I have a json file which I am using in score.py however, it is not being found. When making a post request to the endpoint I get the following error “No such file or directory: ‘/var/azureml-app/model_adjustments.json'” json file is in the same folder as score.py and calling it from a script in my score.py file i have the following I
Tag: azure-machine-learning-service
Unable To Run AzureML Experiment with SDK – Failed to Build Wheel for pynacl / Exit status:1
I am trying to run a AzureML Experiment using sdk (following a Udemy course). When I try to use the Experiment.submit function the experiment prepares and then fails with the following error messages: The Azure env as created within my anaconda navigator for a short period of time and then gets removed. Does anyone know how I can get around
Cant install imbalanced-learn on an Azure ML Environment
I have an Azure ML Workspace which comes by default with some pre-installed packages. I tried to install But I got this error learn) Not sure how to solve this, I have read in other posts to use conda, but that didnt work either. Answer scikit-learn 1.0.1 and up require Python >= 3.7; you use Python 3.6. You need to
AzureML Environment for Inference : can’t add pip packages to dependencies
I can’t find the proper way to add dependencies to my Azure Container Instance for ML Inference. I basically started by following this tutorial : Train and deploy an image classification model with an example Jupyter Notebook It works fine. Now I want to deploy my trained TensorFlow model for inference. I tried many ways, but I was never able
How can I remove the wrapper around the input when using Inference Schema
When using Inference Schema to autogenerate the swagger doc for my AzureML endpoint (as detailed here and here), I see that it creates a wrapper around my input_sample. Is there a way to not wrap the input inside this “data” wrapper? Here is what my score.py looks like: Answer InferenceSchema used with Azure Machine Learning deployments, then the code for