yeah

model*

@<1523701827080556544:profile|JuicyFox94> thanks for the reply, I’ll create a github issue 🙂 !

` from argparse import ArgumentParser

from flask import Flask

from apiserver.config_repo import config
from apiserver.server_init.app_sequence import AppSequence
from apiserver.server_init.request_handlers import RequestHandlers

app = Flask(name, static_url_path="/static")
AppSequence(app).start(request_handlers=RequestHandlers())

=================== MAIN =======================

if name == "main":
p = ArgumentParser(description=doc)
p.add_argument(
"--po...

it requires you set the weights and the framework name

ahh i see so one Task has multiple models that are trained

https://clear.ml/docs/latest/docs/references/sdk/task#update_output_model

i got these extra logs

None

SuccessfulKoala55 do you mean request_handlers.py

bugs i understand are better than ones i dont lol

The only difference that i notice is that when i run it locally the task is completed

i.e.
Task.update_output_model(name="custom_model")

^ it works if i do that and use the jupyter credentials from my work space settings.

since its a logger object

i would greatly appreciate that!

SuccessfulKoala55 thanks alot for your help i greatly appreciate it, i think you are right that there is something happening with the request, I did some checking i can get it running on another computer (my non work computer), so i think you are right there must be something happening with my work related computer which is causing this to occur 😕 I'll keep trying to drill down on this issue on my work computer to see what the difference is, but I can atleast proceed now to as i g...

should it be http://log.info (...)

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from clearml import Task

# Connecting ClearML with the current process,
# from here on everything is logged automatically
# Create a new task, disable automatic matplotlib connect
task = Task.init(project_name='Something', task_name='Something')

# Create plot and explicitly report as figure
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2  # 0 to 15 ...

i think the call is wrong

Failed processing request http://localhost:8008/auth.login : 'Logger' object is not callable

although i wish it would use the credentials in my clearml.conf file automatically

FLASK_DEBUG: "1" -> should 1 be a string or int?

@<1523701070390366208:profile|CostlyOstrich36> maybe cause sagemaker is running in headless mode

https://clear.ml/docs/latest/docs/configs/clearml_conf/

i'm looking at the server.py file and it doesn't have call = self. create_api_call(request)

alternatively you could create a new OutputModel like here: https://github.com/allegroai/clearml/blob/master/examples/reporting/model_reporting.py , not sure if there is a way to stop the automatic uploading

going to double check that rn