Hi @<1545216070686609408:profile|EnthusiasticCow4>

Now that it's running how does one add a new task or remove an existing task from the scheduler?

Did you notice the scheduler stores its own configuration as a config object on the Task?
Notice that you can abort/reset the scheduler, change it's configuration in the UI and relaunch it (i.e. enqueue it). It will use the configuration from the UI (backend) and not the original code that created it. Does that make sense?

Hi SubstantialElk6
clearml-agent was just updated, it should solve the issue.2. Notice that "torch" / "torchvision" packages are resolved by the agent based on the pytorch compatibility table. Is there a way to reproduce the issue where it fails resolving the torch version? could you send a full log?
3. If you want a specific torch version , you can put a direct link to the torch wheel, for example: https://download.pytorch.org/whl/cu102/torch-1.6.0-cp37-cp37m-linux_x86_64.whl

Hi CynicalBee90
Always great to have people joining the conversation, especially if they are the decision makers a.k.a can amend mistakes 🙂
If I can summarize a few points here (and feel free to fill in / edit any mistake or leftovers)
Open-Source license: This is basically the mongodb license, which is as open as possible with the ability to, at the end, offer some protection against Amazon giants stealing APIs (like they did for both mongodb and elastic search) Platform & language agno...

SoreDragonfly16 as SmallDeer34 mentioned, you can iterate over the Tasks, pull metrics (with either task.get_last_scalar_metrics or task.get_reported_scalar ) then report them on the Task that runs the Loop itself with the Logger.
wdyt?

thanks!

Hi VexedCat68
(sorry I just saw the message)

I wanted to ask, how to run pipeline steps conditionally? E.g if step returns a specific value, exit the pipeline or run another step instead of the sequential step

So do do so you can do:
` def pre_execute_callback_example(a_pipeline, a_node, current_param_override):
# if we want to skip this node (and subtree of this node) we return False
...
# ew decided to skip so we return False
return False

pipe.add_step(name='...

Are you saying it only records the last 3 epochs or is it the first three epochs ?
Can you see scalars logged from other epochs ?

Oh if this is the case, then by all means push it into your Task's docker_setup_bash_script
It does not seem to have to be done after the git clone, the only part the I can see is setting the PYTHONPATH to the additional repo you are pulling, and that should work.
The main hurdle might be passing credentials to git, but if you are using SSH it should be transparent
wdyt?

@<1546303254386708480:profile|DisgustedBear75> is think this was a UI bug, they are just releasing a new version that fixes that (i.e. server version), are you running a self-hosted server?

Hi @<1707565838988480512:profile|MeltedLizard16>
Maybe I'm missing something but gust add to your YOLO code :

from clearml import Dataset

my_files_folder = Dataset.get("dataset_id_here").get_local_copy()

what am I missing?

Hi SubstantialElk6
No need for that, you can use the helm chart (or spin them once with kubctl) then they take care of scheduling by themselves.
You can also use the k8s glue (basically spinning kubernetes pods automatically for you, based on the Tasks that you push into the ClearML queue)
https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py

In short, two possible deployments
Static k8s pod running the agent (then the agent runs all the experiments inside t...

Hi MiniatureCrocodile39
I would personally recommend the ClearML show 😉
https://www.youtube.com/watch?v=XpXLMKhnV5k
https://www.youtube.com/watch?v=qz9x7fTQZZ8

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?

No sure I follow, you mean to launch it on the kubernretes cluster from the ClearML UI?
(like the clearml-k8s-glue ?)

'config.pbtxt' could not be inferred. please provide specific config.pbtxt definition.

This basically means there is no configuration on how to serve the mode, i.e. size/type of lower (input) layer and output layer.
You can wither store the configuration on the creating Task, like is done here:
https://github.com/allegroai/clearml-serving/blob/b5f5d72046f878bd09505606ca1147d93a5df069/examples/keras/keras_mnist.py#L51
Or you can provide it as standalone file when registering the mo...

