Pr_ambadal_importdata_build (#2331)
* Import schedule samples added * data import schedule samples added * Corrected formatting * lifecycle settings added * Fixed test fail error * fixed broken test * updates * Updated for auto-delete-settings
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AmarBadal
GitHub
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400ade00e9
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@ -1,7 +1,8 @@
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## Working with Data in Azure Machine Learning CLI 2.0
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This repository contains example `YAML` files for creating `data` using Azure Machine learning CLI 2.0. This directory includes:
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- Sample `YAML` files for creating `data` asset from a `datastore`. These examples use `workspaceblobstore` datastore, which is created by default when a `workspace` is created. The examples use shorthand `azureml` scheme for pointing to a path on the `datastore` using syntax `azureml://datastores/${{datastore-name}}/paths/${{path_on_datastore}}`.<BR>
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- Sample `YAML` files for creating `data` asset from a `datastore`. These examples use `workspaceblobstore` datastore, which is created by default when a `workspace` is created. The examples use shorthand `azureml` scheme for pointing to a path on the `datastore` using syntax `azureml://datastores/${{datastore-name}}/paths/${{path_on_datastore}}`.<BR>
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- Sample `YAML` files for creating a `data` asset by uploading local file or folder.
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- Sample `YAML` files for creating a `data` asset by using `URI` of file or folder on an Azure storage account or `URL` of a file available in the public domain.
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@ -9,12 +10,14 @@ This repository contains example `YAML` files for creating `data` using Azure Ma
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- Sample `YAML` files for creating a `data` asset by using an `MLTable` file on an Azure storage account or `URL` of a file available in the public domain.
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- To create a data asset using any of the sample `YAML` files provided for the above scenarios, execute following command:
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```cli
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> az ml data create -f <file-name>.yml
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```
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- Sample `YAML` files for creating `data` asset by importing data from external data sources. These examples use `workspaceblobstore` datastore, which is created by default when a `workspace` is created. The examples use shorthand `azureml` scheme for pointing to a path on the `datastore` using syntax `azureml://datastores/workspaceblobstore/paths/<my_path>/${{name}}`.
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>__NOTE:__ Choose `path` as "azureml://datastores/${{datastore-name}}/paths/${{path_on_datastore}}" if you wish to cache the imported data in separate locations. This would provide reproducibility capabilities but add to storage cost. If you wish to over-write the data in successive imports, choose `path` as "azureml://datastores/${{datastore-name}}/paths/<my_path>", this would save you from incurring duplicate storage cost but you would lose the reproducibility as the newer version of data asset would have over-written the underlying data in the data path.
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- Sample `YAML` files for creating `data` asset by importing data from external data sources. These examples use `workspaceblobstore` datastore, which is created by default when a `workspace` is created. The examples use shorthand `azureml` scheme for pointing to a path on the `datastore` using syntax `azureml://datastores/workspaceblobstore/paths/<my_path>/${{name}}`.
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>__NOTE:__ Choose `path` as "azureml://datastores/${{datastore-name}}/paths/${{path_on_datastore}}" if you wish to cache the imported data in separate locations. This would provide reproducibility capabilities but add to storage cost. If you wish to over-write the data in successive imports, choose `path` as "azureml://datastores/${{datastore-name}}/paths/<my_path>", this would save you from incurring duplicate storage cost but you would lose the reproducibility as the newer version of data asset would have over-written the underlying data in the data path.
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- Sample `YAML` files for importing data from Snowflake DB and creating `data` asset.
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@ -22,11 +25,77 @@ This repository contains example `YAML` files for creating `data` using Azure Ma
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- Sample `YAML` files for importing data from Amazon S3 bucket and creating `data` asset.
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- To create a data asset using any of the sample `YAML` files provided by data import from external data sources, execute following command:
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- To create a data asset using any of the sample <source>.yml `YAML` files provided by data import from external data sources, execute following command:
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```cli
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> az ml data import -f <file-name>.yml
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```
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> **NOTE: Ensure you have copied the sample data into your default storage account by running the [copy-data.sh](../../../setup/setup-repo/copy-data.sh) script (`azcopy` is required).**
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- To create a data asset in AzureML managed HOBO datastore use snowflake-import-managed.yml which points to path of "workspacemanageddatastore" `YAML` files provided by data import from external data sources, execute following command:
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To learn more about Azure Machine Learning CLI 2.0, [follow this link](https://docs.microsoft.com/azure/machine-learning/how-to-configure-cli).
