Voluminous | An unnecessarily large volume control plug-in targetting | Plugin library

This is a specific instance of a general problem that I run into when updating packages using conda. I have an environment that is working great on machine A. I want to transfer it to machine B. But, machine A has GTX1080 gpus, and due to configuration I cannot control, requires cudatoolkit 10.2. Machine B has A100 gpus, and due to configuration I cannot control, requires cudatoolkit 11.1

I can easily export Machine A's environment to yml, and create a new environment on Machine B using that yml. However, I cannot seem to update cudatoolkit to 11.1 on that environment on Machine B. I try

The question may seem voluminous - but in order to understand the essence in a different way, it seems extremely difficult to formulate it.
While reading the documentation and some other sources, I encountered a certain misunderstanding related to the variety of file types for interaction when working with the database:

I am using google spreadsheet to collaborate on some common data, and processing this data using spreadsheet macros and saving it there itself in cells. This approach is error prone as the macro functions which are processing the data depend on inputs apart from what is given as parameters.

Example of how the common data-looks

Content Sheet

Sections Sheet

Pages Sheet Explanation of this common data

1. The three sheets are filled by various collaborators
2. Content sheet defines base elements of a page, they are referred (using their UUIDs) in the sections sheet (using macros), and finally all sections add together to make publishable html page.
3. The output html format varies depending upon the destination which are multiple - static html, Google Document, Google Slides, Word-press, MediaWiki etc. The formatting is done using GAS macros.

I tried various solutions, but nothing is working properly. Finally I have thought to keep the google spreadsheet as a data source only and move the entire computation to my local machine. This means I have to download the sheets data (3 sheets) which are some 1,000 rows, but can become voluminous with time.

Earlier when the entire computation was on google spreadsheets, I had to only fetch the final data and use it, which amounted to a lot fewer APIs calls. Referring to the example above, It means I would only fetch the output html of the "Pages sheet".

Q1) So my question is, given that I plan to move the entire computation to local machine, if I make only 3 APIs calls to bulk fetch data of three sheets, and pull the entire data, does it still counts as just 3 API calls, or big requests have a different API cost? Are there any hidden risks of over-running the quota in this approach?

Q2) And let's say i use hooks, both spreadsheet hooks and drive hooks to sync with real-time changes, what are my risks of running out of quota. Does each hook call constitute a API call. Any general advice on best practices for doing this?

Thanks

I have to run a query on ElasticSearch with Python to extract some voluminous data. I'm using elasticsearch 7.9.1 with Python 3.

The problem is, my script doesn't return the same number of lines from one run to an other. Sometimes I get ~300,000 results, sometimes more (1 million), sometimes zero. It seems to be happening especially if 2 runs are close to one another. I'm not changing the query nor the time range in which to search.

I noticed changing the scroll argument in es.search method seems to change the behaviour of the script. If scroll = '1s' for example, page['hits']['hits'] is zero, which doesn't really make sense to me.

Here is the class I'm using in my script :

I have a table which includes the last IP of the user. Using the following query I am able to find all duplicate IP addresses

I have a bit of code I was hoping to clean up/shrink down.

I have a function which receives a key and returns the corresponding value from either of two dictionaries, or a default value if the key is present in neither.

Here is a verbose (but explicit) version of the problem:

I need to download files through the Electron app. Files are voluminous, weigh about 2.5-3GB. How can I implement a file download so that the download process can be paused and continued later?

Technologies that I use (Electron, Node.JS, Vue.JS latest versions). Perhaps there are some modules?

I get a List> (Parameter is an Enum). Now I always get 15 of these ListItems, those who are not needed are null or empty. With these values (string part of the pair) I have to fill different api queries. Respectivly to the api endpoints some of the values are named differently, but are the same value. So I would have to change some of the values. I did it this way till now:

Looking for a more elegant way to transmit data with Jetpack

Now I am passing the data as follows: