Running Machine Learning Code in VirtualBox Using the Ubuntu VM Image or Docker with Kubeflow.
Next Steps.
If you’re interested in building and sharing machine learning models, consider joining the Kubeflow.
Machine Learning and the AWS Free Tier.
Note: A deep dive into machine learning and machine learning code will follow later this month in Kubernetes and Machine Learning.
For now, learn more about Apache MXNet, a state-of-the-art open source deep learning framework for Apache MXNet GitHub. It’s an advanced GPU-accelerated machine learning library.
Mxnet and Kubeflow.Q:
Upgrading to an Apigee API Management version that requires the “soft-deploy” feature to complete the upgrade
I’m trying to upgrade a private.apigee.net API Manager v2 to v3 and I’m getting a message that the API Manager is unable to complete the upgrade due to:
The upgrade is not currently supported for the following API Version:
1.
I’m using the apigee-cli and executing the following command:
apigee upgrade --version v2
The API Manager is set to be a soft-deployment API. I do not have an API that’s set to be a hard-deployment API.
I’m a bit confused how to proceed. I’ve seen the following warning regarding soft-deployment for API Manager versions 2 and 3:
Managing soft-deployed APIs on the API Manager 2.x or API Manager 3.x platform can be a little tricky. You will be unable to downgrade or upgrade soft-deployed API Manager versions, including versions that are deployed with a private SSL certificate. If this API is configured as a soft-deployment API, you will be unable to manage it using the Apigee CLI. If this API is configured as a hard-deployment API, you will be able to manage it.
If this is the case and I don’t have any hard-deployed APIs, why do I need to upgrade to API Manager version 3?
My initial thought was to manually configure the API Manager to be a soft-deployment API and make all of my API’s that were
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