Wallaroo SDK Essentials Guide: Pipeline Management
Table of Contents
Pipelines are the method of taking submitting data and processing that data through the models. Each pipeline can have one or more steps that submit the data from the previous step to the next one. Information can be submitted to a pipeline as a file, or through the pipeline’s URL.
A pipeline’s metrics can be viewed through the Wallaroo Dashboard Pipeline Details and Metrics page.
Pipeline Naming Requirements
Pipeline names map onto Kubernetes objects, and must be DNS compliant. Pipeline names must be ASCII alpha-numeric characters or dash (-) only. . and _ are not allowed.
Create a Pipeline
New pipelines are created in the current workspace.
NOTICE
Pipeline names are not forced to be unique. You can have 50 pipelines all named my-pipeline, which can cause confusion in determining which workspace to use.
It is recommended that organizations agree on a naming convention and select pipeline to use rather than creating a new one each time.
To create a new pipeline, use the Wallaroo Client build_pipeline("{Pipeline Name}") command.
The following example creates a new pipeline imdb-pipeline through a Wallaroo Client connection wl:
imdb_pipeline = wl.build_pipeline("imdb-pipeline")
imdb_pipeline.status()
{'status': 'Pipeline imdb-pipeline is not deployed'}
List All Pipelines
The Wallaroo Client method list_pipelines() lists all pipelines in a Wallaroo Instance.
The following example lists all pipelines in the wl Wallaroo Client connection:
wl.list_pipelines()
[{'name': 'ccfraud-pipeline', 'create_time': datetime.datetime(2022, 4, 12, 17, 55, 41, 944976, tzinfo=tzutc()), 'definition': '[]'}]
Select an Existing Pipeline
Rather than creating a new pipeline each time, an existing pipeline can be selected by using the list_pipelines() command and assigning one of the array members to a variable.
The following example sets the pipeline ccfraud-pipeline to the variable current_pipeline:
wl.list_pipelines()
[{'name': 'ccfraud-pipeline', 'create_time': datetime.datetime(2022, 4, 12, 17, 55, 41, 944976, tzinfo=tzutc()), 'definition': '[]'}]
current_pipeline = wl.list_pipelines()[0]
current_pipeline.status()
{'status': 'Running',
'details': None,
'engines': [{'ip': '10.244.5.4',
'name': 'engine-7fcc7df596-hvlxb',
'status': 'Running',
'reason': None,
'pipeline_statuses': {'pipelines': [{'id': 'ccfraud-pipeline',
'status': 'Running'}]},
'model_statuses': {'models': [{'name': 'ccfraud-model',
'version': '4624e8a8-1414-4408-8b40-e03da4b5cb68',
'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
'status': 'Running'}]}}],
'engine_lbs': [{'ip': '10.244.1.24',
'name': 'engine-lb-85846c64f8-mtq9p',
'status': 'Running',
'reason': None}]}
Pipeline Steps
Once a pipeline has been created, or during its creation process, a pipeline step can be added. The pipeline step refers to the model that will perform an inference off of the data submitted to it. Each time a step is added, it is added to the pipeline’s models array.
Pipeline steps are not saved until the pipeline is deployed. Until then, pipeline steps are stored in local memory as a potential pipeline configuration until the pipeline is deployed.
Add a Step to a Pipeline
A pipeline step is added through the pipeline add_model_step({Model}) command.
In the following example, two models uploaded to the workspace are added as pipeline step:
imdb_pipeline.add_model_step(embedder)
imdb_pipeline.add_model_step(smodel)
imdb_pipeline.status()
{'name': 'imdb-pipeline', 'create_time': datetime.datetime(2022, 3, 30, 21, 21, 31, 127756, tzinfo=tzutc()), 'definition': "[{'ModelInference': {'models': [{'name': 'embedder-o', 'version': '1c16d21d-fe4c-4081-98bc-65fefa465f7d', 'sha': 'd083fd87fa84451904f71ab8b9adfa88580beb92ca77c046800f79780a20b7e4'}]}}, {'ModelInference': {'models': [{'name': 'smodel-o', 'version': '8d311ba3-c336-48d3-99cd-85d95baa6f19', 'sha': '3473ea8700fbf1a1a8bfb112554a0dde8aab36758030dcde94a9357a83fd5650'}]}}]"}
Replace a Pipeline Step
The model specified in a pipeline step can be replaced with the pipeline method replace_with_model_step(index, model).
