Navigating the Challenges of Implementing AI in Software Engineering: My Journey with SWE-agent

🚀 Excited to dive into the world of Software Engineering with SWE-agent! 🛠️ Despite my best efforts, encountered some hurdles getting it up and running on my first attempt. Tried tweaking the codebase, exploring different options, but still couldn't crack it.

(swe-agent) PS C:\\Users\\Admin\\Downloads\\AI\\SWE-agent> docker run --rm -it -v //var/run/docker.sock:/var/run/docker.sock -v ${PWD}/keys.cfg:/app/keys.cfg -v ${PWD}/run.py:/app/run.py sweagent/swe-agent-run:latest python run.py --per_instance_cost_limit 100.00 --data_path https://github.com/PrabhashDiss/SARD/issues/1 --config_file config/default_from_url.yaml
INFO     📙 Arguments: actions:
           apply_patch_locally: false
...
...
...
INFO     💽 Loaded dataset from https://github.com/PrabhashDiss/SARD/issues/1
INFO     Found image sweagent/swe-agent-run:latest with tags: ['sweagent/swe-agent-run:latest'], created: 2024-04-22T08:16:17.397663332Z for linux amd64.
DEBUG    Starting container with command: docker run -i --rm --name sweagent-swe-agent-run-latest-b2489b67cb sweagent/swe-agent-run:latest /bin/bash -l -m
INFO     🌱 Environment Initialized
INFO     ▶️  Beginning task 0
INFO     Trying to clone from non-mirror...
ERROR    Failed to source conda: /bin/bash: line 119: /root/miniconda3/etc/profile.d/conda.sh: No such file or directory

Traceback (most recent call last):
  File "/app/run.py", line 356, in main
    self.run(index)
  File "/app/run.py", line 311, in run
    observation, info = self.env.reset(index)
  File "/app/sweagent/environment/swe_env.py", line 255, in reset
    self.communicate_with_handling(
  File "/app/sweagent/environment/swe_env.py", line 576, in communicate_with_handling
    raise RuntimeError(f"{error_msg}: {logs}")
RuntimeError: Failed to source conda: /bin/bash: line 119: /root/miniconda3/etc/profile.d/conda.sh: No such file or directory

WARNING  ❌ Failed on PrabhashDiss__SARD-i1: Failed to source conda: /bin/bash: line 119: /root/miniconda3/etc/profile.d/conda.sh: No such file or directory

INFO     Beginning environment shutdown...
INFO     Agent container stopped
INFO     Found image sweagent/swe-agent-run:latest with tags: ['sweagent/swe-agent-run:latest'], created: 2024-04-22T08:16:17.397663332Z for linux amd64.
DEBUG    Starting container with command: docker run -i --rm --name sweagent-swe-agent-run-latest-b2489b67cb sweagent/swe-agent-run:latest /bin/bash -l -m
INFO     🌱 Environment Initialized
(swe-agent) PS C:\\Users\\Admin\\Downloads\\AI\\SWE-agent>

💻 Next on the agenda: testing it out on Lightning AI! ⚡️ Stay tuned for updates as I navigate through this fascinating journey of merging AI with software development. 🌟

Comments

Post a Comment

Popular posts from this blog

Deploy FastAPI on AWS Lambda: A Step-by-Step Guide

Image
In the world of serverless computing, AWS Lambda stands out as a powerful platform for deploying applications quickly and efficiently. FastAPI, with its high-performance capabilities and easy-to-use framework, is a perfect match for building robust APIs. In this article, we'll walk through the process of deploying a FastAPI application on AWS Lambda, step by step. Setting Up the Environment First, ensure you have Visual Studio Code open. Begin by installing the necessary dependencies: FastAPI, uvicorn, and Mangum, which serves as the handler for AWS Lambda. In your terminal, execute: pip install fastapi uvicorn mangum Next, generate a requirements.txt file to manage dependencies: pip freeze > requirements.txt Now, create the main application file, main.py , where we'll define our FastAPI application along with the necessary handlers: from fastapi import FastAPI from mangum import Mangum app = FastAPI() handler = Mangum(app) @app.get("/") async def hello...
Read more