In the web UI, in the queue/worker tab, you should see a service queue and a worker available in that queue. Otherwise the service agent is not running. Refer to John c above

I don't think agent are aware of each other. Which mean that you can have as many agent as you want and depending on your task usage, they will be fighting for CPU and GPU usage ...

python library don't always use OS certificates ... typically, we have to set REQUESTS_CA_BUNDLE=/path/to/custom_ca_bundle_crt because requests ignore OS certificates

you should know where your latest model is located then just call task.upload_artifact on that file ?

you should be able to use as many agent as you want.
On the same or different queue

ok, so if git commit or uncommit changes differ from previous run, then the cache is "invalidated" and the step will be run again ?

I use ssh public key to access to our repo ... Never tried to provide credential to clearml itself (via clearml.conf ) so I cannot help much here ...

i need to do a git clone

You need to do it to test if it works. Clearml-agent will run it itself when it take in a task

Awesome. I will try that !
And also found this based on your suggestion that clearml use azure sdk underneath: None
Just not sure under which conditions from_config is actually called ...

From SDK ?

Found it: None
And credential are set with :

sdk {      
    azure.storage {
         containers: [
             {
                 account_name: "account"
                 account_key: "xxxx"
                 container_name:"clearml"
             }
         ]
    }
}

you should be able to see int the Console tab that show what is happening

eg: None

Actually, I can set agent.package_manager.pip_version="" in the clearml.conf
And after reading 4x the doc, I can use the env var:
CLEARML_AGENT__AGENT__PACKAGE_MANAGER__PIP_VERSION

can you make train1.py use clearml.conf.server1 and train2.py use clearml.conf2 ?? In which case I would be intersted @<1523701087100473344:profile|SuccessfulKoala55>

what you mean by different script ?

nice !! That is exactly what I am looking for !!

I know that git clone and pip verify all installed is normal. But for some reason in Michael screenshot, I don't see those steps ...

Looks like it's because I did not do the mmpretrain way with dict config file ...

if you are using a self hosted clearml server spin up with docker-compose, then you can just mount your NAS to /opt/clearml/fileserver on the host machine, prior to starting clearml server with docker-compose up

the config that I mention above are the clearml.conf for each agent

but afaik this only works locally and not if you run your task on a clearml-agent!

Isn;t the agent using the same clearml.conf ?
We have our agent running task and uploading everything to Cloud. As I said, we don;t even have file server running

and in the train.py , I have task.add_requirements("requirements.txt")

is task.add_requirements("requirements.txt") redundant ?
Is ClearML always look for a requirements.txt in the repo root ?

something like this: None ?

None

Onprem: User management is not "live" as you need to reboot and password are hardcoded ... No permission distinction, as everyone is admin ...

hard to see with your croppout here an there ...

Please refer to here None
The doc need to be a bit clearer: one require a path and not just true/false

What should I put in there? What is the syntax for git package?