pkl | A simple Python Keylogger for macOS

Overall you should not create an object inside an object for the purpose of overriding a single method, instead you can just do

Here's the globals()-related part of your problem (I guess):

Return the dictionary implementing the current module namespace. For code within functions, this is set when the function is defined and remains the same regardless of where the function is called.

So globals() in your functions is always the namespace of saveWS.py.

And here the dir()-related one:

Without arguments, return the list of names in the current local scope. With an argument, attempt to return a list of valid attributes for that object.

Therefore dir() refers to the local namespace within the function.

You probably could fix that by passing dir() and globals() as arguments:

The python snippet does not appear to use the dask API efficiently. It might be that your actual function is a bit more complex, so map_partitions cannot be avoided, but let's take a look at the simple case first:

Looking at the scaler API and the code there seems to be no way of applying on a column subsample with the sklearn class. You could write your own class taking an optional column mask at transform time and applying it before the scaling. For instance

Call data.decode() or the equivalent str(data, encoding='utf-8') to convert the bytes to a valid base64-encoded string:

Import GensimWord2VecVectorizer in your Flask Web app python file.

Is it ok to have a separate folder for the data to load?

No, it must be inside the package to avoid polluting installation directory.

…__file__ and __path__…

No need, Python adds these variables automatically on import.

load('./datatoload/toload2.pkl') Would this work when someone downloads my package…?

No because ./ means the current directory and the current directory for user could be anything. You need to calculate you package directory using os.path.dirname(__file__). See https://stackoverflow.com/a/56843242/7976758/ for an example.

When you store file in DBFS (/FileStore/...), it's in your account (data plane). While notebooks, etc. are in the Databricks account (control plane). By design, you can't import non-code objects into a workspace. But Repos now has support for arbitrary files, although only one direction - you can access files in Repos from your cluster running in data plane, but you can't write into Repos (at least not now). You can:

• Either export model to your local disk & commit, then pull changes into Repos
• Use Workspace API to put file (only source code as of right now) into Repos. Here is an answer that shows how to do that.

But really, you should use MLflow that is built-in into Azure Databricks, and it will help you by logging the model file, hyper-parameters, and other information. And then you can work with this model using APIs, command tools, etc., for example, to move the model between staging & production stages using Model Registry, deploy model to AzureML, etc.

Receiving raw pointers usually^* means you don't assume ownership of the object. When you receive IReader* in the constructor of C, pybind11 assumes you will still hold the temporary PklReader() object and keep it alive outside. But you don't, so it gets freed and you get a segfault.

I think