the trend step artifact used to keep track the time of the data so we know the expected trend of the input data. For example, on the first data which is trend_step = 1 the trend value is 10, then if the trend_step = 10 (the tenth data) our regressor will predict the trend value of the selected trend_step. this method is still in research to make it more efficient so it doesn't need to upload artifact every request

Make sense! I would suggest you add a GitHub issue with feature request ...

Task.completed(ignore_errors=False)
What are you getting?

@<1734020162731905024:profile|RattyBluewhale45> could you attach the full Task log? Also what do you have under "installed packages" in the original manual execution that works for you?

I can't think of any hack that will satisfy your IT other than than an actual vault...
wdyt?

Can you try to set this in your clearml.conf:

agent.pip_download_cache.enabled: false

this should disable the local caching, of your wheel, I suspect there is some issue with the local cache file in windows...

ClumsyElephant70 the odd thing is the error here:
docker: Error response from daemon: manifest for nvidia/cuda:latest not found: manifest unknown: manifest unknown.I would imagine it will be with "nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu18.04" but the error is saying "nvidia/cuda:latest"
How could that be ?
Also can you manually run the same command (i.e. docker run --gpus device=0 --rm -it nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu18.04 bash )?

BTW: the same hold for tagging multiple experiments at once

HugeArcticwolf77 oh no, I think you are correct 😞
Do you want to quickly PR a fix ?

Hi RoundMosquito25
Hmm I remember this is tricky ... What's the clearml version? also where is the line you had to hack ?

Hi VexedCat68

txt file or pkl file?

If this is a string , it just stored it (not as a file, this is considered a "link")
https://github.com/allegroai/clearml/blob/12fa7c92aaf8770d770c8ed05094e924b9099c16/clearml/binding/artifacts.py#L521

I am trying to see if the user can submit a list of resource requirements (e.g 4GPUs, 12 cores, 100GB diskspace)

This will be quite easy to implement using the cleamrl k8s glue, just use user-properties and change the template based on it. I can point to where you need to modify the code

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

OddAlligator72 sure thing 🙂
This should sort it out:
Task.init('examples', 'train', continue_last_task=True)If you want to continue a specific Task:
continue_last_task='task_id_here'Getting the previous model:
last_checkopoint = task.models['output'][-1]What do you think?

Can you verify this example is not working for you?
https://github.com/allegroai/clearml/blob/master/examples/frameworks/hydra/hydra_example.py

Hmm let me rerun (offline mode right ?)

100% of things with 

task_overrides

 would be the most convenient way

I think the issue is that you have to pass the project ID not project name (the project unique IS is the property that is actually stored on the Task)
@<1523707653782507520:profile|MelancholyElk85> can you check the following works:

pipe.add_task(, ..., task_overrides={'project': Task.get_project_id(project_name='examples')},)

Fix in the next version 🙂

Ohh is this a conda installed ?

Yep it is the scale 🙂 and yes it should appear once you upgrade

I added the following to the

clearml.conf

file

the conf file that is on the worker machine ?

How can I ensure that additional tasks aren’t created for a notebook unless I really want to?

TrickySheep9 are you saying two Tasks are created in the same notebook without you closing one of them ?
(Also, how is the git diff warning there with the latest clearml, I think there was some fix related to that)

Try to upload something to the file server ?
None

Let me check something

Hi GrievingTurkey78
the artifacts are downloaded to the cache folder (and by default the last 100 accessed artifacts are maintained there).

node executes the task all the info will be erased or does this have to be done explicitly?

Are you referring to the trains-agent running a docker?
By default the cache is persistent between execution (i.e. saving time on multiple downloads between experiments)

Hi QuaintJellyfish58
This is odd, this "undefined" project is also marked as "Example" which would explain why you cannot delete it, but not how you ended up with one
Any idea on what changed on your server ?

We listen to you guys 🙂

I see, when you run it manually (i.e. not via an agent) what do you have under the configuration tab in the UI (meaning do you see both argparser arguments there)?

BurlyPig26 if this is about Task.init delaying execution, did you check:
Task.init(..., deferred_init=True)it will execute the initialization in the background without disturbing execution.

If this is about Model auto logging, see Task.init(..., auto_connect_frameworks) you can specify per framework a wild card to log the models, or disable completely https://github.com/allegroai/clearml/blob/b24ed1937cf8a685f929aef5ac0625449d29cb69/clearml/task.py#L370

Make sense ?

Hm, one of the issues I have with this change is that now every dataset hat doesn’t have a semantic version cannot be loaded anymore

Okay we definitely need to solve that.
Any chance I can ask to open a github issue (just so we do not forget).
I will pass it quickly along so that we can maybe offer a fix in the next RC

What’s interesting to me (as a ClearML newbie) is it’s clearly compiling that wheel using my host machine (MacOS).

Hmm kind of, and kind of not.
If you take a look at the Tasks created (regardless on how they are created,. pipeline, manually, etc.), you have a list of python packages required by the code, as they are detected at runtime (i.e. when the code was first executed, on the development machine). When creating a Pipeline controller (runner), the pipeline Tasks are just lists, ...