Are you running it with docker?

I don't think there is a direct way but if you use a docker container with a different python version inside, that works.

Well you could let ClearML create the config file with: https://clear.ml/docs/latest/docs/references/sdk/task#taskset_credentials
store_conf_file=True

And then go edit the file.

But it's probably easier in your case to use https://clear.ml/docs/latest/docs/references/sdk/task#connect_configuration
and pass it your full configuration?

PricklyRaven28 I think then you're looking for: ZipFile.ZIP_STORED

ExasperatedCrab78 do you know how this could be?

This ^
If you're not getting any errors, it should work just fine 🙂

In https://github.com/thepycoder/urbansounds8k/blob/main/preprocessing.py i'm seeing dataset_task.get_logger().report_image , dataset_task.get_logger().report_table , dataset_task.get_logger().report_histogram and dataset_task.get_logger().report_media which are all manual loggings. Hence, why the author probably didn't use any automatic logging.

Both server and agent can be configured with different ports. Which is it you`re looking for?

You've got it all working now?

Can you give me a bit more info what exactly you're trying to log and what framework you're using?

Could you upload the log so I can have a look?

That's pretty weird. I don't see any clear indications something is wrong, it simply doesn't execute the rest it would seem. Did it successfully run the first time before cloning it?

Or you can give it a configuration object: https://clear.ml/docs/latest/docs/clearml_sdk/task_sdk#configuration-objects

Have you tried --verbose ?

Well seems like you have a solution for now?

If you still want to run it as a notebook, the following should make pip install the required packages:

import sys !{sys.executable} -m pip install -r requirements.txt
I'll check if this something we need to update in our documentation or if it's a bug.

Could you try to see if it does work when you log those manually?
https://clear.ml/docs/latest/docs/clearml_sdk/model_sdk#manually-logging-models

Did you use --git-credentials ?
https://clear.ml/docs/latest/docs/apps/clearml_session#accessing-a-git-repository

You can disable it if you want to. Sometimes it's useful to retrace your steps but it works without :)

Have you tried
logger = Logger.current_logger()in your code?
Logger is a singleton so you should get the same object from your previously created task
https://clear.ml/docs/latest/docs/references/sdk/logger/#loggercurrent_logger

Do you mean in the WebUI or via the API?

And this is also remotely?

I don't see SB3 here so PyTorch would be best: https://clear.ml/docs/latest/docs/integrations/libraries

ThoughtfulBadger56 Have you uncommented the existing venvs_cache section in the config file?
https://clear.ml/docs/latest/docs/clearml_agent#virtual-environment-reuse

I'd you've got a self hosted instance you can have a look yourself https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_config
But other then that I'm not sure. AnxiousSeal95 any thoughts?

Could it be multiple metrics that were combined into a single metric later on? Before the optimizer?

Can you try: '${pipeline.docker_image}' ?

Just checking, are you just trying to use a different docker image in a task? Because then you might want to use this: https://clear.ml/docs/latest/docs/apps/clearml_task/#docker
https://clear.ml/docs/latest/docs/clearml_agent#docker-mode

AnxiousSeal95 Do you know this one?

you can pass use the compression parameter in dataset.upload . The supported values are:
ZipFile.ZIP_STORED (no compression) ZipFile.ZIP_DEFLATED (requires zlib) ZipFile.ZIP_BZIP2 (requires bz2) ZipFile.ZIP_LZMA (requires lzma)Note that you need to import ZipFile beforehand: from zipfile import ZipFile
You're probably looking for ZIP_BZIP2 , but I'm not sure about that.

If you're just looking to reuse virtual environments, have a look here: https://clear.ml/docs/latest/docs/clearml_agent/#environment-caching

Also have a look at --memory-swap
It seems you might not anticipated this usage:

If --memory-swap is unset, and --memory is set, the container can use as much swap as the --memory setting, if the host container has swap memory configured. For instance, if --memory="300m" and --memory-swap is not set, the container can use 600m in total of memory and swap.