IMPORTANT NOTE
Pipeline steps can be replaced while a pipeline is deployed. This allows organizations to have pipelines deployed in a production environment and hot-swap out models for new versions without impacting performance or inferencing downtime.The following parameters are used for replacing a pipeline step:
| Parameter | Default Value | Purpose |
|---|---|---|
index | null | The pipeline step to be replaced. Pipeline steps follow array numbering, where the first step is 0, etc. |
model | null | The new model to be used in the pipeline step. |
In the following example, a deployed pipeline will have the initial model step replaced with a new one. A status of the pipeline will be displayed after deployment and after the pipeline swap to show the model has been replaced from ccfraudoriginal to ccfraudreplacement, each with their own versions.
pipeline.deploy()
pipeline.status()
{'status': 'Running',
'details': [],
'engines': [{'ip': '10.244.2.145',
'name': 'engine-75bfd7dc9d-7p9qk',
'status': 'Running',
'reason': None,
'details': [],
'pipeline_statuses': {'pipelines': [{'id': 'hotswappipeline',
'status': 'Running'}]},
'model_statuses': {'models': [{'name': 'ccfraudoriginal',
'version': '3a03dc94-716e-46bb-84c8-91bc99ceb2c3',
'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
'status': 'Running'}]}}],
'engine_lbs': [{'ip': '10.244.2.144',
'name': 'engine-lb-55dcdff64c-vf74s',
'status': 'Running',
'reason': None,
'details': []}],
'sidekicks': []}
pipeline.replace_with_model_step(0, replacement_model).deploy()
pipeline.status()
{'status': 'Running',
'details': [],
'engines': [{'ip': '10.244.2.153',
'name': 'engine-96486c95d-zfchr',
'status': 'Running',
'reason': None,
'details': [],
'pipeline_statuses': {'pipelines': [{'id': 'hotswappipeline',
'status': 'Running'}]},
'model_statuses': {'models': [{'name': 'ccfraudreplacement',
'version': '714efd19-5c83-42a8-aece-24b4ba530925',
'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
'status': 'Running'}]}}],
'engine_lbs': [{'ip': '10.244.2.154',
'name': 'engine-lb-55dcdff64c-9np9k',
'status': 'Running',
'reason': None,
'details': []}],
'sidekicks': []}
Pre and Post Processing Steps
A Pipeline Step can be more than models - they can also be pre processing and post processing steps. For example, the Demand Curve Tutorial has both a pre and post processing steps that are added to the pipeline. The preprocessing step uses the following code:
import numpy
import pandas
import json
# add interaction terms for the model
def actual_preprocess(pdata):
pd = pdata.copy()
# convert boolean cust_known to 0/1
pd.cust_known = numpy.where(pd.cust_known, 1, 0)
# interact UnitPrice and cust_known
pd['UnitPriceXcust_known'] = pd.UnitPrice * pd.cust_known
return pd.loc[:, ['UnitPrice', 'cust_known', 'UnitPriceXcust_known']]
# If the data is a json string, call this wrapper instead
# Expected input:
# a dictionary with fields 'colnames', 'data'
# test that the code works here
def wallaroo_json(data):
obj = json.loads(data)
pdata = pandas.DataFrame(obj['query'],
columns=obj['colnames'])
pprocessed = actual_preprocess(pdata)
# return a dictionary, with the fields the model expect
return {
'tensor_fields': ['model_input'],
'model_input': pprocessed.to_numpy().tolist()
}
It is added as a Python module by uploading it as a model:
# load the preprocess module
module_pre = wl.upload_model("preprocess", "./preprocess.py").configure('python')
And then added to the pipeline as a step:
# now make a pipeline
demandcurve_pipeline = (wl.build_pipeline("demand-curve-pipeline")
.add_model_step(module_pre)
.add_model_step(demand_curve_model)
.add_model_step(module_post))
Remove a Pipeline Step
To remove a step from the pipeline, use the Pipeline remove_step(index) command, where the index is the array index for the pipeline’s steps.
