Discover the Evaluations of EleutherAI Pythia Model

Discover the Evaluations of EleutherAI Pythia Model

Table of Contents:

1. Introduction
2. SSH onto the Luther like stability cluster
3. Navigate to the working directory
4. Downloading the checkpoints 4.1. Creating the repo for the checkpoints 4.2. Using the convert all script
5. Running slurm jobs 5.1. Understanding slurm commands 5.2. Checking job status with sact 5.3. Finding idle clusters with sinfo
6. Modifying configurations 6.1. Modifying the epiphia config file 6.2. Handling permissions issues
7. Evaluating models 7.1. Using zero shot eval scripts 7.2. Using five shot eval scripts 7.3. Automating evaluations with batch queue script
8. Conclusion

Title: A Step-by-Step Guide to Running Evaluations on the Luther Stability Cluster

Introduction: Running evaluations on the Luther Stability Cluster is an essential task for researchers and developers in the field of Natural Language Processing (NLP). This guide will walk you through the entire process of downloading and using the checkpoints, running slurm jobs, modifying configurations, and conducting evaluations on various models. By following these step-by-step instructions, you will be able to effectively evaluate NLP models on the Luther Stability Cluster and obtain valuable insights.

1. SSH onto the Luther Stability Cluster: To begin the evaluation process, you need to SSH onto the Luther Stability Cluster. This will allow you to access the computing resources and perform the necessary operations. Make sure you have the appropriate credentials and permissions to connect to the cluster.

2. Navigate to the working directory: Once you have successfully SSH'd onto the Luther Stability Cluster, navigate to your working directory. In most cases, the working directory is located in the FSX one. This directory will serve as the starting point for all the following steps.

3. Downloading the checkpoints: Before you can run evaluations, you need to download the checkpoints for the desired NLP models. The checkpoints contain pre-trained weights and configurations that are necessary for the evaluation process. Follow the steps below to download the checkpoints:

   4.1. Creating the repo for the checkpoints: First, create a repository to store the downloaded checkpoints. Navigate to the checkpoint directory and create an empty repository for the specific model you are interested in evaluating.

   4.2. Using the convert all script: The convert all script is a powerful tool that allows you to download and convert the checkpoints from S3. Use the convert all script by passing in the model name, the checkpoint step to start with, the interval between steps, and the number of steps. This script will automatically download and convert the checkpoints for you.

4. Running slurm jobs: Once you have downloaded the necessary checkpoints, it's time to run slurm jobs to perform evaluations. Slurm is a tool that enables distributed job execution on scientific clusters. Follow the steps below to run slurm jobs:

   5.1. Understanding slurm commands: Familiarize yourself with the essential slurm commands, such as sact and sinfo. These commands will help you manage and monitor the status of your slurm jobs.

   5.2. Checking job status with sact: Use the sact command to check the status of the jobs you have recently run. This command will provide you with valuable information about the progress and completion of your evaluations.

   5.3. Finding idle clusters with sinfo: Use the sinfo command to identify the idle clusters where you can run your evaluations. This will ensure that your evaluations run smoothly without any resource constraints.

5. Modifying configurations: To ensure the successful execution of evaluations, you might need to modify certain configurations. Specifically, you may need to modify the epiphia config file to reference the correct checkpoints and set the appropriate hyperparameters. Follow the steps below to modify configurations:

   6.1. Modifying the epiphia config file: Locate and modify the epiphia config file. This file contains attributes that determine the initialization of the model, hyperparameters, and other essential parameters for evaluations. Ensure that the file references the correct checkpoints and configurations.

   6.2. Handling permissions issues: You may encounter permissions issues while using default paths or accessing directories belonging to other users. In these cases, replace any references to directories with your own directory. This will resolve any potential permission problems.

6. Evaluating models: Now that everything is set up, it's time to evaluate the NLP models using the downloaded checkpoints. There are two evaluation scripts available: zero shot eval scripts and five shot eval scripts. Follow the steps below to conduct evaluations:

   7.1. Using zero shot Eval scripts: Utilize the zero shot eval scripts by passing the desired model as an argument. These scripts generate evaluations for a single checkpoint.

   7.2. Using five shot eval scripts: Similar to the zero shot eval scripts, use the five shot eval scripts to evaluate models. These scripts also require a single checkpoint as input.

   7.3. Automating evaluations with batch queue script: To streamline the evaluation process and avoid missing any checkpoints, use the batch queue script. This script automates the slurm job creation for each checkpoint, allowing multiple evaluations to run in parallel.

7. Conclusion: By following this comprehensive guide, you have successfully learned how to run evaluations on the Luther Stability Cluster. From downloading the checkpoints to modifying configurations and conducting evaluations, you now have the necessary knowledge and skills to evaluate NLP models effectively. Keep exploring new models and experiment with different configurations to further enhance your NLP research and development efforts.