If there is new issue will let you know in the new thread

Thanks! I would really like to understand what is the correct configuration

🙂

Correct

BTW: how are you using them? should we have a direct interface to those ?

quick update 1.0.2 will be ready in an hour, apologies 😞

in this week I have met at least two people combining ClearML with other tools (one with Kedro and the other with luigi)

I would love to hear how/what is the use case 🙂

If I run the pipeline twice, changing only parameters or code of taskB, ...

I'll start at the end, yes you can clone a pipeline in the UI (or from code) and instruct it to reuse previous runs.
Let's take our A+B example, Let's say I have a pipeline P, and it executed A and then B (which relies on A's output...

Hi @<1561885921379356672:profile|GorgeousPuppy74>

• Could you copy the 3 messages here into your original message, it helps keeping things tidy and nice (press on the 3 dot menu and select edit)
• what do you mean by "currently its not executing in queue-01", you changed it so it should be pushed to queue-02, no? Also notice that you can run the enire pipeline as sub-processes for debugging,
  just call pipe.start_locally(run_pipeline_steps_locally=True)
  You also need an agent on the ser...

So now for it to take place you need to enqueue the Task and set an agent to pick it up and run it.
When the agent is running the Task the new parameter will be passed.
does that make sense ?

then when we triggered a inference deploy it failed

How would you control it? Is it based on a Task ? like a property "match python version" ?

Hmm, interesting, why would you want that? Is this because some of the packages will fail?

Won't it be too harsh to have system wide restriction like that ?

Hmm, we could add an optional test for the python version, and the fail the Task if the python version is not found. wdyt?

The class documentation itself is also there under "References" -> "Trains Python Package"
Notice that due to a bug in the documentation (we are working on a fix) the reference part is not searchable in the main search bar

however can you see the inconsistency between the key and the name there:

Yes that was my point on "uniqueness" ... 😞
the model-key must be unique, and it is based on the filename itself (the context is known, it is inside the Task) but the Model Name is an entity, so it should have the Task Name as part of the entity name, does that make sense ?

@<1523701523954012160:profile|ShallowCormorant89> can you verify it is reproducible in 1.9.3 ? because if it is I'd like to fix that 🙂

will it be possible for us to configure the "new run" button in a way so that it always clones from a particular pipeline ?

What do you mean by "particular pipeline" ? by default it will clone the last successful one, and by right clicking a specific one you can run a copy of that one. what am I missing ?

SoreDragonfly16 . In the hyper parameters Tab, you have "parallel coordinates" (next to the "add experiment" the button saying "values" press on it and there should be " parallel coordinates")
Is that it?

No way?! Is this real chatgpt answer?

Amazing!

PlainSquid19 yes the link is available on in the actual paid product 😞
I don't think they have the documentation open yet...
My recommendation is to fill the contact us form, you'll get a free online tour as well 😉

Hi @<1523701168822292480:profile|ExuberantBat52>
We log all history anonymously here:
https://faq.clear.ml/
See if you can find it there

Hi ExuberantBat52
I do not think you can... i would use aws secret manager to push the entire user list config file wdyt?

DeliciousSeal67
are we talking about the agent failing to install the package ?

In the "installed packages" section you should have "nvidia-dali-cuda110" In the agent's clearml.conf you should add:extra_index_url: [" ", ]https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf#L78
Should solve the issue

DeliciousSeal67 the agent will use the "install packages" section in order to install packages for the code. If you clear the entire section (you can do that in the UI or programmatically) then it will revert to requirementsd.txt
Make sense ?

JitteryCoyote63 with pleasure 🙂
BTW: the Ignite TrainsLogger will be fixed soon (I think it's on a branch already by SuccessfulKoala55 ) to fix the bug ElegantKangaroo44 found. should be RC next week

JitteryCoyote63 okay... but let me explain a bit so you get a better intuition for next time 🙂
The Task.init call, when running remotely, assumes the Task object already exists in the backend, so it ignores whatever was in the code and uses the data stored on the trains-server, similar to what's happening with Task.connect and the argparser.
This gives you the option of adding/changing the "output_uri" for any Task regardless of the code. In the Execution tab, change the "Output Destina...

Hi JitteryCoyote63 ,
upload_artifacts was designed to upload pre made artifacts, which actually covers everything.
With register_artifacts we tried to have something that will constantly log PD artifact, the use case was examples used for training and their order, so we could compare the execution of two different experiments and detect dataset contamination etc.
Not Sure it is actually useful though ...

Retrieving an artifact from a Task is done by:
` Task.get_task(task_id='aaa').artifact...

ElegantKangaroo44 I think TrainsCheckpoint would probably be the easiest solution. I mean it will not be a must, but another option to deepen the integration, and allow us more flexibility.

task.models["outputs"][-1].tags (plural, a list of strings) and yes I mean the UI 🙂

I get the n_saved what's missing for me is how would you tell the TrainsLogger/Trains the current one is the best? Or are we assuming the last saved model is always the best ? (in that case there is no need for tag, you just take the last in the list)

If we are going with: "I'm only saving the model if it is better than the previous checkpoint" then just always use the same name i.e. " http:/...

I'd prefer to use config_dict, I think it's cleaner

I'm definitely with you

Good news:

new 

best_model

 is saved, add a tag 

best

,

Already supported, (you just can't see the tag, but it is there :))

My question is, what do you think would be the easiest interface to tell (post/pre) store, tag/mark this model as best so far (btw, obviously if we know it's not good, why do we bother to store it in the first place...)