In the following example the pipeline imdb_pipeline will have the step with the model smodel-o removed.
imdb_pipeline.status
<bound method Pipeline.status of {'name': 'imdb-pipeline', 'create_time': datetime.datetime(2022, 3, 30, 21, 21, 31, 127756, tzinfo=tzutc()), 'definition': "[{'ModelInference': {'models': [{'name': 'embedder-o', 'version': '1c16d21d-fe4c-4081-98bc-65fefa465f7d', 'sha': 'd083fd87fa84451904f71ab8b9adfa88580beb92ca77c046800f79780a20b7e4'}]}}, {'ModelInference': {'models': [{'name': 'smodel-o', 'version': '8d311ba3-c336-48d3-99cd-85d95baa6f19', 'sha': '3473ea8700fbf1a1a8bfb112554a0dde8aab36758030dcde94a9357a83fd5650'}]}}]"}>
imdb_pipeline.remove_step(1)
{'name': 'imdb-pipeline', 'create_time': datetime.datetime(2022, 3, 30, 21, 21, 31, 127756, tzinfo=tzutc()), 'definition': "[{'ModelInference': {'models': [{'name': 'embedder-o', 'version': '1c16d21d-fe4c-4081-98bc-65fefa465f7d', 'sha': 'd083fd87fa84451904f71ab8b9adfa88580beb92ca77c046800f79780a20b7e4'}]}}]"}
Clear All Pipeline Steps
The Pipeline clear() method removes all pipeline steps from a pipeline. Note that pipeline steps are not saved until the pipeline is deployed.
Manage Pipeline Deployment Configuration
For full details on pipeline deployment configurations, see Wallaroo SDK Essentials Guide: Pipeline Deployment Configuration.
Deploy a Pipeline
When a pipeline step is added or removed, the pipeline must be deployed through the pipeline deploy(). This allocates resources to the pipeline from the Kubernetes environment and make it available to submit information to perform inferences. This process typically takes 45 seconds.
Once complete, the pipeline status() command will show 'status':'Running'.
Pipeline deployments can be modified to enable auto-scaling to allow pipelines to allocate more or fewer resources based on need by setting the pipeline’s This will then be applied to the deployment of the pipelineccfraudPipelineby specifying it'sdeployment_config` optional parameter. If this optional parameter is not passed, then the deployment will defer to default values. For more information, see Manage Pipeline Deployment Configuration.
In the following example, the pipeline imdb-pipeline that contains two steps will be deployed with default deployment configuration:
imdb_pipeline.status
<bound method Pipeline.status of {'name': 'imdb-pipeline', 'create_time': datetime.datetime(2022, 3, 30, 21, 21, 31, 127756, tzinfo=tzutc()), 'definition': "[{'ModelInference': {'models': [{'name': 'embedder-o', 'version': '1c16d21d-fe4c-4081-98bc-65fefa465f7d', 'sha': 'd083fd87fa84451904f71ab8b9adfa88580beb92ca77c046800f79780a20b7e4'}]}}, {'ModelInference': {'models': [{'name': 'smodel-o', 'version': '8d311ba3-c336-48d3-99cd-85d95baa6f19', 'sha': '3473ea8700fbf1a1a8bfb112554a0dde8aab36758030dcde94a9357a83fd5650'}]}}]"}>
imdb_pipeline.deploy()
Waiting for deployment - this will take up to 45s ...... ok
imdb_pipeline.status()
{'status': 'Running',
'details': None,
'engines': [{'ip': '10.12.1.65',
'name': 'engine-778b65459-f9mt5',
'status': 'Running',
'reason': None,
'pipeline_statuses': {'pipelines': [{'id': 'imdb-pipeline',
'status': 'Running'}]},
'model_statuses': {'models': [{'name': 'embedder-o',
'version': '1c16d21d-fe4c-4081-98bc-65fefa465f7d',
'sha': 'd083fd87fa84451904f71ab8b9adfa88580beb92ca77c046800f79780a20b7e4',
'status': 'Running'},
{'name': 'smodel-o',
'version': '8d311ba3-c336-48d3-99cd-85d95baa6f19',
'sha': '3473ea8700fbf1a1a8bfb112554a0dde8aab36758030dcde94a9357a83fd5650',
'status': 'Running'}]}}],
'engine_lbs': [{'ip': '10.12.1.66',
'name': 'engine-lb-85846c64f8-ggg2t',
'status': 'Running',
'reason': None}]}
Troubleshooting Pipeline Deployment
If you deploy more pipelines than your environment can handle, or if you deploy more pipelines than your license allows, you may see an error like the following:
LimitError: You have reached a license limit in your Wallaroo instance. In order to add additional resources, you can remove some of your existing resources. If you have any questions contact us at community@wallaroo.ai: MAX_PIPELINES_LIMIT_EXCEEDED
Undeploy any unnecessary pipelines either through the SDK or through the Wallaroo Pipeline Dashboard, then attempt to redeploy the pipeline in question again.
