Overview

Set Up Benchmarking

Before initiating the benchmarking process, it's essential to consider the method of GPU configuration. There are two primary approaches available: running an Ansible playbook designed for GPU setup and manually executing bash commands. Each approach presents distinct advantages and considerations that should be taken into account to make an informed choice for benchmarking.

Running Ansible Playbook for GPU Configuration:

Advantages:

Automation: Ansible playbooks allow for automated and repeatable GPU configuration, ensuring consistency and reducing the risk of human errors. Scalability: Ansible is designed to manage configurations across multiple machines, making it efficient for large-scale deployments. Collaboration: Playbooks can be shared and reused, enabling collaborative configurations.

Considerations:

Learning Curve: Familiarity with Ansible's syntax and structure might be necessary. Initial Setup: Setting up Ansible and creating playbooks might require upfront investment in time.

Manual Execution of Bash Commands for GPU Configuration:

Advantages:

Simplicity: Manual execution of bash commands is straightforward and immediate, especially for one-time setups. Direct Control: Directly executing commands provides immediate visibility into their effects.

Considerations:

Error-Prone: Manual execution can lead to errors due to typos, inconsistencies, or missed steps. Repeatability: Replicating manual configurations across multiple machines might be time-consuming and error-prone. Maintenance: Maintaining consistent configurations over time can be challenging. Comparison and Implications for Benchmarking:

The choice between running an Ansible playbook and manual bash execution can impact the benchmarking process. While manual execution may suffice for simple, one-off configurations, Ansible playbooks offer automation, scalability, and consistency benefits. In a benchmarking context, Ansible playbooks provide an organized and efficient approach to GPU configuration, reducing the risk of errors and ensuring consistent results across different environments.