Oh I do not think this is possible, this is really deep in a background thread.
That said we can sample the artifacts and re-register the html as a debug media:
url = Task.current_task().artifacts['notebook preview'].url Task.current_task().get_logger().report_media('notebook', 'notebook', iteration=0, url=url)Once the html is uploaded, it will keep updating on the same link so no need to keep registering the "debug media". wdyt?

do you have a video showing the use case for clearml-session

I totally think we should, I'll pass it along 🙂

what is the difference between vscode via clearml-session and vscode via remote ssh extension ?

Nice! remote vscode is usually thought of as SSH, basically you have your vscode running on your machine, and using SSH vscode automatically connects to the remote machine.

Clearml-Session also ads a new capability VSCode inside your browser, where the VSCode itself as well...

Hi WickedGoat98
Regardless on the ingress configuration (which seems like you have the hang of), the API instance itself needs to be configured with persistent volume (the web / file server do not need direct access to the API server).
Can you get the API to run properly ?

Regrading the trains-agent once you have the API/Web/File server configured, you can configure it like the trains-agent-services is configured inside the docker-compose (e.g. set the environment variable with the c...

RipeGoose2 you mean to have the preview html on S3 work as expected (i.e. click on it add credentials , open in a new tab) ?

the services queue (where the scaler runs) will be automatically exposed to new EC2 instance?

Yes, using this extra_clearml_conf parameter you can add configuration that will be passed to the clearml.conf of the instances it will spin.
Now an example to the values you want to add :
agent.extra_docker_arguments: ["-e", "ENV=value"]https://github.com/allegroai/clearml-agent/blob/a5a797ec5e5e3e90b115213c0411a516cab60e83/docs/clearml.conf#L149
wdyt?

Ohh then use the AWS autoscaler, basically it what you want, spin an EC2 and set an agent there, then if the EC2 goes down (for example if this is a spot), it will spin it up again automatically with the running Task on it.
wdyt?

Sorry @<1798525199860109312:profile|IntriguedGoldfish14> just noticed your reply
Yes two inference container, running simultaneously on the cluster. As you said, each one with its own environment (assuming here that the requirements of the models collide)
Make sense

This will allow them to experiment outside of clearml and only switch to it when they are in an OK state. This will also helpnot to pollute clearml spaces with half backed ideas

What's the value of runnign outside of an experiment management context ? don't you want to log it?
There is no real penalty here, no?!

there is probably some way to make an S3 path open up in the browser by default

You should have a pop-up asking for credentials ...
Could you check that if you add the credentials in the profile page it works ?

Hi RipeGoose2
Just to clarify, the issue with the html stuck in cache is a UI, thing, basically the webapp needs to tell the browser not to cache the artifacts, it has nothing to do with how the artifacts are created.
Regardless we love improvements so feel free to mass around with the code and PR once you get something useful 😉
Specifically this is where the html conversion happens
https://github.com/allegroai/clearml/blob/9d108d855f784e1fe7f5691d3b7bf3be64576218/clearml/backend_in...

AFAIK that's the only way right now (see my comment here - https://clearml.slack.com/archives/CTK20V944/p1657720159903739?thread_ts=1657699287.630779&cid=CTK20V944 )
Or then if you have the ClearML paid service, I believe there is a "vaults" service, right AgitatedDove14 ?

Yep UnevenDolphin73 :)

An easier fix for now will probably be some kind of warning to the user that a task is created but not connected

That is a good point, maybe if you do not have a "main" Task, then we print the warning (with some flag to disable the warning) ?

Hi @<1551376687504035840:profile|StraightSealion9>

AWS Autoscaler to create a new instance when you enqueue a task to the relevant queue.

Does that mean that you were able to enqueue a Task and have it launch on the remote EC2 machine ?

Hi @<1619505588100665344:profile|GrievingHare27>

My understanding is that initiating a task with

Task.init()

captures the code for the entire notebook. I'm facing difficulties when attempting to build a final training pipeline (in a separate notebook) that uses only certain functions from the other notebooks/tasks as pipeline steps.

Well this is is kind of the limit of working with jupyter notebooks, referencing code from one to another is not really feasible (of co...

First I would check the CLI command it will basically prefill it for you:
https://clear.ml/docs/latest/docs/apps/clearml_task
Specifically to your question, working directory "." is the root of the git repo
But I would avoid adding it manually, use the CLI, it will either use ask you to provide info or take the git repo details from the local copy