Removed devcontainer from files and readme.

README.md

@@ -13,20 +13,19 @@ To learn more about the code in this repo, check out the accompanying blog post:
 * You need to have an Azure subscription. You can get a [free subscription](https://azure.microsoft.com/en-us/free) to try it out.
 * Create a [resource group](https://docs.microsoft.com/en-us/azure/azure-resource-manager/management/manage-resource-groups-portal).
 * Create a new machine learning workspace by following the "Create the workspace" section of the [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/quickstart-create-resources). Keep in mind that you'll be creating a "machine learning workspace" Azure resource, not a "workspace" Azure resource, which is entirely different!
-* If you have access to GitHub Codespaces, click on the "Code" button in this GitHub repo, select the "Codespaces" tab, and then click on "New codespace."
-* Alternatively, if you plan to use your local machine:
-  * Install the Azure CLI by following the instructions in the [documentation](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli).
-  * Install the ML extension to the Azure CLI by following the "Installation" section of the [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli).
-  * Install and activate the conda environment by executing the following commands:
-  ```
-  conda env create -f environment.yml
-  conda activate aml_command_cli
-  ```
-* In a terminal window, log in to Azure by executing `az login --use-device-code`. 
+* Install the Azure CLI by following the instructions in the [documentation](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli).
+* Install the ML extension to the Azure CLI by following the "Installation" section of the [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli).
+* Install and activate the conda environment by executing the following commands:
+```
+conda env create -f environment.yml
+conda activate aml_command_cli
+```
+* Within VS Code, go to the Command Palette clicking "Ctrl + Shift + P," type "Python: Select Interpreter," and select the environment that matches the name of this project.
+* In a terminal window, log in to Azure by executing `az login`. 
 * Set your default subscription by executing `az account set -s "<YOUR_SUBSCRIPTION_NAME_OR_ID>"`. You can verify your default subscription by executing `az account show`, or by looking at `~/.azure/azureProfile.json`.
 * Set your default resource group and workspace by executing `az configure --defaults group="<YOUR_RESOURCE_GROUP>" workspace="<YOUR_WORKSPACE>"`. You can verify your defaults by executing `az configure --list-defaults` or by looking at `~/.azure/config`.
 * You can now open the [Azure Machine Learning studio](https://ml.azure.com/), where you'll be able to see and manage all the machine learning resources we'll be creating.
-* Install the [Azure Machine Learning extension for VS Code](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai), and log in to it by clicking on "Azure" in the left-hand menu, and then clicking on "Sign in to Azure."
+* Optionally, you can install the [Azure Machine Learning extension for VS Code](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai). Log in by clicking on "Azure" in the left-hand menu, and then clicking on "Sign in to Azure."
 
 
 ## Train and predict locally

aml_command_cli/cloud/conda.yml

@@ -10,5 +10,5 @@ dependencies:
   - tqdm==4.65.0
   - pip==23.2.1
   - pip:
-      - azure-ai-ml==1.8.0
-      - azureml-mlflow==1.52.0
+    - azure-ai-ml==1.8.0
+    - azureml-mlflow==1.52.0

environment.yml

@@ -12,9 +12,4 @@ dependencies:
   - tqdm==4.65.0
   - yapf==0.33.0
   - pylint==2.17.4
-  - mypy==1.4.1
   - pip==23.2.1
-  - pip:
-    - types-tqdm==4.65.0.2
-    - pandas-stubs==1.2.0.58
-    - types-Pillow==10.0.0.1