Undeploy a Pipeline
When a pipeline is not currently needed, it can be undeployed and its resources turned back to the Kubernetes environment. To undeploy a pipeline, use the pipeline undeploy() command.
In this example, the aloha_pipeline will be undeployed:
aloha_pipeline.undeploy()
{'name': 'aloha-test-demo', 'create_time': datetime.datetime(2022, 3, 29, 20, 34, 3, 960957, tzinfo=tzutc()), 'definition': "[{'ModelInference': {'models': [{'name': 'aloha-2', 'version': 'a8e8abdc-c22f-416c-a13c-5fe162357430', 'sha': 'fd998cd5e4964bbbb4f8d29d245a8ac67df81b62be767afbceb96a03d1a01520'}]}}]"}
Get Pipeline Status
The pipeline status() command shows the current status, models, and other information on a pipeline.
The following example shows the pipeline imdb_pipeline status before and after it is deployed:
imdb_pipeline.status
<bound method Pipeline.status of {'name': 'imdb-pipeline', 'create_time': datetime.datetime(2022, 3, 30, 21, 21, 31, 127756, tzinfo=tzutc()), 'definition': "[{'ModelInference': {'models': [{'name': 'embedder-o', 'version': '1c16d21d-fe4c-4081-98bc-65fefa465f7d', 'sha': 'd083fd87fa84451904f71ab8b9adfa88580beb92ca77c046800f79780a20b7e4'}]}}, {'ModelInference': {'models': [{'name': 'smodel-o', 'version': '8d311ba3-c336-48d3-99cd-85d95baa6f19', 'sha': '3473ea8700fbf1a1a8bfb112554a0dde8aab36758030dcde94a9357a83fd5650'}]}}]"}>
imdb_pipeline.deploy()
Waiting for deployment - this will take up to 45s ...... ok
imdb_pipeline.status()
{'status': 'Running',
'details': None,
'engines': [{'ip': '10.12.1.65',
'name': 'engine-778b65459-f9mt5',
'status': 'Running',
'reason': None,
'pipeline_statuses': {'pipelines': [{'id': 'imdb-pipeline',
'status': 'Running'}]},
'model_statuses': {'models': [{'name': 'embedder-o',
'version': '1c16d21d-fe4c-4081-98bc-65fefa465f7d',
'sha': 'd083fd87fa84451904f71ab8b9adfa88580beb92ca77c046800f79780a20b7e4',
'status': 'Running'},
{'name': 'smodel-o',
'version': '8d311ba3-c336-48d3-99cd-85d95baa6f19',
'sha': '3473ea8700fbf1a1a8bfb112554a0dde8aab36758030dcde94a9357a83fd5650',
'status': 'Running'}]}}],
'engine_lbs': [{'ip': '10.12.1.66',
'name': 'engine-lb-85846c64f8-ggg2t',
'status': 'Running',
'reason': None}]}
Anomaly Testing
Anomaly detection allows organizations to set validation parameters. A validation is added to a pipeline to test data based on a specific expression. If the expression is returned as False, this is detected as an anomaly and added to the InferenceResult object’s check_failures array and the pipeline logs.
Anomaly detection consists of the following steps:
- Set a validation: Add a validation to a pipeline that, when returned
False, adds an entry to theInferenceResultobject’scheck_failuresattribute with the expression that caused the failure. - Display anomalies: Anomalies detected through a Pipeline’s
validationattribute are displayed either through theInferenceResultobject’scheck_failuresattribute, or through the pipeline’s logs.
