Wallaroo Connection API with Google BigQuery Tutorial
This can be downloaded as part of the Wallaroo Tutorials repository.
Wallaroo Connection and ML Workload Orchestration with BigQuery House Price Model Tutorial
This tutorial provides a quick set of methods and examples regarding Wallaroo Connections. For full details, see the Wallaroo Documentation site.
Wallaroo provides Data Connections to organizations with a method of creating and managing automated tasks that can either be run on demand or a regular schedule.
Definitions
- Orchestration: A set of instructions written as a python script with a requirements library. Orchestrations are uploaded to the Wallaroo instance as a .zip file.
- Task: An implementation of an orchestration. Tasks are run either once when requested, on a repeating schedule, or as a service.
- Connection: Definitions set by MLOps engineers that are used by other Wallaroo users for connection information to a data source. Usually paired with orchestrations.
This tutorial will focus on using Google BigQuery as the data source.
Tutorial Goals
The tutorial will demonstrate the following:
- Create a Wallaroo connection to retrieving information from a Google BigQuery source table.
- Create a Wallaroo connection to store inference results into a Google BigQuery destination table.
- Upload Wallaroo ML Workload Orchestration that supports BigQuery connections with the connection details.
- Run the orchestration once as a Run Once Task and verify that the inference request succeeded and the inference results were saved to the external data store.
- Schedule the orchestration as a Scheduled Task and verify that the inference request succeeded and the inference results were saved to the external data store.
Prerequisites
- An installed Wallaroo instance.
- The following Python libraries installed. These are included by default in a Wallaroo instance’s JupyterHub service.
- The following Python libraries. These are not included in a Wallaroo instance’s JupyterHub service.
google-cloud-bigquery
: Specifically for its support for Google BigQuery.google-auth
: Used to authenticate for bigquery.db-dtypes
: Converts the BigQuery results to Apache Arrow table or pandas DataFrame.
Tutorial Resources
- Models:
models/rf_model.onnx
: A model that predicts house price values.
- Data:
data/xtest-1.df.json
anddata/xtest-1k.df.json
: DataFrame JSON inference inputs with 1 input and 1,000 inputs.data/xtest-1k.arrow
: Apache Arrow inference inputs with 1 input and 1,000 inputs.- Sample inference inputs in
CSV
that can be imported into Google BigQuery.data/xtest-1k.df.json
: Random sample housing prices.data/smallinputs.df.json
: Sample housing prices that return results lower than $1.5 million.data/biginputs.df.json
: Sample housing prices that return results higher than $1.5 million.
- SQL queries to create the inputs/outputs tables with schema.
./resources/create_inputs_table.sql
: Inputs table with schema../resources/create_outputs_table.sql
: Outputs table with schema../resources/housrpricesga_inputs.avro
: Avro container of inputs table.
Initial Steps
For this tutorial, we’ll create a workspace, upload our sample model and deploy a pipeline. We’ll perform some quick sample inferences to verify that everything it working.
Load Libraries
Here we’ll import the various libraries we’ll use for the tutorial.
import wallaroo
from wallaroo.object import EntityNotFoundError, RequiredAttributeMissing
# to display dataframe tables
from IPython.display import display
# used to display dataframe information without truncating
import pandas as pd
pd.set_option('display.max_colwidth', None)
import pyarrow as pa
import time
import json
# for Big Query connections
from google.cloud import bigquery
from google.oauth2 import service_account
import db_dtypes
import requests
# used for unique connection names
import string
import random
suffix= ''.join(random.choice(string.ascii_lowercase) for i in range(4))
wallaroo.__version__
'2023.2.0rc3'
Connect to the Wallaroo Instance
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 Client settings, see the Client Connection guide.
# Login through local Wallaroo instance
wl = wallaroo.Client()
API URL
The variable APIURL
is used to specify the connection to the Wallaroo instance’s MLOps API URL, and is composed of the Wallaroo DNS prefix and suffix. For full details, see the Wallaroo API Connection Guide
.
The variables wallarooPrefix
and wallarooSuffix
variables will be used to derive the API url. For example, if the Wallaroo Prefix is doc-test.
and the url is example.com
, then the MLOps API URL would be doc-test.api.example.com/v1/api/{request}
.
