The second problem that I am running into now, is that one of the dependencies in the package is actually hosted in a private repo.

Add your private repo to the extra index section in the clearml.conf:
None

Also I can’t call the “preprocess” function since there is no valid endpoint to be hitting

Wait now I'm confused, when you are calling " None " you are actually calling the preprocess function running on the inference container, and this one in turn (automatically) calls the Triton container.

Are you calling the Triton manually?
Could you share your preprcoess.py , and the command line you have used to register the two model versions ?
(based on ...

Now in case I needed to do it, can I add new parameters to cloned experiment or will these get deleted?

Adding new parameters is supported 🙂

SteadySeagull18 btw: in post-callback the node.job will be completed
because it is a called after the Task is completed

Hi VexedCat68
Check this example:
https://github.com/allegroai/clearml/blob/4f9aaa69ed2d5b8ea68ebee5508610d0b1935d5f/examples/scheduler/trigger_example.py#L44

generally speaking the agent will convert the repo url to the auth scheme it is configured with, ssh->hhtp if using user/pass, and http->ssh if using ssh

Could you maybe send a screenshot? This is very strange? Also what's the trains version?

It doesn't not seem to be related to the upload. The upload itself finished... What's your Trains version?

Hmm if this is case, you can add some prints in here:
None
the service/action will tell you what you are sending
wdyt?

Is there a quicker way to abort all running experiments in a project? I have over a thousand running anonymous data tasks in a specific project and I want to abort them before debugging them.

We are adding "select" all in the next UI version to do that as quickly as possible 🙂

So was the issue solved?

Hi DilapidatedDucks58

eg, we want max validation accuracy and all other metric values for the corresponding epoch

Is this the equivalent of nested sort ?
Wouldn't you get the requested behavior if you add all metric columns but sort based on the "accuracy" column ?

I'm assuming these are the Only packages that are imported directly (i.e. pandas requires other packages but the code imports pandas so this is what listed).
The way ClearML detect packages, it first tries to understand if this is a "standalone" scrip, if it does, than only imports in the main script are logged. Then if it "thinks" this is not a standalone script, then it will analyze the entire repository.
make sense ?

owning the agent helps, but still it's much better if the credentials don't show up in logs,

They are not, they are always filtered out,

• how does force_git_ssh_protocol help please? it doesn't solve the issue of the agent simply not having accessIt automatically maps the host .ssh into the container, so that git can use SSH to clone.
  What exactly is not working?
  and how are you configuring it?

I update my-private-dep to 1.8.0

Not sure how this is connected with the venv, could you expand ?

Things to check:
Task.connect called before the dictionary is actually used Just in case, do configs['training_configuration']=Task.connect(configs['training_configuration']) add print(configs['training_configuration']) after the Task.connect call, making sure the parameters were passed correctly

I think it would make sense to have one task per run to make the comparison on hyper-parameters easier

I agree. Could you maybe open a GitHub issue on it, I want to make sure we solve this issue 🙂

My pleasure

we have some other parts, and for some cases we get initialization time can be about 10 times the experiment time

Before I dive into some agent in agent hacking, I would consider "caching" this preprocessing on an auxiliary Task as an artifact. Basically add another argument for the auxiliary Task, and fetch the data from it (obviously you will need to run it once before the optimizer launches the first experiment).
Now that is out of the way (which really would be the preferred engin...

DeterminedCrab71 that is a good point, how does plotly adjust for nans on graphs?

BTW: if you want to sync between artifacts / settings, I would recommend calling task.reload() to get the latest values back from the server.

Hi BitterStarfish58
What's the clearml version you are using ?

dataset upload both work fine

Artifacts / Datasets are uploaded correctly ?
Can you test if it works if you change " http://files.community.clear.ml " to " http://files.clear.ml " ?

Hi @<1610083503607648256:profile|DiminutiveToad80>

<h1>Request Entity Too Large</h1>

What's the size of the file? how are you running your clearml-server?

But essentially Prefect also has agents to run jobs on machines where the processes run (which seems to be exactly the same model as in ClearML),

Yes ait is conceptually very similar

this data is highly regulated data, ...

The main difference that with ClearML the agents are running on Your machines (either local or on Your cloud account) the clearml-server does not actually have access to the data streaming through it.
Does that make sense ?

clearml-agent deployment file

What do you mean by that? is that the helm of the agent ?

think perhaps it came across as way more passive aggressive than I was intending.

Dude, you are awesome for saying that! no worries 🙂 we try to assume people have the best intention at heart (the other option is quite depressing 😉 )

I've been working on a Azure load balancer example, ...

This sounds exciting, let me know if we can help in any way

What if I have multiple files that are not in the same folder? (That is the current use-case)

I think you can do weights_filenames= ['a_folder/firstfile.bin', 'b_folder/secondfile.bin']
(it will look for a common file path for both so it retains the folder structure)

Our workaround now for using a

Dataset

as we do, is to store the dataset ID as a configuration parameter, so it's always included too

Exactly, so with Input Model it's the same only kind of ...

Should work out of the box, as long as the task was started. You can forcefully start the task with:
task.mark_started()

I'm checking now to see where the extra ' could come from

If you spin two agent on the same GPU, they are not ware of one another ... So this is expected behavior ...
Make sense ?