merlin

Extractor for Machine Learning(ML) Models from Merlin.

The extractor uses the REST API exposed by Merlin to extract models. The REST API has been documented with Swagger and can be seen here.

Usage

source:
  name: merlin
  scope: staging
  config:
    url: my-company.com/api/merlin/
    service_account_base64: |
      ____base64_encoded_service_account_credentials____

Inputs

Key	Value	Example	Description	Required?
url	string	my-company.com/api/merlin/	Merlin's API base URL	✅
service_account_base64	string	____BASE64_ENCODED_SERVICE_ACCOUNT____	Service Account credentials in base64 encoded string.	❌
request_timeout	string	10s	Timeout for HTTP requests to Merlin API	❌
worker_count	int	5	Number of workers to spawn for extracting projects parallely from Merlin.	❌

Notes

• Leaving service_account_base64 blank will default to Google's default authentication. It is recommended if Meteor instance runs inside the same Google Cloud environment as the BigQuery project.

Outputs

The models are mapped to an Asset with model specific metadata stored using Model. Please refer the proto definitions for more information.

A single model asset includes all the active model versions. A model version is considered active if it has an endpoint.

Field	Value	Sample Value
resource.urn	urn:merlin:{scope}:model:{model.project_name}.{model.name}	urn:merlin:staging:model:food.restaurant-image
resource.name	{model.name}	tensorflow-sample
resource.service	merlin	merlin
resource.type	model	model
resource.url	{model.endpoints[0].url}	tensorflow-sample.integration-test.models.mycompany.com
namespace	{project.name}	integration-test
flavor	model.type	pyfunc
versions	[]ModelVersion
attributes.merlin_project_id	project.id	23
attributes.mlflow_experiment_id	model.mlflow_experiment_id	721
attributes.mlflow_experiment_url	model.mlflow_url	http://mlflow.mycompany.com/#/experiments/721
attributes.endpoint_urls[]	model.endpoints[].url	["tensorflow-sample.integration-test.models.mycompany.com"]
create_time	model.created_at	2021-03-01T18:42:50.564685Z
update_time	model.updated_at	2022-01-27T10:21:26.121941Z
resource.owners[].urn	{project.administrators[]}	[email protected]
resource.owners[].email	{project.administrators[]}	[email protected]
lineage.upstreams	[]Resource upstreams
resource.labels	{"team": {project.team}, "stream": {project.stream} + project.labels	{"stream": "relevance","team": "search"}

ModelVersion

A ModelVersion is used to represent each combination of Merlin model's version and it's 'endpoint' destination. A single model version will have an 'endpoint' for each environment it is deployed in. Please refer the proto definitions for more information.

Field	Value	Sample Value
status	model_version.status	running
version	model_version.id	11
attributes.endpoint_id	endpoint.id	187
attributes.mlflow_run_id	model_version.mlflow_run_id	3c7067f3770441ebbd66a0dce91b8724
attributes.mlflow_run_url	model_version.mlflow_url	http://mlflow.mycompany.com/#/experiments/721/runs/3c7067f3770441ebbd66a0dce91b8724
attributes.endpoint_url	endpoint.url	tensorflow-sample.integration-test.models.mycompany.com
attributes.version_endpoint_url	version_endpoint.url	http://tensorflow-sample-11.integration-test.models.mycompany.com/v1/models/tensorflow-sample-11
attributes.monitoring_url	version_endpoint.monitoring_url	https://grafana.mycompany.com/graph/d/z9MBKR1Az/model-version-dashboard?params
attributes.message	version_endpoint.message	timeout creating inference service
attributes.environment_name	endpoint.environment_name	aws-staging
attributes.deployment_mode	version_endpoint.deployment_mode	serverless
attributes.service_name	version_endpoint.service_name	tensorflow-sample-11-predictor-default.integration-test.models.mycompany.com
attributes.env_vars	version_endpoint.env_vars	{"INIT_HEAP_SIZE_IN_MB": "2250","WORKERS": "1"}
attributes.transformer	version_endpoint.transformer	Attributes including transformer.{enabled, type, image, command, args, env_vars}
attributes.weight	endpoint.rule.destinationsp[].weight	100
labels	model_version.labels
create_time	model_version.created_at	2022-11-13T07:21:07.888150Z
update_time	model_version.updated_at	2022-11-13T07:21:07.888150Z

Resource upstreams

The extractor currently has limited support for constructing the upstreams for Model that utilises the env vars for standard transformer. It parses the feature table specs that specify the project name and feature table name of the CaraML Store Feature Table from the env vars. This information is used to construct the upstreams for the model.

Field	Value	Sample Value
urn	urn:caramlstore:{scope}:feature_table:{ft.project}.{ft.name}	urn:kafka:int-kafka.yonkou.io:topic:staging_30min_demand
type	feature_table	topic
service	caramlstore	kafka

Contributing

Refer to the contribution guidelines for information on contributing to this module.