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M kovalsky/smmnotebook (#382)
* added notebook * added notebook * updated restore function to only take dataset name.
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‎notebooks/Semantic Model Management.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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"id": "5c27dfd1-4fe0-4a97-92e6-ddf78889aa93",
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"metadata": {
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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"### Install the latest .whl package\n",
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"\n",
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"Check [here](https://pypi.org/project/semantic-link-labs/) to see the latest version."
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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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"id": "d5cae9db-cef9-48a8-a351-9c5fcc99645c",
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"metadata": {
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"jupyter": {
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"outputs_hidden": true,
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"source_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"outputs": [],
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"source": [
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"%pip install semantic-link-labs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b195eae8",
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"metadata": {},
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"source": [
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"### Import the library and necessary packages"
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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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"id": "1344e286",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sempy_labs as labs\n",
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"source_dataset = '' # Name of the semantic model to backup\n",
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"target_dataset = '' # Name of the semantic model to restore\n",
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"source_workspace = '' # Name of the workspace in which the semantic model resides\n",
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"target_workspace = '' # Destination workspace of the semantic model\n",
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"source_file_path = '' # Name/path of the backup file to create\n",
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"target_file_path = '' # Name/path of the backup file to be copied to the target workspace\n",
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"storage_account = '' # Name of the ADLS Gen2 storage account associated with both source & target workspaces"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d4f5356a",
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"metadata": {},
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"source": [
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"#### Prerequisites\n",
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"* [Create an ADLS Gen2 storage account (in the Azure Portal)](https://learn.microsoft.com/azure/storage/common/storage-account-create?tabs=azure-portal)\n",
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"* Assign the ADLS Gen2 storage account to both source and target workspaces\n",
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" * Navigate to your workspace.\n",
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" * Select 'Workspace settings'.\n",
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" * Select 'Azure connections'.\n",
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" * Within 'Azure Data Lake Gen2 Storage' click 'Configure'.\n",
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" * Enter your Subscription, Resource Group and Storage Account.\n",
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" * Click 'Save'.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "55e5ca67",
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"metadata": {},
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"source": [
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"### Backup, copy and restore a semantic model to a new workspace"
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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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"id": "5a985c1f",
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"metadata": {},
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"outputs": [],
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"source": [
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"labs.backup_semantic_model(\n",
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" dataset=source_dataset,\n",
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" file_path=source_file_path,\n",
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" workspace=source_workspace,\n",
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")\n",
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"labs.copy_semantic_model_backup_file(\n",
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" source_workspace=source_workspace,\n",
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" target_workspace=target_workspace,\n",
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" source_file_name=source_file_path,\n",
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" target_file_name=target_file_path,\n",
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" storage_account=storage_account,\n",
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")\n",
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"labs.restore_semantic_model(\n",
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" dataset=target_dataset,\n",
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" file_path=target_file_path,\n",
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" workspace=target_workspace,\n",
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")"
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]
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}
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],
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"metadata": {
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"kernel_info": {
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"name": "synapse_pyspark"
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},
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"kernelspec": {
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"display_name": "Synapse PySpark",
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"language": "Python",
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"name": "synapse_pyspark"
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},
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"language_info": {
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"name": "python"
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},
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"microsoft": {
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"language": "python"
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},
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"nteract": {
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"version": "nteract-front-end@1.0.0"
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},
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"spark_compute": {
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"compute_id": "/trident/default"
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},
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"synapse_widget": {
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"state": {},
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"version": "0.1"
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},
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"widgets": {}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎src/sempy_labs/_clear_cache.py‎

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@log
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def restore_semantic_model(
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dataset: str | UUID,
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dataset: str,
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file_path: str,
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allow_overwrite: bool = True,
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ignore_incompatibilities: bool = True,
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Parameters
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----------
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dataset : str | uuid.UUID
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Name or ID of the semantic model.
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dataset : str
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Name of the semantic model.
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file_path : str
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The location in which to backup the semantic model. Must end in '.abf'.
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Example 1: file_path = 'MyModel.abf'
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)
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(workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
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(dataset_name, dataset_id) = resolve_dataset_name_and_id(dataset, workspace_id)
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tmsl = {
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"restore": {
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"database": dataset_name,
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"database": dataset,
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"file": file_path,
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"allowOverwrite": allow_overwrite,
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"security": "copyAll",
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fabric.execute_tmsl(script=tmsl, workspace=workspace_id)
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print(
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f"{icons.green_dot} The '{dataset_name}' semantic model has been restored to the '{workspace_name}' workspace based on the '{file_path}' backup file."
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f"{icons.green_dot} The '{dataset}' semantic model has been restored to the '{workspace_name}' workspace based on the '{file_path}' backup file."
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)
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