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.
**Amazon Bedrock provides access to many open-source foundation models from leading AI companies along with a broad set of capabilities needed to build generative AI applications with security, privacy, and responsible AI.
Bedrock allows for more advanced customization of your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG) which you’ll learn more about later in this lesson., as well as image generation models.**
Before you can try out these models, you'll have to set up access to them on the AWS platform.
Step 1: Go to Amazon Bedrock in the AWS Console
Launch the cloud gateway in your classroom, and open the AWS cloud console. Search for Amazon Bedrock using the search box at the top of the AWS console, and select it from the list of services.
You might see a button to get started with Amazon Bedrock. Click on Get Started to be taken to the Amazon Bedrock interface.
Step 2: Manage Model Access in Amazon Bedrock
Once you're in the Amazon Bedrock interface, look for the Model Access link on the bottom of the left side navigation panel in the Amazon Bedrock menu. Click on Model Access.
This page lists text, image, and embedding models. The modality lists whether the model is used for text, embedding, or images. Click on Manage model access button in the top right of the page.
You'll see the same list of models, but now they have a check box next to them. Select the check box next to the following models:
**Amazon Bedrock provides access to many open-source foundation models from leading AI companies along with a broad set of capabilities needed to build generative AI applications with security, privacy, and responsible AI.
Bedrock allows for more advanced customization of your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG) which you’ll learn more about later in this lesson., as well as image generation models.**
Before you can try out these models, you'll have to set up access to them on the AWS platform.
Step 1: Go to Amazon Bedrock in the AWS Console
Launch the cloud gateway in your classroom, and open the AWS cloud console. Search for Amazon Bedrock using the search box at the top of the AWS console, and select it from the list of services.
You might see a button to get started with Amazon Bedrock. Click on Get Started to be taken to the Amazon Bedrock interface.
Step 2: Manage Model Access in Amazon Bedrock
Once you're in the Amazon Bedrock interface, look for the Model Access link on the bottom of the left side navigation panel in the Amazon Bedrock menu. Click on Model Access.
This page lists text, image, and embedding models. The modality lists whether the model is used for text, embedding, or images. Click on Manage model access button in the top right of the page.
You'll see the same list of models, but now they have a check box next to them. Select the check box next to the following models:
Jurassic-2 Mid (under AI21 Labs) Llama 2 Chat 70B (under Meta)
Select the Save Changes button in the lower right corner of the page. It may take several minutes to save changes to the Model access page.
The Jurassic-2 Mid and Llama 2 Chat 70B models will now show as Access granted on the Model access page under Access status.
Once access to these models is granted, you'll be able to test them out in an Amazon Bedrock Playground in the AWS console.