Set A Validation
Validations are added to a pipeline through the wallaroo.pipeline add_validation method. The following parameters are required:
| Parameter | Type | Description |
|---|---|---|
| name | String (Required) | The name of the validation |
| validation | wallaroo.checks.Expression (Required) | The validation expression that adds the result InferenceResult object’s check_failures attribute when expression result is False. The validation checks the expression against both the data value and the data type. |
Validation expressions take the format value Expression, with the expression being in the form of a :py:Expression:. For example, if the model housing_model is part of the pipeline steps, then a validation expression may be housing_model.outputs[0][0] < 100.0: If the output of the housing_model inference is less than 100, then the validation is True and no action is taken. Any values over 100, the validation is False which triggers adding the anomaly to the InferenceResult object’s check_failures attribute.
IMPORTANT NOTE
Validations test for the expression value and the data type. For example: 100 is considered an integer data type, while 100.0 is considered a float data type.
If the data type is an integer, and the value the expression is testing against is a float, then the validation check will always be triggered. Verify that the data type is properly set in the validation expression to ensure correct validation check results.
Note that multiple validations can be created to allow for multiple anomalies detection.
In the following example, a validation is added to the pipeline to detect housing prices that are below 100 (represented as $100 million), and trigger an anomaly for values above that level. When an inference is performed that triggers a validation failure, the results are displayed in the InferenceResult object’s check_failures attribute.
p = wl.build_pipeline('anomaly-housing-pipeline')
p = p.add_model_step(housing_model)
p = p.add_validation('price too high', housing_model.outputs[0][0] < 100.0)
pipeline = p.deploy()
test_input = {"dense_16_input":[[0.02675675, 0.0, 0.02677953, 0.0, 0.0010046, 0.00951931, 0.14795322, 0.0027145, 2, 0.98536841, 0.02988655, 0.04031725, 0.04298041]]}
response_trigger = pipeline.infer(test_input)
print("\n")
print(response_trigger)
[InferenceResult({'check_failures': [{'False': {'expr': 'anomaly-housing.outputs[0][0] < 100'}}],
'elapsed': 15110549,
'model_name': 'anomaly-housing',
'model_version': 'c3cf1577-6666-48d3-b85c-5d4a6e6567ea',
'original_data': {'dense_16_input': [[0.02675675,
0.0,
0.02677953,
0.0,
0.0010046,
0.00951931,
0.14795322,
0.0027145,
2,
0.98536841,
0.02988655,
0.04031725,
0.04298041]]},
'outputs': [{'Float': {'data': [350.46990966796875], 'dim': [1, 1], 'v': 1}}],
'pipeline_name': 'anomaly-housing-model',
'time': 1651257043312})]
Display Anomalies
Anomalies detected through a Pipeline’s validation attribute are displayed either through the InferenceResult object’s check_failures attribute, or through the pipeline’s logs.
To display an anomaly through the InferenceResult object, display the check_failures attribute.
In the following example, the an InferenceResult where the validation failed will display the failure in the check_failures attribute:
test_input = {"dense_16_input":[[0.02675675, 0.0, 0.02677953, 0.0, 0.0010046, 0.00951931, 0.14795322, 0.0027145, 2, 0.98536841, 0.02988655, 0.04031725, 0.04298041]]}
response_trigger = pipeline.infer(test_input)
print("\n")
print(response_trigger)
[InferenceResult({'check_failures': [{'False': {'expr': 'anomaly-housing-model.outputs[0][0] < '
'100'}}],
'elapsed': 12196540,
'model_name': 'anomaly-housing-model',
'model_version': 'a3b1c29f-c827-4aad-817d-485de464d59b',
'original_data': {'dense_16_input': [[0.02675675,
0.0,
0.02677953,
0.0,
0.0010046,
0.00951931,
0.14795322,
0.0027145,
2,
0.98536841,
0.02988655,
0.04031725,
0.04298041]]},
'outputs': [{'Float': {'data': [350.46990966796875], 'dim': [1, 1], 'v': 1}}],
'pipeline_name': 'anomaly-housing-pipeline',
'shadow_data': {},
'time': 1667416852255})]
The other methods is to use the pipeline.logs() method with the parameter valid=False, isolating the logs where the validation was returned as False.
