Hi William!
1 So if I understand correctly, you want to get an artifact from another task into your preprocessing.
You can do this using the Task.get_task() call. So imagine your anomaly detection task is called anomaly_detection it produces an artifact called my_anomaly_artifact and is located in the my_project project you can do:
` from clearml import Task
anomaly_task = Task.get_task(project_name='my_project', task_name='anomaly_detection')
treshold = anomaly_task.artifacts['my_anomaly_artifact'].get() You can do this anywhere to get details from any task! So also in preprocessing 🙂 In this case I use project name and task name to get the task you need, but you can also use id! Task.get_task(task_id='...') `
2 I will take a look, which docker-compose version do you have? Are you running linux, windows, mac? Which GPU are you running on and have you configured docker to allow access to your GPU?
3 Can you give the exact command you ran and also a screenshot of the error? 🙂 You should not have to upload it manually, the add model command should work!
1 Can you give a little more explanation about your usecase? It seems I don't fully understand yet. So you have multiple endpoints, but always the same preprocessing script to go with it? And you need to gather a different threshold for each of the models?
2 Not completely sure of this, but I think an AMD APU simply won't work. ClearML serving is using triton as inference engine for GPU based models and that is written by nvidia, specifically for nvidia hardware. I don't think triton will run on an AMD APU
3 Well spotted! Indeed it seems the sklearn documentation has the same problem. Would you mind opening a PR for it, then you can be contributor 😄
Ok I check 3: The commandclearml-serving --id <your_id> model add --engine triton --endpoint "test_model_keras" --preprocess "examples/keras/preprocess.py" --name "train keras model" --project "serving examples" --input-size 1 784 --input-name "dense_input" --input-type float32 --output-size -1 10 --output-name "activation_2" --output-type float32should beclearml-serving --id <your_id> model add --engine triton --endpoint "test_model_keras" --preprocess "examples/keras/preprocess.py" --name "train keras model - serving_model" --project "serving examples" --input-size 1 784 --input-name "dense_input" --input-type float32 --output-size -1 10 --output-name "activation_2" --output-type float32It seems to have been a mistype in the docs 🙂
Okay thank you Sonckie.
Yup I tried that method. In my application's use case it needs to connect to multiple model endpoint with the same preprocess method. So I need to change the task id in the preproccess.py code everytime I deploy the model? My docker-compose version is 1.29.2. I am running docker desktop apps in my windows 10 with WSL. My Laptop use AMD APU, I haven't searched about configuring my docker to run with GPU. Thank you for the answer. It seems the error is same with the sklearn documentation command
yup so the multiple endpoints has only difference in weight value but the same preprocessing script. And each preprocessing script use different threshold. okay I will test it on my friend's pc then. I will give the update soon when this problem solved. I'm not familiar with term 'PR' since I've never joined a community before, is it pull request? If yes, then I will open a PR and informed the update at this thread