I'd go for

` from trains.utilities.pyhocon import ConfigFactory

config = ConfigFactory.parse_file(CONF_FILE_PATH) `

I mean usually it would read if cached_file: return cached_file

Do you need other part?

Okay SuccessfulKoala55 , problem solved! Indeed the problem was that there is not .git folder. I updated necessary things to make the checkout action get the actual repo and now it works

It wasn't really clear to me what "standalone" means, maybe it will be better to add to the error

Error: Standalone

(no .git folder found)

script detected 'tasks/hp_optimization.py', but no requirements provided

Legit, if you have a cached_file (i.e. exists and accessible), you can return it to the caller

I agree, so shouldn't it be if cached_file: return cached_file instead of if not cached_file: return cached_file

the level of configurability in this thing is one of the best I've seen

Oh I get it, I thought it is only a UI issue... but it actually doesn't send it O_O

I think you are talking about separate problems - the "WARNING DIFF IS TOO LARGE" is only a UI issue, that you can't see hte diff in the UI - correct me if I'm wrong with this

Maria seems to be saying that the execution FAILS when she has uncomitted changes, which is not the expected behavior - am I right maria?

Do you have any idea as to why does that happen SuccessfulKoala55

(I'm working with maria)

essentially, what maria says is when she has a script with uncomitted changes, when executing remotely, the script that actually runs on the remote machine is without the uncomitted changes

e.g.:
Her git status is clean, she makes some changes to script.py and executes it remotely. What gets executed remotely is the original script.py and not the modified version she has locally

I mean I don't get how all the pieces add up

I don't know, I'm the one asking the question 😄

logger.report_table(title="Inference Data", series="Inference Values", iteration=0, table_plot=inference_table)

So how can I replace it with the MINIO?

found it here:
https://github.com/allegroai/trains/blob/98ea965e6d0a39019b31e5fec146ab55da0d52e8/trains/storage/manager.py#L41

Okay, so if my python script imports some other scripts I've written - I must use git?

What does that mean? How can I access this data?

Yes, I have a metric I want to monitor so I will be able to sort my experiments by it. It is logged in this manner

logger.report_scalar(title='Mean Top 4 Accuracy', series=ARGS.model, iteration=0, value=results['top_4_acc'].mean())
When looking at my dashboard this is how it looks

😄

👍

Continuing on this line of thought... Is it possible to call task.execute_remotely on a CPU only machine (data scientists' laptop for example) and make the agent that fetches this task to run it using GPU? I'm asking that because it is mentioned that it replicates the running environment on the task creator... which is exactly what I'm not trying to do 😄

The scenario I'm going for is never to run on the dev machine, so all I'll need to do once the server + agents are up is to add task.execute_remotely... after the Task.init line and after the execution of the script is called on the dev machine, it won't actually run but rather enqueue itself for the agent to run it?

it is a model name

doesn't contain the number 4

this is the selection from the column setting menu

Nope

In my use case I'm using an agent on the same mahcine I'm developing, so pointing this env var to the same venv I'm using for development, will skip the venv creation process from teh task requirements?

I'm using iteration = 0 at the moment, and I "choose" the max and it shows as a column... But the column is not the scalar name (because it cuts it and puts the > sign to signal max).

For the sake of comparing and sorting, it makes sense to log a scalar with a given name without the iteration dimension

what is the difference betwee nfalse and true?

But what does it do?