Note the .
is part of the prefix. If there is no prefix, then wallarooPrefix = ""
Set the Wallaroo Prefix and Suffix in the code segment below based on your Wallaroo instance.
Variable Declaration
The following variables will be used for our big query testing.
We’ll use two connections:
- bigquery_input_connection: The connection that will draw inference input data from a BigQuery table.
- bigquery_output_connection: The connection that will upload inference results into a BigQuery table.
Not that for the connection arguments, we’ll retrieve the information from the files ./bigquery_service_account_input_key.json
and ./bigquery_service_account_output_key.json
that include the service account key file(SAK) information, as well as the dataset and table used.
Field | Included in SAK |
---|---|
type | √ |
project_id | √ |
private_key_id | √ |
private_key | √ |
client_email | √ |
auth_uri | √ |
token_uri | √ |
auth_provider_x509_cert_url | √ |
client_x509_cert_url | √ |
database | 🚫 |
table | 🚫 |
# Setting variables for later steps
wallarooPrefix = "YOUR PREFIX."
wallarooSuffix = "YOUR SUFFIX"
APIURL = f"https://{wallarooPrefix}api.{wallarooSuffix}"
# Setting variables for later steps
workspace_name = 'bigqueryapiworkspace'
pipeline_name = 'bigqueryapipipeline'
model_name = 'bigqueryapimodel'
model_file_name = './models/rf_model.onnx'
bigquery_connection_input_name = f"bigqueryhouseapiinput{suffix}"
bigquery_connection_input_type = "BIGQUERY"
bigquery_connection_input_argument = json.load(open('./bigquery_service_account_input_key.json'))
bigquery_connection_output_name = f"bigqueryhouseapioutputs{suffix}"
bigquery_connection_output_type = "BIGQUERY"
bigquery_connection_output_argument = json.load(open('./bigquery_service_account_output_key.json'))
Helper Methods
The following helper methods are used to either create or get workspaces, pipelines, and connections.
# helper methods to retrieve workspaces and pipelines
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
def get_connection(name, connection_type, connection_arguments):
try:
connection = wl.get_connection(name)
except RequiredAttributeMissing:
connection =wl.create_connection(name,
connection_type,
connection_arguments)
return connection
Create the Workspace and Pipeline
We’ll now create our workspace and pipeline for the tutorial. If this tutorial has been run previously, then this will retrieve the existing ones with the assumption they’re for us with this tutorial.
We’ll set the retrieved workspace as the current workspace in the SDK, so all commands will default to that workspace.
workspace = get_workspace(workspace_name)
wl.set_current_workspace(workspace)
workspace_id = workspace.id()
pipeline = get_pipeline(pipeline_name)
Upload the Model and Deploy Pipeline
We’ll upload our model into our sample workspace, then add it as a pipeline step before deploying the pipeline to it’s ready to accept inference requests.
# Upload the model
housing_model_control = wl.upload_model(model_name, model_file_name, framework=wallaroo.framework.Framework.ONNX).configure()
# Add the model as a pipeline step
pipeline.add_model_step(housing_model_control)
name | bigqueryapipipeline |
---|---|
created | 2023-05-17 16:06:45.400005+00:00 |
last_updated | 2023-05-17 16:06:48.150211+00:00 |
deployed | True |
tags | |
versions | 5e78de4f-51f1-43ce-8c2c-d724a05f856a, d2f03a9c-dfbb-4a03-a014-e70aa80902e3 |
steps | bigqueryapimodel |
#deploy the pipeline
pipeline.deploy()
name | bigqueryapipipeline |
---|---|
created | 2023-05-17 16:06:45.400005+00:00 |
last_updated | 2023-05-17 16:11:23.317215+00:00 |
deployed | True |
tags | |
versions | 153a11e5-7968-450c-aef5-d1be17c6b173, 5e78de4f-51f1-43ce-8c2c-d724a05f856a, d2f03a9c-dfbb-4a03-a014-e70aa80902e3 |
steps | bigqueryapimodel |
Connection Management via the Wallaroo MLOps API
The following steps will demonstration using the Wallaroo MLOps API to:
- Create the BigQuery connections
- Add the connections to the targeted workspace
- Use the connections for inference requests and uploading the results to a BigQuery dataset table.
