But there is no need for 2FA for cloning repo

pass :
task_filter=dict(system_tags=['-archived'])

Manually I was installing the

leap

package through

python -m pip install .

when building the docker container.

NaughtyFish36 what happnes if you add to your "installed packages" /opt/keras-hannd ? This should translate to "pip install /opt/keras-hannd" which seems like exactly what you want, no ?

Seems the apiserver is out of connections, this is odd...
SuccessfulKoala55 do you have an idea ?

Ohh, like a query based only of the stored url ?
Do you also has the creating Task ?

Hi GiganticTurtle0

The problem is that the packages that I define in 'required_packages' are not in the scripts corresponding

What do you mean by that? is "Xarray" a wheel package? is it instllable from a git repo (example: pip install git+ http://github.com/user/xarray/axrray.git )

OutrageousSheep60 so if this is the case I think you need to add "external links" i.e. upload the individual files to GCS, then register the links to GCS, does that make sense ?

Hi LooseClams37
From the docker compose, I see the agent is running in venv mode, is that correct?
Also notice that when configuring the minio credentials you can specify if this is an https connection (secure: true) which by default it is not.
See here: https://github.com/allegroai/clearml-agent/blob/5a6caf6399a0128ad81e8723d0a847e2ded5b75e/docs/clearml.conf#L287

I want to be able to install the venv in multiple servers and start the "simple" agents in each one on them. You can think of it as some kind of one-off agent for a specific (distributed) hyperparameter search task

ExcitedFish86 Oh if this is the case:
in your cleaml.conf:
agent.package_manager.type: conda agent.package_manager.conda_env_as_base_docker: truehttps://github.com/allegroai/clearml-agent/blob/36073ad488fc141353a077a48651ab3fabb3d794/docs/clearml.conf#L60
https://git...

Yey!

Many thanks! I'll pass on to technical writers 🙂

I commented the upload_artifact at the end of the code and it finishes correctly now

upload_artifact caused the "failed" issue ?

Yeah I think using voxel for forensics makes sense. What's your use case ?

Hi HelplessCrocodile8
yes there is:
in the first case, the new_key will be automatically logged:
a_dict = {} a_dict = task.connect(a_dict) a_dict['new_key'] = 42In the second example changes to the "object" passed to connect are not tracked
make sense ?

Hi RipeGoose2
Any logs on the console ?
Could you test with a dummy example on the demoserver ?

CooperativeFox72 you can you start by checking the latest RC :)
pip install trains==0.15.2rc0

It seems to fail when trying to download the model
local_download = StorageManager.get_local_copy(uri, extract_archive=False) File "/opt/venv/lib/python3.7/site-packages/clearml/storage/manager.py", line 47, in get_local_copy cached_file = cache.get_local_copy(remote_url=remote_url, force_download=force_download) File "/opt/venv/lib/python3.7/site-packages/clearml/storage/cache.py", line 55, in get_local_copy if helper.base_url == "file://":And based on the error I suspect the...

Could you see if that makes a difference ?

While if I just download the right packages from the requirements.txt than I don't need to think about that

I see you point, the only question how come these packages are not automatically detected ?

if it ain't broke, don't fix it

😄

Up to you, just a few features & nicer UI.
BTW: everything is backwards compatible, there is no need to change anything all the previous trains/trains-agent packages will work without changing anything 🙂
(This even includes the configuration file, so you can keep the current ~/trains.conf and work with whatever combination you like of trains/clearml on the same machine)

Hmm I would have the docker file contain the default Azure credentials/output_uri, and then have the users clearml credentials passed as env variable in runtime. wdyt?
(I'm checking if you can pass the azure credentials as env in a minute)

this topic is about the issue with reporting a configuration with a string inside a tuple that has backslash

So the encoding itself is done YAML style, and based on your example \b Has to be encoded to \b because this is string encoding, like \n will become "new line"
Make sense ?

if we look at the host machine we can see a single python process that is actually busy

Only one?! can you see the other python processes ?

How do you currently report images, with the Logger or Tensorboard or Matplotlib ?

off the top of my head, the self hosted is missing the autoscalers (there is an AWS CLI, but no UI or others), also missing a the HPO UI feature,
but you should just check the detailed table here: None

OutrageousSheep60

I found the task in the UI -

and in the

UNCOMMITTED CHANGES

execution section there is

No changes logged

This is the issue.

and then run the

session

via docker

clearml-session --docker nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 \ --packages "clearml" "tensorflow>=2.2" "keras" \ --queue MY_QUEUE \ --verboseAre you running the "cleamrl-session" from your machine? (i.e. not from inside a docker) ?...

This is part if a more advanced set of features of the scheduler, but only available in the enterprise edition 🙂

Hi @<1635088270469632000:profile|LividReindeer58>
You mean the clearml.conf?
You can do:

from clearml.config import config_obj

you should have the entire configuration file as an object (dict interface)
fyi: under the hood it uses pyHOCON

SubstantialElk6 I know they have full permission control in the enterprise edition, if this is something you need I suggest you contact http://allegro.ai 🙂

If this is the case:
dataset = Dataset.get(...) dataset.get_dependency_graph()https://clear.ml/docs/latest/docs/references/sdk/dataset#get_dependency_graph