arrow format with 1,000 and 10,000 records respectively.
• wallaroo-model-endpoints-api.py: A code-only version of this tutorial as a Python script.

This tutorial and sample data comes from the Machine Learning Group’s demonstration on Credit Card Fraud detection.

Prerequisites

The following is required for this tutorial:

• A deployed Wallaroo instance with Model Endpoints Enabled
• The following Python libraries:
  • os
  • requests
  • pandas
  • pyarrow
  • wallaroo (Installed in the Wallaroo JupyterHub service by default).

Tutorial Goals

This demonstration provides a quick tutorial on performing inferences using the Wallaroo MLOps API using a deployed pipeline’s Inference URL. This following steps will be performed:

• Connect to a Wallaroo instance using the Wallaroo SDK and environmental variables. This bypasses the browser link confirmation for a seamless login, and provides a simple method of retrieving the JWT token used for Wallaroo MLOps API calls. For more information, see the Wallaroo SDK Essentials Guide: Client Connection and the Wallaroo MLOps API Essentials Guide.
• Create a workspace for our models and pipelines.
• Upload the ccfraud model.
• Create a pipeline and add the ccfraud model as a pipeline step.
• Run sample inferences with pandas DataFrame inputs and Apache Arrow inputs.

Retrieve Token

There are two methods of retrieving the JWT token used to authenticate to the Wallaroo instance’s API service:

• Wallaroo SDK. This method requires a Wallaroo based user.
• API Client Secret. This is the recommended method as it is user independent. It allows any valid user to make an inference request.

This tutorial will use the Wallaroo SDK method for convenience with environmental variables for a seamless login without browser validation. For more information, see the Wallaroo SDK Essentials Guide: Client Connection.

API Request Methods

All Wallaroo API endpoints follow the format:

• https://$URLPREFIX.api.$URLSUFFIX/v1/api$COMMAND

Where $COMMAND is the specific endpoint. For example, for the command to list of workspaces in the Wallaroo instance would use the above format based on these settings:

• $URLPREFIX: smooth-moose-1617
• $URLSUFFIX: example.wallaroo.ai
• $COMMAND: /workspaces/list

This would create the following API endpoint:

• https://smooth-moose-1617.api.example.wallaroo.ai/v1/api/workspaces/list

Connect to Wallaroo

For this example, a connection to the Wallaroo SDK is used. This will be used to retrieve the JWT token for the MLOps API calls.

This example will store the user’s credentials into the file ./creds.json which contains the following:

{
    "username": "{Connecting User's Username}", 
    "password": "{Connecting User's Password}", 
    "email": "{Connecting User's Email Address}"
}

Replace the username, password, and email fields with the user account connecting to the Wallaroo instance. This allows a seamless connection to the Wallaroo instance and bypasses the standard browser based confirmation link. For more information, see the Wallaroo SDK Essentials Guide: Client Connection.

Update wallarooPrefix = "YOUR PREFIX." and wallarooSuffix = "YOUR SUFFIX" to match the Wallaroo instance used for this demonstration. Note the . is part of the prefix. If there is no prefix, then wallarooPrefix = ""

import wallaroo
from wallaroo.object import EntityNotFoundError

import pandas as pd
import os
import base64

import pyarrow as pa

import requests
from requests.auth import HTTPBasicAuth

# Used to create unique workspace and pipeline names
import string
import random

# make a random 4 character prefix
suffix= ''.join(random.choice(string.ascii_lowercase) for i in range(4))
display(suffix)

import json

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

'atwc'

# Retrieve the login credentials.
os.environ["WALLAROO_SDK_CREDENTIALS"] = './creds.json.example'

# wl = wallaroo.Client(auth_type="user_password")

# Client connection from local Wallaroo instance
wallarooPrefix = ""
wallarooSuffix = "autoscale-uat-ee.wallaroo.dev"

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

wallarooPrefix = "YOUR PREFIX."
wallarooPrefix = "YOUR SUFFIX"

wallarooPrefix = ""
wallarooSuffix = "autoscale-uat-ee.wallaroo.dev"

APIURL=f"https://{wallarooPrefix}api.{wallarooSuffix}"
APIURL

'https://api.autoscale-uat-ee.wallaroo.dev'

Retrieve the JWT Token

As mentioned earlier, there are multiple methods of authenticating to the Wallaroo instance for MLOps API calls. This tutorial will use the Wallaroo SDK method Wallaroo Client wl.auth.auth_header() method, extracting the token from the response.

