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ivan-valkovivan-valkov
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Add an example notebook for model metadata service
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‎examples/.gitignore

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#Ipython Notebook
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.ipynb_checkpoints

‎examples/metadata_demo.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Model Metadata API Demo"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The Model Metadata API is still in developement. This demo showcases some of the functionality of the new API."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Api Configuration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from seldon_deploy_sdk.rest import ApiException\n",
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"\n",
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"from seldon_deploy_sdk import V1Model, ModelMetadataServiceApi, Configuration, ApiClient\n",
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"from seldon_deploy_sdk.auth import OIDCAuthenticator\n",
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"from pprint import pprint\n",
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"\n",
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"config = Configuration()\n",
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"config.host = \"http://localhost:8080\"\n",
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"# auth if needed\n",
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"# config.oidc_server = \"http://localhost:8080/auth/realms/deploy-realm\"\n",
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"# config.oidc_client_id = \"sd-cli\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# auth = OIDCAuthenticator(config)\n",
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"api_client = ApiClient(config)\n",
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"# config.access_token = auth.authenticate(\"[email protected]\", \"12341234\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"api_instance = ModelMetadataServiceApi(api_client)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create a model metadata entry"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = V1Model(\n",
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" uri=\"gs://test-model-alpha-v1.0.0\",\n",
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" name=\"alpha\",\n",
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" version=\"v1.0.0\",\n",
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" implementation=\"XGBOOST\",\n",
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" task_type=\"regression\",\n",
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" tags={\n",
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" \"source\": \"https://github.com/some-test-model-alpha-repo\",\n",
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" \"an arbitrary tag\": \"true\",\n",
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" },\n",
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")\n",
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"try:\n",
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" # Create a Model Metadata entry.\n",
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" api_response = api_instance.model_metadata_service_create_model_metadata(model)\n",
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" pprint(api_response)\n",
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"except ApiException as e:\n",
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" print(\n",
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" \"Exception when calling ModelMetadataServiceApi->model_metadata_service_create_model_metadata: %s\\n\"\n",
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" % e\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create multiple metadata entries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"models = [\n",
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" # Same model different versions\n",
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" {\n",
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" \"uri\": \"gs://test-model-beta-v1.0.0\",\n",
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" \"name\": \"beta\",\n",
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" \"version\": \"v1.0.0\",\n",
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" \"implementation\": \"SKLEARN\",\n",
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" \"task_type\": \"classification\",\n",
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" \"tags\": {\"author\": \"Jon\"},\n",
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" },\n",
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" {\n",
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" \"uri\": \"gs://test-model-beta-v2.0.0\",\n",
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" \"name\": \"beta\",\n",
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" \"version\": \"v2.0.0\",\n",
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" \"implementation\": \"SKLEARN\",\n",
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" \"task_type\": \"classification\",\n",
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" \"tags\": {\"author\": \"Bob\"},\n",
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" },\n",
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" {\n",
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" \"uri\": \"gs://test-model-beta-v3.0.0\",\n",
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" \"name\": \"beta\",\n",
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" \"version\": \"v3.0.0\",\n",
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" \"implementation\": \"SKLEARN\",\n",
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" \"task_type\": \"classification\",\n",
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" \"tags\": {\"author\": \"Bob\"},\n",
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" },\n",
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"]\n",
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"\n",
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"for model in models:\n",
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" body = V1Model(**model)\n",
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" try:\n",
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" # Create a Model Metadata entry.\n",
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" api_response = api_instance.model_metadata_service_create_model_metadata(body)\n",
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" pprint(api_response)\n",
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" except ApiException as e:\n",
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" print(\n",
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" \"Exception when calling ModelMetadataServiceApi->model_metadata_service_create_model_metadata: %s\\n\"\n",
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" % e\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## List all model metadata entries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" # List Model Metadata entries.\n",
