python – How to get Numba’s prange threads to collaborate by storing / seeing the result in a shared variable?

I have a test code like so:

from numba import njit, prange
import random

def func():
    highest_val = 0
    for i in prange(100):
        for j in range(1000):
            val = random.randint(0, 100000000000)
            if val > highest_val:
                highest_val = val
                print('New highest val: ', highest_val)


Without parallel=True, it will run and always print higher and higher values.

However, if I switch parallel to True, then all threads started by that prange call seem to get a copy of the initial value of highest_val. They all compete printing many repeated highest values, and the value set by one thread is not visible to other threads.

As new threads start to execute they start from scratch again, finding highest values that are lower than the ones found by previous threads. That assignment doesn’t seem to be altering the highest_val variable in a way that is visible to the other threads.

I’m trying to understand how this works.

How can I accomplish this?

Email sent to 2 addresses with shared same organization domain and one bounced back. Was it successfully delivered to the other address?

It is my first time asking questions, so my apologies if there is any mistakes. I sent an email to 2 addresses (2 different departments in same organization with shared, one bounced back from due to ‘address not found’. I later found out it was a generated email address. Could someone please tell me if my email was successfully delivered to the other ‘good’ address (the other department)?

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On Windows 10 version 1809, I have a shared folder mapped.

When I rename or create a sub-directory I lose access to the shared folder. Despite being disconnected, the change does go through.
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Little context – today I have a monolith application that I am planning to split into micro-services due to it’s growth and the need to partial re-deployments.

I’m designing a development process where I have a number micro-services in a multi repo environment (All written in python).

  • There is a one “Foundation” repository that stores ~30 different packages of
    shared code.
  • Each micro-service is stored in a different repo.
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An example of the packages and dependencies:

Package 1
Package 2 --> 1
Package 3 --> 1,2
Package 4

"Micro-service 1"
Package 5
Package 6 --> 5,1,3

"Micro-service 2"
Package 7 --> 4

I’m trying to understand how to handle the flow where Package 1 introduces
new change (let’s say – feature change that causes a minor version bump)
How I should keep up with changing all the depended libraries ?

  1. Package 3 depends on 1 directly and indirectly – think i want to avoid case where they have different versions – it will cause a problem in the deployment.
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