Just one more question, do you have any idea about how I could change the x-axis label from "Iterations" to "Epochs"

You mean in the UI (i.e. just the title) ? or are you actually reporting iterations instead of epochs? and if so is this auto connected to tensorboard or is it reported manually ?

Seems that api has changed pretty much since a few versions back.

Correct, notice that your old pipelines Tasks use the older package and will still work.

There seems to be no need in 

controller_task

 anymore, right?

Correct, you can just call pipeline.start() 🙂

The pipeline creates the tasks, but never executes/enqueues them (they are all in 

Draft

 mode). No DAG graph appears in 

RESULTS/PLOTS

 tab.

Which vers...

Hi HealthyStarfish45
You can disable the entire TB logging :
Task.init('examples', 'train', auto_connect_frameworks={'tensorflow': False})

Ohhh yes, that the problem

One last thing make sure you spin the pod container with privileged mode, because the trains-agent docker will spin a sibling docker for your actual experiment.

Hi DeliciousBluewhale87
So basically no webhooks, the idea is that you have full API to query everything in the system and launch task based on any logic. You can check the slack monitoring example, it is basically doing that. Wdyt?

Thanks!

I was able to see a configuration file under artifacts

Okay that is odd, it should have been under the configuration section, but as your screenshot shows this is not the case...
but the code specifically adds it:
None
called here:
None
wdyt?

but then an error message in the web-app pops up

Fetch parents failed

and the Scheduler task disappears

And the Task is still running? What's he clearml python version and webui version ?

Hmm, might be, check if your files server is running and configured properly

btw: I'm assuming that args is not the ArgParser object, as the ArgParser is automatically "connected" ?

MelancholyElk85 assuming we are running with clearml 1.1.1 , let's debug the pipeline and instead of pipeline start/wait/stop :
Let's do:
pipeline.start_locally(run_pipeline_steps_locally=False)

MuddySquid7 the fix was pushed to GitHub, you can now install directly from the repo:
pip install git+

DilapidatedDucks58 I see ...
This might be more complicated that one would imagine, a simple solution might be to store a snapshot of the values every-time we reach a new maximum, a quick hack might be to add it as text on one of the task's parameters or properties (that we can later add to the table as custom column).
wdyt?

(ignoring still having to fix the problem with 

LazyEvalWrapper

 return values).

fix will be pushed post weekend 🙂

such as displaying the step execution DaG in the PLOTS tab . (edited)

Wait, what are you getting on the DAG plot ? I think we "should" be able to see all the steps

Looks great, let me see if I can understand what's missing, because it should have worked ...

I should mention this is run within a TF v1 session context

This should not be connected.

everything gets stored as intended (to clearML dashboard)

So in jupyter it works? But from command line it does not ? what's the difference ?

WickedGoat98 Forever 🙂
The limitation is on the storage size

Sure 🙂
https://clear.ml/docs/latest/docs/references/sdk/task#get_reported_scalars

Hi JitteryCoyote63
The easiest is to inherit the ResourceMonitor class and change the default logging rate (you could also disable some of the metrics).
https://github.com/allegroai/clearml/blob/701fca9f395c05324dc6a5d8c61ba20e363190cf/clearml/task.py#L565
Then pass the new class to Task.init as auto_resource_monitoring

BattyLion34 are you saying you do not have the "APP CREDENTIALS" section in the profile page?

So I checked the code, and the Pipeline constructor internally calls Task.init, that means that after you constructs the pipeline object, Task.current_task() should return a valid object....
let me know what you find out

why are there indefinitely growing anonymous tasks, even after i've closed the main schedulers.

The anonymous Tasks are The Dataset you are creating (a Dataset version is also a Task of a certain type with artifacts, the idea is usually Datasets are created from code, hence the need to combine the two).
Make sense ?

What do you have in "server_info['url']" ?

GiddyTurkey39

A flag would be really cool, just in case if theres any problem with the package analysis.

Trying to think if this is a system wide flag (i.e. trains.conf) or a flag in task.init.
What do you think?

SweetGiraffe8 Works when I'm using plotly...
Can you please copy paste the code with the plotly, it's probably something I'm missing

Would you have an example of this in your code blogs to demonstrate this utilisation?

Yes! I definitely think this is important, and hopefully we will see something there 🙂 (or at least in the docs)

TrickyRaccoon92 actually Click is on the to do list as well ...

LovelyHamster1
Also you can use pip freeze instead of the static code analysis , on your development machines set:
detect_with_pip_freeze: false
https://github.com/allegroai/clearml/blob/e9f8fc949db7f82b6a6f1c1ca64f94347196f4c0/docs/clearml.conf#L169

I'm answering on the original thread