Ok gotchu. I'll do that as soon as I can.

This is a minimal comet example. I'm afraid I don't know what it does under the hood.. There are no callbacks on the metrics tracked in model.fit and yet if you check out your project in the website, your training and validation losses are tracked automatically, live.

https://www.comet.ml/docs/python-sdk/keras/

can you elaborate a bit on the token side? i'm not sure exactly what would be a bad practice here

Yeah, I simply used a different port but I got this output:
` (prediction_module) emilio@unicorn:~/clearml-serving$ docker run -v ~/clearml.conf:/root/clearml.conf -p 9501:9501 -e CLEARML_SERVING_TASK_ID=7ce187d2218048e68fc594fa49db0051 -e CLEARML_SERVING_POLL_FREQ=5 clearml-serving-inference:latest
CLEARML_SERVING_TASK_ID=7ce187d2218048e68fc594fa49db0051
CLEARML_SERVING_PORT=
CLEARML_USE_GUNICORN=
EXTRA_PYTHON_PACKAGES=
CLEARML_SERVING_NUM_PROCESS=
CLEARML_SERVING_POLL_FREQ=5
CLEARML_DEFAULT...

i'm just interested in actually running a prediction with the serving engine and all

well, what are the types of input expected? is parent_datasets a list of names or IDs?

i can't get some sort of response from curl

So far I have taken one mnist image, and done the following:

` from PIL import Image
import numpy as np

def preprocess(img, format, dtype, h, w, scaling):

sample_img = img.convert('L')


resized_img = sample_img.resize((1, w*h), Image.BILINEAR)
resized = np.array(resized_img)
resized = resized.astype(dtype)

return resized

png img file

img = Image.open('./7.png')

preprocessed img, FP32 formated numpy array

img = preprocess(img, format, "float32", 28, 28, None)

...

well, i have run the keras mnist example that is in the clearml-serving READme. Now I'm just trying to send a request to make a prediction via curl

i'm also not sure what this is
-H "Content-Type: application/octet-stream" -H' NV-InferRequest:batch_size: 1 input { name: "dense_input" dims: [-1, 784] } output { name: "activation_2" cls { count: 1 } }'

it's from the github issue you sent me but i don't know what the "application" part is or the "NV-InferRequest:...."

fp32 seems to be floating point 32 so my preprocessing seems wrong

i don't know ahahaha

` Using cached repository in "/root/.clearml/vcs-cache/DeployKit_cloud.git.3e6952dd2fa4054e353465fe2d40daa3/DeployKit_cloud.git"

fatal: Could not read from remote repository. `

Because sometimes it clones a cached version of a private repository, instead of cloning the requested version

If 3e5962dd is the commit it's trying to clone, it doesn't exist because I deleted it.
it should be cloning a more up-to-date version of the repository

Yeah, that would be nice!

um, this line is not doing anything for me 🤔
controller_clearml_task = Task.current_task() controller_clearml_task.set_resource_monitor_iteration_timeout( seconds_from_start=10 )

I have this inside my pipeline defined with decorator

AgitatedDove14 I noticed a lot of my tasks don't contain these graphs though...

Hi SuccessfulKoala55 , do you have an update on this?

hi SuccessfulKoala55 ! has the docker compose been updated with this?>

In fact I just did that yesterday. I'll let you know how it goes

hi SuccessfulKoala55 with the clearml server update, does it use a newer ES docker?

Hi CostlyOstrich36
I added this instruction at the very end of my postprocess function
shutil.rmtree("~/.clearml")

where should I look for this folder?
I am talking about serving

nevermind! I have figured out my issue

they are taking longer than 30 secs, but admittedly not much longer: 1-3 minutes

I understand! this is my sysadmin message:
"if nothing else, they could publish a new elasticsearch image of 7.6.2 (ex. 7.6.2-1) which uses a newer patched version of JDK (1.13.x but newer than 1.13.0_2)"

is this a possibility?