MsgPack | MessagePack encoder for types | Serialization library

Ondřej has removed support for 16.04 just a few days ago:

As of this moment, all the packages for the Ubuntu 16.04 LTS (Xenial) have been removed from the repositories.

This explains how it worked ok for you last week, but isn't working today.

Highly doubt that anyone would have the same scenario, but I solved it by just using a separate docker container for memcached and connecting my application to that instead.

Sorry you ran into that, we've had one report of that error before. It seems like something is weird with cupy on colab specifically. Based on the previous report, you should start with a clean Python environment and should not install cupy directly. I think colab uses a special version or something.

I'm not really sure what happened and why but I tried doing this on my rails container and I was no longer receiving the said error.

1. docker-compose run --rm bash
2. cd to project directory
3. bundle install

Adding this to my runtime scripts solve the problem. Instead of loading it as a module i'm loading my model from the path

When you use PyInstaller to collect data files into the bundle as you are doing here, the files are actually compiled into the resulting exe itself. This is transparently handled for Python code by PyInstaller when import statements are evaluated.

However, for data files you must handle this yourself. For instance, spacy is likely looking for the model in the current working directory. It won’t find your model because it is compiled into the .exe instead and therefore isn’t present in the current working directory.

You will need to use this API:

https://pyinstaller.readthedocs.io/en/stable/spec-files.html#using-data-files-from-a-module

This allows you to read a data file from the exe that PyInstaller creates. You can then write it to the current working directory and then spacy should be able to find it.

Your dask and distributed versions have gone out of sync, 2021.4.0 versus 2021.4.1. Updating dask should fix this. Note that you need to ensure that the exact same versions are also in the environment you are using for YARN.

Since big_array somehow needs to be shipped to all the workers I'm willing to believe that we encounter some large bytes objects along the way.

Here is your clue - don't make large function definitions like this. You should use scatter to move your array to the workers, if you cannot have them load the array.

Channel pypi means that the package was installed with pip. You may need to upgrade it with pip as well