So it's seemingly not the image, but maybe something to do with how Studio runs it as a kernel.

Yeah I think that for some reason it fails detecting this is actually jupyter noteboko (not really sure why), Thank you for double checking on the container !!

and the only calls to "uri": "/api/sessions" are the ones I made during testing - sagemaker doesn't seem to ever call that itself

sadly no

print(os.environ)

the problem is here: None

looks like the same as in server_info

I can get it to run up to here: None

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',
        'REGION_NAME': 'us-east-1',
        'AWS_INTERNAL_IMAGE_OWNER': 'Custom',
        'AWS_DEFAULT_REGION': 'us-east-1',
        'PWD': '/home/sagemaker-user',
        'AWS_REGION': 'us-east-1',
        'SHLVL': '1',
        'HOME': '/home/sagemaker-user',
        'AWS_SAGEMAKER_PYTHONNOUSERSITE': '0',
        'AWS_ACCOUNT_ID': 'xxx',
        '_': '/opt/.sagemakerinternal/conda/bin/jupyter-kernelgateway',
        'LC_CTYPE': 'C.UTF-8',
        'KERNEL_LAUNCH_TIMEOUT': '40',
        'KERNEL_WORKING_PATH': '',
        'KERNEL_GATEWAY': '1',
        'JPY_PARENT_PID': '9',
        'PYDEVD_USE_FRAME_EVAL': 'NO',
        'TERM': 'xterm-color',
        'CLICOLOR': '1',
        'FORCE_COLOR': '1',
        'CLICOLOR_FORCE': '1',
        'PAGER': 'cat',
        'GIT_PAGER': 'cat',
        'MPLBACKEND': '

_inline'}

We will add this to the SDK soon

As another test I ran Jupyter Lab locally using the same custom Docker container that we're using for Sagemaker Studio, and it works great there, just like the native local Jupyter Lab. So it's seemingly not the image, but maybe something to do with how Studio runs it as a kernel.

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

nice! Just tested it on my end as well, looks like it works!

Hi @<1532532498972545024:profile|LittleReindeer37> @<1523701205467926528:profile|AgitatedDove14>
I got the session with a bit of "hacking".
See this script:

import boto3, requests, json
from urllib.parse import urlparse

def get_notebook_data():
    log_path = "/opt/ml/metadata/resource-metadata.json"
    with open(log_path, "r") as logs:
        _logs = json.load(logs)
    return _logs

notebook_data = get_notebook_data()
client = boto3.client("sagemaker")
response = client.create_presigned_domain_url(
    DomainId=notebook_data["DomainId"],
    UserProfileName=notebook_data["UserProfileName"]
)
authorized_url = response["AuthorizedUrl"]
authorized_url_parsed = urlparse(authorized_url)
unauthorized_url = authorized_url_parsed.scheme + "://" + authorized_url_parsed.netloc
with requests.Session() as s:
    s.get(authorized_url)
    print(s.get(unauthorized_url + "/jupyter/default/api/sessions").content)

Basically, we can get the session directly from AWS, but we need to be authenticated.
One way I found was to create a presigned url through boto3, by getting the domain id and profile name from a resoure-metadata file that is found on the machine None .
Then use that to get the session...
Maybe there are some other ways to do this (safer), but this is a good start. We know it's possible

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'}]

Hmm and you are getting empty list for thi one:

server_info['url'] = f"http://{server_info['hostname']}:{server_info['port']}/"

Yes, I'm running a notebook in Studio. Where should it be captured?

yep