SuccessfulKoala55 Ok. Here is my code. Thanks.
` def plot_feature_scatter(df1, df2, features):
i = 0
sns.set_style("whitegrid")
plt.figure

fig, ax = plt.subplots(5, 4, figsize=(20, 20))

for feature in features:
    i += 1
    plt.subplot(5, 4, i)
    plt.scatter(df1[feature], df2[feature], marker="+", color='#2B3A67', alpha=0.2)
    plt.xlabel(feature, fontsize=9)
    
plt.show()

plot_feature_scatter(train_df.sample(50000), test_df.sample(50000), features) `

SuccessfulKoala55 No, I don't know how to turn off and turn on the auto-logging. Could you tell me? Thanks.

Performance issues are actually derived from the size of the plot object

I just thought maybe I could assist in explaining how to downscale the plot when saving 🙂

So I assume what grows is the features list?

SuccessfulKoala55 The image is something like this.
My questions are two
Is it possible to let ClearML not record this plot manually? Doesn't ClearML have performance issue for this kind of plot?

CostlyOstrich36 Ok. Thanks.

ScaryBluewhale66 , please look in:
https://clear.ml/docs/latest/docs/references/sdk/task#taskinit
The relevant section for you is auto_connect_frameworks
The usage would be along these lines:
Task.init(..., auto_connect_frameworks={'matplotlib': False})

SuccessfulKoala55 I don't understand your meaning.

Hi ScaryBluewhale66 , do you have such an example of a plot?

You can turn off the auto-logging, and just report what you need manually, will that do?

Hi SuccessfulKoala55 Do u mean give you the code to plot the image (but not including the data) or the image or the experiment I encountered this performance issue?