GPT-ENGINEER安装教程

Table of Contents

1. Introduction
2. Background Information
3. Trending Repository in GitHub
4. Setting Up the Environment
5. Cloning the Repository
6. Installing Necessary Packages
7. Getting the API Key
8. Running the Code
9. Preprocessing the Data
10. Modifying the Model
11. Conclusion

Introduction

Welcome back, GPT engineer! In this video, we will be discussing a trending repository in GitHub called "GPT Engineer." This repository has been gaining popularity recently, with a Current count of 3160 stars.

Background Information

Before we dive into implementing the GPT Engineer repository, let's first understand why it's trending. The repository allows You to specify what you want to build, and the AI system asks for clarifications before generating the desired output. This automated process makes it convenient and efficient for developers looking to automate certain tasks.

Trending Repository in GitHub

The GPT Engineer repository is currently trending due to its innovative approach in using AI to generate code. While the documentation mentions the use of GPT 4, many developers do not have access to it. However, in this video, we will be using the GPT 3.5 turbo, which yields similar results.

Setting Up the Environment

To avoid any conflicts in setting up the environment, we will be using the GitHub code space. If you are following along locally, please make sure to follow the provided steps. Let's navigate to the GitHub code space and Create a query space on the main branch.

Cloning the Repository

In order to proceed, we need to clone the GPT Engineer repository. If you are following along locally, make sure to navigate to the GPT Engineer folder.

Installing Necessary Packages

Before installing the necessary packages, it is important to create a virtual environment. Once that is done, we can proceed with the installation of the required packages.

Getting the API Key

To access the GPT 4 SS, we need to obtain the API key. However, since most of us don't have access to GPT 4, we will be using GPT 3.5 instead. You can obtain the API keys by visiting platform.OpenAI.com.

Running the Code

To get started, we need to run the command "python3 main.py" from the root folder. This command will create a new project and workspace for us.

Preprocessing the Data

In the example provided, We Are using logistic regression to implement a machine learning model. We have already uploaded the necessary data, and we need to specify the target variable, purpose of using MSE and R2 for evaluation, and the name of the file where the code will be saved.

Modifying the Model

After running the code, we may encounter errors related to preprocessing. In such cases, we can modify the code accordingly. For example, if there is an issue with categorical features, we can implement one-hot encoding or label encoding. Additionally, we can explore using different models like random forest or XGBoost to improve our results.

Conclusion

In conclusion, the GPT Engineer repository provides an efficient way to automate code generation. Despite the initial setup and possible modifications required, this project is constantly evolving, with regular updates and contributions from the community. Feel free to refer to the official GitHub repository for more information and to contribute to the project.