SageMaker Studio:

api/kernels does report back the active kernel, but doesn't give notebook paths or anything

thanks for the thoughtful response, @<1523701205467926528:profile|AgitatedDove14> ! I think I'll need to test out some workflows to see what works

if I change it to 0.0.0.0 it works

that fails

local Jupyter Lab:

cool, thanks! the first one was what I had thought of but seemed unpythonic, so I'll give the second a shot

cool, if I call clearml.binding.hydra_bind.PatchHydra._register_omegaconf(cfg) directly inside the notebook I can manually cause it to log the Hydra config, it just doesn't seem to autodetect if you're doing a manual call to Hydra Compose

None

poking around a little bit, and clearml.backend_interface.task.repo.scriptinfo.ScriptInfo._get_jupyter_notebook_filename() returns None

done: None

seems like it's using None and that doesn't provide the normal api/sessions endpoint - or, it does, but returns an empty list

environ{'PYTHONNOUSERSITE': '0',
        'HOSTNAME': 'gfp-science-ml-t3-medium-d579233e8c4b53bc5ad626f2b385',
        'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/_sagemaker-instance-credentials/xxx',
        'JUPYTER_PATH': '/usr/share/jupyter/',
        'SAGEMAKER_LOG_FILE': '/var/log/studio/kernel_gateway.log',
        'PATH': '/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tmp/miniconda3/condabin:/tmp/anaconda3/condabin:/tmp/miniconda2/condabin:/tmp/anaconda2/condabin'...

and that requests.get() throws an exception:

ConnectionError: HTTPConnectionPool(host='default', port=8888): Max retries exceeded with url: /jupyter/default/api/sessions (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f7ba9cadc30>: Failed to establish a new connection: [Errno -2] Name or service not known'))

I can get it to run up to here: None

I will once I figure out the fix!

at least in 2018 it returned sessions! None

and is there any way to capture hydra from a notebook as a Configuration? you don't use the typical @hydra.main() but rather call the compose API , and so far in my testing that doesn't capture the OmegaConf in ClearML

I additionally tried using a Sagemaker Notebook instance, to see if it was the kernel dockerization that Studio uses that was messing things up. But it seems to actually log less information from a Notebook instance vs Studio .


![image](https://clearml-web-assets.s3.amazonaws.com/scoold/im...

the server_info is

[{'base_url': '/jupyter/default/',
  'hostname': '0.0.0.0',
  'password': False,
  'pid': 9,
  'port': 8888,
  'root_dir': '/home/sagemaker-user',
  'secure': False,
  'sock': '',
  'token': '',
  'url': '

',
  'version': '1.23.2'}]

the problem is here: None

But we're also testing out new models all the time, which are typically implemented as git branches - they run on the same set of inputs but don't output their results into production

so my reading of the jupyter-kernel-gateway docs is that each session is containerized, so each notebook "session" is totally isolated

I could just loop through and create separate pipelines with different parameters, but seems sort of inefficient. the hyperparameter optimization might actually work in this case utilizing grid search, but seems like kind of a hack

So I'm thinking maybe a Project for each thing we're forecasting, and then new Tasks for each time we run it

yep! None

right now I'm doing

# clear existing configs in case we're rerunning the notebook
if hydra.core.global_hydra.GlobalHydra().is_initialized():
    hydra.core.global_hydra.GlobalHydra.instance().clear()

# initialize Hydra
hydra.initialize(
    version_base=None,
    config_path=".",
    job_name="test_app",
)

# use the compose API since we're running in a notebook instead of __main__
cfg = hydra.compose(
    config_name="config", 
    overrides=[],
)

# report in ClearML Config UI
clearm...

Yeah, I think that'd work