TartSeagull57 , can you please open a github issue for tracking? 🙂
what framework are you on? What version of ClearML are you using?
I'm using PyTorch and my clearml version is 1.3.2.
What are you referring yo specifically? The data plots seem to be identical.
The top subplot disappears entirely. Also the 'child' labels are added, it seems like it tries to combine the two plots?
Do you mean the x/y intersection?
The x labels are not at the x-axis but in the middle of the plot. Also the y-axis seems to be duplicated? There are different labels written over each other.
TartSeagull57 , I couldn't make the sample you gave me work 😞
Can you please provide a self contained example that would reproduce the issue?
The problem was that the plot I created myself
How was the plot created? Can you give me a small snippet to try and play around with?
Hi TartSeagull57 ,
Which one is fig 1 and which one is fig 2?
How are you logging them?
TartSeagull57 , what framework are you on? What version of ClearML are you using?
CostlyOstrich36 The problem was that the plot I created myself (with the loss and accuracy) was not stored properly in the results/plot tab. I removed the plotting from my script and instead I save the loss and accuracy using Logger.current_logger().report_scalar(). With the loss and accuracy properly saved as scalars, they are plotted nicely in the results/scalar tab.
Hi Natan,
Thanks for your reply.
Fig1 = the left one, with the subplots
Fig2 = the right one, with the 'child' labels (which I did not create)
This is the plot that is automatically registered in the details of the experiment.
TartSeagull57 , you said the problem was with automatic reporting. Can you give an example of how you solved the issue for yourself?
Why does the figure change so drastically? And how can I solve it?
What are you referring yo specifically? The data plots seem to be identical.
Sidenote: there seems to be a bug in the plot viewer, as the axis are a bit chaotic..
Do you mean the x/y intersection?
fig, axs = plt.subplots(2, 1) axs[1].plot(range(epoch), accuracy_train, range(epoch), accuracy_valid) axs[1].legend(['Training accuracy', 'Validation accuracy']) axs[0].plot(range(epoch), loss_train, range(epoch), loss_valid) axs[0].legend(['Training loss', 'Validation loss']) plt.show()This is the code I used on the output of my model (epoch, accuracy_train, accuracy_valid, loss_train, loss_valid)
I circumvented the problem by saving the variables as scalars, those automatic plots work well.
Thanks for your help!