In this example, a set of logs where the validation returned as False will be displayed:
pipeline.logs(valid=False)
| Timestamp | Output | Input | Anomalies |
|---|---|---|---|
| 2022-02-Nov 19:20:52 | [array([[350.46990967]])] | [[0.02675675, 0.0, 0.02677953, 0.0, 0.0010046, 0.00951931, 0.14795322, 0.0027145, 2, 0.98536841, 0.02988655, 0.04031725, 0.04298041]] | 1 |
A/B Testing
A/B testing is a method that provides the ability to test competing ML models for performance, accuracy or other useful benchmarks. Different models are added to the same pipeline steps as follows:
- Control or Champion model: The model currently used for inferences.
- Challenger model(s): The model or set of models compared to the challenger model.
A/B testing splits a portion of the inference requests between the champion model and the one or more challengers through the add_random_split method. This method splits the inferences submitted to the model through a randomly weighted step.
Each model receives inputs that are approximately proportional to the weight it is assigned. For example, with two models having weights 1 and 1, each will receive roughly equal amounts of inference inputs. If the weights were changed to 1 and 2, the models would receive roughly 33% and 66% respectively instead.
When choosing the model to use, a random number between 0.0 and 1.0 is generated. The weighted inputs are mapped to that range, and the random input is then used to select the model to use. For example, for the two-models equal-weight case, a random key of 0.4 would route to the first model, 0.6 would route to the second.
Add Random Split
A random split step can be added to a pipeline through the add_random_split method.
The following parameters are used when adding a random split step to a pipeline:
| Parameter | Type | Description |
|---|---|---|
| champion_weight | Float (Required) | The weight for the champion model. |
| champion_model | Wallaroo.Model (Required) | The uploaded champion model. |
| challenger_weight | Float (Required) | The weight of the challenger model. |
| challenger_model | Wallaroo.Model (Required) | The uploaded challenger model. |
| hash_key | String(Optional) | A key used instead of a random number for model selection. This must be between 0.0 and 1.0. |
Note that multiple challenger models with different weights can be added as the random split step.
add_random_split([(champion_weight, champion_model), (challenger_weight, challenger_model), (challenger_weight2, challenger_model2),...], hash_key)
In this example, a pipeline will be built with a 2:1 weighted ratio between the champion and a single challenger model.
pipeline = (wl.build_pipeline("randomsplitpipeline-demo")
.add_random_split([(2, control), (1, challenger)]))
The results for a series of single are displayed to show the random weighted split between the two models in action:
results = []
results.append(experiment_pipeline.infer_from_file("data/data-1.json"))
results.append(experiment_pipeline.infer_from_file("data/data-1.json"))
results.append(experiment_pipeline.infer_from_file("data/data-1.json"))
results.append(experiment_pipeline.infer_from_file("data/data-1.json"))
results.append(experiment_pipeline.infer_from_file("data/data-1.json"))
for result in results:
print(result[0].model())
print(result[0].data())
('aloha-control', 'ff81f634-8fb4-4a62-b873-93b02eb86ab4')
[array([[0.00151959]]), array([[0.98291481]]), array([[0.01209957]]), array([[4.75912966e-05]]), array([[2.02893716e-05]]), array([[0.00031977]]), array([[0.01102928]]), array([[0.99756402]]), array([[0.01034162]]), array([[0.00803896]]), array([[0.01615506]]), array([[0.00623623]]), array([[0.00099858]]), array([[1.79337805e-26]]), array([[1.38899512e-27]])]
('aloha-control', 'ff81f634-8fb4-4a62-b873-93b02eb86ab4')
[array([[0.00151959]]), array([[0.98291481]]), array([[0.01209957]]), array([[4.75912966e-05]]), array([[2.02893716e-05]]), array([[0.00031977]]), array([[0.01102928]]), array([[0.99756402]]), array([[0.01034162]]), array([[0.00803896]]), array([[0.01615506]]), array([[0.00623623]]), array([[0.00099858]]), array([[1.79337805e-26]]), array([[1.38899512e-27]])]
('aloha-challenger', '87fdfe08-170e-4231-a0b9-543728d6fc57')
