gm folks, really liking ClearML so far as my top choice (after looking at dvc, mlflow), and thank you for your help here!

Thanks HurtWoodpecker30 !

Is there a recommended workflow to be able to “drop into” the

exact

env

(code, venv, data) of a previous experiment (which may have been several commits ago), to reproduce that experiment?

You can use clearml-agent on your local machine to build the env of any Task,
` clearml-agent build --id <ta...

@<1556812486840160256:profile|SuccessfulRaven86> is the issue with flask reproducible ? if so could you open a github issue, so we do not forget to look into it?

Yey!

DepressedChimpanzee34
so parsing bask is done via a yaml reader:
https://github.com/allegroai/clearml/blob/49fcbd7bbf3236f4175cdff29fa951847b0923cc/clearml/backend_interface/task/args.py#L506
We could add extra test here, checking for \ in the string, that should solve it and will be backwards compatible (I think)
https://github.com/allegroai/clearml/blob/49fcbd7bbf3236f4175cdff29fa951847b0923cc/clearml/backend_interface/task/task.py#L935

@<1710827340621156352:profile|HungryFrog27> the venv-build folder is supposed to be deleted after each task is done. How did you end up with leftovers? Could it be windows was failing to delete it for some reason? That actually connects with you initial issue no?

Okay so my thinking is, on the pipelinecontroller / decorator we will have:
abort_all_running_steps_on_failure=False (if True, on step failing it will abort all running steps and leave)
Then per step / component decorator we will have
continue_pipeline_on_failure=False (if True, on step failing, the rest of the pipeline dag will continue)
GiganticTurtle0 wdyt?

However, are you thinking of including this callbacks features in the new pipelines as well?

Can you see a good use case ? (I mean the infrastructure supports it, but sometimes too many arguments is just confusing, no?!)

GiganticTurtle0 My apologies, I made a mistake, this will not work 😞
In the example above "step_two" is executed "instantaneously" , meaning it is just launching the remote task, it is not actually waiting for it.
This means an exception will not be raised in the "correct" context (actually it will be raised in a background thread).
That means that I think we have to have a callback function, otherwise there is no actual way to catch the failed pipeline task.
Maybe the only re...

Thanks StrongHorse8
Where do you think would be a good place to put a more advanced setup? Maybe we should add an entry for DevOps? Wdyt?

Yes you have to spin the server in order to generate the access/secret key...

YEY!

I think the real issue is that I am not able to specify a platform for the model,

None
there is no need to specify it, remove it from the config.pbtxt - the clearml-serving will automatically add the background

Yes docker was not installed in the machine

Okay make sense, we should definitely check that you have docker before starting the daemon 😉

Ok, it would be nice to have a --user-folder-mounted that do the linking automatically

It might be misleading if you are running on k8s cluster, where one cannot just -v mount volume...
What do you think?

WackyRabbit7 my apologies for the lack of background in my answer 🙂
Let me start from the top, one of the goal of the trains-agent is to reproduce the "original" execution environment. Once that is done, it will launch the code and monitor it. In order to reproduce the original execution environment, trains-agent will install all the needed python packages, pull the code, and apply the uncommitted changes.
If your entire environment is python based, then virtual-environment mode is proba...

If you could provide the specific task ID then it could fetch the training data and study from the previous task and continue with the specified number of trainings.

Yes exactly, and also all the definitions for the HPO process (variables space, study etc.)

The reason that being able to continue from a past study would be useful is that the study provides a base for pruning and optimization of the task. The task would be stopped by aborting when the gpu-rig that it is using is neede...

Hi GrievingTurkey78
First, I would look at the CLI clearml-data as a baseline for implementing such a tool:
Docs:
https://github.com/allegroai/clearml/blob/master/docs/datasets.md
Implementation :
https://github.com/allegroai/clearml/blob/master/clearml/cli/data/main.py
Regrading your questions:
(1) No, a new dataset version will only store the diff from the parent (if files are removed it stored the metadata that says the file was removed)
(2) Yes any get operation will downl...

