Well actually I have tried a different approach and it works.
` task = Task.init(project_name=args['cml_project_name'],
task_type=TaskTypes.data_processing,
task_name=f'Dataset for {os.path.basename(OBJECT_NAME)}',
tags=args['cml_tags'].split(','),
output_uri = args['cml_output_uri'],
auto_connect_frameworks=True)

    dataset = Dataset.create(
        dataset_n...

AgitatedDove14 thanks - that makes it clear.
In particular there are some optional dependencies - in my case I am using pandas.read_xls and it requires openpyxl .
Meanwhile I have changed a script and inserted an explicit import - now agent makes this install.

Are you running in docker mode ? the venv inside the docker inherits from all the installed packages, how come it is missing?

My fault - I was in pip mode...

Yeap. Thanks - I've already did that and faced another issue - https://github.com/allegroai/clearml/issues/740
Now model stays local and is not uploaded to s3.

SweetBadger76 thanks for looking into this. Here's a screenshot that displays files in clearML that should be available in minio. I can see them in clearML (I refer to this as clearML metadata) but when I press the link it redirects me to minio and shows that this file is not there. Also when I explore minio with console - I don't see those files there. But notebooks and datasets get uploaded just fine.

I am using
WebApp: 1.5.0-186
Server: 1.5.0-186
API: 2.18
On client side:
clearml==1.4.1
clearml-agent==1.2.3

It was some issue with the server - after restarting all seems to work.

no prob - will run now

and this is inside a container to check that package is installed:
docker run -it --rm torch2022 pip show torch
Name: torch Version: 1.11.0 Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration Home-page: Author: PyTorch Team Author-email: packages@pytorch.org License: BSD-3 Location: /opt/conda/lib/python3.8/site-packages Requires: typing_extensions Required-by: torchmetrics, pytorch-lightning, torchvision, torchtext, torchelastic
I build my own ...

So I have switched back to ssl to give a try to the script again - and it works with ssl now.
I even have tried it with big files - still works.
SweetBadger76 thanks for giving a hand - don't know what was the issue but now that works.

Hi SweetBadger76
So - I have turned off SSL for minio and tried a test script for uploading those two artifacts.
The result is that it works - the file got uploaded to a bucket.
Although it has taken a long time to finish upload and the files are less than 1Mb
$ python3 test.py ClearML Task: overwriting (reusing) task id=72e7c0b098e14197a9ffe82d7444337f ClearML results page: `
2022-06-10 14:14:00,894 - clearml.Task - INFO - Waiting to finish uploads
2022-06-10 14:14:11,888 - clearml.T...

Thanks Jake SuccessfulKoala55 !
I used to have problems with clearML agents and multi-GPU training with agents - have put it on hold.
Now my problem is with ClearML serving.
I have managed to run a demo https://clear.ml/docs/latest/docs/clearml_serving/clearml_serving_tutorial
But had problems :
` clearml-serving --id c605bf64db3740989afdd9bee87e6353 model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "initial model training" --p...

Hi Martin. Sorry - missed your reply.
Yeap I am aware that docker_internal_mounts is inside agent section.
Here is the actual docker command from the log
` INFO Executing: ['docker', 'run', '-t', '--gpus', '"device=0"', '-v', '/tmp/ssh-XXXXXXnfYTo5/agent.8946:/tmp/ssh-XXXXXXnfYTo5/agent.8946', '-e', 'SSH_AUTH_SOCK=/tmp/ssh-XXXXXXnfYTo5/agent.8946', '-l', 'clearml-worker-id=agent-gpu:gpu0', '-l', 'clearml-parent-worker-id=agent-gpu:gpu0', '-e', 'CLEARML_WORKER_ID=agent-gpu:gpu0', '-e', 'CLEARM...

Hi AgitatedDove14
Either I do smth wrong or it works only in theory 😃
Here are my mounts in agent clearml.conf
sdk_cache: "/clearml_agent_cache" ssh_folder: "/home/testuser/.ssh" pip_cache: "/home/testuser/.cache/pip" vcs_cache: "/home/testuser/.clearml/vcs-cache" venv_build: "/home/testuser/.clearml/venvs-builds" pip_download: "/home/testuser/.clearml/pip-download-cache"testuser is inside Docker container
and I run agent from local user and I would expect...