[array([[0.00151959]]), array([[0.98291481]]), array([[0.01209957]]), array([[4.75912966e-05]]), array([[2.02893716e-05]]), array([[0.00031977]]), array([[0.01102928]]), array([[0.99756402]]), array([[0.01034162]]), array([[0.00803896]]), array([[0.01615506]]), array([[0.00623623]]), array([[0.00099858]]), array([[1.79337805e-26]]), array([[1.38899512e-27]])]
('aloha-challenger', '87fdfe08-170e-4231-a0b9-543728d6fc57')
[array([[0.00151959]]), array([[0.98291481]]), array([[0.01209957]]), array([[4.75912966e-05]]), array([[2.02893716e-05]]), array([[0.00031977]]), array([[0.01102928]]), array([[0.99756402]]), array([[0.01034162]]), array([[0.00803896]]), array([[0.01615506]]), array([[0.00623623]]), array([[0.00099858]]), array([[1.79337805e-26]]), array([[1.38899512e-27]])]
('aloha-challenger', '87fdfe08-170e-4231-a0b9-543728d6fc57')
[array([[0.00151959]]), array([[0.98291481]]), array([[0.01209957]]), array([[4.75912966e-05]]), array([[2.02893716e-05]]), array([[0.00031977]]), array([[0.01102928]]), array([[0.99756402]]), array([[0.01034162]]), array([[0.00803896]]), array([[0.01615506]]), array([[0.00623623]]), array([[0.00099858]]), array([[1.79337805e-26]]), array([[1.38899512e-27]])]
Replace With Random Split
If a pipeline already had steps as detailed in Add a Step to a Pipeline, this step can be replaced with a random split with the replace_with_random_split method.
The following parameters are used when adding a random split step to a pipeline:
| Parameter | Type | Description |
|---|---|---|
| index | Integer (Required) | The pipeline step being replaced. |
| champion_weight | Float (Required) | The weight for the champion model. |
| champion_model | Wallaroo.Model (Required) | The uploaded champion model. |
| **challenger_weight | Float (Required) | The weight of the challenger model. |
| challenger_model | Wallaroo.Model (Required) | The uploaded challenger model. |
| hash_key | String(Optional) | A key used instead of a random number for model selection. This must be between 0.0 and 1.0. |
Note that one or more challenger models can be added for the random split step:
replace_with_random_split(index, [(champion_weight, champion_model), (challenger_weight, challenger_model)], (challenger_weight2, challenger_model2),...], hash_key)
A/B Testing Logs
A/B Testing logs entries contain the model used for the inferences in the column out._model_split.
logs = experiment_pipeline.logs(limit=5)
display(logs.loc[:,['time', 'out._model_split', 'out.main']])
| time | out._model_split | out.main | |
|---|---|---|---|
| 0 | 2023-03-03 19:08:35.653 | [{“name”:“aloha-control”,“version”:“89389786-0c17-4214-938c-aa22dd28359f”,“sha”:“fd998cd5e4964bbbb4f8d29d245a8ac67df81b62be767afbceb96a03d1a01520”}] | [0.9999754] |
| 1 | 2023-03-03 19:08:35.702 | [{“name”:“aloha-challenger”,“version”:“3acd3835-be72-42c4-bcae-84368f416998”,“sha”:“223d26869d24976942f53ccb40b432e8b7c39f9ffcf1f719f3929d7595bceaf3”}] | [0.9999727] |
| 2 | 2023-03-03 19:08:35.753 | [{“name”:“aloha-challenger”,“version”:“3acd3835-be72-42c4-bcae-84368f416998”,“sha”:“223d26869d24976942f53ccb40b432e8b7c39f9ffcf1f719f3929d7595bceaf3”}] | [0.6606688] |
| 3 | 2023-03-03 19:08:35.799 | [{“name”:“aloha-control”,“version”:“89389786-0c17-4214-938c-aa22dd28359f”,“sha”:“fd998cd5e4964bbbb4f8d29d245a8ac67df81b62be767afbceb96a03d1a01520”}] | [0.9998954] |
| 4 | 2023-03-03 19:08:35.846 | [{“name”:“aloha-control”,“version”:“89389786-0c17-4214-938c-aa22dd28359f”,“sha”:“fd998cd5e4964bbbb4f8d29d245a8ac67df81b62be767afbceb96a03d1a01520”}] | [0.99999803] |
Pipeline Shadow Deployments
Wallaroo provides a method of testing the same data against two different models or sets of models at the same time through shadow deployments otherwise known as parallel deployments or A/B test. This allows data to be submitted to a pipeline with inferences running on several different sets of models. Typically this is performed on a model that is known to provide accurate results - the champion - and a model or set of models that is being tested to see if it provides more accurate or faster responses depending on the criteria known as the challenger(s). Multiple challengers can be tested against a single champion to determine which is “better” based on the organization’s criteria.