Reference: MLOps API Retrieve Token Through Wallaroo SDK

# Retrieve the token
headers = wl.auth.auth_header()
display(headers)

{'Authorization': 'Bearer eyJhbGciOiJSUzI1NiIsInR5cCIgOiAiSldUIiwia2lkIiA6ICJEWkc4UE4tOHJ0TVdPdlVGc0V0RWpacXNqbkNjU0tJY3Zyak85X3FxcXc0In0.eyJleHAiOjE2ODg3NTE2NjQsImlhdCI6MTY4ODc1MTYwNCwianRpIjoiNGNmNmFjMzQtMTVjMy00MzU0LWI0ZTYtMGYxOWIzNjg3YmI2IiwiaXNzIjoiaHR0cHM6Ly9rZXljbG9hay5hdXRvc2NhbGUtdWF0LWVlLndhbGxhcm9vLmRldi9hdXRoL3JlYWxtcy9tYXN0ZXIiLCJhdWQiOlsibWFzdGVyLXJlYWxtIiwiYWNjb3VudCJdLCJzdWIiOiJkOWE3MmJkOS0yYTFjLTQ0ZGQtOTg5Zi0zYzdjMTUxMzA4ODUiLCJ0eXAiOiJCZWFyZXIiLCJhenAiOiJzZGstY2xpZW50Iiwic2Vzc2lvbl9zdGF0ZSI6Ijk0MjkxNTAwLWE5MDgtNGU2Ny1hMzBiLTA4MTczMzNlNzYwOCIsImFjciI6IjEiLCJyZWFsbV9hY2Nlc3MiOnsicm9sZXMiOlsiZGVmYXVsdC1yb2xlcy1tYXN0ZXIiLCJvZmZsaW5lX2FjY2VzcyIsInVtYV9hdXRob3JpemF0aW9uIl19LCJyZXNvdXJjZV9hY2Nlc3MiOnsibWFzdGVyLXJlYWxtIjp7InJvbGVzIjpbIm1hbmFnZS11c2VycyIsInZpZXctdXNlcnMiLCJxdWVyeS1ncm91cHMiLCJxdWVyeS11c2VycyJdfSwiYWNjb3VudCI6eyJyb2xlcyI6WyJtYW5hZ2UtYWNjb3VudCIsIm1hbmFnZS1hY2NvdW50LWxpbmtzIiwidmlldy1wcm9maWxlIl19fSwic2NvcGUiOiJwcm9maWxlIGVtYWlsIiwic2lkIjoiOTQyOTE1MDAtYTkwOC00ZTY3LWEzMGItMDgxNzMzM2U3NjA4IiwiZW1haWxfdmVyaWZpZWQiOmZhbHNlLCJodHRwczovL2hhc3VyYS5pby9qd3QvY2xhaW1zIjp7IngtaGFzdXJhLXVzZXItaWQiOiJkOWE3MmJkOS0yYTFjLTQ0ZGQtOTg5Zi0zYzdjMTUxMzA4ODUiLCJ4LWhhc3VyYS1kZWZhdWx0LXJvbGUiOiJ1c2VyIiwieC1oYXN1cmEtYWxsb3dlZC1yb2xlcyI6WyJ1c2VyIl0sIngtaGFzdXJhLXVzZXItZ3JvdXBzIjoie30ifSwibmFtZSI6IkpvaG4gSGFuc2FyaWNrIiwicHJlZmVycmVkX3VzZXJuYW1lIjoiam9obi5odW1tZWxAd2FsbGFyb28uYWkiLCJnaXZlbl9uYW1lIjoiSm9obiIsImZhbWlseV9uYW1lIjoiSGFuc2FyaWNrIiwiZW1haWwiOiJqb2huLmh1bW1lbEB3YWxsYXJvby5haSJ9.QE5WJ6NI5bQob0p2M7KsVXxrAiUUxnsIjZPuHIx7_6kTsDt4zarcCu2b5X6s6wg0EZQDX22oANWUAXnkWRTQd_E6zE7DkKF7H5kodtyu90ewiFM8ULx2iOWy2GkafQTdiuW90-BGDIjAcOiQtOkdHNaNHqJ9go2Lsom1t_b4-FOhh8bAGhMM3aDS0w-Y8dGKClxW_xFSTmOjNLaPxbFs5NCib-_QAsR_PiyfSFNJ_kjIV8f2mdzeyOauj0YOE-w5nXjhbrDvhS1kJ3n_8C2J2eOnEg85OGd3m6VKVzoR7oPzoZH15Jtl8shKTDS6BEUWpzZNfjYjwZdy1KTenCbzAQ'}

Create Workspace

In a production environment, the Wallaroo workspace that contains the pipeline and models would be created and deployed. We will quickly recreate those steps using the MLOps API. If the workspace and pipeline have already been created through the Wallaroo SDK Inference Tutorial, then we can skip directly to Deploy Pipeline.

