report_scalar pernits to manually report a scalar series. This is the dedicated function. There could be other ways to report a scalar, for example through tensorboard - in this case you would have to report to tensorboard, and clearML will automatically report the values

When the pipeline or any step is executed, a task is created, and it name will be taken from the decorator parameters. Additionally, for a step, the name parameter is optional : if not provided, the function name will be used instead.

It seems to me that your script fails creating the pipeline controller task because it fails pulling the name parameter. which is weird ... Weird because in the last error line, we can see that name !

those are the credentials you got from your self hosted server ?
what about the logs before the error ? i think it relevant to have them all. i try to isolate the error, and to understand if it comes from the cred, the servers addresses, a file error or a network error

can you try again after having upgraded to 3.6.2 ?

TenderCoyote78 i pm you to avoid overfilling the thread here

i am not sure i get you here.
when pip installing clearml-agent, it doesnt fire any agent. the procedure is that after having installed the package, if there isnt any config file, you do clearml-agent init and you enter the credentials, which are stored in clearml.conf. If there is a conf file, you simply edit it and manually enter the credentials. so i dont understand what you mean by "remove it"

hi WickedElephant66
you can log your models as artifacts on the pipeline task, from any pipeline steps. Have a look there :
https://clear.ml/docs/latest/docs/pipelines/pipelines_sdk_tasks#models-artifacts-and-metrics
I am trying to find you some example, hold on 🙂

hi AbruptHedgehog21
which s3 service provider will you use ?
do you have a precise list of the var you need to add to the configuration to access your bucket ? 🙂

Hi SmugTurtle78
We currently don't support GitHub deploy keys, but there might be a way to make the task use SSH (and not HTTPS), so that you could put the SSH key on the AWS machine. Please let me check if I can find such a solution, and come back to you after.

oups please pardon me I made a confusion, this answer is not related to your issue. my fault 🙏

can you describe the issue ?

hi PanickyMoth78
from within your function my_pipeline_function here is how to access the project and task names :

task = Task.current_task()
task_name = task.name
full_project_path = task.get_project_name()
project_name = full_project_path.split('/')[0]

Note that you could also use the full_project_path to get both project and task name
task_name = full_project_name.split('/')[-1]

can you also check that you can access the servers ?
try to do curl http://<my server>:port for your different servers ? and share the results 🙂

for controlling the number of cpus cores, i am not sure. i keep you updated asap

hi AbruptHedgehog21
clearml-serving will use your clearml.conf file
Configure it to access your s3 bucket - that is the place for bucket, host etc

thanks for those info. i check that and come back to you

With pleasures ! Hope that will help

btw here is the content of the imported file:

import torch
from torchvision import datasets, transforms
import os
MY_GLOBAL_VAR = 32

def my_dataloder ():
return torch.utils.data.DataLoader(
datasets.MNIST(os.path.join('./', 'data'), train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor()
` ...

No, it is supposed to have its status updated automatically. We may have a bug. Can you share some example code with me, so that i could try to figure out what is happening here ?

for the scalars :

` import pandas as pd
import plotly.graph_objects as go

scalars = client.events.scalar_metrics_iter_histogram(task=task.id).to_dict()['metrics']

for graph in scalars.keys():
for i, metric in enumerate(scalars[graph].keys()):
df = pd.DataFrame(scalars[graph][metric]).iloc[:, 1:]
fig = go.Scatter(
scalars[graph][metric],
mode='lines',
name=metric,
showlegend=True
)
plio.write_image(fig=go.Fi...

however the model is saved in the step task - this is what i am trying to figure out

Yep sorry I have not pasted the import line. You should add something like this :

from clearml.backend_api.services import events

🙏

Hi Alon
This is indeed a known bug, we are currently working on a fix.

Hi CheerfulGorilla72
You have an example of implementation here :
https://github.com/allegroai/clearml/tree/master/examples/services/monitoring

Hope it will help 🙂

Avec plaisir 😉

Hey Atalya 🙂

Thanks for your feedback. This is indeed a good feature to think asbout.
So far there is no other ordering than the alphabetical. Could you please create a feature request on github ?

Thanks

Hi SmugSnake6
I might have found you a solution 🎉 I answered on the GH thread https://github.com/allegroai/clearml-agent/issues/111

i guess it is... but still 😉

can you share some code ? about how you build the pipeline