you can also increase the limit here:
https://github.com/allegroai/clearml/blob/2e95881c76119964944eaa0289549617e8afeee9/docs/clearml.conf#L32

Are you running it in venv mode or docker mode?

quick update 1.0.2 will be ready in an hour, apologies 😞

Hi GiganticTurtle0
you should actually get " file://home/user/local_storage_path "
With "file://" prefix.
We always store the file:// prefix to note that this is a local path

BTW: what happens if you pass the same s3://bucket to Task.init output_uri ? I assume you are getting the same access issue ?

The agent ip? Generally what’s the expected pattern to deploy and scale this for multiple models?

Yes the agent's IP, and with multiple agents, one would probably use k8s for the nodes, then configure ingest. This is the next step for the cleaml-serving, adding support for KFServing or manually configuring the ingest. wdyt?

Yes, but as you mentioned everything is created inside the lib, which means the python is not able to intercept the metrics so that clearml can send them to the backend.

SmarmySeaurchin8 it could be a switch, the problem is that when you have automatic stopping flows, they will abort a task, which is legitimate (e.g. should not considered failed)
How come you have aborted tasks in the pipeline ? If you want to abort the pipeline, you need to first abort the pipeline Task then the tasks themselves.

Is this a correct assumption ?

Hi RipeGoose2

when creating a task the default path is still there

What do you mean by "PATH" do you want to provide path for the config file? is it for trains manual execution or the agent ?

CurvedHedgehog15 is it plots or scalars you are after ?

Correct (with the port mapping service in it)

Hi DilapidatedDucks58 ,
Just making sure all 8 works have different worker ids? (you can see 8 in the workers page in the UI)
Also, are they running this docker or venv mode?

No worries 🙂

@<1527459125401751552:profile|CloudyArcticwolf80> what are you seeing in the Args section ?
what exactly is not working ?

👍

So in summary: subprocess calls appear to break clearML tracking, even if I do Task.init() in both main.py and train.py.

Okay let me see if we can reproduce & fix this, it should not be long

(I think the GCP is already up, I'll double check)

Sounds good, I assumed that was the case but I was not sure.
Let's make sure that in the clearml.conf we write it in the comment above the use_credentials_chain option, so that when users look for IAM roles configuration they can quick search for it 🙂

PleasantGiraffe85 you can disable the SSL verification on the client end:
https://github.com/allegroai/clearml-agent/blob/21c4857795e6392a848b296ceb5480aca5f98e4b/docs/clearml.conf#L12
Basically you can just manually create the clearml.comf with only the following:
api { api_server: web_server: files_server: `
credentials {"access_key": "EGRTCO8JMSIGI6S39GTP43NFWXDQOW", "secret_key": "x!XTov_G-#vspE*Y(h$Anm&DIc5Ou-F)jsl$PdOyj5wG1&E!Z8"}

# verify...

But functionality is working

Awesome , I will wait with the merge until tested internally .
There is a resale coming out after the weekend, once it is out I expect we will merge it.

Thanks for checking NastyFox63
I double checked with both front/backend , there should not be any limit...
Could you maybe provide a toy demo to reproduce the issue ?

FriendlySquid61 could you help?

The problem is, the configuration is loaded at import time, so there is no "time" to pass anything other than environment variable.
That said if the only difference is server config you can use
Task.set_credentials

Hi NastyFox63 yes I think the problem was found (actually backend side).
It will be solved in the upcoming release (due after this weekend 🙂 )

No, I just want to register a new model in the storage.

Is the model file is already uploaded, you can register it without a Task:
InputModel.import_model(...)https://github.com/allegroai/clearml/blob/b3a2b3425c5098ebfc0598c9dfb3e670d4a87706/clearml/model.py#L521

I need to create a separate task for this right?

If you want the model to be uploaded, then yes you have to create a Task.

👍

 are models technically 

Task

s and can they be treated as such? If not, how to delete a model permanently (both from the server and from AWS storage)?

When you call Task.delete() it actually goes over a;; the models/artifacts and deletes them from the storage

We are always looking for additional talented people 😉 DM me...

, is the team open to PRs from external people?

Yes please do! PRs are welcomed! I thought we fixed the GitHub readme to reflect it, anyhow I'll make sure we do 🙂