Enhancing wfl with Large Language Models: Researching the Power of GPT for (HPC/AI) Job Workflows (2023-05-14)

In today's world of ever-evolving technology, the need for efficient and intelligent job workflows is more important than ever. With the advent of large language models (LLMs) like GPT, we can now leverage the power of AI to create powerful and sophisticated job workflows. In this blog post, I'll explore how I've enhanced wfl, a versatile workflow library for Go, by integrating LLMs like OpenAI's GPT. I'll dive into three exciting use cases: job error analysis, job output transformation, and job template generation.

wfl: A Brief Overview

wfl is a flexible workflow Go library designed to simplify the process of creating and managing job workflows. It's built on top of the DRMAA2 standard and supports various backends like Docker, Kubernetes, Google Batch, and more. With wfl, users can create complex workflows with ease, focusing on the tasks at hand rather than the intricacies of job management.

Enhancing the go wfl library with LLMs

I've started to enhance wfl by integrating large language models (OpenAI), enabling users to harness the power of AI to enhance their job workflows even further. By utilizing GPT's natural language understanding capabilities, we can now create more intelligent and adaptable workflows that can be tailored to specific requirements and challenges. This not only expands the possibilities for research but also increases the efficiency of job workflows. These workflows can span various domains, including AI workflows and HPC workflows.

It's important to note that this is a first research step in applying LLMs to wfl, and I expect to find new and exciting possibilities build upon these three basic use cases.

1. Job Error Analysis

Errors are inevitable in any job workflow, but understanding and resolving them can be a time-consuming and tedious process. With the integration of LLMs in wfl, we can now analyze job errors more efficiently and intelligently. By applying a prompt to an error message, the LLM can provide a detailed explanation of the error and even suggest possible solutions. This can significantly reduce the time spent on debugging and increase overall productivity.

2. Job Output Transformation

Sometimes, the raw output of a job can be difficult to understand or may require further processing to extract valuable insights. With LLMs, we can now apply a prompt to the output of a job, transforming it into a more understandable or usable format. For example, we can use a prompt to translate the output into a different language, summarize it, or extract specific information. This can save time and effort while enabling to extract maximum value from my job outputs.

3. Job Template Generation

Creating job templates can be a complex and time-consuming process, especially when dealing with intricate workflows. With the integration of LLMs in wfl, we can now also generate job templates based on textual descriptions, making the process more intuitive and efficient. By providing a prompt describing the desired job, the LLM can generate a suitable job template that can be analyzed, customized and executed. This not only simplifies the job creation process but also enables users to explore new possibilities and ideas more quickly. Please use this with caution and do not execute generated job templates without additional security verifications! I guess such verifications when automated could be a whole new research area.


The integration of large language models like GPT into wfl has opened up a world of possibilities for job workflows in HPC, AI, and enterprise jobs. By leveraging the power of AI, you can now create more intelligent and adaptable workflows that can address specific challenges and requirements. Further use cases, like building whole job flows upon the building blocks, needs to be investigated

To learn more about wfl and how to harness the power of LLMs for your job workflows, visit the WFL GitHub repository: https://github.com/dgruber/wfl/

A basic sample application demonstrating the Go interface is here: https://github.com/dgruber/wfl/tree/master/examples/llm_openai