Hi LivelyLion31
Yes, the reason we designed Trains with an automagic integration is exactly that reason, so users do not need to learn another package and that with almost no effort you get most of the benefits.
Regrading the TB files, from experience most users will use the TB files short after they executed the experiment, usually for debugging and in depth capabilities (like network debugger profile etc), metric view is something that is much easier to do on a centralized server (like on...

is there something else in the conf that i should change ?

I'm assuming the google credentials?
https://github.com/allegroai/clearml/blob/d45ec5d3e2caf1af477b37fcb36a81595fb9759f/docs/clearml.conf#L113

Hi CooperativeSealion8
Seems like your NoScript addon is blocking the site :)

I did nothing to generate a command-line. Just cloned the experiment and enqueued it. Used the server GUI.

Who/What created the initial experiment ?

I noticed that if I run the initial experiment by "python -m folder_name.script_name"

"-m module" as script entry is used to launch entry points like python modules (which is translated to "python -m script")
Why isn't the entry point just the python script?
The command line arguments are passed as arguments on the Args section of t...

Hi MortifiedCrow63
I have to admit this is very strange, I think the fact it works for the artifacts and not for the model is kind of a fluke ...
If you use "wait_on_upload" argument in the upload_artifact you end up with the same behavior. Even if uploaded in the background, the issue is still there, for me it was revealed the minute I limited the upload bandwidth to under 300kbps.It seems the internal GS timeout assumes every chunk should be uploaded in under 60 seconds.
The default chunk...

because a pipeline is composed of multiple tasks, different tasks in the pipeline could run on different machines.

Yes!

. Or more specifically, they could run on different queues, and as you said, in your other response, we could have a Q for smaller CPU-based instances, and another queue larger GPU-based instances.

Exactly !

I like the idea of having a queue dedicated to CPU-based instances that has multiple agents running on it simultaneously. Like maybe four agents.

Th...

OddAlligator72 I like this idea.
The single thing I'm not sure about is the "function entry point"
Why would one do that? Meaning why wouldn't you have a proper python entry-point.
The reason I'm reluctant is that you might have calls/functions/variables in global scope of the file storing the function, and then users will not know why something broke, ans it will be very cumbersome to debug.
A simple script entry point seems trivial to launch and debug locally.
What do you think ? What woul...

Hi SubstantialElk6 I'll start at the end, you can run your code directly on the remote GPU machine 🙂
See clearml-task documentation, on how to create a task from existing code and launch it
https://github.com/allegroai/clearml/blob/master/docs/clearml-task.md

That said, the idea is that you add the Task.init call when you are writing/coding the code itself, then later when you want to run it remotely you already have everything defined in the UI.
Make sense ?

I pass my dataset as parameter of pipeline:

@<1523704757024198656:profile|MysteriousWalrus11> I think you were expecting the dataset_df dataframe to be automatically serialized and passed, is that correct ?
If you are using add_step, all arguments are simple types (i.e. str, int etc.)
If you want to pass complex types, your code should be able to upload it as an artifact and then you can pass the artifact url (or name) for the next step.

Another option is to use pipeline from dec...

ValueError('Task object can only be updated if created or in_progress')

It seems the task is not "running" hence the error, could that be

Fixed in pip install clearml==1.8.1rc0 🙂

SubstantialElk6 I just realized 3 weeks passed, wow!
So the good news we have some new examples:
https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_decorator.py
https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_functions.py
The bad news the documentation was postponed a bit, as we are still messaging the interface (the community is constantly pushing for great ideas and uses cases , and they are just too good to miss out 🙂 )...

okay let's PR this fix ?

CrookedWalrus33 from the log it seems the code is trying to use "kwcoco" but it is not listed under any "Installed packages" nor do you see any attempt to install it. Can you confirm ?