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```cli
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> az ml data import -f <file-name>.yml
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```
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- To import data asset using schedule we have two options - either call any of the <source>.yml in a Schedule YAML as in simple_import-schedule.yml or define an "inline schedule YAML" where you define both the schedule and import details in one single YAML as in data_import_schedule_database_inline.yml
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```cli
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> az ml schedule create -f <file-name>.yml
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```
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- The import data asset that is imported on to workspacemanageddatastore has data lifecycle management capability. There will be a default value and condition set for "auto-delete-settings" that could be altered.
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- Use the following command for the imported dataset on to workspacemanageddatastore to check the current auto-delete-settings -
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```cli
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> az ml data show -n <imported-data-asset-name> -v <version>
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```
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- To update the settings -
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- use the following command to change the value
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```cli
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> az ml data update --name 'data_import_to_managed_datastore' --version '1' --set auto_delete_setting.value='45d'
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```
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- To update the settings -
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- use the following command to change the condition - valid values for condition are - 'created_greater_than' and 'last_accessed_greater_than'
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```cli
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> az ml data update --name 'data_import_to_managed_datastore' --version '1' --set auto_delete_setting.condition='created_greater_than'
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```
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- To update the settings -
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- use the following command to change the condition and values -
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```cli
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> az ml data update --name 'data_import_to_managed_datastore' --version '1' --set auto_delete_setting.condition='created_greater_than' auto_delete_setting.value='30d'
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```
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- To delete the settings -
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- use the following command to remove the auto-delete-setting -
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```cli
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> az ml data update --name 'data_import_to_managed_datastore' --version '1' --remove auto_delete_setting
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```
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- To add back the settings -
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- use the following command -
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```cli
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> az ml data update --name 'data_import_to_managed_datastore' --version '1' --set auto_delete_setting.condition='created_greater_than' auto_delete_setting.value='30d'
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```
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- Use the following command to query all the imported data assets that have certain values for condition or value
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>```cli
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auto_delete_setting.value: az ml data list --name 'data_import_to_managed_datastore' --query "[?auto_delete_setting.value=='30d']"
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>az ml data list --name 'data_import_to_managed_datastore' --query "[?auto_delete_setting.condition=='last_accessed_greater_than']"
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```
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>**NOTE: Ensure you have copied the sample data into your default storage account by running the [copy-data.sh](../../../setup/setup-repo/copy-data.sh) script (`azcopy` is required).**
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To learn more about Azure Machine Learning CLI 2.0, [follow this link](https://docs.microsoft.com/azure/machine-learning/how-to-configure-cli).
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$schema: http://azureml/sdk-2-0/Schedule.json
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name: schedule_data_import_inline
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display_name: Schedule data import inline
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description: Schedule data import inline
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trigger:
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type: cron
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expression: "15 10 * * 1"
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time_zone: "Pacific Standard Time" # optional - default will be UTC
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import_data:
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type: mltable
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name: my_snowflake_ds
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path: azureml://datastores/workspaceblobstore/paths/snowflake/${{name}}
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source:
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type: database
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query: select * from my_table
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connection: azureml:my_snowflake_connection
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$schema: http://azureml/sdk-2-0/Schedule.json
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name: schedule_data_import
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display_name: Schedule data import
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description: Schedule data import
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trigger:
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type: cron
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expression: "15 10 * * 1"
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time_zone: "Pacific Standard Time" # optional - default will be UTC
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import_data: ./snowflake-import.yml
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$schema: http://azureml/sdk-2-0/DataImport.json
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# Supported connections include:
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# Connection: azureml:<workspace_connection_name>
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# Supported paths include:
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# Datastore: azureml://datastores/<data_store_name>/paths/<my_path>/${{name}}
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type: mltable
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name: snowflake_sample
