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'}]
Just ran the same notebook in a local Jupyter Lab session and it worked as I expected it might, saving a copy to Artifacts
if I use the same kernel there'll be two
yeah, even then it'll run but return 0 notebooks
it does return kernels, just not sessions
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.
seems like it's using None and that doesn't provide the normal api/sessions endpoint - or, it does, but returns an empty list
so my reading of the jupyter-kernel-gateway docs is that each session is containerized, so each notebook "session" is totally isolated
poking around a little bit, and clearml.backend_interface.task.repo.scriptinfo.ScriptInfo._get_jupyter_notebook_filename() returns None
This is very odd ... let me check something
but maybe that doesn't matter, actually - it might be one session per host I guess
but the call to jupyter_server.serverapp.list_running_servers() does return the server
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
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}
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" ?
and the only calls to "uri": "/api/sessions" are the ones I made during testing - sagemaker doesn't seem to ever call that itself
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 !!
but the only exception handler is for requests.exceptions.SSLError
right now I can't figure out how to get the session in order to get the notebook path
which I looked at previously to see if I could import sagemaker.kg or kernelgateway or something, but no luck
. 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 ..
Yep I think you are correct, you should have had the same output as a local jupyter notebook, and it seems that in sagemaker studio it is not working 😞
Let me check something
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 .



