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Rename Operational_Instructions/AWS_and_Pinecone_Accounts_for_LiveRAG.md to Operational_Instructions/AWS_Accounts_for_LiveRAG.md

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Operational_Instructions/{AWS_and_Pinecone_Accounts_for_LiveRAG.md β†’ AWS_Accounts_for_LiveRAG.md} RENAMED
@@ -1,20 +1,18 @@
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- # AWS and Pinecone Accounts for LiveRAG
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-
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- ## Using AWS Accounts
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  During the LiveRAG Challenge, participants may use two AWS accounts:
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- - **Personal AWS Account** – This is your private AWS account, where you can apply AWS credits provided for the competition.
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- - **TII-Provided AWS Account** – This account is managed by TII and grants access to the dense (Pinecone) and sparse (OpenSearch) pre-built indices.
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  Using these accounts is optional but highly recommended to optimize both cost and effort.
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- ## Using Your Personal AWS Account with AWS Credits
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  Each participating group receives AWS credits for the duration of the competition.
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  The credits are limited, and any usage beyond the allocated amount will be charged to your personal payment method.
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  To ensure your credits are properly applied, follow the official AWS instructions [here](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/useconsolidatedbilling-credits.html).
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- ### Personal AWS Account Setup and Credit Application Steps
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  1. **Create an AWS Account** – Sign up for an AWS account and add a payment method (typically a credit card). If you have an existing AWS account you may reuse it.
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  2. **Apply AWS Credits** – Follow the instructions in the provided AWS documentation to redeem and apply your credits.
@@ -22,26 +20,12 @@ To ensure your credits are properly applied, follow the official AWS instruction
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  - Use AWS Cost Management tools to monitor spending.
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  - Set up billing alarms using AWS CloudWatch to receive notifications when costs exceed predefined thresholds.
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- ### Cost Optimization Recommendations
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-
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- TII and AWS have estimated the cost of a typical team’s infrastructure, including GPU usage, and AWS has provided credits accordingly.
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- However, individual teams may have varying requirements, so cost management is essential:
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- - **Shut down unused resources** – Always shut down GPUs and other compute instances when not in use.
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- - **Experiment on smaller datasets first** – This approach speeds up iteration cycles and reduces expenses before scaling up to larger datasets.
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-
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- ## Using the TII-Provided AWS Account
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-
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- The TII-provided AWS account is strictly limited to specific use cases, primarily for accessing pre-built indices. These include:
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-
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- - A **Sparse OpenSearch Index** hosted by AWS.
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- - A **Dense Pinecone Index** hosted by Pinecone, with access credentials stored on AWS.
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-
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  ### Access and Setup Instructions
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- To gain access and configure the TII-Provided AWS account, follow these steps:
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- 1. **Receive Sign-In Details** – You will receive an email with login credentials from the organizers.
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  2. **Sign In & Change Password** – Log in to the AWS console and update your password.
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  3. **Create AWS Command Line Interface (CLI) Credentials** – Generate an access key and secret for CLI usage.
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  4. **Configure AWS CLI Profile** – Run the following command to set up the CLI for this account and follow the instructions:
@@ -73,7 +57,12 @@ You're welcome to build your own Pinecone/OpenSearch, or other indices. We encou
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  For instructions pertaining to building your own Pinecone index see [here](Pinecone_for_LiveRAG.md).
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- ## Summary of Key Recommendations
 
 
 
 
 
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  | **Action** | **Recommendation** |
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  |:-----------------------|:-------------------------------------------------------------|
 
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+ # AWS Accounts for LiveRAG
 
 
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  During the LiveRAG Challenge, participants may use two AWS accounts:
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+ - **AWS Team Account** – This is your private AWS account for your team, where you can apply AWS credits provided for the competition.
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+ - **AWS LiveRAG Account** – This account is managed by TII and grants access to the dense (Pinecone) and sparse (OpenSearch) pre-built indices.
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  Using these accounts is optional but highly recommended to optimize both cost and effort.
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+ ## Using Your AWS Team Account with AWS Credits
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  Each participating group receives AWS credits for the duration of the competition.
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  The credits are limited, and any usage beyond the allocated amount will be charged to your personal payment method.
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  To ensure your credits are properly applied, follow the official AWS instructions [here](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/useconsolidatedbilling-credits.html).
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+ ### AWS Team Account Setup and Credit Application Steps
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  1. **Create an AWS Account** – Sign up for an AWS account and add a payment method (typically a credit card). If you have an existing AWS account you may reuse it.
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  2. **Apply AWS Credits** – Follow the instructions in the provided AWS documentation to redeem and apply your credits.
 
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  - Use AWS Cost Management tools to monitor spending.
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  - Set up billing alarms using AWS CloudWatch to receive notifications when costs exceed predefined thresholds.
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  ### Access and Setup Instructions
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+ To gain access and configure the AWS LiveRAG account, follow these steps:
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+ 1. **Receive Sign-In Details** – You should have received an email with login credentials from the organizers.
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  2. **Sign In & Change Password** – Log in to the AWS console and update your password.
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  3. **Create AWS Command Line Interface (CLI) Credentials** – Generate an access key and secret for CLI usage.
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  4. **Configure AWS CLI Profile** – Run the following command to set up the CLI for this account and follow the instructions:
 
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  For instructions pertaining to building your own Pinecone index see [here](Pinecone_for_LiveRAG.md).
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+ ### Cost Optimization Recommendations
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+
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+ We have estimated the cost of a typical team’s infrastructure, including GPU usage, and AWS has provided credits accordingly.
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+ However, individual teams may have varying requirements, so cost management is essential:
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+ - **Shut down unused resources** – Always shut down GPUs and other compute instances when not in use.
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+ - **Experiment on smaller datasets first** – This approach speeds up iteration cycles and reduces expenses before scaling up to larger datasets.
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  | **Action** | **Recommendation** |
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  |:-----------------------|:-------------------------------------------------------------|