How to Use Powerful Benchmarker for Domain Adaptation

If you’re delving into the world of machine learning, specifically focusing on domain adaptation, you’ll want to explore the Powerful Benchmarker framework. This comprehensive guide will walk you through the steps to set it up and run experiments effectively.

Choosing the Right Git Branch

Before you begin, it’s essential to select the correct branch for your project:

• The domain-adaptation branch has code for:
  • Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptation
• The metric-learning branch contains code for:
  • A Metric Learning Reality Check

Currently, technical support is only available for the domain-adaptation branch.

Installation Guide

Let’s get started by cloning the repository and installing the required packages.

git clone https://github.com/KevinMusgrave/powerful-benchmarker.git
cd powerful-benchmarker
pip install -r requirements.txt

Setting Up Configuration

To ensure the framework runs smoothly, you’ll need to set paths in the constants.yaml file:

• exp_folder: This is where your experiments will be saved as sub-folders.
• dataset_folder: Specify where datasets will be downloaded (e.g., dataset_folder: mnist).
• conda_env: (optional) Specify the conda environment for Slurm jobs.
• slurm_folder: Logs will be saved to exp_folder/…/slurm_folder.
• gdrive_folder: (optional) This is where you can upload logs to Google Drive.

Folder Organization

The repository is well-organized; here are some important folders to check:

• latex – Code for creating LaTeX tables from experiment data.
• notebooks – Jupyter notebooks.
• powerful_benchmarker – Code for hyperparameter searches for training models.
• scripts – Bash scripts, including those for uploading logs to Google Drive.
• unit_tests – Tests to check for bugs.
• validator_tests – Code for evaluating validation methods (validators).

Using Top-Level Scripts

This framework comes with several useful scripts to enhance your workflow:

• delete_slurm_logs.py: Deletes all Slurm logs or specific logs based on experiment groups.
• kill_all.py: Kill all model training jobs or validator test jobs.
• print_progress.py: Prints the number of completed hyperparameter trials and provides detailed summaries.
• simple_slurm.py: Run programs via Slurm easily.
• upload_logs.py: Upload Slurm logs to Google Drive at regular intervals.

Analogy to Understand the Code Structure

Imagine setting up a new office space:

• The Git branches are like different departments in a company; each department has its specific role.
• The constants.yaml file is like your office layout plan, determining where every piece of equipment goes—ensuring everyone has what they need to work efficiently.
• The folder organization resembles the filing cabinets in each office, where all important documents (code) are stored neatly, making it easy for employees (scripts) to find what they need.

Troubleshooting

If you encounter any issues while using the Powerful Benchmarker, here are some troubleshooting tips:

• Ensure that all dependencies are correctly installed by checking the requirements.txt file.
• Verify that your paths in constants.yaml are correct and accessible.
• Check your Git branch to ensure you’re using the right one for your needs.

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Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.