Ohh wow

it should be fairly easy to write such a daemon

from clearml.backend_api.session.client import APIClient
client = APIClient()
timestamp = time() - 60 * 60 * 2  # last 2 hours
tasks = client.tasks.get_all(
            status=["in_progress"],
            only_fields=["id"],
            order_by=["-last_update"],
            page_size=100,
            page=0,
            created =[">{}".format(datetime.utcfromtimestamp(timestamp))],
        )
...

references:
[None](https://clear.ml/...

SmarmySeaurchin8 yes, you should avoid that (we are saving it for a future feature 😉 )

named as 

venv_update

 (I believe it's still in beta). Do you think enabling this parameter significantly helps to build environments faster?

This is deprecated... it was a test to use the a package that can update pip venvs, but it was never stable, we will remove it in the next version

Yes, I guess. Since pipelines are designed to be executed remotely it may be pointless to enable an 

output_uri

 parameter in the 

PipelineDecorator.componen...

Okay, so the idea behind the new decorator is not to group all the defined steps under the same script so that they share the same environment, but rather to simplify the process of creating scripts for each step and avoid manually calling 

Task.init

 on those scripts.

Correct, and allow users to more easily create Tasks from code.

Regarding virtual environment creation from caching, I will keep running benchmarks (from what you say it might be due to high workload ...

Hmm ConvincingSwan15

WARNING - Could not find requested hyper-parameters ['Args/patch_size', 'Args/nb_conv', 'Args/nb_fmaps', 'Args/epochs'] on base task

Is this correct ? Can you see these arguments on the original Task in the UI (i.e. Args section, parameter epochs?)

That is actually what you are looking for:
https://github.com/allegroai/trains/blob/04b3fa809bb73d7101d1995327684ebe5b2911e3/trains/storage/cache.py#L46

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.

Do you want to PR it? should be a quick fix

packages are updated, and I don't know which python version I get, + changing the python version of the OS is not really recommended

Wait I'm confused, this is inside a container, no?

and the python version running my code should not depend of the python version running the clearml-agent (especially for experiments running in containers)

Generally speaking you are correct, but some packages will not have the same version for all python versions

Specifically in this case I think...

WickedGoat98 I suspect the main difference is with GitHub your are cloning with https (i.e. not credentials needed) , but with gitlab you are using SSH authentication to clone the repository .If on the machine running the trains-agent you can "git clone" your repository (i.e. from command line), the trains-agent should be able to do the same (basically make sure you have the SSH keys in your ~/.ssh folder.

Are you testing the trains-agent service from (i.e. from the docker compose) o...

Okay, so I think it doesn't find the correct Task, otherwise it wouldn't print the warning,
How do you setup the HPO class ? Could you copy paste the code?

JitteryCoyote63 I think I found the bug in clearml-task it adds it at the end instead of before everything else

Hi JitteryCoyote63
I think this is the default python str() casting.
But you can specify the preview test when you call upload_artifact:
https://clear.ml/docs/latest/docs/references/sdk/task#upload_artifact
see preview argument

By your description it seems to make no difference whether I added the files via sync or add, since I will have to create a new dataset either way.

Sync is design to take a local folder/s and add/remove files from a dataset based on the local changes (it does that automatically based on file existence / content)
The changes (i.e. added files) are uploaded as delta changes relative to the parent version, this means we are not always uploading all files.

Add on the other hand means you...

Well I guess you can say this is definitely not self explanatory line 😉
but, it is actually asking whether we should extract the code, think of it as:
if extract_archive and cached_file: return cls._extract_to_cache(cached_file, name)

Please go ahead with the PR 🙂

sure thing 🙂

Hi RobustGoldfish9 ,

I'd much rather just have trains-agent just automatically build the image defined there than have to build the image separately and make it available for all the agents to pull.

Do you mean there is no docker image in the artifactory built based on your Dockerfile ?

Hmm MiniatureHawk42 how many files in the zip ?

Hi MortifiedDove27
I think you can resize the plot area in the UI (try to drag the horizontal separator)

yes 🙂
But I think that when you get the internal_task_representation.execution.script you are basically already getting the API object (obviously with the correct version) so you can edit it in place and pass it too

SmilingFrog76 this is not a weird mechanism at all , this is proper HPC scheduler 🙂
trains-agent is not actually aware of other nodes, it is responsible for launching a Task on its own hardware (with whatever configuration it was set). What can be done is to use the trains-agent inside a 3rd party scheduler and have the scheduler allocate the node and trains-agent spin the experiment. There is a k8s example here: basically pulling jobs for the trains-server queue and pushing ...

@<1571308003204796416:profile|HollowPeacock58> seems like an internal issue copying this object config.model
This is a complex object, and it seems that for some reason
None

As a workaround just do not connect this object. it seems you cannot pickle it / copy it (see GH issue)

I assume here:
https://github.com/allegroai/trains/blob/04b3fa809bb73d7101d1995327684ebe5b2911e3/trains/storage/cache.py#L47

We should probably change it so it is more human readable 🙂

I see, would having this feature solve it (i.e. base docker + bash init script)?
https://github.com/allegroai/trains/issues/236

HandsomeCrow5 if you want to edit the Task object you can just use:
internal_task_representation = task.data internal_task_representation.execution.script = ... task._edit(execution=internal_task_representation.execution)This will make sure you do not need to worry about API version etc. the Task object will take care of it.
BTW: it seems a few more people wanted this ability, maybe we should edit a proper .edit method to Task. Thoughts ?

Legit, if you have a cached_file (i.e. exists and accessible), you can return it to the caller

Hi @<1562973095227035648:profile|ThoughtfulOctopus83>
The host should be just the host name, no https prefix, I'm assuming that's the issue