Module tf.about.faq


It does not work. Why?

Stay up to date!

Always use the latest version of TF, because there is still a lot of development going on.

A working installation contains three parts that are updated occasionally, sometimes slowly, other times rapidly:

  • TF itself, the Python library that you obtained by pip install text-fabric;
  • TF apps, the apps that are specialized in a specific corpus; you obtained it when you said tf org/repo or A = use("org/repo");
  • TF data, which was downloaded by that same statement that downloaded the app.

See tf.about.install for instructions how to upgrade these things.

Latest TF

TF cannot be found!

Most likely, you installed TF into another Python than you use when you run your Python programs. See Python Setup below.

Failed to upgrade TF!

When you get errors doing pip install text-fabric, there is probably an older version around. You have to say

pip install --upgrade text-fabric

If this still does not download the most recent version of text-fabric, it may have been caused by caching. Then say:

pip install --upgrade --no-cache-dir text-fabric

You can check what the newest distributed version of TF is on PyPi.

Failed to upgrade TF (still)!

Old versions on your system might get in the way.

Sometimes pip uninstall text-fabric fails to remove all traces of TF. Here is how you can remove them manually:

  • locate the bin directory of the current Python, it is something like

    • (MacOS regular Python) /Library/Frameworks/Python.framework/Versions/3.7/bin
    • (Windows Anaconda) C:\Users\You\Anaconda3\Scripts

    Remove the file text-fabric from this directory if it exists.

  • locate the site-packages directory of the current Python, it is something like

    • (MacOS regular Python) /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages

      Remove the subdirectory tf from this location, plus all files with text-fabric in the name.

  • After this, you can make a fresh install of text-fabric:

pip install text-fabric

Python setup

If you are new to Python, it might be tricky to set up Python the right way. If you make unlucky choices, and work with trial and error, things might get messed up. Most of the times when TF does not appear to work, it is because of this. Here are some hints to recover from that.

Upgrade of TF does not have any effect!

Most likely, you have multiple Pythons on your system. You installed TF in one Python, but you are using it in another Python.

This can happen if you installed Python from and then later installed Jupyter from Anaconda, which brings its own Python.

You can check whether you are in this situation.

First, on the command-line, do


You will see the version of TF that is used when you call the TF browser.

Then, in a Jupyter notebook, say

from tf.parameters import VERSION

You will see the version of TF that you use in a Jupyter notebook.

If they are equal, you might use the same Python in both cases.

If they are different, you have to clean up your Python installation. Ask a local guru, or google your way out. Or read on.

Other Python versions

The following hygienic measures are known to be beneficial when you have multiple versions of Python on your system.

When you have upgraded Python, remove PATH statements for older versions from your system startup files.

  • For the MacOS: look at .zshrc, .bashrc, and .bash_profile in your home directory.
  • For Windows: on the command prompt, say echo %path% to see what the content of your PATH variable is. If you see references to older versions of python than you actually work with, they need to be removed. Here is how

Do not remove references to Python 2.*, but only outdated Python 3.* versions.

TF browser

Internal Server Error!

When the TF browser opens with an Internal Server error, the most likely reason is that the TF web server has not started up without errors.

Look back at the terminal or command prompt where you started tf.

If somewhere down the road you see Error, I offer you my apologies!

Copy and paste that error and send it to me, and I'll fix it as soon as I can, and I let you know on the issue list.

Out of memory!

If TF has run out of memory, you might be able to do something about it.

In this case, during loading TF did not have access too enough RAM memory. Maybe you had too many programs (or browser tabs) open at that time.

Close as many programs as possible (even better, restart your machine) and try again. TF is know to work on Windows 10 machines with only 3GB RAM on board, but only in the best of circumstances.

If your machine has 4GB of RAM, it should be possible to run TF, with care.


TF uses the GitHub API to get its apps and data on the fly.

GitHub Rate Limit Exceeded!

Several solutions:

  1. (recommended) ask the provider of the dataset to use zipAll() to create a zip file of the complete dataset and attach it to the latest release on GitHub. Then TF uses a method to get your data which does not involve the GitHub API, and your problem is gone.
  2. use previously downloaded data or get data manually from GitHub, e.g. by cloning the GitHub repository that holds the dataset. This requires confidence with git / GitHub operations such as cloning and pulling.
  3. increase your rate limit by making yourself known to GitHub. The work needed to increase the rate is fairly simple, but it assumes a bit more knowledge about how your terminal and operating system works.

See also tf.advanced.repo.

Expand source code Browse git
.. include:: ../docs/about/