Repository: openai/evals
Issue: Error when install evals with pyarrow 
Root cause: Environment Incompatibility

Background : Python 3.10 & conda 22.11.1
I got this error when install evals with `pip install -e .`
pod5 0.1.5 requires pyarrow~=8.0.0, but you have pyarrow 10.0.1 which is incompatible.

Then I used `pip install pyarrow==8.0` to down pyarrow to 8.0. And the second time I tried to install evals, I got the same error as beginning: pyarrow 10.0.1 was installed and popped out the error.

Any help? Thank you.

I just created a new conda environment, gave it the option python=3.10, installed it, and it worked great.

@fish2022Jul  Just try to make the virtual environment 1st then use `pip install -e .`

follow the steps

1. Create a virtual environment (` python3 -m venv yourenv`)
2. active the environment ( `. yourenv/bin/active` for windows `. venv/Scripts/activate`)
3. `pip install -e .`

should works:

```bash
python -m pip install --upgrade pip && pip install -e .
```

It seems that there is a conflict between the package requirements of your project and the pyarrow version installed in your environment. To resolve this issue, you can create a new isolated Conda environment for your project and install the required packages there.

_**### Follow these steps:**_

Create a new Conda environment and activate it:
bash
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conda create --name my_project_env python=3.10
conda activate my_project_env


Replace my_project_env with the desired name for your environment.

Install the required version of pyarrow:
bash
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pip install pyarrow==8.0.0

Install evals in your new environment:
bash
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pip install -e .


Make sure you are in the same directory as your project's setup.py file before running this command.

This process will create an isolated environment, preventing conflicts between the packages required for your project and the global Python installation. If you still encounter issues, consider checking the evals project's dependencies and requirements to ensure you are using the appropriate package versions.

Here is a set of instructions for creating a virtual environment using venv, activating it, and installing your project with pip install -e .:

Create a virtual environment:

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python3 -m venv yourenv


Replace yourenv with your desired virtual environment name.

Activate the virtual environment:

For non-Windows systems (Linux, macOS):

bash
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source yourenv/bin/activate


For Windows:

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.\yourenv\Scripts\activate


Install your project in the virtual environment:

Make sure you are in the same directory as your project's setup.py file, then run:

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pip install -e .


Using a virtual environment will help you isolate your project's dependencies and prevent conflicts with your global Python installation.

To add the ARC Challenge (1 or 2) JSONs to an evaluation script in the simplest way possible, follow these steps:

Download the ARC dataset:

Download the ARC-800-tasks.zip file from https://lab42.global/wp-content/uploads/2022/08/ARC-800-tasks.zip
Extract the contents of the zip file to a folder.

Locate the evaluation script you want to use with the ARC dataset. Make sure the script is capable of reading JSON files, or modify it to read JSON data.

In the evaluation script, add code to load the ARC Challenge JSON files. You can use the Python json module to load the JSON files:

python
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import json

with open('path/to/arc_challenge_1.json', 'r') as f:
    arc_challenge_1_data = json.load(f)

with open('path/to/arc_challenge_2.json', 'r') as f:
    arc_challenge_2_data = json.load(f)


Replace 'path/to/arc_challenge_1.json' and 'path/to/arc_challenge_2.json' with the actual paths to the JSON files extracted from the ARC-800-tasks.zip file.

Process the loaded ARC Challenge data according to the requirements of your evaluation script. This may involve preprocessing the data or transforming it into a format that the script can work with.

Run the evaluation script. The script should now use the ARC Challenge data as input and generate the desired results.

Keep in mind that the specific steps to incorporate the ARC dataset into your evaluation script may vary depending on the script's design and data processing requirements. The general guidance provided here should help you get started, but you may need to make additional adjustments depending on your specific use case.

@fish2022Jul Were you able to resolve your issue using one of the proposed solutions?

Closing due to lack of activity. Let me know if there's still an issue and we can reopen.