This doesn't seem to be running inside a container...
What's the clearml-agent launch command you are using ? (i.e. do you have --docker flag)

Issue Verified, thanks!

Yes I think the difference is running conda install with arguments vs conda install with env file...

Hmm, and when you zoom out, still cropped ?

Thank you!

@<1523707653782507520:profile|MelancholyElk85> what are you trying to change ? maybe there is a better way?
BTW: if you do step_base_task.export_task() you can use the parts that you need in the dict and pass them to:
task_overrides argument in add_step (you might need to flatten the nested arguments with '.' , and thinking about it, maybe we should do that automatically?!)

ModelCheckpoint('best_model', save_best_only=True)That worked for me now, what's the diff

GreasyPenguin66 you can pass:
AZURE_STORAGE_ACCOUNT AZURE_STORAGE_KEYAs the default azure access/secret 🙂

I "think" I have a clue on the issue that is lost here in the translation:
Specifically to me it all comes down to the definition of "pipeline"
From the clearml perspective:
Manual Task - code that is executed by the user (or any other mechanism Outside of the agent)
Remote Task - code that is executed by the Agent

Pipeline is a Task
Pipeline can be "manual task" but also "remote task"
Pipeline generates "remote tasks"
Task status (e.g. pipeline status as it is also a Task) can be: draft, a...

Edit the cloned version and enqueue it?

Hi GreasyPenguin14
Yes, I think you are right the series name should be next to the title. Let me check it...

and clearml version ?

Do you think the local agent will be supported someday in the future?

We can take this ode sample and extent it. can't see any harm in that.
It will enable very easy to ran "sweeps" without any "real agent" installed.

I'm thinking roll out multiple experiments at once

You mean as multiple subprocesses, sure if you have the memory for it

GrievingTurkey78 please feel free to send me code snippets to test 🙂

Essentially the example provide just prints out ids to the log file,

What do mean?

@<1523710674990010368:profile|GreasyPenguin14> make sure it to uses https not ssh:
edit ~/clearml.conf

force_git_ssh_protocol: false

and that you have both git_user & git_pass set in your clearml.conf

Hi FierceFly22 I'll answer according to q order?

Hi SparklingHedgehong28
What would be the use for "end of docker hook" ? is this like an abort callback? completion ?

instance protection

Do you mean like when instance just died (line spot in AWS) ?

EnviousStarfish54 you can also run the docker-compose on one of the machines on your local LAN. but then you will not be able to access it from home 🙂

Yep, this seems like a bug in the display

Not yet 😞
It should not be complex to implement,
The actual aws auto scaler class is implementing just two functions:

def spin_up_worker(self, resource, worker_id_prefix, queue_name):
https://github.com/allegroai/clearml/blob/e9f8fc949db7f82b6a6f1c1ca64f94347196f4c0/clearml/automation/auto_scaler.py#L104

def spin_down_worker(self, instance_id):
https://github.com/allegroai/clearml/blob/e9f8fc949db7f82b6a6f1c1ca64f94347196f4c0/clearml/automation/auto_scaler.py#L...

Hmm let me check something

replace the base-docker-image and it should work fine 🙂

The issue is the 400 returned form the server, let me check with backend guys

What's the difference between the example pipeeline and this code ?
Could it be the "parents" argument ? what is it?

Just making sure i understand, you are to upload your models with clearml to the Yandex compatible s3 storage?

Also what do you have in the "Configuration" section of the serving inference Task?

ohh that is odd. Let me check a minute

Is the agent idle ? it is running something else ?

It doesn't not seem to be related to the upload. The upload itself finished... What's your Trains version?