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source:
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type: database
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query: select * from my_sample_table
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connection: azureml:my_snowflakedb_connection
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path: azureml://datastores/workspacemanageddatastore
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@ -1,6 +1,7 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -39,6 +40,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -89,6 +91,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -141,6 +144,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -181,6 +185,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -209,6 +214,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -239,6 +245,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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@ -485,6 +492,380 @@
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"ml_client.data.import_data(data_import=data_import)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Importing data on a Schedule. \n",
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"You can import data on a schedule created on a recurrence trigger or a cron trigger"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Here is an example of importing data from Snowflake on Recurrence trigger"
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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 azure.ai.ml.data_transfer import Database\n",
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"from azure.ai.ml.constants import TimeZone\n",
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"from azure.ai.ml.entities import (\n",
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" ImportDataSchedule,\n",
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" RecurrenceTrigger,\n",
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" RecurrencePattern,\n",
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")\n",
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"from datetime import datetime\n",
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"\n",
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"source = Database(connection=\"azureml:my_sf_connection\", query=\"select * from my_table\")\n",
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"\n",
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"path = \"azureml://datastores/workspaceblobstore/paths/snowflake/schedule/${{name}}\"\n",
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"\n",
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"\n",
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"my_data = DataImport(\n",
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" type=\"mltable\", source=source, path=path, name=\"my_schedule_sfds_test\"\n",
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")\n",
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"\n",
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"schedule_name = \"my_simple_sdk_create_schedule_recurrence\"\n",
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"\n",
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"schedule_start_time = datetime.utcnow()\n",
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"\n",
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"recurrence_trigger = RecurrenceTrigger(\n",
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" frequency=\"day\",\n",
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" interval=1,\n",
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" schedule=RecurrencePattern(hours=1, minutes=[0, 1]),\n",
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" start_time=schedule_start_time,\n",
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" time_zone=TimeZone.UTC,\n",
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")\n",
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"\n",
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"\n",
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"import_schedule = ImportDataSchedule(\n",
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" name=schedule_name, trigger=recurrence_trigger, import_data=my_data\n",
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")\n",
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"\n",
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"ml_client.schedules.begin_create_or_update(import_schedule).result()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Here is a similar example of creating an import data on a schedule - this time it is a Cron Trigger"
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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 azure.ai.ml.entities import CronTrigger, ImportDataSchedule\n",
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"\n",
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"source = Database(connection=\"azureml:my_sf_connection\", query=\"select * from my_table\")\n",
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"\n",
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"path = \"azureml://datastores/workspaceblobstore/paths/snowflake/schedule/${{name}}\"\n",
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"\n",
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"\n",
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"my_data = DataImport(\n",
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" type=\"mltable\", source=source, path=path, name=\"my_schedule_sfds_test\"\n",
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")\n",
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"\n",
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"schedule_name = \"my_simple_sdk_create_schedule_cron\"\n",
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"\n",
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"cron_trigger = CronTrigger(\n",
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" expression=\"15 10 * * 1\",\n",
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" start_time=datetime.utcnow(),\n",
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" end_time=\"2023-12-03T18:40:00\",\n",
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")\n",
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"import_schedule = ImportDataSchedule(\n",
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" name=schedule_name, trigger=cron_trigger, import_data=my_data\n",
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")\n",
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"ml_client.schedules.begin_create_or_update(import_schedule).result()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"NOTE: The import schedule is a schedule, so all the other CRUD operations of Schedule are available on this as well."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Disable the schedule"
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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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"ml_client.schedules.begin_disable(name=schedule_name).result()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Check the detail of the schedule"
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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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"created_schedule = ml_client.schedules.get(name=schedule_name)\n",
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"[created_schedule.name]"
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]
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},
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{
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||||
