From 97920a29a9050f03b2946e8ae6576e16fa8b4777 Mon Sep 17 00:00:00 2001 From: Moitreyee Hazarika <72728390+MoitreyeeHazarika@users.noreply.github.com> Date: Thu, 17 Oct 2024 18:42:42 +0530 Subject: [PATCH] PR request for Building Block (blocks) and Task update for oml (#316) * notebook-ea file modified Added new tasks - 7, 8, 9 * Screenshots updated for oml notebook lab * Screenshot updates * Conda lab updated Addressed Sherry's comments. * Conda images replaced Shows Py 3.12 version now * Screenshot replaced cmd-import-py-lib.png * Conda lab updated With info on ssl cert info and new screenshots. * Conda lab Note modified in Task 2, Step 1 * Image modified Screenshot modified to show OML_LABS as DB name. * Removed the word "enhanced" * typo corrected * Date updated --- .../oml/oml-conda/images/activate-mypyenv.png | Bin 15619 -> 8967 bytes .../oml-conda/images/cmd-import-py-lib.png | Bin 48750 -> 19606 bytes .../oml-conda/images/cmd-load-iris-data.png | Bin 0 -> 9603 bytes 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Here, you will also install the Python packages - _tensorflow_ and _seaborn_ from the _conda-forge_ channel. +This task demonstrates how to create a Conda environment called `mypyenv` for Python packages, with Python 3.12 that is compatible with OML4Py. Here, you will also install the Python packages - _tensorflow_ and _seaborn_ from the _conda-forge_ channel. * Conda channels are the locations where packages are stored. They serve as the base for hosting and managing packages. Conda packages are downloaded from remote channels, which are URLs to directories containing conda packages. The conda command searches a set of channels. By default, packages are automatically downloaded and updated from the default channel. -* The _conda-forge_ channel is a community channel made up of thousands of contributors, and is free for all to use. You can modify what remote channels are automatically searched. You might want to do this to maintain a private or internal channel. +* The _conda-forge_ channel is a community channel made up of thousands of contributors, and is free for all to use. You can modify what remote channels are automatically searched. ->*Note:* You must be signed in as ADMIN user to create the Conda environment. +>*Note:* You must be signed in as ADMIN user to create the Conda environment and upload it to Object Storage. To create a Conda environment: @@ -65,7 +67,7 @@ To create a Conda environment: %conda - create -n mypyenv -c conda-forge --override-channels --strict-channel-priority python=3.10 tensorflow seaborn + create -n mypyenv -c conda-forge --strict-channel-priority python=3.12.1 pandas==2.1.1 numpy==1.26.4 seaborn tensorflow ``` @@ -73,7 +75,7 @@ To create a Conda environment: * ``-n`` : This is the name of the environment. In this example, it is _mypyenv_. * ``-c`` : This is the channel name. In this example, it is _conda-forge_ * ``--override-channels`` : This argument ensures that the system does not search default, and requires a channel to be mentioned. - * ``--strict-channel-priority`` : This argument ensures that packages in lower priority channels are not considered if a package with the same name appears in a higher priority channel. In this example, the priority is given to python 3.10, tensorflow, and seaborn + * ``--strict-channel-priority`` : This argument ensures that packages in lower priority channels are not considered if a package with the same name appears in a higher priority channel. In this example, the priority is given to python 3.12, tensorflow, and seaborn @@ -94,7 +96,7 @@ To create a Conda environment: ![Command to list env and shows mypyenv](images/env-list-mypyenv.png) - >*Note:* The _mypyenv_ environment is not yet active. The active environment is marked with an asterisk (*), and in this list, it is present against the base environment usr. + >*Note:* The _mypyenv_ environment is not yet active. The active environment is marked with an asterisk (*), and in this list, it is present against the base environment /opt/conda. 