Hi ReassuredTiger98
So let's assume we call:
logger.report_image(title='training', series='sample_1', iteration=1, ...)And we report every iteration (keeping the same title.series names). Then in the UI we could iterate back on the last 100 images (back in time) for this title / series.
We could also report a second image with:
logger.report_image(title='training', series='sample_2', iteration=1, ...)which means that for each one we will have 100 past images to review ( i.e. same title/series only diff iteration, i.e. progress in time).
Make sense ?

I don't know for sure but this is what I understand from the code. But you need to have 100 experiment running at the same time, so unless you have access to 100 GPUs you should be fine

Ohh sorry. task_log_buffer_capacity is actually internal buffer for the console output, on how many lines it will store before flushing it to the server.
To be honest, I can't think of a reason to expose / modify it...

AgitatedDove14 I have to problem that "debug samples" are not shown anymore after running many iterations. What's appropriate to use here: A colleague told me increasing task_log_buffer_capacity worked. Is this the right way? What is the difference to file_history_size ?

Thanks for answering. I don't quite get your explanation. You mean if I have 100 experiments and I start up another one (experiment "101"), then experiment "0" logs will get replaced?

Thank you very much!!!

Thanks for answering, but I still do not get it. file_history_size decides how many past files are shown? So if file_history_size=100 and I have 1 image/iteration and ran 1000 iterations, I will see images for iteration 900-1000?

I did my best in explanation.
You have buffer of tasks, for example 100. When you add task #101 the task under #1 is replaced with new and you keep now tasks from #2 to #101.
Because I have > 100 saved experiment, I don't think that anyone should bother to change it, unless you are running more than 100 experiments at the same time

AgitatedDove14 Could you elaborate?

Right, if this is the case, then just use 'title/name 001' it should be enough (I think this is how TB separates title/series or metric/variant )

Hi Martin, thank you for your reply.
Could you please show an example about image title/series?
My have names like Epoch_001_first_batch_train, Epoch_001_first_batch_val,
Epoch_001_first_batch_val_balanced,
Epoch_002_first_batch_train, and so on

You mean I can do Epoch001/ and Epoch002/ to split them into groups and make 100 limit per group?

yes then the 100 limit is per "Epoch001" and another 100 limit for "Epoch002" etc. 🙂

ReassuredTiger98 why don't you take 5 minutes time and check out source code? https://github.com/allegroai/clearml/blob/701fca9f395c05324dc6a5d8c61ba20e363190cf/clearml/backend_interface/task/log.py

this is pretty obvious, it replaces last task with new task when the buffer is full

MortifiedDove27 Sure did, but I do not understand it very well. Else I would not be asking here for an intuitive explanation 🙂 Maybe you can explain it to me?

Thanks, that makes sense. Can you also explain what task_log_buffer_capacity does?

from torch.utils.tensorboard import SummaryWriter
writer.add_figure('name', figure=fig)
where fig is matplotlib

How do you currently report images, with the Logger or Tensorboard or Matplotlib ?

Hi MortifiedDove27

Looks like there is a limit of 100 images per experiment,

The limit is 100 unique combination of title/series per image.
This means that changing the title or the series name will add 100 more images (notice the 100 limit is for previous iterations)

Guys I think I lost context here 🙂 what are we talking about? Can I help in anyway ?

You mean I can do Epoch001/ and Epoch002/ to split them into groups and make 100 limit per group?

Thank you, I will try

I have to problem that "debug samples" are not shown anymore after running many iterations.

ReassuredTiger98 could you expand on it? What do you mean by "not shown anymore" ?
Can you see other reports ?

AgitatedDove14 I think Tim wanted to know what is task_log_buffer_capacity and what functionality it provides

you can also increase the limit here:
https://github.com/allegroai/clearml/blob/2e95881c76119964944eaa0289549617e8afeee9/docs/clearml.conf#L32

AgitatedDove14

But would this not have to be a server parameter instead of a clearml.conf parameter then? Maybe someone from clearml can confirm MortifiedDove27 's explaination?

What is the difference to 

file_history_size

Number of unique files per titles/series combination (aka how many images to store in the history, when the iteration is constantly increasing)