Change default to no code upload and update docs

docs/source/03_quickstart.rst

@@ -51,66 +51,61 @@ created in Azure and have their **names** ready to input to the plugin:
    or set appopriate settings
    (`https://github.com/kedro-org/kedro-plugins/tree/main/kedro-telemetry <https://github.com/kedro-org/kedro-plugins/tree/main/kedro-telemetry>`__).
 6. Install the requirements ``pip install -r src/requirements.txt``
-7. Create an Azure ML Environment for the project:
-
-For the project's code to run on Azure ML it needs to have an environment
-with the necessary dependencies. Here is it shown how to do this from a
-local Docker build context. Please refer to the
-`Azure ML CLI documentation <https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2#create-an-environment>`
-for more options.
-
-Start by executing the following command:
+7. Initialize Kedro Azure ML plugin, it requires the Azure resource
+   names as stated above. Experiment name can be anything you like (as
+   long as it's allowed by Azure ML). The environment name is the name
+   of the Azure ML Environment to be created in the next step. You can
+   use the syntax <environment_name>@latest for the latest version or
+   <environment-name>:<version> for a specific version.
 
 .. code:: console
 
-   kedro docker init
+   #Usage: kedro azureml init [OPTIONS] RESOURCE_GROUP WORKSPACE_NAME
+   #                          EXPERIMENT_NAME CLUSTER_NAME STORAGE_ACCOUNT_NAME
+   #                          STORAGE_CONTAINER ENVIRONMENT_NAME
+   kedro azureml init <resource-group-name> <workspace-name> <experiment-name> <compute-cluster-name> <storage-account-name> <storage-container-name> <environment-name>
 
-This command creates a several files, including ``Dockerfile`` and 
-``.dockerignore``. These can be adjusted to match the workflow for
-your project.
 
-Depending on whether you want to use code upload when submitting an
-experiment or not, you would need to add the code and any possible input
-data to the Docker image.
+8. Create an Azure ML Environment for the project:
 
-   - If using code upload:
-     Everything apart from the section "install project requirements"
-     can be removed from the ``Dockerfile``. You can add a
-     ``.amlignore`` file to specify which files should be uploaded.
-   - If not using code upload:
-     Keep the sections in the ``Dockerfile`` and adjust the ``.dockerignore``
-     file to add any other files to be added to the Docker image,
-     such as ``!data/01_raw`` for the raw data files.
-
-     Set ``code_directory: null`` in the ``azureml.yml`` config file.
+   For the project's code to run on Azure ML it needs to have an environment
+   with the necessary dependencies. Here is it shown how to do this from a
+   local Docker build context. Please refer to the
+   `Azure ML CLI documentation <https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2#create-an-environment>`
+   for more options.
 
-Create or update an Azure ML Environment by running the following command:
+   Start by executing the following command:
 
 .. code:: console
 
-   az ml environment create --name <environment-name> --version <version> --build-context . --dockerfile-path Dockerfile
+   kedro docker init
 
+   This command creates a several files, including ``Dockerfile`` and 
+   ``.dockerignore``. These can be adjusted to match the workflow for
+   your project.
 
-8. Initialize Kedro Azure ML plugin, it requires the Azure resource
-   names as stated above. Experiment name can be anything you like (as
-   long as it's allowed by Azure ML). The environment name is the name
-   of the Azure ML Environment created in the previous step. You can
-   use the syntax <environment_name>@latest for the latest version or
-   <environment-name>:<version> for a specific version.
+   Depending on whether you want to use code upload when submitting an
+   experiment or not, you would need to add the code and any possible input
+   data to the Docker image.
 
-.. code:: console
+   - If using code upload:
+      Everything apart from the section "install project requirements"
+      can be removed from the ``Dockerfile``. You can add a
+      ``.amlignore`` file to specify which files should be uploaded.
 
-   #Usage: kedro azureml init [OPTIONS] RESOURCE_GROUP WORKSPACE_NAME
-   #                          EXPERIMENT_NAME CLUSTER_NAME STORAGE_ACCOUNT_NAME
-   #                          STORAGE_CONTAINER ENVIRONMENT_NAME
-   kedro azureml init <resource-group-name> <workspace-name> <experiment-name> <compute-cluster-name> <storage-account-name> <storage-container-name> <environment-name>
+      Set ``code_directory: "."`` (or a subdirectory containing the code to upload)
+      in the ``azureml.yml`` config file.
+
+   - If not using code upload:
+      Keep the sections in the ``Dockerfile`` and adjust the ``.dockerignore``
+      file to add any other files to be added to the Docker image,
+      such as ``!data/01_raw`` for the raw data files.
+      
+   Create or update an Azure ML Environment by running the following command:
 
 .. code:: console
 
-   Configuration generated in /Users/marcin/Dev/tmp/kedro-azureml-demo/conf/base/azureml.yml
-   It's recommended to set Lifecycle management rule for storage container kedro-azure-storage to avoid costs of long-term storage of the temporary data.
-   Temporary data will be stored under abfs://kedro-azure-storage/kedro-azureml-temp path
-   See https://docs.microsoft.com/en-us/azure/storage/blobs/lifecycle-management-policy-configure?tabs=azure-portal
+   az ml environment create --name <environment-name> --version <version> --build-context . --dockerfile-path Dockerfile
 
 9. Adjust the Data Catalog - the default one stores all data locally,
    whereas the plugin will automatically use Azure Blob Storage. Only

kedro_azureml/config.py

@@ -67,7 +67,7 @@ class KedroAzureRunnerConfig(BaseModel):
   # Azure ML Environment to use during pipeline execution
   environment_name: "{environment_name}"
   # Path to directory to upload, or null to disable code upload
-  code_directory: "."
+  code_directory: null
   # Path to the directory in the Docker image to run the code from
   # Ignored when code_directory is set
   working_directory: /home/kedro