Yeah its trying to plan down the line into model deployment. Whilst its easy to save out a keras SavedModel or similar and have that artifact uploaded into the store, just wanted to check if there was a more generic solution. I could just create a Python class and serialise that out such that it has a standard interface, but good to check. So for example, some artifact representing an arbitrary math function. For better context, the idea is to make deploying any artifact we upload using clear...

Ah I see, it does print out the top experiments, you jus thave to make sure the metric and what not agrees. If I was looking to just attach some basic information to the task (after its been rerun, instead of printing it to the log), would the best option be to use the Logger to try and attach it, or set parameters, set comment, or is there a general way to set some metadata that is intended to be used in that capacity.

Ah fantastic, thanks! Another one for me - is there support for custom python models at all? For example, dummy models that simply return the output of an equation run over the dataframe after transforming some of the input columns. Something similar to mlflows custom pyfunc that allows a standard way of interfacing with custom models as you do with keras/sklearn/pytorch models

Phew! If I find any bugs or potential issues in the doco/comments too, where would be the best place to send that so I dont spam slack if I find tiny issues. Github issues / DM / a specific slack channel?

I meant the object instance itself so we can see what class it is and if its initialised. Also that looks like a matplotlib plot, not a plotly plot which you logging call indicates

Ah that did the trick, thanks a ton! Would be good to add deleting cookies to the upgrading process in the docs below if thats an easy thing to do 🙂 https://allegro.ai/clearml/docs/docs/deploying_clearml/clearml_server_linux_mac.html

Compared to the settings I can see when I go through the online portal and use the free hosted version:

How so? The view can be used to just initialise the plot above like I had done all the steps by hand, but any flexibility (add in other scalars/parameters) Id expect would still be viable options in the UI

Ah yup, found it, I was in the server Tasks doco and not the clearml Task doco, oops!

Gorgeous!

Can you throw a screenshot of the code where fig is defined?

The lack of this info might be to do with some API issues shown in the web dev console:

And heres the profile page if I access it via the URL. No option to add credentials

Alas no, apologies. Are you saying that in the global_min case, if a trial returns an MAE of 1.3, but the previous trial got an MAE of 0.5, the optimiser gets told that the MAE of the latest model is 0.5 instead of the truth?

So Im guessing either return_fig is False when you ran this, or plt.cdf is having issues (which it can). Id always recommend explicitly creating the figure instead of using the plt shortcuts.

fix, ax = plt.subplots()
ax.plot(…)

etc

Okay, that makes sense then. Whats still got me scratching my head is the examples printing out the WOOT WOOT for breaking the record, despite it clearly not. Hmm.

Ah fantastic, Ill use that over IDEs suggestions from now on 🙂

Oh btw the set_system_tags we talked about previously to auto-archive by adding the archived tag is throwing me an error.

for t in top_exp: t.set_tags(t.get_tags() + ["trial"]) t.set_system_tags(t.get_system_tags() + ["archived"])With error: ValueError: Task object can only be updated if created or in_progress

I think its because the _edit call happens after the set_task_property call?

Im trying to do it at the end of the optimisation. The same place in the example where you print the ids to the log. Im just hoping theres a way to get said table simply rather than going through a bunch of api calls to construct it myself

Heres the landing page, now with no option for settings or sidebar navigation:

+1 for autoarchiving. Right now the interface feels incredibly clunky to use once the number of HPO trials starts to increase. I currently have a demo project and have different algos to make predictions (a simple keras model, a RF, etc). Ideally I’d want to see the HPO execution just once with all the trials underneath it, or just the top (few) models. At the moment, I have pages and pages of models, 99% of them I dont care about. Is it possible to archive models and set tags in the code rat...

Yeah I was imagining the artifact, id, link to the child task, etc, would all be saved out. I have the HPO experiment open in the UI at the moment, and yup, I can see in the Results>Plots a table summary, but that wasnt the issue, it was trying to clean up the project wide experiments view without making a large number of projects. Are tagging / archiving available in the API for a task? Also, thanks for the help so far 🙂

Yes, this is expected, because you are trying to log a matplotlib figure as if it was a plotly figure. Try using report_matplotlib_figure

Yup, I see. My apologies for not catching that before posting 🙂

How odd, its not in my task.py file!

Let me check…

Ah. The code inspector is jumping to backend_interface.task.py, not clearml.task.py, how embarrassing for me!

Awesome ,thanks mate

To go off the online example, it finds the top 3 performing models and prints out the ID. What would be better would be to take those 3 IDs and - in the python code - grab the model artifacts for each, put them into the parent HPO model as its artifacts, and then go through the archive everything. Doesnt solve the issue if a HPO run is going to take a few days (in which the UI would become unusable), but once its done then the auto archiving would clean it up a lot. Is that possible at all, u...

Can you print out fig to verify it at all? The logger should call to_plotly_json on the figure, so Im guessing its None

Yup, thats how Ive been doing it now. Will happily update to a simpler method call whenever one gets made. Trying to make use of the HPO is a big thing Im trying to sell the team on, as its what sets ClearML apart from MLFlow or neptune - useful task orchestration and cloning 🙂

Yeah, post execution in a separate script / task