do you have your Task.init call inside the "train.py" script ? (and if you do, what are you getting in the Execution tab of the task) ?

Is there a way I could move the JWT authentication (not authorization) logic into an API Gateway or Load Balancer?

Hmm in theory, but not in practice 😞

if ClearML is following OAuth 2.0, t

This is for the SSO part, not for the API, API is only using JWT for verification, the login process itself is with external SSO (OAuth 2.0). But the open-source version does not support SSO 😞

Why are you trying to add another ELB with JWT verification on it ? ...

You described getting a secret key pair from the UI and feeding it back into the compose file. Does this mean it's not possible to seed the secrets in the compose file, starting from clean state? If so, that would explain why I can't get it to work.

Long story short, no. This would basically mean you have a pre-build credentials in the docker, this sounds dangerous 🙂
I'm not sure I'm following the use case here, what exactly are we trying to do?
(or maybe I missed something here?)

The goal is to be able to run

docker-compose up

in CI, which starts a clearml-server. And then make several API calls to the started ClearML server to prove that the VS Code extension code is working.

Oh I see, if this is CI workflow, why not run in offline mode ?
None

When you login with user/pass in the UI the same "process" happens and you get a Token to work with, this is the same as secret/key
Since in both cases you provide credentials and get back access token, it should work
(This is of course only if you are setting user/pass manually and disabling pass_hashed as you have)

Hi @<1541954607595393024:profile|BattyCrocodile47>

It seems to me that instead of implementing webhooks to react to things like adding a tag to a model

Did you look at this example ?
None

Can we straightforwardly stream ALL ClearML events to another system?

what would you consider an event?
The "basic" object type is Task, a state in task is changed via an api call, would that be an e...

But I believe it would be harder for our team to detect and respond to failures in the event handler functions if they were placed there because it seems unclear how we could use our existing systems and practices to do that.

Okay I think this is the issue, handler functions are not "supposed" to fail, they are supposed to trigger Tasks, these can fail.
e.g.:
Model Tag Trigger -> handler function creates a Task -> Task does something, like build container, trigger CI/CD etc -> Task...

Okay, I think this might be a bit of an overkill, but I'll entertain the idea 🙂
Try passing the user as key, and password as secret?

Hi @<1541954607595393024:profile|BattyCrocodile47>
Do you mean to start a remote session instead of the cli directly from the vscode ui and connect to it? If so, that would be awesome!! We have a remote session from the web were it spins you remote session and launches vscode inside the container so you work on it in your browser. But a VSCode plugin is a great idea, do you have a ref code to similar plugins?

I see, good point. It does look like mostly boiler plate code, not sure where it actually runs the python command, but I'm sure it is there (python.ts, but could not locate who is actually using it)

This is good stuff! Keep us posted here, I'll do my best to support live 😍

LOL, if you can get it to run any python code, I can help with the rest. We just need to make sure we can capture the output, and then start the VScode remote debugging feature directly from the extension.

s like the

would be a really good starting place.

This is actually JS (typescript) ... not python, not sure on how to continue from there 😞

Hi @<1541954607595393024:profile|BattyCrocodile47>
see here: None
Try with app.clearml.mlops-club.org
and the rest of them

Hi DeliciousBluewhale87
When you say "workflow orchestration", do you mean like a pipeline automation ?

at means I need to pass a single zip file to 

path

 argument in 

add_files

 , right?

actually the opposite, you pass a folder (of files) to add_files. Then add_files remembers the files location (and pre calculates the hash of the files content). When you call upload it will actually compress the files that changed into a zip file (or files depending on the chunk size), and upload the files to the destination (as specified in the upload call...

With 

pipe.start(queue='services')

, it still tries to run some docker for some reason

The services agent is always running with --docker:
https://github.com/allegroai/clearml-agent/blob/e416ab526ba9fe05daa977b34c9e46b50fb214a0/docker/services/entrypoint.sh#L16
Actually I think we should have it as an argument, so it is easier to control from docker-compose

I'll be waiting for the full log to check the "git clone" issue

sure

UptightMouse31 You can add any metric (KPI) with "manual" logging
Logger.current_logger().report_scalar("KPI", "metric", iteration=0, value=1.1)This means you can later add a column KPI/metric to your experiment table.
Will this do the trick ?

When I passed specific arguments (for example --steps) it ignored them...

script.py test blah1 blah2 blah3 42

Is this how it is intended to be used ?

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 ?

is number of calls performed, not what those calls were.

oh, yes this is just a measure of how many API calls are sent.
It does not really matter which ones

but actually that path doesn't exist and it is giving me an error

So you are saying you only uploaded the "meta-data" i.e. a text file with links to the files, and this is why it is missing?

Is there a way to change the path inside the .txt file to clearml cache, because my images are stored in clearml cache only

I think a good solution would be to store the path in the txt file as relative path, i.e. instead of /Users/adityachaudhry/data/folder... as ./data/folder

Yes exactly like a Task (pipeline is a type of task)
'''
clonedpipeline=Task.clone(pipeline_uid_here)
Task.enqueue(...)
'''

Hi ShaggyHare67 ,
Yes the trains.conf created by trains-agent is basically an extension of the trains usage (specifically it adds a section for the agent)
I'm assuming you are running the agent on the same development machine.
I guess the easiest is to rename the trains.conf to trains.conf.old and run trains-agent init
(No need to worry, the trains package supports it , so the new configuration file that will be generated will work just fine)

Hi guys, sorry, this thread was some reason forgotten,

pipeline I ran but during its execution the overrides (which is filled) is not taken into account and I have my default params.

Could this be the same issue as this one?
None

@<1558986821491232768:profile|FunnyAlligator17> can you provide the full log ?

Yeah that sounds like it, let me check if we can quickly reproduce

Hi @<1567321739677929472:profile|StoutGorilla30>

Is it necessary to serve keras model using triton engine?

It is not, but it is the most efficient way to serve keras models, and this is why by default clearml-serving is using Nvidia Triton (we are talking 10x factors)
I would start with the keras example, see that it works and then work your way into your example (notice you always need to provide the layers form the in/out of the model)
[None](https://github.com/allegroai/clearml-s...

Thanks JitteryCoyote63 !
Any chance you want to open github issue with the exact details or fix with a PR ?
(I just want to make sure we fix it as soon as we can 🙂 )