I see, ok!
I will try that out.
Another thing I noticed: none of my pipeline tasks are reporting these graphs, regardless of runtime. I guess this line would also fix that?
I think the issue is that the host is not trusted... it looks like it looks into the index
Ok, going to ask the server admins, will keep you posted, thanks!
i don't think the conf is an issue. it's been deployed for a long time and working. models from yesterday correctly display the url
yes, I just ran steps 6-12 again from https://allegro.ai/docs/deploying_trains/trains_server_linux_mac/
can you elaborate a bit on the token side? i'm not sure exactly what would be a bad practice here
right, and why can't a particular version be found? how it does it try to find python versions?
yeah, that's fair enough. is it possible to assign cpu cores? I wasn't aware
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 } }'
i'm just interested in actually running a prediction with the serving engine and all
i'm probably sending the request all wrong + i'm not sure how the model expects the input
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
but it's been that way for over 1 hour.. I remember I can force the task to wait for the upload. how do i do this?
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)
...
And this is what I get with the curl inference example on the README.md(prediction_module) emilio@unicorn:~/clearml-serving$ curl -X POST "
` " -H "accept: application/json" -H "Content-Type: application/json" -d '{"x0": 1, "x1": 2}'
<html> <head><title>405 Not Allowed</title></head> <body> <center><h1>405 Not Allowed</h1></center> <hr><center>nginx/1.20.1</center> </body> </html> `