AgitatedDove14 So what you are saying is that since I have trains-server 0.16.1, I should use trains>=0.16.1? And what about trains-agent? Only version 0.16 is released atm, this is the one I use

with 1.1.1 I get
User aborted: stopping task (3)

It could be: I am running the clearml aws autoscaler in an ec2 instance having iam roles allowing for creating/deleting instances, but I get Warning! exception occurred: An error occurred (UnauthorizedOperation) when calling the RunInstances operation: You are not authorized to perform this operation. Encoded authorization failure message: ...
I suspect that since the agent is running in docker mode, the boto3 lib doesn’t automatically get the right permissions from the ec2-instance. To...

Thanks!

Hi AgitatedDove14 , I don’t see any in the https://pytorch.org/ignite/_modules/ignite/handlers/early_stopping.html#EarlyStopping but I guess I could overwrite it and add one?

So when I create a task using `task = Task.init(project_name=config.get("project_name"), task_name=config.get("task_name"), task_type=Task.TaskTypes.training, output_uri=" s3://my-bucket ") locally, the artifact is correctly logged remotely, but when I create the task remotely (from an agent) the artifact is logged locally (in the agent machine, not on s3)

So get_registered_artifacts() only works for dynamic artifacts right? I am looking for a download_artifacts() which allows me to retrieve static artifacts of a Task

So the migration from one server to another + adding new accounts with password worked, thanks for your help!

What do you mean by aws scalar?

Guys the experiments I had running didn't fail, they just waited and reconnected, this is crazy cool

SuccessfulKoala55 Could you please point me to where I could quickly patch that in the code?

No, I want to launch the second step after the first one is finished and all its artifacts are uploaded

I want to make sure that an agent did finish uploading its artifacts before marking itself as complete, so that the controller does not try to access these artifacts while they are not available

yes please, I think indeed that’s the problen

Yes 🙂 Thanks!

SO I updated the config with:
resource_configurations { A100 { instance_type = "p3.2xlarge" is_spot = false availability_zone = "us-east-1b" ami_id = "ami-04c0416d6bd8e4b1f" ebs_device_name = "/dev/xvda" ebs_volume_size = 100 ebs_volume_type = "gp3" key_name = "<my-key-name>" security_group_ids = ["<my-sg-id>"] subnet_id = "<my-subnet-id>" } }
but I get in the logs of the autoscaler:
` Warning! exception occurred: An error occurred (InvalidParam...

extra_configurations = {'SubnetId': "<subnet-id>"}with brackets right?

Probably something's wrong with the instance, which AMI you used? the default one?

The default one is not existing/accessible anymore, I replaced it with the one that was shown in the NVIDIA Deep Learning AMI markplace page https://aws.amazon.com/marketplace/pp/B076K31M1S?qid=1610377938050&sr=0-1&ref_=srh_res_product_title that is: ami-04c0416d6bd8e4b1f

But we can easily extend, right?

If I remove security_group_ids and just let subnet_id in the configuration, it is not taken into account (the instances are created in a the default subnet)

extra_configurations = {"SubnetId": "<subnet-id>"}

That fixed it 😄

Thanks for your answer! I am in the process of adding subnet_id/security_groups_id/key_name to the config to be able to ssh in the machine, will keep you informed 😄

` resource_configurations {
A100 {
instance_type = "p3.2xlarge"
is_spot = false
availability_zone = "us-east-1b"
ami_id = "ami-04c0416d6bd8e4b1f"
ebs_device_name = "/dev/xvda"
ebs_volume_size = 100
ebs_volume_type = "gp3"
}
}

queues {
aws_a100 = [["A100", 15]]
}

extra_trains_conf = """
agent.package_manager.system_site_packages = true
agent.package_manager.pip_version = "==20.2.3"
"""

extra_vm_bash_script = """

sudo apt-get install -y libsm6 libxext6 libx...

you mean to run it on the CI machine ?

yes

That should not happen, no? Maybe there is a bug that needs fixing on clearml-agent ?

It just to test that the logic being executed in if not Task.running_locally() is correct

Because it lives behind a VPN and github workers don’t have access to it

I found, the filter actually has to be an iterable:
Task.get_tasks(project_name="my-project", task_name="my-task", task_filter=dict(type=["training"])))

yes 🙂

Thanks!

trains==0.16.4

Thanks SuccessfulKoala55 !
Maybe you could add to your docker-compose file an option for limiting the size of the logs, since there is no limit by default, their size will grow for ever, which doesn't sound ideal https://docs.docker.com/compose/compose-file/#logging