Hi @<1523702786867335168:profile|AdventurousButterfly15>
Make sure you pass output_uri=true in Task.init
It will automatically upload your model to the file server. You can also configure it in the clearml.conf, look for defualt_output_uri

Hmm yeah I can see why...
Now that I think about it, at least in theory the second process that torch creates, should inherit from the main one, and as such Task.init is basically "ignored"
Now I wonder why your first version of the code did not work?
Could it be that we patched the argparser on the subprocess and that we should not have?

• Yes Task.init should be called on each subprocess (because torch forks them before they ar epatched)
• I think the main issue is that we patch the argparse on the Subprocess (this is assuming you did not manually parse non argv argument)
• If you can create a mock test I think we can work around the issue, as long as the way you spin it is the standard pytorch distub way

wdyt?
https://clear.ml/docs/latest/docs/clearml_serving/clearml_serving
https://clear.ml/docs/latest/docs/clearml_serving/clearml_serving_cli#metrics
https://clear.ml/docs/latest/docs/clearml_serving/clearml_serving_tutorial#model-monitoring-and-performance-metrics

Is there any documentation on versioning for Datasets?

You mean how to select the version name ?

what if the preexisting venv is just the system python? my base image is python:3.10.10 and i just pip install all requirements in that image. Does that not avoid venv still?

it will basically create a new venv inside the container forking the existing preinistalled stuff (i.e. the new venv already has everything the python system has preinstalled)
then it will call "pip install" on all the "installed packages of the Task.
Which should just check everything is there and install nothing...

BTW:

Error response from daemon: cannot set both Count and DeviceIDs on device request.

Googling it points to a docker issue (which makes sense considering):
https://github.com/NVIDIA/nvidia-docker/issues/1026
What is the host OS?

Hi MelancholyChicken65
I'm not sure you an control it, the ui deduces the URL based on the address you are browsing to: so if you go yo http://app.clearml.example.com you will get the correct ones, but you have to put them on the right subdomains:
https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_config#subdomain-configuration

Sure, ReassuredTiger98 just add them after the docker image in the "Base Docker image" section under the execution Tab. The same applies for setting it from code.
example:
nvcr.io/nvidia/tensorflow:20.11-tf2-py3 -v /mnt/data:/mnt/dataYou can also always force extra docker run arguments by changing the clearml.conf on the agent itself:
https://github.com/allegroai/clearml-agent/blob/822984301889327ae1a703ffdc56470ad006a951/docs/clearml.conf#L121

or even different task types

Yes there are:
https://clear.ml/docs/latest/docs/fundamentals/task#task-types
https://github.com/allegroai/clearml/blob/b3176a223b192fdedb78713dbe34ea60ccbf6dfa/clearml/backend_interface/task/task.py#L81

Right now I dun see differences, is this a deliberated design?

You mean on how to use them? I.e. best practice ?
https://clear.ml/docs/latest/docs/fundamentals/task#task-states

JitteryCoyote63 hacky but sure 🙂
` from trains.config import config_obj

print(config_obj) `

I know about clearml.conf but wanted to avoid ssh-ing through 50 instances to edit it.

LOL yeah, btw: this is exactly the reason the enterprise version has a vault feature, so one could edit the base configuration in the UI and it automatically propagates everywhere

but docker_arguments doesn't propagate if I leave docker_image as None

yeah, that's correct, you have to select a container to be used

Worker just installs by name from pip, and it installs not my package!

Oh dear ...
Did you configure additional pip repositories in the Agent's clearml.conf ? https://github.com/allegroai/clearml-agent/blob/178af0dee84e22becb9eec8f81f343b9f2022630/docs/clearml.conf#L77 It might be that (1) is not enough, as pip will first try to search the package in the pip repository, and only then in the private one. To avoid that, in your code you can point directly to an https of your package` Ta...

Make sense. BTW: you can manually add data visualization to a Dataset with dataset.get_logger().report_table(...)

Hi SmallDeer34
I need some help what is the difference between the manual one and the automatic one ?
from your previous log, this is the bash command executed inside the container, can you try to "step by step" try to catch who/what is messing it up ?
` docker run -it --gpus "device=1" -e CLEARML_WORKER_ID=Gandalf:gpu1 -e CLEARML_DOCKER_IMAGE=nvidia/cuda:11.4.0-devel-ubuntu18.04 -v /home/dwhitena/.git-credentials:/root/.git-credentials -v /home/dwhitena/.gitconfig:/root/.gitconfig -v /tmp/...

2. ...that file and the logs of the agent service always say the same thing as before:

Oh in that case you need feel in Your credentials here:
https://github.com/allegroai/clearml-server/blob/5de7c120621c2831730e01a864cc892c1702099a/docker/docker-compose.yml#L137
Basically CLEARML_API_ACCESS_KEY / CLEARML_API_SECRET_KEY will let the agent running inside the docker talk to the server itself. Just put your own credentials there as a start, it should solve the issue

We almost didn't go with ClearML because of this, but then I suggested some steps,

😞
Just in case @<1557175205510516736:profile|ShallowSwan53>
None
None
None
None

Hi ConvolutedBee40

If we deploy a task to

clearml-server

, will it automatically scale?

The way it works is with agents and agent glue, basically using k8s as a resource allocator and the clearml agent as orchestrator, did that answer the question ?

In that case I suggest you turn on the venv cache, it will accelerate the conda environment building because it will cache the entire conda env.

is this repo installed on the machine creating the pipeline ?
You can also manually add it here `packages={"link_to_internal_python_package",]
None

Archived is actually just a "flag" on the Task. If you actually want to delete it (incl artifacts), in the archived view, right click and select delete

The .ssh is mounted, but the owner is my local user,

sudo -H clearml-agent ...to allow sudo to access home

Sure :
task = Task.init(..., auto_connect_arg_parser={'arg_not_to_log': False})This will cause all argparse to automatically be logged (and later editable) with the exception of the argument arg_not_to_log
Notice that if you have --arg-something, to exclude it add to the dict arg_something': False

@<1523701601770934272:profile|GiganticMole91> really nice!

but can we scheduled new task here?

@<1523701260895653888:profile|QuaintJellyfish58> do you mean schedule a Task from the scheduled function? if yes, you can do something similar to @<1523701601770934272:profile|GiganticMole91> , you create/clone existing Task, change arguments and push it into an execution queue. wdyt?

Hi @<1619867971730018304:profile|WhimsicalGorilla67>
No 😞 only the "admin" (owner) of the workspace has access to it

, I generate some more graphs with a file called 

graphs.py

  and want to attach/upload to this training task

Make total sense to use Task.get_task, I just want to make sure that you are aware of all the options, so you pick the correct one for you :)

Hi @<1561885921379356672:profile|GorgeousPuppy74>
Please use threads to ask questions, so we keep everything tidy
(and if you can please remove your first message, and merge it with the above one, this one and edit this one, for better readability)

regrading the issue, you need to either have clearm.conf in your Home folder, I'm assuming thisis /root/
not /home/ubuntu/.
Also not sure why you need to expose ports...

https://github.com/allegroai/clearml-serving

Sure, venv mode

Yey! 🙂