actually no

hmm, are those packages correct ?

I thought this is the issue on the thread you linked, did I miss something ?

So the naming is a by product of the many TB created (one per experiment), if you add different naming ot the TB files, then this is what you'll be seeing in the UI. Make sense ?

(torchvision vs. cuda compatibility, will work on that),

The agent will pull the correct torch based on the cuda version that is available at runtime (or configured via the clearml.conf)

Do you mean it recently become part of enterprise version?

I do not think so, but it seems this the support for the open-source is more like a PoC
https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py

Hi VexedElephant56
Yes it is:
Define CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1
(if running in doecker mode add -e CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1 as container args)
https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_env_var

Okay, this seems to be the problem

Follow up: I see that if I move an Experiment to a new project, it does not copy the associated model files and must be done manually. Once I moved the models to the new project, the query works as expected.

Correct 🙂
Nice catch!

Can you test with the hydra example? if the example works, any chance you can send a toy to reproduce it ?
https://github.com/allegroai/clearml/tree/master/examples/frameworks/hydra

Hi ColossalAnt7
Following on SuccessfulKoala55 answer

I saw that there is a config file where you can specify specific users and passwords, but it currently requires

3. mount the configuration file (the one holding the user/pass) into the pod from a persistent volume .

I think the k8s way to do this would be to use mounted config maps and secrets.

You can use ConfigMaps to make sure the routing is always correct, then add a load-balancer (a.k.a a fixed IP) for the users a...

Update us if it solved the issue (for increased visibility)

, but what I really want to achieve is to share this code:

You mean to share the code between them, unless this is a "preinstalled" package in the container, each endpoint has it's own separate set of modules / files
(this is on purpose, so you could actually change them, just image diff versions of the same common.py file)

BTW: any specific reason for going the RestAPI way and not using the python SDK ?

Hi @<1623491856241266688:profile|TenseCrab59>
It is kind of dark or are you asking about the graphs ?

Hi JuicyDog96
The easiest way at the moment (apologies for still lack of RestAPI documentation, it is coming:)
Is actually the code (full docstring doc)
https://github.com/allegroai/trains/tree/master/trains/backend_api/services/v2_8
You can access it all with an easy Pythonic interface, for example:
from trains.backend_api.session.client import APIClient client = APIClient() tasks = client.tasks.get_all()

Hi UnsightlyBeetle11
Is it possible to report the model's architecture (PyTorch model) automatically on ClearML, as we do it via Netron or other neural network visualisation tools?You mean like the actual network layout? Unfortunately, there is currently no option to do that, you can however manually store a plot/image that represents it
BTW:I think that at the beginning Netron was somehow integrated, but it was rarely used and support for it was not trivial so it was phased out. You can ho...

ReassuredTiger98 when you look for task "dca2e3ded7fc4c28b342f912395ab9bc" there are no artifacts ?
Could you add some prints? this should have worked...

Bake to the error:

clearml_agent: ERROR: Failed getting token (error 401 from

): Unauthorized (invalid credentials) (failed to locate provided credentials)

See here:
https://github.com/allegroai/clearml-server/blob/3f2b96266bc51bfce680bd759c7fa9d635ae36d3/docker/docker-compose.yml#L131
You need to provide an access key so it can actually "talk" to the server next to it.

The imports inside the functions are because the function itself becomes a stand-alone job running on a remote machine, not the entire pipeline code. This also automatically picks packages to be installed on the remote machine. Make sense?

The other order (with custom decorator above pipeline fails - just for you info

)

This is on "purpose" the pipeline decorator has to be the top decorator.
Glad it works!

Nice!

JitteryCoyote63 This seems like exactly what you are saying, elastic license issue...

Does it work if I launch the clearml-agent on a docker and pip doesn't know the packages to install

Not sure I follow... the "detect_with_pip_freeze" flag (when set) will tell clearml (at runtime) to create the "installed packages" directly from pip freeze (instead of analyzing the code)

Hi StraightCoral86

When I run an experiment using 

Task.create()

 ,

Use Task.init 🙂
Task.create is meant to create an extranl Task (i.e. Job) ins the system, Not to auto-gernerate a job from the running code. Make sense ?

Can't figure out what made it get to this point

I "think" this has something to do with loading the configuration and setting up the "StorageManager".
(in other words setting the google.storage)... Or maybe it is the lack of google storage package?!
Let me check

I tried specifying helpers functions but it still gives the same error.

What's the error you are getting ?

Hi @<1643060801088524288:profile|HarebrainedOstrich43>
You are absolutely correct we just fixed nested decorators in pipeline a week ago, let me check if he RC is already out with a fix.

Thanks!

It seems that I solved the problem by moving all of the local code (local repos) imports to after the Task.init

PunyPigeon71 I'm confused, how did that solve the issue on the remote machine?

Hi TrickySheep9
could you verify the fix 😉
!git install git+