Using Dagger and GPTScript to build CI Pipelines with AI
April 29, 2024
Apr 29, 2024
Dagger Functions let you distill complex CI operations into discrete, containerized code units, then share them with the community. We've shown in the past how Dagger can be used for AI use cases, but did you know AI models can also use Dagger Functions to generate CI Pipelines?
By using publicly available Dagger Functions from the Daggerverse in conjunction with Large Language Models (LLM), you can now have an AI model dynamically assemble a CI pipeline for you by combining different Dagger Functions to achieve your stated goal.
In a recent demo from an AI meetup in Paris, Solomon demonstrated a Dagger Function that dynamically generates a CI pipeline using the Dagger API, based on natural language prompts. Internally, the Dagger Function uses GPTScript, a new scripting language designed to create a simpler and more natural programming experience leveraging OpenAI.
The AI model can be seen exploring a subset of the Dagger API, then using it to dynamically construct a pipeline that pulls containers, executes commands, reads files, and produces a results table. With the Daggerverse as playground, it's not hard to imagine this simple example being extended to more complex AI-powered use cases.
Watch the full demo below and join our fortnightly community call for more awesome demos from the Dagger community!
Dagger Functions let you distill complex CI operations into discrete, containerized code units, then share them with the community. We've shown in the past how Dagger can be used for AI use cases, but did you know AI models can also use Dagger Functions to generate CI Pipelines?
By using publicly available Dagger Functions from the Daggerverse in conjunction with Large Language Models (LLM), you can now have an AI model dynamically assemble a CI pipeline for you by combining different Dagger Functions to achieve your stated goal.
In a recent demo from an AI meetup in Paris, Solomon demonstrated a Dagger Function that dynamically generates a CI pipeline using the Dagger API, based on natural language prompts. Internally, the Dagger Function uses GPTScript, a new scripting language designed to create a simpler and more natural programming experience leveraging OpenAI.
The AI model can be seen exploring a subset of the Dagger API, then using it to dynamically construct a pipeline that pulls containers, executes commands, reads files, and produces a results table. With the Daggerverse as playground, it's not hard to imagine this simple example being extended to more complex AI-powered use cases.
Watch the full demo below and join our fortnightly community call for more awesome demos from the Dagger community!
Dagger Functions let you distill complex CI operations into discrete, containerized code units, then share them with the community. We've shown in the past how Dagger can be used for AI use cases, but did you know AI models can also use Dagger Functions to generate CI Pipelines?
By using publicly available Dagger Functions from the Daggerverse in conjunction with Large Language Models (LLM), you can now have an AI model dynamically assemble a CI pipeline for you by combining different Dagger Functions to achieve your stated goal.
In a recent demo from an AI meetup in Paris, Solomon demonstrated a Dagger Function that dynamically generates a CI pipeline using the Dagger API, based on natural language prompts. Internally, the Dagger Function uses GPTScript, a new scripting language designed to create a simpler and more natural programming experience leveraging OpenAI.
The AI model can be seen exploring a subset of the Dagger API, then using it to dynamically construct a pipeline that pulls containers, executes commands, reads files, and produces a results table. With the Daggerverse as playground, it's not hard to imagine this simple example being extended to more complex AI-powered use cases.
Watch the full demo below and join our fortnightly community call for more awesome demos from the Dagger community!