Create Connections via API
We will create the data source connection via the Wallaroo api request:
/v1/api/connections/create
This takes the following parameters:
name (String Required): The name of the connection.
type (String Required): The user defined type of connection.
details (String Required): User defined configuration details for the data connection. These can be
{'username':'dataperson', 'password':'datapassword', 'port': 3339}
, or{'token':'abcde123==', 'host':'example.com', 'port:1234'}
, or other user defined combinations.IMPORTANT NOTE: Data connections names must be unique. Attempting to create a data connection with the same
name
as an existing data connection will result in an error.
# retrieve the authorization token
headers = wl.auth.auth_header()
url = f"{APIURL}/v1/api/connections/create"
# input connection
data = {
'name': bigquery_connection_input_name,
'type' : bigquery_connection_input_type,
'details': bigquery_connection_input_argument
}
response=requests.post(url, headers=headers, json=data).json()
display(response)
# saved for later steps
connection_input_id = response['id']
{'id': '839779d7-d4b3-4e1c-953a-2f5ef55f1bb2'}
# retrieve the authorization token
headers = wl.auth.auth_header()
url = f"{APIURL}/v1/api/connections/create"
# output connection
data = {
'name': bigquery_connection_output_name,
'type' : bigquery_connection_output_type,
'details': bigquery_connection_output_argument
}
response=requests.post(url, headers=headers, json=data).json()
display(response)
# saved for later steps
connection_output_id = response['id']
{'id': 'ef0c62e7-e59f-4c43-bdff-05ed0976cffa'}
Add Connections to Workspace via API
The connections will be added to the sample workspace with the MLOps API request:
/v1/api/connections/add_to_workspace
This takes the following parameters:
- workspace_id (String Required): The name of the connection.
- connection_id (String Required): The UUID connection ID
# retrieve the authorization token
headers = wl.auth.auth_header()
url = f"{APIURL}/v1/api/connections/add_to_workspace"
data = {
'workspace_id': workspace_id,
'connection_id': connection_input_id
}
response=requests.post(url, headers=headers, json=data)
display(response.json())
data = {
'workspace_id': workspace_id,
'connection_id': connection_output_id
}
response=requests.post(url, headers=headers, json=data)
display(response.json())
{'id': '3877da9d-e450-4b9a-85dd-7f68127b1313'}
{‘id’: ‘15f247ab-833f-468a-b7c5-9b50831052e6’}
Connect to Google BigQuery
With our connections set, we’ll now use them for an inference request through the following steps:
- Retrieve the input data from a BigQuery request from the input connection details.
- Perform the inference.
- Upload the inference results into another BigQuery table from the output connection details.
Create Google Credentials
From our BigQuery request, we’ll create the credentials for our BigQuery connection.
We will use the MLOps API call:
/v1/api/connections/get
to retrieve the connection. This request takes the following parameters:
- name (String Required): The name of the connection.
# get the connection input details
# retrieve the authorization token
headers = wl.auth.auth_header()
url = f"{APIURL}/v1/api/connections/get"
data = {
'name': bigquery_connection_input_name
}
connection_input_details=requests.post(url, headers=headers, json=data).json()['details']
# get the connection output details
# retrieve the authorization token
headers = wl.auth.auth_header()
url = f"{APIURL}/v1/api/connections/get"
data = {
'name': bigquery_connection_output_name
}
connection_output_details=requests.post(url, headers=headers, json=data).json()['details']
# Set the bigquery credentials
bigquery_input_credentials = service_account.Credentials.from_service_account_info(
connection_input_details)
bigquery_output_credentials = service_account.Credentials.from_service_account_info(
connection_output_details)
Connect to Google BigQuery
We can now generate a client from our connection details, specifying the project that was included in the big_query_connection
details.
