It's just that to access that comparison page, you have to make a comparison first.

Make total sense to me 🙂

JitteryCoyote63 How is it so far ?

BTW: you should probably update the server, you're missing out on a lot of cool features 🙂

Is is across the board for any Task ?
What would you expect to happen if you clone a Task that used the requirements.txt, would you ignore the full "pip freeze" and use the requirements .txt again, or is this thime we want to use the "installed packages" ?

Sorry @<1798525199860109312:profile|IntriguedGoldfish14> just noticed your reply
Yes two inference container, running simultaneously on the cluster. As you said, each one with its own environment (assuming here that the requirements of the models collide)
Make sense

it seems it's following the path of the script i'm using to task.create, eg:

The folder it should run it is the script path you are passing (i.e. "script=ep_fn," )
Wrong path would imply that is it not finding the correct repository, is that the case ?

How much free RAM / disk do you have there now? How's the CPU utilization ? how many Tasks are working with this machine at the same time

So it seems to get the "hint" from the type:
This will work
tf.summary.image('toy255', (ex * 255).astype(np.uint8), step=step, max_outputs=10)wdyt, should it actually check min/max and manually cast it ?

What does spin mean in this context?

This line:
docker-compose --env-file example.env -f docker-compose-triton-gpu.yml up

But these have: different task ids, same endpoints (from looking through the tabs)
So I am not sure why they are here and why not somewhere else

You can safely ignore them for the time being 🙂

but is it true that I can have multiple models on the same docker instance with different endpoints?

Yes! this is exactly the idea (and again I'm not sure ...

a CI for the vscode extension ? So spin server + agent & connect to it as part of the CI?

I'm not, not sure about WickedElephant66 / DefiantHippopotamus88

BTW, VexedKangaroo32 are you using torch launch ?

I have made a PR request.

Thanks you!!! 🎉 we will merge shortly 🙂

BTW: how is it missing listing torch ? Do you have "import torch" in the code ?

so moving b in to a won’t work if some subfolders are already there

I though that if they are already there you would merge / overwrite, isn't that what you need ?
a/b/c/2.txt seems like the result of moving b from dataset B into folder b in Dataset A, what am I missing?
(My assumption is that you have both datasets locally on the same machine and that you can just copy the files from b of Datasset B into b folder of Dataset A)

GreasyPenguin14

Is it possible in ClearML to have a main task (the complete cross validation) and subtasks (one for each fold)?

You mean to see it as nested in the UI? or Auto logged by the code ?

/opt/clearml/data/fileserver this is ion the host machine and it is mounted Into the container to /mnt/fileserer

What do you have in the artifacts of this task id: 4a80b274007d4e969b71dd03c69d504c

For example:
examples/k8s_glue_example.py --queue k8s_gpu - --namespace pod-clearml-conf ~/trains.conf --template-yaml example/base.yml

print(os.environ)

Thanks ReassuredTiger98 , yes that makes sense.
What's the python version you are using ?

Hmm can you test with the latest RC? or even better from the GitHub (that said the Github will break the interface, as we upgraded the pipeline 🙂 )

Well that depends on how you think about the automation. If you are running your experiments manually (i.e. you specifically call/execute them), then at the beginning of each experiment (or function) call Task.init and when you are done call Task.close . This can be done in parallel if you are running them from separate processes.
If you want to automate the process, you can start using the trains-agent which could help you spin those experiments on as many machines as you l...

This means that in your "Installed packages" you should see the line:
Notice that this is not a pypi artifactory (i.e. a server to add to the extra index url for pip), this is a direct pip install from a git repository, hence it should be listed in the "installed packages".
If this is the way the package was installed locally, you should have had this line in the installed packages.
The clearml agent should take care of the authentication for you (specifically here, it should do nothing).
If ...

Hmm can you test with the latest RC?
pip install clearml==0.17.6rc1

Hi Guys, just curious here, what's was the final issue?
Also out of curiosity, what does that mean? "1.12.2 because some bug that make fastai lag 2x" ?

This is what I think you should end up with
DiscreteParameterRange('General/dataset_url', values=["option 1 for url", "option 2 for url"])If args['dataset_url'] is a list, you should just do values=args['dataset_url']

-e

:user/private_package.git@57f382f51d124299788544b3e7afa11c4cba2d1f#egg=private_package

Is this the correct link to the repo and a valid commit id ?

Can you post a few more lines from the agent's log ?
Something is failing to install I'm just not sure what

Hi ShortElephant92

This isn't an issue if the user is using a Service Account JSON Key,

Are you saying that when you are using GS python sdk directly it works?

For context, the google cloud storage SDK allows an authorized user credentials.

ClearML actually uses the google python SDK, the JSON is just a way to pass the credentials to the google SDK, I'm not sure it points to "service account"? where did that requirement came from ?
is it from here ` Service account info was n...

The remaining problem is that this way, they are visible in the ClearML web UI which is potentially unsafe / bad practice, see screenshot below.

Ohhh that makes sense now, thank you 🙂
Assuming this is a one time credntials for every agent, you can add these arguments in the "extra_docker_arguments" in clearml.conf
Then make sure they are also listed in: hide_docker_command_env_vars which should cover the console log as well
https://github.com/allegroai/clearml-agent/blob/26e6...