Yeap. It is configured this way
force_git_ssh_protocol: trueBut I don't see the mount of .ssh
One thing though - my container is running on behalf of non-root user.
` Here are my docker mounts:
docker_internal_mounts {
sdk_cache = /clearml_agent_cache
# apt_cache = /var/cache/apt/archives
ssh_folder = /home/testuser/.ssh
pip_cache = /home/testuser/.cache/pip
poetry_cache = /home/testuser/.cache/pypoetry
vcs_cache = /home/testuser/.clearml/vcs-cache
venv_build =...

Hi Martin.
I think a proper place would be somewhere in https://clear.ml/docs/latest/docs/clearml_agent#execution

And one more question - I have compared two docker commands - 1st without cleaning SSH_AUTH_SOCK
It's portion regarding SSH mount looks like this:
'-v', '/tmp/ssh-XXXXXXnfYTo5/agent.8946:/tmp/ssh-XXXXXXnfYTo5/agent.8946', '-e', 'SSH_AUTH_SOCK=/tmp/ssh-XXXXXXnfYTo5/agent.8946'To me all seems to be correct it maps temporary folder with ssh keys and points SSH_AUTH_SOCK to it...

Hi! any update on that fix? @<1523703436166565888:profile|DeterminedCrab71>
maybe it is not present in 1.8 and will just use that version?

cool with report shortly!

Hi David,
In my case I have a remote minio with ssl enabled - do you want me to run a local one with HTTP to test if all works fine in that config?

Good weekend to all! Any update on this? Thanks!

Hi AgitatedDove14
Thanks for the update.
Well, it's a pain... I use specifically pytorch docker image and still agent will download it?
My image is build based on FROM pytorch/pytorch:1.11.0-cuda11.3-cudnn8-devel
And a portion of agent log on top of that image:
` Package(s) not found: torch
Torch CUDA 113 download page found
Found PyTorch version torch==1.11.0 matching CUDA version 113
Package(s) not found: torchvision
Found PyTorch version torchvision==0.12.0 matching CUDA version 113
Co...

This time it runs smoothly - here's the output:
` Local file not found [torch @ file:///home/testuser/.clearml/pip-download-cache/cu113/torch-1.11.0%2Bcu113-cp39-cp39-linux_x86_64.whl], references removed
Local file not found [torchvision @ file:///home/testuser/.clearml/pip-download-cache/cu113/torchvision-0.12.0%2Bcu113-cp39-cp39-linux_x86_64.whl], references removed
Adding venv into cache: /home/nino/.clearml/venvs-builds/3.9
Running task id [b15553c045ab4c3283bbdb040ec19f1f]:
[src/models]...

thanks a bunch - that has worked just fine

clearml-agent --version CLEARML-AGENT version 1.2.3

Made an upgrade to the latest version from 1.5 and have stumbled upon an issue with webserver:
I am saving all artefacts to a custom s3 server. Used to work fine - saving and downloading them from webserver. Now I can now download anything that resides on s3 - getting the following errors in browser console:
Unable to parse "https None " as a whatwg URL.
ERROR EndpointError: Custom endpoint https [None](//storage.yandexcloud.net) was not a valid URI

Back at 1....

Thanks @<1523701087100473344:profile|SuccessfulKoala55>
I've looked into the docker-compose and found a new image async_delete
Not sure what it does and if I should include it into upgraded installation.
If I do - there is a parameter CLEARML__services__async_urls_delete__fileserver__url_prefixes: "[${CLEARML_FILES_HOST:-}]"
I guess I should set it to fileserver in case of one docker-compose?

Thanks @<1523703436166565888:profile|DeterminedCrab71>
I've tried that - it does not work - I have a valid endpoint in settings but a missing colon in js console
Waiting for a fix 🙏

Currently I have the following config re S3:
` aws {
s3 {
# default, used for any bucket not specified below
key: ""
secret: ""
region: ""

        credentials: [
            {
                # This will apply to all buckets in this host (unless key/value is specifically provided for a given bucket)
                host: "mydomain.com:9000"
                key: "minio"
                secret: "secret data"

...

Hi David. Sorry I got stuck with agent in docker mode training on multiple GPUs. Will get that sorted and finish that stuff with minio.

Sure will do and get back to you - not that difficult.