ok, so there is no way to cache it and detect when the ref changes?

yes, in setup.py I have:
..., install_requires= [ "my-private-dep @ git+ ", ... ], ...

I call task._update_requirements(my_reqs) regardless whether I am in the local machine or in the clearml agent, so "installed packages" section is always updated to the list my_reqs that I pass to the function, in this case ["."]

This one doesn’t have _to_dict unfortunately

yes that makes sense, I will do that. Thanks!

yes

Hi CostlyOstrich36 , I mean insert temporary access keys

They are, but this doesn’t work - I guess it’s because temp IAM accesses have an extra token, that should be passed as well, but there is no such option on the web UI, right?

I get the following error:

automatically promote models to be served from within clearml

Yes!

I am confused now because I see in the master branch, the clearml.conf file has the following section:
# Or enable credentials chain to let Boto3 pick the right credentials. # This includes picking credentials from environment variables, # credential file and IAM role using metadata service. # Refer to the latest Boto3 docs use_credentials_chain: falseSo it states that IAM role using metadata service should be supported, right?

btw SuccessfulKoala55 the parameter is not documented in https://allegro.ai/clearml/docs/docs/references/clearml_ref.html#sdk-development-worker

Awesome, thanks!

This is what I get with mprof on this snippet above (I killed the program after the bar reaches 100%, otherwise it hangs trying to upload all the figures)

Well no luck - using matplotlib.use('agg') in my training codebase doesn't solve the mem leak

Some context: I am trying to log an HTML file and I would like it to be easily accessible for preview

Or even better: would it be possible to have a support for HTML files as artifacts?

Ok, I am asking because I often see the autoscaler starting more instances than the number of experiments in the queues, so I guess I just need to increase the max_spin_up_time_min

Yes, it did spin two instances for the same task

Here is what happens with polling_interval_time_min=1 when I add one task to the queue. The instance takes ~5 mins to start and connect. During this timeframe, the autoscaler starts to new instances, then spin them down. So it acts as if max_spin_up_time_min=10 is not taken into account

Why would it solve the issue? max_spin_up_time_min should be the param defining how long to wait after starting an instance, not polling_interval_time_min , right?

I will try with that and keep you updated

1.1.2

Thanks! Corrected both, now its building

btw, I tried with alpine instead of ubuntu:18.04, got :

Unable to find image 'alpine:latest' locally
latest: Pulling from library/alpine
df20fa9351a1: Pulling fs layer
df20fa9351a1: Verifying Checksum
df20fa9351a1: Download complete
df20fa9351a1: Pull complete
Digest: sha256:185518070891758909c9f839cf4ca393ee977ac378609f700f60a771a2dfe321
Status: Downloaded newer image for alpine:latest
docker: Error response from daemon: OCI runtime create failed: container_linux.go:349: starting containe...

Ok, so what worked for me in the end was:
config = task.connect_configuration(read_yaml(conf_path)) cfg = OmegaConf.create(config._to_dict())

Are you planning to add a server-backup service task in the near future?

Ok, I won't have time to venture to check the different database components, the first option (shuting down the server) sounds like the easiest option for me, I would then run manually the script once a month or so

both are repos for python modules (experiment one and dependency of the experiment)

(Just to know if I should wait a bit or go with the first solution)