Oh! With the sleep() function? Let me try it again

Let me check

What's the trains version / trains-server version ?

SmarmyDolphin68 , All looks okay to me...
Could you verify you still get the plot on debug samples as image with the latest trains RC
pip install trains==0.16.4rc0

0.16.1-320

you mean 0.16?

Ah my bad, I picked up the version from docker-compose file :D

I tried it, and it is uploading it to Debug Samples ( Task.get_task() ) with task.get_logger().report_matplotlib_figure() , but with a Task.init() , it's uploading it to Plots.

Checking with the RC package now

AgitatedDove14 , thanks a lot! I'll get back with a script in a day or two.

Thanks you !
I'll look into it

Understood, I'll look into it!

My use case is that, let's say I'm training with a file called train.py in which I have Task.init() , now after the training is finished, I generate some more graphs with a file called graphs.py and want to attach/upload to this training task which has finished. That's when I realised Task.get_task() is not working as intended, but it is when I have a Task.init() before it.

Could you amend the original snippet (or verify that it also produces plots in debug samples) ?
(Basically I need something that I can run 🙂 )

What's the matplotlib version ? and python version?

Okay, my bad, the code snippet I sent is correctly uploading it to 'Plots' but in the actual script I use this:
` def plot_graphs(input_df,output_df,task):

# Plotting all the graphs
for metric in df_cols:


    # Assigning X and Y axes data
    in_x = input_df["frameNum"]
    in_y = input_df[metric]
    out_x = output_df["frameNum"]
    out_y = output_df[metric]

    # Creating a new figure
    plt.figure()

    plt.xlabel('Frame Number')
    plt.ylabel('{} Score'.format(metric))

    # Plotting a simple line graph
    plt.plot(in_x,in_y)
    plt.plot(out_x,out_y)

    # Adding legend
    plt.legend(["Input {}".format(metric),"Output {}".format(metric)])

    # Reporting the plot through Trains Logger API
    task.get_logger().report_matplotlib_figure('Input vs Output {}'.format(metric),'Test',iteration=3,figure=plt,report_image=False)
    # task.logger.flush()
    time.sleep(3.0) `This is the code I'm using, not much different from the snippet. I've used plt.figure() to create a new plot since it's in the for loop, could that be causing this?

trains 0.16.3
trains-server 3.6

SmarmyDolphin68 is it the same code as in the snippet ?

` from trains import Task
import matplotlib.pyplot as plt
import numpy as np
import time

task = Task.get_task(task_id='task_id')

for i in range(0,10):

x_1 = np.random.rand(50)
y_1 = np.random.rand(50)

x_2 = np.random.rand(50)
y_2 = np.random.rand(50)
plt.figure()
plt.scatter(x_1, y_1, alpha=0.5)
plt.scatter(x_2, y_2, alpha=0.5)
# Plot will be reported automatically
# plt.show()
task.get_logger().report_matplotlib_figure(title="My Plot Title", series="My Plot Series", iteration=i, figure=plt)
time.sleep(6.0) `Weird that this code is also uploading to the 'Plots'. I replicated the same thing as my main script, but main script is still uploading to Debug Samples.

Python - 3.8.5
Matplotlib - 3.3.3
(Using same env to run test script and main script)

trains-server 3.6

you mean 0.16?

Are you doing 

plt.imshow

Nope

And yes, I set the report_image=False

, I generate some more graphs with a file called 

graphs.py

  and want to attach/upload to this training task

Make total sense to use Task.get_task, I just want to make sure that you are aware of all the options, so you pick the correct one for you :)

It's going to Debug Samples with the RC too

SmarmyDolphin68 if you can reproduce the behavior in a standalone script , it will really accelerate fixing this issue

Weird that this code is also uploading to the 'Plots'. I replicated the same thing as my main script, but main script is still uploading to Debug Samples.

SmarmyDolphin68 are you saying the same code behaves differently ?

Go to https://demoapp.trains.allegro.ai/profile
You should see something like 0.16.2-123

SmarmyDolphin68
BTW: there is no automatic reporting when you have task = Task.get_task(task_id='your_task_id')
It's only active when you have one "main" task.
You can also check the continue_last_task argument in Task.init , it might be a good fit for your scenario
https://allegro.ai/docs/task.html#trains.task.Task.init

SmarmyDolphin68 What's the matplotlib version ? and python version?

SmarmyDolphin68

Debug Samples tab and not the Plots,

Are you doing plt.imshow ?
Also make sure you have report_image=False when calling the report_matplotlib_figure
(if it is true it will upload it as an image to "debug samples")

BTW: I tested the code you previously attached, and it showed the plot in the "Plots" section
(Tested with latest trains from GitHub)

AgitatedDove14 , here's the code snippet you requested

No, the sample code I sent above works as intended, uploads to 'Plots'. But the main code that I've written, which is almost exactly similar to the sample code behaves differently.

SmarmyDolphin68 okay what's happening is the process exists before the actual data is being sent (report_matplotlib_figure is an async call, and data is sent in the background)
Basically you should just wait for all the events to be flushed
task.flush(wait_for_uploads=True)That said, quickly testing it it seems it does not wait properly (again I think this is due to the fact we do not have a main Task here, I'll continue debugging)
In the meantime you can just do
sleep(3.0)And it will solve the issue 🙂

Indeed, sleep() did the trick but it's going into the Debug Samples tab and not the Plots, any reason why? Earlier (with Task.init() followed by Task.get_task() ) the same plt plots got reported to 'Plots'.