Ok, sorry, this is my mistake, it's actually inside a loop, so this make sense.

it seems that if I don't use plt.show() it won't show up in Allegro, is this a must?

I am not sure what are those example/1/2/3 are, I only have one chart

Cool, versioning the difference is useful. It also depends on what kind of data. For example, for tabular data, database might be a natural choice, however, how to integrate it and keep track of the metadata could be tricky. While for images, it probably more suitable for blob storage or per file basis.

It's good that you have version your dataset with name, I have seen many trained model that people just replace the dataset directly.

Oh I did not realize I asked this in a old thread, sorry about that.

I don't want user making heavy requests

Sorry for late reply, you mention there will be built-in way to version data. May I asked is there a release date for it?

I am interested in machine learning experiment mangament tools.

I understand Trains already handle a lot of things on the model side, i.e. hyperparameters, logging, metrics, compare two experiments.

I also want it to help reproducible. To achieve that, I need code/data/configuration all tracked.

For code and configuration I am happy with current Trains solution, but I am not sure about the data versioning.

So if you have more details about the dataset versioning with the enterprise offer...

Btw, I am able to isolate the code that causing the problem. It maybe easier for you to debug.

` import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
from trains import Task

task = Task.init(project_name='examples', task_name='Matplotlib example')
x = [ 1,2,3]
y = [1,2,3]
x = list(x)
y = list(y)
f, ax = plt.subplots(figsize=(50, 0.7 * len(x)))
sns.barplot(y, x, orient="h", ax=ax)
plt.show() `

wow, new data version feature and session could be useful

one does record the package, the other does not

lovely, thanks Jake!

No, I mean it capture the plot somehow, as you can see the left side there are a list of plot, but it does not show up.

lol...... mine is best_model_20210611_v1.pkl and better_model_20210611_v2.pkl or best_baseline_model_with_more_features.pkl

this is a bit weird, I have two Window machine, and both point to the public server.

yes

The "incremental" config seems does not work well if I add handlers in the config. This snippets will fail with the incremental flag.

` import logging
from clearml import Task

conf_logging = {
"version": 1,
"incremental": True,
"formatters": {
"simple": {"format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"}
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"level": "INFO",
"formatter": "s...

Thanks! That's great

I see, I will look into the documentation of it, thanks Jake.

I just remove it and it works now.. LOL

SuccessfulKoala55 task.connect()

Thanks for the help!

I just need a way to check if the web/app host is configured.

If yes, go ahead. If not, offline/throw an error

AgitatedDove14 Thanks! This seems to be a more elegant solution

I need this as I want to write a wrapper for internal use.

I need to block the default behavior that link to public server automatically when user has no configuration file.

and the 8 charts are actually identical

I use Yaml config for data and model. each of them would be a nested yaml (could be more than 2 layers), so it won't be a flexible solution and I need to manually flatten the dictionary

And the plotting area is completely empty, only some chart titles show up on the left.

AgitatedDove14 I believe you mean plt.savefig? I used this function to save my charts, but it does not show up as well.