bigqueryinputclient = bigquery.Client(
credentials=bigquery_input_credentials,
project=connection_input_details['project_id']
)
bigqueryoutputclient = bigquery.Client(
credentials=bigquery_output_credentials,
project=connection_output_details['project_id']
)
Query Data
Now we’ll create our query and retrieve information from out dataset and table as defined in the file bigquery_service_account_key.json
. The table is expected to be in the format of the file ./data/xtest-1k.df.json
.
inference_dataframe_input = bigqueryinputclient.query(
f"""
SELECT tensor
FROM {connection_input_details['dataset']}.{connection_input_details['table']}"""
).to_dataframe()
inference_dataframe_input.head(5)
tensor | |
---|---|
0 | [4.0, 2.5, 2900.0, 5505.0, 2.0, 0.0, 0.0, 3.0, 8.0, 2900.0, 0.0, 47.6063, -122.02, 2970.0, 5251.0, 12.0, 0.0, 0.0] |
1 | [2.0, 2.5, 2170.0, 6361.0, 1.0, 0.0, 2.0, 3.0, 8.0, 2170.0, 0.0, 47.7109, -122.017, 2310.0, 7419.0, 6.0, 0.0, 0.0] |
2 | [3.0, 2.5, 1300.0, 812.0, 2.0, 0.0, 0.0, 3.0, 8.0, 880.0, 420.0, 47.5893, -122.317, 1300.0, 824.0, 6.0, 0.0, 0.0] |
3 | [4.0, 2.5, 2500.0, 8540.0, 2.0, 0.0, 0.0, 3.0, 9.0, 2500.0, 0.0, 47.5759, -121.994, 2560.0, 8475.0, 24.0, 0.0, 0.0] |
4 | [3.0, 1.75, 2200.0, 11520.0, 1.0, 0.0, 0.0, 4.0, 7.0, 2200.0, 0.0, 47.7659, -122.341, 1690.0, 8038.0, 62.0, 0.0, 0.0] |
Sample Inference
With our data retrieved, we’ll perform an inference and display the results.
result = pipeline.infer(inference_dataframe_input)
display(result.head(5))
time | in.tensor | out.variable | check_failures | |
---|---|---|---|---|
0 | 2023-05-17 16:12:53.970 | [4.0, 2.5, 2900.0, 5505.0, 2.0, 0.0, 0.0, 3.0, 8.0, 2900.0, 0.0, 47.6063, -122.02, 2970.0, 5251.0, 12.0, 0.0, 0.0] | [718013.75] | 0 |
1 | 2023-05-17 16:12:53.970 | [2.0, 2.5, 2170.0, 6361.0, 1.0, 0.0, 2.0, 3.0, 8.0, 2170.0, 0.0, 47.7109, -122.017, 2310.0, 7419.0, 6.0, 0.0, 0.0] | [615094.56] | 0 |
2 | 2023-05-17 16:12:53.970 | [3.0, 2.5, 1300.0, 812.0, 2.0, 0.0, 0.0, 3.0, 8.0, 880.0, 420.0, 47.5893, -122.317, 1300.0, 824.0, 6.0, 0.0, 0.0] | [448627.72] | 0 |
3 | 2023-05-17 16:12:53.970 | [4.0, 2.5, 2500.0, 8540.0, 2.0, 0.0, 0.0, 3.0, 9.0, 2500.0, 0.0, 47.5759, -121.994, 2560.0, 8475.0, 24.0, 0.0, 0.0] | [758714.2] | 0 |
4 | 2023-05-17 16:12:53.970 | [3.0, 1.75, 2200.0, 11520.0, 1.0, 0.0, 0.0, 4.0, 7.0, 2200.0, 0.0, 47.7659, -122.341, 1690.0, 8038.0, 62.0, 0.0, 0.0] | [513264.7] | 0 |
Upload the Results
With the query complete, we’ll upload the results back to the BigQuery dataset.
output_table = bigqueryoutputclient.get_table(f"{connection_output_details['dataset']}.{connection_output_details['table']}")
bigqueryoutputclient.insert_rows_from_dataframe(
output_table,
dataframe=result.rename(columns={"in.tensor":"in_tensor", "out.variable":"out_variable"})
)
[[], []]
Verify the Upload
We can verify the upload by requesting the last few rows of the output table.
task_inference_results = bigqueryoutputclient.query(
f"""