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Answered
What Sort Of Integration Is Possible With Clearml And Sagemaker? On The Page

What sort of integration is possible with ClearML and SageMaker? On the page describing ClearML Remote it says:

Create a remote development environment (e.g. AWS SageMaker, GCP CoLab, etc.) on any on-prem machine or any cloud.

But the only mention of SageMaker I see in the docs is the release notes for 0.13 saying "Add support for SageMaker".

I have SageMaker Studio up and running with access to my ClearML server and it's successfully able to log plots and scalars from experiments, but in terms of code it just logs the code used to launch the kernel:

"""Entry point for launching an IPython kernel.
This is separate from the ipykernel package so we can avoid doing imports until
after removing the cwd from sys.path.
"""
import sys

if __name__ == '__main__':
    # Remove the CWD from sys.path while we load stuff.
    # This is added back by InteractiveShellApp.init_path()
    if sys.path[0] == '':
        del sys.path[0]
    from ipykernel import kernelapp as app
    app.launch_new_instance()

Is it possible to capture more than that while using SageMaker?

  
  
Posted 2 years ago
Votes Newest

Answers 77


Hmm what do you have here?

os.system("cat /var/log/studio/kernel_gateway.log")
  
  
Posted 2 years ago

but maybe that doesn't matter, actually - it might be one session per host I guess

  
  
Posted 2 years ago

sounds good, thanks!

  
  
Posted 2 years ago

as best I can tell it'll only have one .ipynb in $HOME with this setup, which may work...

  
  
Posted 2 years ago

the problem is here: None

  
  
Posted 2 years ago

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

  
  
Posted 2 years ago

nope, that's wrong

  
  
Posted 2 years ago

lots of things like {"__timestamp__": "2023-02-23T23:49:23.285946Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.0007679462432861328, "method": "GET", "uri": "/api/kernels/6ba227af-ff2c-4b20-89ac-86dcac95e2b2", "status": 200}

  
  
Posted 2 years ago

but the call to jupyter_server.serverapp.list_running_servers() does return the server

  
  
Posted 2 years ago

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'}]
  
  
Posted 2 years ago

I think it just ends up in /home/sagemaker-user/{notebook}.ipynb every time

  
  
Posted 2 years ago

print(os.environ)
  
  
Posted 2 years ago

I can get it to run up to here: None

  
  
Posted 2 years ago

What happens when you call:

from clearml.backend_interface.task.repo import ScriptInfo

print(ScriptInfo._ScriptInfo__legacy_jupyter_notebook_server_json_parsing(None))
  
  
Posted 2 years ago

if I instead change the request url to f"http://{server_info['hostname']}:{server_info['port']}/api/sessions" then it gets a 200 response... however , the response is an empty list

  
  
Posted 2 years ago

Hmm and you are getting empty list for thi one:

server_info['url'] = f"http://{server_info['hostname']}:{server_info['port']}/"
  
  
Posted 2 years ago

sh-4.2$ cat /var/log/studio/kernel_gateway.log | head -n10
{"__timestamp__": "2023-02-23T21:48:28.036559Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.0012829303741455078, "method": "GET", "uri": "/api", "status": 200}
{"__timestamp__": "2023-02-23T21:48:39.111068Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.0012879371643066406, "method": "GET", "uri": "/api/kernels", "status": 200}
{"__timestamp__": "2023-02-23T21:48:39.116324Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.0007715225219726562, "method": "GET", "uri": "/api/terminals", "status": 200}
{"__timestamp__": "2023-02-23T21:48:39.272822Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.0007491111755371094, "method": "GET", "uri": "/api/terminals", "status": 200}
{"__timestamp__": "2023-02-23T21:48:43.000795Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 2.539133071899414, "method": "POST", "uri": "/api/kernels", "status": 201}
{"__timestamp__": "2023-02-23T21:48:43.073568Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.0013430118560791016, "method": "GET", "uri": "/api/kernels/6ba227af-ff2c-4b20-89ac-86dcac95e2b2", "status": 200}
{"__timestamp__": "2023-02-23T21:48:43.469751Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.0013761520385742188, "method": "GET", "uri": "/api/kernels/6ba227af-ff2c-4b20-89ac-86dcac95e2b2", "status": 200}
{"__timestamp__": "2023-02-23T21:48:43.702549Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.0013780593872070312, "method": "GET", "uri": "/api/kernels/6ba227af-ff2c-4b20-89ac-86dcac95e2b2", "status": 200}
{"__timestamp__": "2023-02-23T21:48:43.986808Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.0007445812225341797, "method": "GET", "uri": "/api/kernels/6ba227af-ff2c-4b20-89ac-86dcac95e2b2", "status": 200}
{"__timestamp__": "2023-02-23T21:48:43.992860Z", "__schema__": "sagemaker.kg.request.schema", "__schema_version__": 1, "__metadata_version__": 1, "account_id": "", "duration": 0.001028299331665039, "method": "GET", "uri": "/api/kernels", "status": 200}
  
  
Posted 2 years ago

local Jupyter Lab:
image
image
image

  
  
Posted 2 years ago

image

  
  
Posted 2 years ago

if I change it to 0.0.0.0 it works

  
  
Posted 2 years ago

right now I can't figure out how to get the session in order to get the notebook path

you mean the code that fires "HTTPConnectionPool" ?

  
  
Posted 2 years ago

image

  
  
Posted 2 years ago

if there are any tests/debugging you'd like me to try, just let me know

  
  
Posted 2 years ago

This is strange, let me see if we can get around it, because I'm sure it worked 🙂

  
  
Posted 2 years ago

. I'm thinking it's generically a kernel gateway issue, but I'm not sure if other platforms are using that yet

The odd thing is that you can access the notebook, but it returns zero kernels ..

  
  
Posted 2 years ago

I've poked around both the internal URL that Jupyter kernel is running on and some of the files in /sagemaker/.jupyter but no luck so far - I can find plenty of kernel info, but not session

  
  
Posted 2 years ago

yeah, even then it'll run but return 0 notebooks

  
  
Posted 2 years ago

weird that it won't return that single session

  
  
Posted 2 years ago

still empty
image

  
  
Posted 2 years ago

sadly no

  
  
Posted 2 years ago
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