MagnificentSeaurchin79
"requirements.txt" is ignored if the Task has an "installed packges" section (i.e. not completely empty) Task.add_requirements('pandas') needs to be called before Task.init() (I'll make sure there is a warning if called after)

I'm saying that because in the task under "INSTALLED PACKAGES" this is what appears

This is exactly what I was looking for. Thanks!
Yes that makes sense, I think this bug was fixed a long time ago, and this is why I could not reproduce it.
I also think you can use a later version of clearml 🙂

Do people generally update the same model “entry”? That feels so wrong to me…how do you reproduce a older model version or do a rollback etc?

Correct, they do not 🙂 On the Task itself the output models will reflect the diff filenames you saved, usually ppl just add a running number.

🎊

I see, give me a minute to check what would be the easiest

https://github.com/allegroai/clearml/blob/master/examples/services/monitoring/slack_alerts.py

The class documentation itself is also there under "References" -> "Trains Python Package"
Notice that due to a bug in the documentation (we are working on a fix) the reference part is not searchable in the main search bar

Hmmm are you saying the Dataset Tasks do not have the "dataset" system_tag as well as the type ?

Hi SubstantialElk6

Generically, we would 'export' the preprocessing steps, setup an inference server, and then pipe data through the above to get results. How should we achieve this with ClearML?

We are working on integrating the OpenVino serving and Nvidia Triton serving engiones, into ClearML (they will be both available soon)

Automated retraining

In cases of data drift, retraining of models would be necessary. Generically, we pass newly labelled data to fine...

So I might be a bit out of sync, but I think there should be Triton serving and OpenVino serving built into it (or at least in progress).

Correct (copied == uploaded)

BTW: GreasyPenguin14 you can also upload them as debug samples (when setting the output_uri, the debug samples will be uploaded to the same destination)
https://github.com/allegroai/clearml/blob/6b9297660e0ed83a77bce3da2fab384c552206fd/examples/reporting/image_reporting.py#L21

Could it be the code is not in a git repository ?
clearml support either a single script or a git repository, but Not a collection of standalone files. wdyt?

GiganticTurtle0 you mean the repo for the function itself ?
the default assumes the function is "standalone", you can specify a repo with:
@PipelineDecorator.component(..., repo='.')
will take the current folder's repo (i.e. the local one)
you can also specify repo url/commit etc (repo=' https://github/user/repo/repo.git ' ....)
See here:
https://github.com/allegroai/clearml/blob/dd3d4cec948c9f6583a0b69b05043fd60d8c103a/clearml/automation/controller.py#L1931

Hi GiganticTurtle0
You can keep clearml following the dictionary auto updating the UI
args = task.connect(args)

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

So where is it coming from ?

These both point to nvidia docker runtime installation issue.
I'm assuming that in both cases you cannot run the docker manually as well, which is essentially what the agent will have to do ...

Hi GleamingGrasshopper63

How well can the ML Ops component handle job queuing on a multi-GPU server

This is fully supported 🙂
You can think of queues as a way to simplify resources for users (you can do more than that,but let's start simple)
Basicalli qou can create a queue per type of GPU, for example a list of queues could be: on_prem_1gpu, on_prem_2gpus, ..., ec2_t4, ec2_v100
Then when you spin the agents, per type of machine you attach the agent to the "correct" queue.

Int...

CurvedHedgehog15 there is not need for :
task.connect_configuration( configuration=normalize_and_flat_config(hparams), name="Hyperparameters", )Hydra is automatically logged for you, no?!

Ohh SubstantialElk6 please use agent RC3, (latest RC is somewhat broken sorry, we will pull it out)

Hmm I wonder, can you try with this line before?
Task._report_subprocess_enabled = False frameworks = { 'tensorboard': True, 'pytorch': False } Task.init(...)

Correct :)

Hi ShallowCat10
What's the TB your are using?
Is this example working correctly for you?
https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorboard_pr_curve.py

According to you the VPN shouldn't be a problem right?

Correct as long as all parties are on the same VPN it should work, all the connections are always http so basically trivial communication

Correct 🙂

SmugDog62 so on plain vanilla Jupyter/lab everything seems to work.
What do you think is different in your setup ?

Nice! 🙂

Oh :)
task.get_parameters_as_dict()

ShinyWhale52 any time 🙂
Feel free to followup with more questions