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" api_response = api_instance.model_metadata_service_list_model_metadata()\n",
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" pprint(api_response)\n",
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"except ApiException as e:\n",
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" print(\n",
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" \"Exception when calling ModelMetadataServiceApi->model_metadata_service_list_model_metadata: %s\\n\"\n",
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" % e\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Filter model metadata entries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# uri = 'uri_example' # str | (optional)\n",
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"# name = 'name_example' # str | (optional)\n",
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"# version = 'version_example' # str | (optional)\n",
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"# implementation = 'implementation_example' # str | (optional)\n",
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"# task_type = 'task_type_example' # str | (optional)\n",
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"# model_type = 'model_type_example' # str | (optional)\n",
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"\n",
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"try:\n",
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" # List Model Metadata entries.\n",
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" api_response = api_instance.model_metadata_service_list_model_metadata(name=\"beta\")\n",
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" print(\"Filter by name=beta\")\n",
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" pprint(api_response)\n",
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"except ApiException as e:\n",
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" print(\n",
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" \"Exception when calling ModelMetadataServiceApi->model_metadata_service_list_model_metadata: %s\\n\"\n",
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" % e\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Modify a metadata entry"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" # List Model Metadata entries.\n",
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" api_response = api_instance.model_metadata_service_list_model_metadata(uri=\"gs://test-model-alpha-v1.0.0\")\n",
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" print(\"Before update:\")\n",
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" pprint(api_response)\n",
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"except ApiException as e:\n",
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" print(\n",
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" \"Exception when calling ModelMetadataServiceApi->model_metadata_service_list_model_metadata: %s\\n\"\n",
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" % e\n",
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" )\n",
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"\n",
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"\n",
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"model = V1Model(\n",
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" uri=\"gs://test-model-alpha-v1.0.0\",\n",
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" name=\"alpha\",\n",
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" version=\"v1.0.0\",\n",
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" implementation=\"XGBOOST\",\n",
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" task_type=\"regression\",\n",
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" tags={\n",
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" \"source\": \"https://github.com/some-other-repo\",\n",
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" \"an arbitrary tag\": \"true\",\n",
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" \"an additional tag\": \"123\",\n",
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" },\n",
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")\n",
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"try:\n",
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" # Update a Model Metadata entry.\n",
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" api_response = api_instance.model_metadata_service_update_model_metadata(model)\n",
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" pprint(api_response)\n",
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"except ApiException as e:\n",
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" print(\"Exception when calling ModelMetadataServiceApi->model_metadata_service_update_model_metadata: %s\\n\" % e)\n",
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" \n",
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"try:\n",
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" # List Model Metadata entries.\n",
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" api_response = api_instance.model_metadata_service_list_model_metadata(uri=\"gs://test-model-alpha-v1.0.0\")\n",
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" print(\"After update:\")\n",
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" pprint(api_response)\n",
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"except ApiException as e:\n",
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" print(\n",
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" \"Exception when calling ModelMetadataServiceApi->model_metadata_service_list_model_metadata: %s\\n\"\n",
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" % e\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get runtime information for a model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" # List Runtime Metadata for all deployments associated with a model.\n",
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" api_response = api_instance.model_metadata_service_list_runtime_metadata_for_model(model_uri=\"gs://kfserving-samples/models/sklearn/iris\", deployment_status=\"Running\")\n",
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" pprint(api_response)\n",
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"except ApiException as e:\n",
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" print(\"Exception when calling ModelMetadataServiceApi->model_metadata_service_list_runtime_metadata_for_model: %s\\n\" % e)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get model information for a deployment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" # List Runtime Metadata for all deployments associated with a model.\n",
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" api_response = api_instance.model_metadata_service_list_runtime_metadata_for_model(deployment_name=\"iris\", deployment_namespace=\"seldon\")\n",
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" pprint(api_response)\n",
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"except ApiException as e:\n",
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" print(\"Exception when calling ModelMetadataServiceApi->model_metadata_service_list_runtime_metadata_for_model: %s\\n\" % e)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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