For example in our case we do reinforcement learning and the we would call a script like this: python run_openai_gym.py some_ http://package.my _agent .

It also seems like the deletion operation will slow down the server substantially.

Good to know!
I think the current solutions are fine. I will try it first and probably will have some more questions/problems 🙂

And which is for metrics?

The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a model is working.

Maybe the difference is that I am using pipnow and I used to use conda! The NVIDIA PyTorch container uses conda. Could that be a reason?

Hi KindChimpanzee37 I was more asking about the general idea to make these settings task-specific, but thank you for the suggestion anyways, I will definitely apply it.

Is ther a way to see the contents of /tmp/conda_envaz1ne897.yml ? Seems to be deleted after the task is finihsed

Yea, give me a minute.

And in the WebUI I can see arguments similar to the second print statement's.

These are the errors I get if I use file_servers without a bucket ( s3://my_minio_instance:9000 )

2022-11-16 17:13:28,852 - clearml.storage - ERROR - Failed creating storage object Reason: Missing key and secret for S3 storage access ( ) 2022-11-16 17:13:28,853 - clearml.metrics - WARNING - Failed uploading to ('NoneType' object has no attribute 'upload_from_stream') 2022-11-16 17:13:28,854 - clearml.storage - ERROR - Failed creating storage object ` Reason: Missing key...

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 

)