👍

Did not solve this

But what does it do?

I couldn't do it with clearml task as it was looking for a requirements file and I'm workgin with poetry

AgitatedDove14 since this is a powerful feature, I think this should be documented. I'm at a point where I want to use the AWS autoscaler and i'm not sure how.

I see in the docs that I need to supply the access+secret keys, which are associated with an IAM, but nowhere does it say what permissions does this IAM need in order to execute.

Also using the name "AWS Autoscaler" immediately suggests that behind the scene, trains uses the https://docs.aws.amazon.com/autoscaling/ec2/userguide/wha...

In my use case I'm using an agent on the same mahcine I'm developing, so pointing this env var to the same venv I'm using for development, will skip the venv creation process from teh task requirements?

the level of configurability in this thing is one of the best I've seen

why does it deplete so fast?

I'm asking that because the DSes we have are working on multiple projects, and they have only one trains.conf file, I wouldn't want them to edit it each time they switch project

Yes, I have a metric I want to monitor so I will be able to sort my experiments by it. It is logged in this manner

logger.report_scalar(title='Mean Top 4 Accuracy', series=ARGS.model, iteration=0, value=results['top_4_acc'].mean())
When looking at my dashboard this is how it looks

but I can't seem to run docker-compose down

👍

Thanks a lot, that clarifies things

What about permissions to the machines that are being spun up? For exampel if I want the instances to have specific permissions to read/write to S3 for example, how do I mange those?

I'm using ip address show

I'm not, just want to be very precise an consice about them when I do ask... but bear with me, its coming 🙂

Depending on where the agent is, the value of DATA_DIR might change

my current version of the images used:

essentially editing apiserver.conf section auth.fixed_users.users

Could be, my message is that in general, the ability to attach a named scalar (without iteration/series dimension) to an experiment is valuable and basic when looking to track a metric over different experiments

Confirmed working 😄

I'd go for

` from trains.utilities.pyhocon import ConfigFactory

config = ConfigFactory.parse_file(CONF_FILE_PATH) `

and I'm using the same credentials

So the scale will also appear?

I was here, but I can't find info for the questions I mentioned

could be 192.168.1.255?

you want to see its contents?

Okay, I'll try that

now I'm curious what is the effect of _allow_extra_fields_

ShinyLobster84