TightElk12 are you still looking for a way to create a new "sub-task" ?
ReassuredTiger98 when you look for task "dca2e3ded7fc4c28b342f912395ab9bc" there are no artifacts ?
Could you add some prints? this should have worked...
Thanks! Let me check something
Ok, so it doesn't follow the exact same rules as
Task.init
?
Correct
I was afraid all the logs and outputs of a hyperparameter optimization task would be deleted just because no artifacts were created. (edited)
Should not happen 🙂
if you have an automation process, then you should have the Task object, no?
then you have task.id
What am I missing here?
Yes, only task.execute_remotely() should be the last call. because it literally will stop the local run before you add the Args section
I'm assuming TF was not part of the original requirements, and was automatically pulled by one of the packages, hence the latest version ....
Hi ShinyWhale52
Luigi's approach is basically an extension of a functional dag, where each node is a single function. Let's think of Kedro as extension of this approach.
With both the assumption is that a node is a single function (sometimes it really is) and we just want to create a meta execution path (i.e. the execution dag, quite similar to TF v1).
ClearML pipelines are a different story (in a way).
The main difference is that with ClearML each node is a Task, not a function. That mean...
hi @<1546303293918023680:profile|MiniatureRobin9>
I can still see the metrics in Grafana. I
it will not delete it from grafana, it means it's no longer collected, make sense ?
FranticCormorant35
See here https://github.com/allegroai/trains/blob/master/examples/manual_reporting.py#L42
ResponsiveCamel97
BTW: any reason not to allow this flexibility ?
Hi CooperativeSealion8
Seems like your NoScript addon is blocking the site :)
Thanks GiganticTurtle0
So the bug is "mock_step" is storing "NUMBER_2" argument value in the second instance?
Okay, I think this might be a bit of an overkill, but I'll entertain the idea 🙂
Try passing the user as key, and password as secret?
Hi SkinnyPanda43
Do you mean the cleaml-agent or the cleaml python (a.k.a the auto package detection) ?
Hi RoughTiger69
but still get the semantics of knowing when an (external) file changed?
How would you know it changed?
This implies you have a way to verify hash, which means you download the data , no?
Woot woot! 🤩
Hi ContemplativeCockroach39
Assuming you wrap your model with a flask app (or using any other serving solution), usually you need:
Get the model Add some metrics on runtime performance package in a dockerGetting a pretrained model is straight forward one you know either the creating Task or the Model ID
` from clearml import Task, Model
model_file_from_task = Task.get_task(task_id).models['output'][-1].get_local_copy()
or
model_file_from_model = Model(model_id=<moedl_id>).get_local_copy()...
Oh sure that makes sense, clone the experiment in the UI (right click, clone) then everything is editable :) both uncommitted changes, and branch / commit
I did nothing to generate a command-line. Just cloned the experiment and enqueued it. Used the server GUI.
Who/What created the initial experiment ?
I noticed that if I run the initial experiment by "python -m folder_name.script_name"
"-m module" as script entry is used to launch entry points like python modules (which is translated to "python -m script")
Why isn't the entry point just the python script?
The command line arguments are passed as arguments on the Args section of t...
EnviousStarfish54 Notice that you can configure it on the agent machine only, so in development you are not "wasting" storage when uploading debug checkpoints/models 🙂
Hi SkinnyPanda43
Every "commit" is a new version, so sync changes you need to either create a new version (with parent version as the previous one), and sync the local folder (or manually add/remove files).
If you do not need to actually store the "current" version, you can just reset the Task, and sync it again.
wdyt?
AbruptHedgehog21 could it be the console log itself is huge ?
My task starts up and checks the mounted EFS volume for x data, if x data does not exist there, it then pulls x data from S3.
BoredHedgehog47 you can just use StorageManager and configure clearml cache for the EFS, it will essentially do the same 🙂
Regrading helm chart with EFS,
you need to configure the clearml-glue pod template with the EFS mount
example :
https://github.com/kubernetes-sigs/aws-efs-csi-driver/blob/e7f647f4e6fc76f983d61522e635353005f1472f/examples/kubernetes/volu...
Hi @<1544853721739956224:profile|QuizzicalFox36>
http:/34.67.35.46:8081/...
notice there is a / missing in the link, how is that possible? it should be http://
Are you trying to upload an artifact post execution ?