Workspaces are created through the MLOps API with the /v1/api/workspaces/create command. This requires the workspace name be provided, and that the workspace not already exist in the Wallaroo instance.

Reference: MLOps API Create Workspace

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json'

# Create workspace
apiRequest = f"{APIURL}/v1/api/workspaces/create"

workspace_name = f"apiinferenceexampleworkspace{suffix}"

data = {
  "workspace_name": workspace_name
}

response = requests.post(apiRequest, json=data, headers=headers, verify=True).json()
display(response)
# Stored for future examples
workspaceId = response['workspace_id']

{'workspace_id': 374}

Upload Model

The model is uploaded using the /v1/api/models/upload_and_convert command. This uploads a ML Model to a Wallaroo workspace via POST with Content-Type: multipart/form-data and takes the following parameters:

• Parameters
  • name - (REQUIRED string): Name of the model
  • visibility - (OPTIONAL string): The visibility of the model as either public or private.
  • workspace_id - (REQUIRED int): The numerical id of the workspace to upload the model to. Stored earlier as workspaceId.

Directly after we will use the /models/list_versions to retrieve model details used for later steps.

Reference: Wallaroo MLOps API Essentials Guide: Model Management: Upload Model to Workspace

## upload model

# Retrieve the token
headers = wl.auth.auth_header()

apiRequest = f"{APIURL}/v1/api/models/upload_and_convert"

framework='onnx'

model_name = f"{suffix}ccfraud"

data = {
    "name": model_name,
    "visibility": "public",
    "workspace_id": workspaceId,
    "conversion": {
        "framework": framework,
        "python_version": "3.8",
        "requirements": []
    }
}

files = {
    "metadata": (None, json.dumps(data), "application/json"),
    'file': (model_name, open('./ccfraud.onnx', 'rb'), "application/octet-stream")
    }

response = requests.post(apiRequest, files=files, headers=headers).json()
display(response)
modelId=response['insert_models']['returning'][0]['models'][0]['id']

{'insert_models': {'returning': [{'models': [{'id': 176}]}]}}

# Get the model details

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json'

apiRequest = f"{APIURL}/v1/api/models/get_by_id"

data = {
  "id": modelId
}

response = requests.post(apiRequest, json=data, headers=headers, verify=True).json()
display(response)

{'msg': 'The provided model id was not found.', 'code': 400}

# Get the model details

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json'

apiRequest = f"{APIURL}/v1/api/models/list_versions"

data = {
  "model_id": model_name,
  "models_pk_id" : modelId
}

response = requests.post(apiRequest, json=data, headers=headers, verify=True).json()
display(response)

[{'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
  'models_pk_id': 175,
  'model_version': 'fa4c2f8c-769e-4ee1-9a91-fe029a4beffc',
  'owner_id': '""',
  'model_id': 'vsnaccfraud',
  'id': 176,
  'file_name': 'vsnaccfraud',
  'image_path': 'proxy.replicated.com/proxy/wallaroo/ghcr.io/wallaroolabs/mlflow-deploy:v2023.3.0-main-3481',
  'status': 'ready'},
 {'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
  'models_pk_id': 175,
  'model_version': '701be439-8702-4896-88b5-644bb5cb4d61',
  'owner_id': '""',
  'model_id': 'vsnaccfraud',
  'id': 175,
  'file_name': 'vsnaccfraud',
  'image_path': 'proxy.replicated.com/proxy/wallaroo/ghcr.io/wallaroolabs/mlflow-deploy:v2023.3.0-main-3481',
  'status': 'ready'}]

model_version = response[0]['model_version']
display(model_version)
model_sha = response[0]['sha']
display(model_sha)

'fa4c2f8c-769e-4ee1-9a91-fe029a4beffc'
‘bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507’

Create Pipeline

Create Pipeline in a Workspace with the /v1/api/pipelines/create command. This creates a new pipeline in the specified workspace.