"attachments": {},
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"cell_type": "markdown",
|
||||
"metadata": {},
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"source": [
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"List schedules in a 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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"metadata": {},
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"outputs": [],
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"source": [
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"schedules = ml_client.schedules.list()\n",
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"[s.name for s in schedules]"
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]
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||||
},
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||||
{
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||||
"attachments": {},
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||||
"cell_type": "markdown",
|
||||
"metadata": {},
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||||
"source": [
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||||
"Enable a schedule"
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
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"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
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"source": [
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||||
"ml_client.schedules.begin_enable(name=schedule_name).result()"
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||||
]
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||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Update a schedule"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
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"source": [
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"# Update trigger expression\n",
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"import_schedule.trigger.expression = \"10 10 * * 1\"\n",
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"import_schedule = ml_client.schedules.begin_create_or_update(\n",
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||||
" schedule=import_schedule\n",
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").result()\n",
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"print(import_schedule)"
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||||
]
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||||
},
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||||
{
|
||||
"attachments": {},
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||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Delete the schedule"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
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||||
"# Only disabled schedules can be deleted\n",
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"ml_client.schedules.begin_disable(name=schedule_name).result()\n",
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||||
"ml_client.schedules.begin_delete(name=schedule_name).result()"
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||||
]
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||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Data Management on Workspace managed datastore:\n",
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||||
"\n",
|
||||
"Data import can be performed to an AzureML managed HOBO storage called \"workspacemanageddatastore\" by specifying \n",
|
||||
"path: azureml://datastores/workspacemanageddatastore\n",
|
||||
"The datastore will be automatically back-filled if not present.\n",
|
||||
"\n",
|
||||
"When done so, it comes with the added benefit of data life cycle management.\n",
|
||||
"\n",
|
||||
"The following shows a simple data import on to the workspacemanageddatastore. Same can be done using the schedules defined above - "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azure.ai.ml.entities import DataImport\n",
|
||||
"from azure.ai.ml.data_transfer import Database\n",
|
||||
"from azure.ai.ml import MLClient\n",
|
||||
"\n",
|
||||
"# Supported connections include:\n",
|
||||
"# Connection: azureml:<workspace_connection_name>\n",
|
||||
"# Supported paths include:\n",
|
||||
"# Datastore: azureml://datastores/<data_store_name>/paths/<my_path>/${{name}}\n",
|
||||
"\n",
|
||||
"data_import = DataImport(\n",
|
||||
" name=\"my_sf_managedasset\",\n",
|
||||
" source=Database(\n",
|
||||
" connection=\"azureml:my_snowflakedb_connection\",\n",
|
||||
" query=\"select * from my_sample_table\",\n",
|
||||
" ),\n",
|
||||
" path=\"azureml://datastores/workspacemanageddatastore\",\n",
|
||||
")\n",
|
||||
"ml_client.data.import_data(data_import=data_import)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Following are the examples of doing data lifecycle management aka altering the auto_delete_settings"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Get imported data asset details:\n",
|
||||
"```python\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Get data asset details\n",
|
||||
"name = \"my_sf_managedasset\"\n",
|
||||
"version = \"1\"\n",
|
||||
"my_data = ml_client.data.get(name=name, version=version)\n",
|
||||
"\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Update auto delete settings - "
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"from azure.ai.ml.entities import Data\n",
|
||||
"from azure.ai.ml.entities._assets.auto_delete_setting import AutoDeleteSetting\n",
|
||||
"from azure.ai.ml.constants import AssetTypes\n",
|
||||
"\n",
|
||||
"# update auto delete setting\n",
|
||||
"name = \"my_sf_managedasset\"\n",
|
||||
"version = \"1\"\n",
|
||||
"my_data = ml_client.data.get(name=name, version=version)\n",
|
||||
"my_data.auto_delete_setting = AutoDeleteSetting(\n",
|
||||
" condition=\"created_greater_than\", value=\"45d\"\n",
|
||||
")\n",
|
||||
"my_data = ml_client.data.create_or_update(my_data)\n",
|
||||
"print(\"Update auto delete setting:\", my_data)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Update auto delete settings - "
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"from azure.ai.ml.entities import Data\n",
|
||||
"from azure.ai.ml.entities._assets.auto_delete_setting import AutoDeleteSetting\n",
|
||||
"from azure.ai.ml.constants import AssetTypes\n",
|
||||
"\n",
|
||||
"# update auto delete setting\n",
|
||||
"name = \"my_sf_managedasset\"\n",
|
||||
"version = \"1\"\n",
|
||||
"my_data = ml_client.data.get(name=name, version=version)\n",
|
||||
"my_data.auto_delete_setting = AutoDeleteSetting(\n",
|
||||
" condition=\"last_accessed_greater_than\", value=\"30d\"\n",
|
||||
")\n",
|
||||
"my_data = ml_client.data.create_or_update(my_data)\n",
|
||||
"print(\"Update auto delete setting:\", my_data)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Delete auto delete settings - "
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"from azure.ai.ml.entities import Data\n",
|
||||
"from azure.ai.ml.entities._assets.auto_delete_setting import AutoDeleteSetting\n",
|
||||
"from azure.ai.ml.constants import AssetTypes\n",
|
||||
"\n",
|
||||
"# remove auto delete setting\n",
|
||||
"name = \"my_sf_managedasset\"\n",
|
||||
"version = \"1\"\n",
|
||||
"my_data = ml_client.data.get(name=name, version=version)\n",
|
||||
"my_data.auto_delete_setting = None\n",
|
||||
"my_data = ml_client.data.create_or_update(my_data)\n",
|
||||
"print(\"Remove auto delete setting:\", my_data)\n",
|
||||
"\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
|
|
@ -528,6 +909,7 @@
|
|||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
|
|
@ -557,6 +939,7 @@
|
|||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
|
|
|
|||
Загрузка…
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