As described in the Wallaroo blog post The What, Why, and How of Model A/B Testing:
In data science, A/B tests can also be used to choose between two models in production, by measuring which model performs better in the real world. In this formulation, the control is often an existing model that is currently in production, sometimes called the champion. The treatment is a new model being considered to replace the old one. This new model is sometimes called the challenger….
Keep in mind that in machine learning, the terms experiments and trials also often refer to the process of finding a training configuration that works best for the problem at hand (this is sometimes called hyperparameter optimization).
When a shadow deployment is created, only the inference from the champion is returned in the InferenceResult Object data, while the result data for the shadow deployments is stored in the InferenceResult Object shadow_data.
Create Shadow Deployment
Create a parallel or shadow deployment for a pipeline with the pipeline.add_shadow_deploy(champion, challengers[]) method, where the champion is a Wallaroo Model object, and challengers[] is one or more Wallaroo Model objects.
Each inference request sent to the pipeline is sent to all the models. The prediction from the champion is returned by the pipeline, while the predictions from the challengers are not part of the standard output, but are kept stored in the shadow_data attribute and in the logs for later comparison.
In this example, a shadow deployment is created with the champion versus two challenger models.
champion = wl.upload_model(champion_model_name, champion_model_file).configure()
model2 = wl.upload_model(shadow_model_01_name, shadow_model_01_file).configure()
model3 = wl.upload_model(shadow_model_02_name, shadow_model_02_file).configure()
pipeline.add_shadow_deploy(champion, [model2, model3])
pipeline.deploy()
| name | cc-shadow |
| created | 2022-08-04 20:06:55.102203+00:00 |
| last_updated | 2022-08-04 20:37:28.785947+00:00 |
| deployed | True |
| tags | |
| steps | ccfraud-lstm |
Shadow Deploy Outputs
Model outputs are listed by column based on the model’s outputs. The output data is set by the term out, followed by the name of the model. For the default model, this is out.{variable_name}, while the shadow deployed models are in the format out_{model name}.variable, where {model name} is the name of the shadow deployed model.
sample_data_file = './smoke_test.df.json'
response = pipeline.infer_from_file(sample_data_file)
| time | in.tensor | out.dense_1 | check_failures | out_ccfraudrf.variable | out_ccfraudxgb.variable | |
|---|---|---|---|---|---|---|
| 0 | 2023-03-03 17:35:28.859 | [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] | [0.0014974177] | 0 | [1.0] | [0.0005066991] |
Retrieve Shadow Deployment Logs
Shadow deploy results are part of the Pipeline.logs() method. The output data is set by the term out, followed by the name of the model. For the default model, this is out.dense_1, while the shadow deployed models are in the format out_{model name}.variable, where {model name} is the name of the shadow deployed model.
logs = pipeline.logs()
display(logs)
| time | in.tensor | out.dense_1 | check_failures | out_ccfraudrf.variable | out_ccfraudxgb.variable | |
|---|---|---|---|---|---|---|
| 0 | 2023-03-03 17:35:28.859 | [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] | [0.0014974177] | 0 | [1.0] | [0.0005066991] |
Get Pipeline URL Endpoint
The Pipeline URL Endpoint or the Pipeline Deploy URL is used to submit data to a pipeline to use for an inference. This is done through the pipeline _deployment._url() method.
In this example, the pipeline URL endpoint for the pipeline ccfraud_pipeline will be displayed:
ccfraud_pipeline._deployment._url()
'http://engine-lb.ccfraud-pipeline-1:29502/pipelines/ccfraud-pipeline'