• Parameters
  • pipeline_id - (REQUIRED string): Name of the new pipeline.
  • workspace_id - (REQUIRED int): Numerical id of the workspace for the new pipeline. Stored earlier as workspaceId.
  • definition - (REQUIRED string): Pipeline definitions, can be {} for none.

For our example, we are setting the pipeline steps through the definition field. This will direct inference requests to the model before output.

Reference: Wallaroo MLOps API Essentials Guide: Pipeline Management: Create Pipeline in a Workspace

# Create pipeline

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json'

apiRequest = f"{APIURL}/v1/api/pipelines/create"

pipeline_name=f"{suffix}apiinferenceexamplepipeline"

data = {
  "pipeline_id": pipeline_name,
  "workspace_id": workspaceId,
  "definition": {'steps': [{'ModelInference': {'models': [{'name': f'{model_name}', 'version': model_version, 'sha': model_sha}]}}]}
}

response = requests.post(apiRequest, json=data, headers=headers, verify=True).json()

pipeline_id = response['pipeline_pk_id']
pipeline_variant_id=response['pipeline_variant_pk_id']
pipeline_variant_version=['pipeline_variant_version']

Deploy Pipeline

With the pipeline created and the model uploaded into the workspace, the pipeline can be deployed. This will allocate resources from the Kubernetes cluster hosting the Wallaroo instance and prepare the pipeline to process inference requests.

Pipelines are deployed through the MLOps API command /v1/api/pipelines/deploy which takes the following parameters:

• Parameters
  • deploy_id (REQUIRED string): The name for the pipeline deployment.
  • engine_config (OPTIONAL string): Additional configuration options for the pipeline.
  • pipeline_version_pk_id (REQUIRED int): Pipeline version id. Captured earlier as pipeline_variant_id.
  • model_configs (OPTIONAL Array int): Ids of model configs to apply.
  • model_ids (OPTIONAL Array int): Ids of models to apply to the pipeline. If passed in, model_configs will be created automatically.
  • models (OPTIONAL Array models): If the model ids are not available as a pipeline step, the models’ data can be passed to it through this method. The options below are only required if models are provided as a parameter.
    • name (REQUIRED string): Name of the uploaded model that is in the same workspace as the pipeline. Captured earlier as the model_name variable.
    • version (REQUIRED string): Version of the model to use.
    • sha (REQUIRED string): SHA value of the model.
  • pipeline_id (REQUIRED int): Numerical value of the pipeline to deploy.
• Returns
  • id (int): The deployment id.

Reference: Wallaroo MLOps API Essentials Guide: Pipeline Management: Deploy a Pipeline

# Deploy Pipeline

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json'

apiRequest = f"{APIURL}/v1/api/pipelines/deploy"

exampleModelDeployId=pipeline_name

data = {
    "deploy_id": exampleModelDeployId,
    "pipeline_version_pk_id": pipeline_variant_id,
    "model_ids": [
        modelId
    ],
    "pipeline_id": pipeline_id
}

response = requests.post(apiRequest, json=data, headers=headers, verify=True).json()
display(response)
exampleModelDeploymentId=response['id']

# wait 45 seconds for the pipeline to complete deployment
import time
time.sleep(45)

{'id': 260}

Get Deployment Status

This returns the deployment status - we’re waiting until the deployment has the status “Ready.”

• Parameters
  • name - (REQUIRED string): The deployment in the format {deployment_name}-{deploymnent-id}.

Example: The deployed empty and model pipelines status will be displayed.

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json'

# Get model pipeline deployment

api_request = f"{APIURL}/v1/api/status/get_deployment"

data = {
  "name": f"{pipeline_name}-{exampleModelDeploymentId}"
}

response = requests.post(api_request, json=data, headers=headers, verify=True).json()
response

{'status': 'Running',
 'details': [],
 'engines': [{'ip': '10.244.17.3',
   'name': 'engine-f77b5c44b-4j2n5',
   'status': 'Running',
   'reason': None,
   'details': [],
   'pipeline_statuses': {'pipelines': [{'id': 'vsnaapiinferenceexamplepipeline',
      'status': 'Running'}]},
   'model_statuses': {'models': [{'name': 'vsnaccfraud',
      'version': 'fa4c2f8c-769e-4ee1-9a91-fe029a4beffc',
      'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
      'status': 'Running'}]}}],
 'engine_lbs': [{'ip': '10.244.17.4',
   'name': 'engine-lb-584f54c899-q877m',
   'status': 'Running',
   'reason': None,
   'details': []}],
 'sidekicks': []}

Get External Inference URL

The API command /admin/get_pipeline_external_url retrieves the external inference URL for a specific pipeline in a workspace.