4. Now, run the following command to activate the environment _mypyenv_. ``` @@ -119,7 +121,7 @@ The asterisk (*) next to the environment name confirms the activation. This comp ## Task 2: Create a Conda Environment for R and Install R Packages In this task, you will create a Conda environment called _myrenv_ for R packages, with R-4.0.5 for OML4R compatibility, and install the forecast and ggplot2 packages. ->*Note:* You must be signed in as ADMIN user to create the Conda environment. +>*Note:* You must be signed in as ADMIN user to create the Conda environment and upload it to Object Storage. To create a Conda environment named _myrenv_ with R-4.0.5 for OML4R compatibility and install the _forecast_ and _ggplot2_ packages: @@ -131,7 +133,7 @@ To create a Conda environment named _myrenv_ with R-4.0.5 for OML4R compatibilit %conda - create -n myrenv -c conda-forge --override-channels --strict-channel-priority r-base=4 r-forecast r-ggplot2 + create -n myrenv -c conda-forge --override-channels --strict-channel-priority r-base=4.0.5 r-forecast r-ggplot2 ``` In this command: @@ -140,6 +142,7 @@ To create a Conda environment named _myrenv_ with R-4.0.5 for OML4R compatibilit * ``--override-channels`` : This argument ensures that the system does not search default, and requires a channel to be mentioned. * ``--strict-channel-priority`` : This argument ensures that packages in lower priority channels are not considered if a package with the same name appears in a higher priority channel. In this example, the priority is given to R-4.0.5, forecast, and ggplot2. + >*Note:* You must set ``r-base`` to ``4.0.5`` for compatibility with the R version you are using in your notebooks. The command returns the following: @@ -163,7 +166,6 @@ The application tag is required for use with embedded execution. For example, OM >*Note:* There is one Object Storage bucket for each data center region. The Conda environment is saved to a folder in Object Storage corresponding to the tenancy and database. The folder is managed by Autonomous Database and is only available to users through OML Notebooks. There is an 8G maximum size for a single Conda environment, and no size limit on Object Storage. -To get help for the upload command, type upload --help in the %conda interpreter. 1. Run the following command to upload the mypyenv to the Object Storage bucket associated with the ADB. ``` @@ -245,7 +247,7 @@ The steps in task 4 must be performed as the non-ADMIN OML user with the `OML_DE import keras from keras.models import Sequential from keras.layers import Dense - from tensorflow.keras.optimizers import Adam + from keras.optimizers import Adam import seaborn as sns import pandas as pd @@ -265,12 +267,18 @@ The steps in task 4 must be performed as the non-ADMIN OML user with the `OML_DE %python - df = sns.load_dataset("iris") + import ssl + ssl._create_default_https_context = ssl._create_unverified_context + df = sns.load_dataset("iris") z.show(df) ``` - ![Command to load data](images/cmd-load-data.png) + ![Command to load the iris data](images/cmd-load-iris-data.png) + + After you run the command successfully, the iris dataset is displayed. + + ![Iris dataset](images/iris-data-loaded.png) 4. Plot the relationship pairwise @@ -289,6 +297,7 @@ The steps in task 4 must be performed as the non-ADMIN OML user with the `OML_DE Scroll down to view the output: ![Viewing the Plotwise relationship](images/output-plotwise-relship.png) + ## Task 5: Download, Activate and Use the Conda environment for R The steps in task 5 must be performed as the non-ADMIN OML user with the `OML_DEVELOPER` role. Here, you will download and activate the Conda environment for R. @@ -346,4 +355,4 @@ The steps in task 5 must be performed as the non-ADMIN OML user with the `OML_DE * **Author** - Moitreyee Hazarika, Principal User Assistance Developer, Database User Assistance Development * **Contributors** - Mark Hornick, Senior Director, Data Science and Machine Learning; Marcos Arancibia Coddou, Product Manager, Oracle Data Science; Sherry LaMonica, Consulting Member of Tech Staff, Machine Learning -* **Last Updated By/Date** - Moitreyee Hazarika, March 2024 +* **Last Updated By/Date** - Moitreyee Hazarika, October 2024 diff --git a/building-blocks/blocks/oml/oml-monitoring/oml-monitoring.md b/building-blocks/blocks/oml/oml-monitoring/oml-monitoring.md index 5673928f..dce0b1ea 100644 --- a/building-blocks/blocks/oml/oml-monitoring/oml-monitoring.md +++ b/building-blocks/blocks/oml/oml-monitoring/oml-monitoring.md @@ -3,7 +3,7 @@ This lab demonstrates how to use the OML monitoring feature. OML monitoring provides you with the functionality to monitor your data as well as monitor your machine learning models. -*Data Monitoring* evaluates how your data evolves over time. It helps you with insights on trends and multivariate dependencies in the data. It also gwarns you about data drift. +*Data Monitoring* evaluates how your data evolves over time. It helps you with insights on trends and multivariate dependencies in the data. It also warns you about data drift. Data drift occurs when data diverges from the original baseline data over time. 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