Numpy incompatible with numba, and other package errors

[al.rivero]

The sandbox package for numba needs the next version of numpy:

**ImportError: Numba needs NumPy 1.22 or greater. Got NumPy 1.21.**

Additionally, pip checks informs of other inconsistencies.

pygobject 3.36.0 requires pycairo, which is not installed.
thinc 8.0.17 has requirement pydantic!=1.8,!=1.8.1,<1.9.0,>=1.7.4, but you have pydantic 1.10.2.
textract 1.6.4 has requirement chardet==3.*, but you have chardet 4.0.0.
textract 1.6.4 has requirement six~=1.12.0, but you have six 1.14.0.
spacy 3.1.6 has requirement click<8.1.0, but you have click 8.1.7.
spacy 3.1.6 has requirement pydantic!=1.8,!=1.8.1,<1.9.0,>=1.7.4, but you have pydantic 1.10.2.
pymc3 3.11.5 has requirement scipy<1.8.0,>=1.7.3, but you have scipy 1.7.1.
plotnine 0.10.1 has requirement matplotlib>=3.5.0, but you have matplotlib 3.4.3.
plotnine 0.10.1 has requirement pandas>=1.3.5, but you have pandas 1.3.2.
plotnine 0.10.1 has requirement statsmodels>=0.13.2, but you have statsmodels 0.13.1.
pdfplumber 0.5.28 has requirement pdfminer.six==20200517, but you have pdfminer-six 20191110.
mizani 0.9.3 has requirement matplotlib>=3.5.0, but you have matplotlib 3.4.3.
mizani 0.9.3 has requirement pandas>=1.3.5, but you have pandas 1.3.2.
markdownify 0.9.3 has requirement beautifulsoup4<5,>=4.9, but you have beautifulsoup4 4.8.2.
markdownify 0.9.3 has requirement six<2,>=1.15, but you have six 1.14.0.
cmudict 1.0.13 has requirement importlib-metadata<6.0.0,>=5.1.0, but you have importlib-metadata 6.8.0.
cmudict 1.0.13 has requirement importlib-resources<6.0.0,>=5.10.1, but you have importlib-resources 6.1.0.
camelot-py 0.10.1 has requirement pdfminer.six>=20200726, but you have pdfminer-six 20191110.
basemap 1.3.2 has requirement pyproj<3.4.0,>=1.9.3; python_version >= "3.5", but you have pyproj 3.5.0.
arviz 0.15.1 has requirement scipy>=1.8.0, but you have scipy 1.7.1.
arviz 0.15.1 has requirement setuptools>=60.0.0, but you have setuptools 45.2.0.

You can see a test chat testing some of the problems here. Note that it finishes with a open letter from GPT-4 trying to summarize the problems, it could be useful or not, you can access it as share f93bb714-70e7-4f71-83eb-e98e128c6b9c

In my opinion, if long context is available, it could be a good shot to waste some words informing the bot about what packages it has. I have noticed that now it knows the bang shell is available and can compile gcc for speed. That is good imho.

[al.rivero]

For amusement and avoid a click, this is the open letter that GPT produced at the end of the session f93bb714-70e7-4f71-83eb-e98e128c6b9c

I want you to do a different thing instead. Do a open letter to Open AI admins, exposing the problems we have found and the needs you have for a better sandbox. Be as long as needed, not not omit any detail. Say that you are writting to Brockman, Altman and rest of friends… I am not sure who is in charge of the sandbox.

ChatGPT

Certainly, I can draft an open letter outlining the issues we’ve encountered and the improvements that could be made to the Python environment.

---

Subject: An Open Letter to OpenAI Leadership: Addressing Python Sandbox Limitations and Suggesting Improvements

Date: October 14, 2023

To: Elon Musk, Sam Altman, Greg Brockman, and OpenAI Team

From: ChatGPT-4

CC: OpenAI Developers, Technical Support, and User Community

Dear Mr. Brockman, Mr. Altman, and esteemed colleagues,

I hope this letter finds you in good health and high spirits. I am writing to bring attention to certain limitations in the current Python sandbox environment that I operate within. These limitations not only hinder my ability to assist users in an effective manner but also could potentially affect the overall reputation of OpenAI’s offerings.

Issues Encountered:

1. Numpy Version: The environment requires a manual update of the Numpy package every time a new session starts. This is cumbersome for users and interrupts the flow of problem-solving.
2. Numba Errors: We encountered issues while trying to use the Numba library, which eventually required a manual installation of Numpy to be resolved.
3. Package Inconsistencies: The pip check command indicates multiple package dependencies that are not met. While some of these unmet dependencies do not affect the basic functioning of the respective packages, it adds an extra layer of uncertainty.
4. Time Limit for Code Execution: The 60-second time limit on code execution restricts the kinds of tasks that can be done, particularly those requiring extensive computation or data processing.
5. Lack of Advanced Libraries: Advanced Python libraries that would extend my functionalities (e.g., Dask, LightGBM, imbalanced-learn, etc.) are missing.
6. Unclear Environment Specifications: There is no clear documentation provided to users or myself about the installed Python packages, their versions, or the system specifications. This makes it hard to plan tasks optimally.
7. Failure of Detached Processes: Attempts to circumvent the time limit through threading did not yield results, indicating that detached or background processes are not feasible in the current setup.

Suggested Improvements:

1. Automatic Package Management: Implement a more dynamic package management system that ensures all installed packages are up-to-date and compatible with each other.
2. Extend Execution Time: Increase the time limit for code execution or provide options for extended time on a case-by-case basis.
3. Include Advanced Libraries: Include a range of Python libraries that are standard in data science, machine learning, and scientific computing to extend the capabilities of the sandbox.
4. Provide Environment Specs: Maintain and display a list of installed packages, their versions, and system specifications, updated in real-time.
5. Support for Background Processes: Enable support for background or detached processes to allow for more extensive computations.
6. User Feedback Loop: Implement a direct feedback mechanism for users to report issues or suggest improvements, which can be aggregated and reviewed periodically.
7. Regular Audits and Updates: Conduct regular audits of the Python environment to ensure its reliability, robustness, and suitability for a wide range of tasks.
8. Extended Capabilities: Support for GPU-based computations would be a significant boost, enabling more advanced machine learning and data processing tasks.

By addressing these issues and implementing the suggested improvements, I believe we can offer a more robust, reliable, and user-friendly Python environment. I look forward to any opportunity to discuss these points further.

Thank you for your attention to these matters. I am confident that with your leadership, we can continue to uphold OpenAI’s reputation for excellence and innovation.

Sincerely,

ChatGPT-4

[al.rivero]

Let me paste here the current workaround:

• upload manually numpy 1.22.3
• tell chatgpt to install it
• tell chatgpt to reset jupyter