• delete documents in auth.user and backend.user - > see picture

How can I ensure that whether this email has existed in the server?

Check mongo?

hi , @<1687645223846416384:profile|GracefulToad94> .
About Upgrade.

This version is deployed in the productive environment, so it is not easy to upgrade it.

About special user.

an ordinary user with the following information:
url:

http://{webServerHost}/api/v2.13/auth/create_user

createRequest:

type AuthCreateUserReq struct {
   Avatar     string 
   // brain, this value is also configured for apiserver's env CLEARML__APISERVER__DEFAULT_COMPANY(brain)
  ...

which collection?

ok, I check it now

Sorry, I can not understand what does nut from mean.

I check it with the following result:

action:

auth.create_user

mongo:

see picture

apiserver logs:
`[2022-05-17 10:02:50,024] [8] [WARNING] [clearml.service_repo] Returned 400 for auth.create_user in 8ms, msg=Value combination already exists: email=

`

No, this is the first user in the brain company.

I am sure that I delete this user in auth.user and backend.user before call auth.create_user

no, I just suspend this issue.

Got it!

Thanks

I want to know the total fields(string reference mentioned above) which can be used as input of step2. Are these fields of ClearML Task?

Hi BurlySeagull48 AgitatedDove14 , does upgrading clearml package and images to 1.0.4 really work? https://clearml.slack.com/archives/CTK20V944/p1628112607184300?thread_ts=1628108536.180100&cid=CTK20V944

I set it up with the following documentation: https://allegro.ai/clearml/docs/docs/deploying_clearml/clearml_server_kubernetes_helm.html

I have another question. Does the following apis in the picture are served by both of apiserver and webserver, or just one of them ?

In any case the user associated with the credentials you use is not assigned to the same cinoany ID you replaced

More in detail?

Thanks a lot! I’ ll replace credentials with new user in assigned to a new company ID to call users.get_all again.

AgitatedDove14 , I will try it with latest version. But why the url in es is different from it in web UI? I have checked the codes that evnets.debug_images api which will return url to web UI will get the url from es.

code commit

Another question: can I mix steps with Task and Function?

README introduces that no real scheduling when using Kubernetes. But I think no matter launch a Pod or Job that the Pod will be scheduled through k8s scheduler exception specifying the nodeName in Pod specification. This confuses me a lot. What is the meaning of no real scheduling I don’t understand no verification of docker image

And no real scheduling in README exactly means that ClearML will not do scheduling for task which may provided in paid tier? In other words, Kubernetes will schedule the Pod for me. Is it right?

What is meaning of ‘the scheduling is done before that’? Does it mean the ClearML will schedule the task for me?

Since k8s has no real priority/order the scheduling order is not guaranteed form this point.

K8s can schedule pod with different priorities. So maybe no real scheduling means there is no ClearML scheduling after applying pod to k8s.

only if it is sure there are enough resources to actually spin the job now

In 1.0.2 version, I do not find any logic about checking k8s resource in k8s.py. Does it will implement in the future?

Thanks for your reply 😀 ！

Hi, SuccessfulKoala55 Maybe this is not a good question. In other words, docs introduce that ClearML Open Source supports orchestration, how can I found the relating codes? And what the role of clearml-agent in orchestration, a combination of kube-scheduler and kubelet?

Hi TimelyPenguin76 , thanks for you reply. I still confuse about ClearML’s orchestration. Could you describe it in detail?

According to pipeline_from_functions.py , it is easy to understand that step1 returns data_frame and I can use it as input of step2. But I have no idea what string reference could be used when steps come from Task?

code

pipe.add_function_step(
    name='step_one',
    function=step_one,
    function_kwargs=dict(pickle_data_url='${pipeline.url}'),
    function_return=['data_frame'],
    cache_executed_step=True,
)
pipe.add_function_step(
    name='step_two',
    # parents=['st...