EnviousStarfish54 quick update, regardless of the logging.config.dictConfig issue, I will make sure that even when the logger is removed, the clearml logging will continue to function 🙂
The commit will be synced after the weekend

Yey!

Could you test with the latest "cleaml"
pip install git+Task.add_requirement(".") should be supported now 🙂

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

is it normal that it's slower than my device even though the agent is much more powerful than my device? or because it is just a simple code

Could be the agent is not using the GPU for some reason?

I am struggling with configuring ssh authentication in docker mode

GentleSwallow91 Basically the agent will automatically mount the .ssh into the container , just make sure you set the following in the clearml.conf:
force_git_ssh_protocol: truehttps://github.com/allegroai/clearml-agent/blob/178af0dee84e22becb9eec8f81f343b9f2022630/docs/clearml.conf#L30

GiganticTurtle0 is there any git redundancy on your network ? maybe you could configure a fallback server ?

Hi WackyRabbit7
So I'm assuming after the start_locally is called ?
Which clearml version are you using ?
(just making sure, calling Task.current_task() before starting the pipeline returns the correct Task?)

it fails but with COMPLETED status

Which Task is marked "completed" the pipeline Task or the Step ?

and I have no way to save those as clearml artifacts

You could do (at the end of the code
task.upload_artifact('profiler', Path('./fil-result/'))wdyt?

Hi SkinnyPanda43
Are you trying to access the same Task or an external one ?

https://github.com/allegroai/clearml-serving

Hmm let me test the call itself.

Yes
Are you trying to upload_artifact to a Task that is already completed ?

👍

Will the new fix avoid this issue and does it still requires the 

incremental

 flag?

It will avoid the issue, meaning even when incremental is not specified, it will work
That said the issue any other logger will be cleared as well, so, just good practice ...

From the 

logging

 documentation ...

Hmmm so I guess Kedro should not use dictConfig ?! I'm not sure on the exact use case, but just clearing all loggers seems like a harsh approach

https://github.com/allegroai/trains-web

DeliciousBluewhale87 out of curiosity , what do you mean by "deployment functionality" ? is it model serving ?

correct on both.
notice that with upload you can specify any storage (S3/GS/Azure atc)

BroadMole98 as one can expect long answer as well 🙂

I have a workflow with 19000 job nodes in it.

wow, 19k job nodes? as in a single pipeline 19k steps?

The main idea of the trains-agent is to allow multi-node workloads, and creating pipelines on top of a scheduler without worrying about docker packaging (done automatically for you), and to have a proper scheduler with priority (that is missing from k8s)

If the first step is just "logging" all the steps, you can easily add "Task...

Nice debugging experience

Kudos on the work !
BTW, I feel weird to add an issue on their github, but someone should, this generic setup will break all sorts of things ...

I am writing quite a bit of documentation on the topic of pipelines. I am happy to share the article here, once my questions are answered and we can make a pull request for the official documentation out of it.

Amazing please share once done, I will make sure we merge it into the docs!

Does this mean that within component or add_function_step I cannot use any code of my current directories code base, only code from external packages that are imported - unless I add my code with ...

Hi SubstantialElk6
Yes this is the queue the glue will pull jobs from and push into the k8s. You can create a new queue from the UI (go to the workers&queues page and to the Queue Tab and press on "create new" Ignore it 🙂 this is if you are using config maps and need TCP routing to your pods As you noted this is basically all the arguments you need to pass for (2). Ignore them for the time being This is the k8s overrides to use if launching the k8s job with kubectl (basically --override...

the parameter datatypes are not being changed when loading them up.

These are the auto logged parameters , inside YOLO, correct?
Just to make sure, you can actually see the value None in the UI, is that correct? (if everything works as expected, you should see empty string there)

Hi MelancholyChicken65
I'm assuming you need ssh protocol not https user/token, set this one to true 🙂
force_git_ssh_protocol: true
https://github.com/allegroai/clearml-agent/blob/76c533a2e8e8e3403bfd25c94ba8000ae98857c1/docs/clearml.conf#L39

Hi SubstantialElk6

The ClearML session ended up tunneling into the physical machine that my agent is running on,

Yes that is the correct behavior. basically the clearml-session is using the agent to "schedule" a machine, then spin a container with JupyterLab/VSCode , and finally connect your CLI directly with that machine.
You can think of it as a way to solve the resource allocation problem.
Make sense ?

Hi StrangePelican34 , you mean poetry as package manager of the agent? The venvs cache will only work for pip and conda, poetry handles everything internally:(

Hi ClumsyElephant70

Any idea how to get the credentials in there?

How about to map it into the docker with -v you can set it here:
https://github.com/allegroai/clearml-agent/blob/0e7546f248d7b72f762f981f8d9033c1a60acd28/docs/clearml.conf#L137
extra_docker_arguments: ["-v", "/host/folder/cred.json:/gcs/cred.json"]

Hi ReassuredTiger98
So let's assume we call:
logger.report_image(title='training', series='sample_1', iteration=1, ...)And we report every iteration (keeping the same title.series names). Then in the UI we could iterate back on the last 100 images (back in time) for this title / series.
We could also report a second image with:
logger.report_image(title='training', series='sample_2', iteration=1, ...)which means that for each one we will have 100 past images to review ( i.e. same ti...

is there GPU support

That's basically depends on your template yaml resources, you can have multiple of those each one "connected" with a diff glue pulling from a diff queue. This way the user can enqueue a Task in a specific queue, say single_gpu , then the glue listens on that queue and for each clearml Task it creates a k8s job the single gpu as specified in the pod template yaml.