Here is the log

Also tried conda version 4.7.12. Same problem.

Is there a simple way to get the response of the MinIO instance? Then I can verify whether it is the MinIO instance or my client

I will debug this myself a little more.

name: core
channels:
  - pytorch
  - anaconda
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1
  - _openmp_mutex=4.5
  - blas=1.0
  - bzip2=1.0.8
  - ca-certificates=2020.10.14
  - certifi=2020.6.20
  - cloudpickle=1.6.0
  - cudatoolkit=11.1.1
  - cycler=0.10.0
  - cytoolz=0.11.0
  - dask-core=2021.2.0
  - decorator=4.4.2
  - ffmpeg=4.3
  - freetype=2.10.4
  - gmp=6.2.1
  - gnutls=3.6.13
  - imageio=2.9.0
  - jpeg=9b
  - kiwisolver=1.3.1
  - lame=3.100
  - lcms2=2.11
  -...

Thank you very much! 😃

Setting the api.files_server: s3://myhost:9000/clearml in clearml.conf works!

Nvm, that does not seem to be a problem. I added a part to the logs in the post above. It shows that some packages are found from conda.

TRAINS_LOG_ENVIRONMENT=MYENVVAR works. Thank you!

Is there a way to capture uncommited changes with Task.create just like Task.init does? Actually, I would like to populate the repo, branch and packages automatically...

When I passed specific arguments (for example --steps) it ignored them...

I am not sure what you mean by this. It should not ignore anything.

One question: Does clearml resolve the CUDA Version from driver or conda?

` args = parser.parse_args()
print(args) # args PRINTED HERE ON LOCAL

command = args.command
enqueue = args.enqueue
track_remote = args.track_remote
preset_name = args.preset
type_name = args.type
environment_name = args.environment
nvidia_docker = args.nvidia_docker

# Initialize ClearML Task
task = (
    Task.init(
        project_name="reinforcement-learning/" + type_name,
        task_name=args.name or preset_name,
        tags=...

That seems to be the case. After parsing the args I run task = Task.init(...) and then task.execute_remotely(queue_name=args.enqueue, clone=False, exit_process=True) .

Python 3.8.8, clearml 1.0.2

Good, at least now I know it is not a user-error 😄

clearml does everything for me 😄

Thank you, good to know!

(btw: the simulator is called carla, not clara :))

I use this snippet:

Logger.current_logger().set_default_upload_destination(
    "

" # or 

)

Artifact Size: 74.62 MB

Now it works.

Thank you very much!

So if understand correctly, something like this should work?

task = Task.init(...) task.connect_configuration( {"agent.package_manager.system_site_packages": False} ) task.execute_remotely(queue_name, clone=False, exit_process=True)

Exactly. I don't want people to circumvent the queue 🙂

But I do not have anything linked correctly since I rely in conda installing cuda/cudnn for me

You can add and remove clearml-agents to/from the clearml-server anytime.

So actually deleting from client (e.g. an dataset with clearml-data) works.

Same error.

I am not sure what happened, but my experiments are gone. However, the data directory is still filled.

Here it is