PanickyMoth78 thank you for the mock code, I can verify it reproduces the issue. It seem that for some reason (bug) when the same function is called multiple times it "collects" parents, hence the odd graph,
BTW: if you want to see exactly what is passed to the step you can press on the step's full_details, and see the hyperparameter section.
I'll make sure we fix this bug in the next RC.

Yey! 🙂

Generally speaking I would say the Nvidia deep-learning AMI:
https://aws.amazon.com/marketplace/pp/prodview-7ikjtg3um26wq

Hi SuperficialGrasshopper36

/home/ubuntu/.clearml/venvs-builds.1/3.8/task_repository/repository_name/.venv

This is the problem, they should not be installed there, it should be in
/home/ubuntu/.clearml/venvs-builds.1/3.8/
Could you post the poetry.lock file? Maybe it is something there?
What's the poetry version and cleaml-agent versions ?

FYI:
ssh -R 8080:localhost:8080 -R 8008:localhost:8008 -R 8081:localhost:8081 replace_with_username@ubuntu_ip_heresolved the issue 🙂

SubstantialElk6
The ~<package name with first name dropped> == a.b.c is a known conda/pip temporary install issue. (Some left over from previous package install)
The easiest way is to find the site-packages folder and delete the package, or create a new virtual environment
BTW:
pip freeze will also list these broken packages

However, SNPE performs quantization with precompiled CLI binary instead of python library (which also needs to be installed). What would be the pipeline in this case?

I would imagine a container with preinstalled SNPE compiler / quantizer, and a python script triggering the process ?

one more question: in case of triggering the quantization process, will it be considered as separate task?

I think this makes sense, since you probably want a container with the SNE environment, m...

That is odd, can you send the full Task log? (Maybe some oddity with conda/pip ?!)

Awesome ! thank you so much!
1.0.2 will be out in an hour

And having a pdf is easier/better than sharing a link to the results page ?

Woot woot! 🎊

So what is the difference?!

(BTW: you can disable the auto-logging feature of joblib)
Task.init(..., auto_connect_frameworks={'scikit': False})

Then try to add the missing apt packages

extra_docker_shell_script: ["apt-get install -y ???", ]

None

Task.connect is "automagic" i.e. to server when in Manual mode, from server in agent mode,
set_parameter is one way only and should be used to set an external Task's parameters.

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...

GrievingTurkey78 notice that when enqueuing an aborted Task, the agent will not deleted the previously reported metrics/logs

VexedCat68 are you manually creating the OutputModel object?

is there a built in programmatic way to adjust 

development.default_output_uri

?

How about: In your Task.init(output_uri='...')

And you are seeing a bunch of the GS SSL errors?

got to love the ascii art 😍

Also what is the docker vserion?

It does not upload, the default behavior is to log the artifact (so you know where you stored, but not enforce unnecessary uploads)
If you were to change:
task = Task.init(project_name='examples', task_name='Keras with TensorBoard example')to:
task = Task.init(project_name='examples', task_name='Keras with TensorBoard example', output_uri=" ")It would also upload the model

RoundMosquito25 do notice the agent is pulling the code from the remote repo, so you do need to push the local commits, but the uncommitted changes clearml will do for you. Make sense?

Hi @<1523701523954012160:profile|ShallowCormorant89>
This is generally based on number of agents, or am I missing something ? Also is it based on Task or decorated functions ?

It could be the model storing? could it be the peak is at the end of the epoch ?

Yes very

Thanks you!

And If I create myself a Pro account

Then you have the UI and implementation of both AWS & GCP autoscalers, am I missing something?

Yes, I mean use the helm chart to deploy the server, but manually deploy the agent glue.
wdyt?