• Parameters
  • workspace_id (REQUIRED integer): The workspace integer id.
  • pipeline_name (REQUIRED string): The name of the pipeline.

In this example, a list of the workspaces will be retrieved. Based on the setup from the Internal Pipeline Deployment URL Tutorial, the workspace matching urlworkspace will have it’s workspace id stored and used for the /admin/get_pipeline_external_url request with the pipeline urlpipeline.

The External Inference URL will be stored as a variable for the next step.

Reference: Wallaroo MLOps API Essentials Guide: Pipeline Management: Get External Inference URL

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json'

## Retrieve the pipeline's External Inference URL

apiRequest = f"{APIURL}/v1/api/admin/get_pipeline_external_url"

data = {
    "workspace_id": workspaceId,
    "pipeline_name": pipeline_name
}

response = requests.post(apiRequest, json=data, headers=headers, verify=True).json()
deployurl = response['url']
deployurl

'https://api.autoscale-uat-ee.wallaroo.dev/v1/api/pipelines/infer/vsnaapiinferenceexamplepipeline-260/vsnaapiinferenceexamplepipeline'

Perform Inference Through External URL

The inference can now be performed through the External Inference URL. This URL will accept the same inference data file that is used with the Wallaroo SDK, or with an Internal Inference URL as used in the Internal Pipeline Inference URL Tutorial.

For this example, the externalUrl retrieved through the Get External Inference URL is used to submit a single inference request through the data file data-1.json.

Reference: Wallaroo MLOps API Essentials Guide: Pipeline Management: Perform Inference Through External URL

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json; format=pandas-records'

## Inference through external URL using dataframe

# retrieve the json data to submit
data = [
    {
        "tensor":[
            1.0678324729,
            0.2177810266,
            -1.7115145262,
            0.682285721,
            1.0138553067,
            -0.4335000013,
            0.7395859437,
            -0.2882839595,
            -0.447262688,
            0.5146124988,
            0.3791316964,
            0.5190619748,
            -0.4904593222,
            1.1656456469,
            -0.9776307444,
            -0.6322198963,
            -0.6891477694,
            0.1783317857,
            0.1397992467,
            -0.3554220649,
            0.4394217877,
            1.4588397512,
            -0.3886829615,
            0.4353492889,
            1.7420053483,
            -0.4434654615,
            -0.1515747891,
            -0.2668451725,
            -1.4549617756
        ]
    }
]

# submit the request via POST, import as pandas DataFrame
response = pd.DataFrame.from_records(
    requests.post(
        deployurl, 
        json=data, 
        headers=headers)
        .json()
    )

display(response.loc[:,["time", "out"]])

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/vnd.apache.arrow.file'

# set accept as apache arrow table
headers['Accept']="application/vnd.apache.arrow.file"

# Submit arrow file
dataFile="./data/cc_data_10k.arrow"

data = open(dataFile,'rb').read()

response = requests.post(
                    deployurl, 
                    headers=headers, 
                    data=data, 
                    verify=True
                )

# Arrow table is retrieved 
with pa.ipc.open_file(response.content) as reader:
    arrow_table = reader.read_all()

# convert to Polars DataFrame and display the first 5 rows
display(arrow_table.to_pandas().head(5).loc[:,["time", "out"]])

Undeploy the Pipeline

With the tutorial complete, we’ll undeploy the pipeline with /v1/api/pipelines/undeploy and return the resources back to the Wallaroo instance.

Reference: Wallaroo MLOps API Essentials Guide: Pipeline Management: Undeploy a Pipeline

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json'

apiRequest = f"{APIURL}/v1/api/pipelines/undeploy"

data = {
    "pipeline_id": pipeline_id,
    "deployment_id":exampleModelDeploymentId
}

response = requests.post(apiRequest, json=data, headers=headers, verify=True).json()
display(response)

None

Wallaroo supports the ability to perform inferences through the SDK and through the API for each deployed pipeline. For more information on how to use Wallaroo, see the Wallaroo Documentation Site for full details.