This Python script creates a password-protected ZIP file using the pyzipper library. It allows you to specify the files to include in the ZIP and set a password for encryption. The resulting ZIP file requires the provided password to access its contents, providing an additional layer of security.
Configuring Your Project Environment
Follow these steps to configure these required resources on the AWS platform.
An AWS SageMaker IAM Role
An AWS SageMaker Notebook Instance
A GPU instance for fine-tuning training
Download the project starter files(opens in a new tab)
You can start and stop the SageMaker Notebook Instance and your Jupyter Notebook Python files will be saved. Remember to stop your notebook instance when you are not using it.
Ensure you are always working in the US-west-2 Region by checking and changing the region in the dropdown menu in the top of the AWS console to US West Oregon (us-west-2)
Configuring an IAM Role for AWS SageMaker
The first step is to make an IAM role you'll use throughout the project. This role will have all of the necessary permissions to run your Jupyter Notebook files, access AWS S3 Storage Buckets containing datasets, model deployment, and training jobs.
In the left navigation menu, choose Role manager, and click on "Create a role"
Name the Sagemaker role udacitySagemakerRole so you can identify it later. Configure the role with the following settings:
Validate the permissions in your role against these values to ensure you have assigned all the necessary permissions to complete the project.
Creating an AWS SageMaker Notebook Instance
The next step is to create an AWS SageMaker Notebook Instance you'll use throughout the project. You'll create the Python Jupyter notebook files for the project in this instance. You'll use the IAM role you created in step 1 for this notebook instance.
An Amazon SageMaker Notebook instance is a EC2 virtual machine with the open-source Jupyter server installed The virtual machine costs money every hour it is running. Make sure you stop your notebook instance to stop the virtual machine when you're not using it so you don't run out of budget
In the AWS console, navigate to the Sagemaker home page, and choose Notebook -> Notebook instances from the left navigation menu
Click on "Create notebook instance" from the top of the page
Name your notebook instance and ensure you choose the IAM Sagemaker role you created in Step 1 above.
Request a GPU Instance for Training
You'll need to use an EC2 instance with a GPU to fine-tune the Meta Llama 2 7B model. Follow these instructions to visit the AWS Service Quotas dashboard and request a ml.g5.2xlarge instance for training job usage.
Visit the AWS Service Quota Dashboard(opens in a new tab)
In the AWS Services dropdown menu at the top of the page, choose or type Sagemaker, then click "View quotas" In the "Search by quota name" search box, enter ml.g5.2xlarge.
Locate the row that says ml.g5.2xlarge for training job usage, and fill in the circle next to it.
Click on the "Request increase at account-level" button at the top of the page, and enter 1 instances in the request form.
Submit the request
Configuring Your Project Environment Follow these steps to configure these required resources on the AWS platform.
An AWS SageMaker IAM Role An AWS SageMaker Notebook Instance A GPU instance for fine-tuning training Download the project starter files(opens in a new tab) You can start and stop the SageMaker Notebook Instance and your Jupyter Notebook Python files will be saved. Remember to stop your notebook instance when you are not using it.
Ensure you are always working in the US-west-2 Region by checking and changing the region in the dropdown menu in the top of the AWS console to US West Oregon (us-west-2)
Configuring an IAM Role for AWS SageMaker The first step is to make an IAM role you'll use throughout the project. This role will have all of the necessary permissions to run your Jupyter Notebook files, access AWS S3 Storage Buckets containing datasets, model deployment, and training jobs.
Log into the AWS console using the "Launch Cloud Gateway" button in the course menu. Navigate to the Sagemaker home page(opens in a new tab).
In the left navigation menu, choose Role manager, and click on "Create a role"
Name the Sagemaker role udacitySagemakerRole so you can identify it later. Configure the role with the following settings:
Validate the permissions in your role against these values to ensure you have assigned all the necessary permissions to complete the project.
Creating an AWS SageMaker Notebook Instance The next step is to create an AWS SageMaker Notebook Instance you'll use throughout the project. You'll create the Python Jupyter notebook files for the project in this instance. You'll use the IAM role you created in step 1 for this notebook instance.
An Amazon SageMaker Notebook instance is a EC2 virtual machine with the open-source Jupyter server installed The virtual machine costs money every hour it is running. Make sure you stop your notebook instance to stop the virtual machine when you're not using it so you don't run out of budget
In the AWS console, navigate to the Sagemaker home page, and choose Notebook -> Notebook instances from the left navigation menu Click on "Create notebook instance" from the top of the page Name your notebook instance and ensure you choose the IAM Sagemaker role you created in Step 1 above.
Request a GPU Instance for Training You'll need to use an EC2 instance with a GPU to fine-tune the Meta Llama 2 7B model. Follow these instructions to visit the AWS Service Quotas dashboard and request a ml.g5.2xlarge instance for training job usage.
Visit the AWS Service Quota Dashboard(opens in a new tab) In the AWS Services dropdown menu at the top of the page, choose or type Sagemaker, then click "View quotas" In the "Search by quota name" search box, enter ml.g5.2xlarge. Locate the row that says ml.g5.2xlarge for training job usage, and fill in the circle next to it. Click on the "Request increase at account-level" button at the top of the page, and enter 1 instances in the request form. Submit the request
genaiwithawsprojectstarter (2).zip