How can I create a new Azure Machine Learning workspace when creating a new Azure Container Instance from Azure cloud shell.
Here is a sample of the command am using to create the ACI.
az container create --name dev-container –resource-group XXX –location eastus –image mcr.microsoft.com/XXX –cpu 2 –memory 6 –environment-variables WORKSPACE_NAME=XXX
Thanks
I think you're approaching the problem from the opposite direction than the Azure ML PG. My understanding is that when you make an Azure ML workspace, an Azure Container Instance service is automatically spun up and is inherently tied to the Azure ML workspace. Check out a similar question another user had this week
More generally, Azure ML has a core feature called Environments which provides a simple interface for creating custom Docker/Conda environments.
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We have a web applications developed with Angular and .Net, which is deployed on an Azure Cloud platform, lets say External A-Cloud.
We need to lift the same application and host in a different Internal Cloud Platform, lets say Internal B-Cloud.
How can we achieve this, please share some thoughts to do the ground work to start the process,
Warm Regards
KdM
Migrate an externally hosted cloud based application to our Cloud platform.
We have both AWS and Azure, but the externally hosted one is in Azure cloud platform
We can move from any Cloud to any Cloud. But we need to understand few points first.
How are the Angular and .Net hosted in Azure
If they are hosted on simple Virtual Machines - Then we can create a Virtual Machine in AWS and Migrate or host the apps in AWS ( Yes we definitely need to consider foundation of AWS like VNETs , hope thats already done )
If the Angular and .Net hosted in Azure is of Kubernetes and docker based
We need to Create EKS in AWS and then as its docker based, the same Manifest files etc would work in EKS as well with minor changes
We can look at migration tools as well if they are Windows VM based
I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.
I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.
I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free).
Hub allows you to load data from anywhere. Hub works locally, on Google Cloud, MinIO, AWS as well as Activeloop storage (no servers needed!). So, it allows you to load data and directly stream datasets from cloud storage to ML workflows.
You can find more information about storage authentication in the Hub docs.
Then, Hub allows you to stream data to PyTorch or TensorFlow with simple dataset integrations as if the data were local since you can connect Hub datasets to ML frameworks.
I have an active Azure container Instance which is running, How can I add it to my Workspace using the Azure ML SDK.
I'm afraid you misunderstand the ML. As I know it's just a way to deploy the docker image into ACI (if you want to deploy ACI). And when you finish deploying, then the ACI is already a resource and has no relationship with the ML. It's no difference when the ACI is created. So there is no need to add the ACI to the ML workspace when it's running.
As part of our pipelines, we currently use a deployment tool that has connectivity to our various instances and we can upload revisions/versions of our app to a central repository, archive them, and redeploy them at any time. Is Spinnaker intended to replace an existing deployment automation tool (there are many on market today) or is more meant for us to create pipelines that call the API of our other tool(s) when actually deploying our code to different servers?
Spinnaker has native support for deployment to supported cloud platforms (AWS, Google, CloudFoundry, and soon Azure).
In those environments, the Spinnaker model is an immutable infrastructure style deployment where new VMs are created to push new software versions.
If that fits your needs, then Spinnaker could replace an existing deployment automation tool.
If that doesn't fit your model, then Spinnaker also supports calling out to an external execution environment as a pipeline stage (currently Jenkins is well supported) where you could implement custom behaviors to integrate to an existing deployment tool.
I have a question about whether cloud vendors have an inter-operable mechanism. For example, I am developing a WCF service and hosting in Azure successfully. After a pro-long time using Azure, can I use the same code for deploying it in AWS? Will it be possible? Does the API of both matches the same for deploying? If not, what are all the extra care needed for hosting the same service when switching over other Cloud Vendors like Salesforce.com, OpenStack, etc.,
In general, you can't just take what you develop for one Cloud platform and put it on another: they have different functionality sets and expose different APIs. However, the more low-level you make your code, the more likely it is that you'll find another vendor with a very similar API, since virtualizing infrastructure is simpler (and closer to standardized) than virtualizing a CMS application.
If you're using just IaaS, you can probably port fairly rapidly but you have to do more work to make your application. If you're using PaaS (or SaaS!) then you're more locked-in but you get more support for developing rapidly: it's that support platform which is both the value-add and the lock-in, and you won't get one without the other.
If you're using an Azure web role for hosting your WCF service then from deployment point of view you will not have many problems with AWS. You'll simply use facilities offered by AWS SDK for .NET (aka Publish to AWS CloudFormation). For sure you'll have to change the logging part if you've used Azure Diagnostic and alla Azure services with related AWS services. We did this multiple times in the last year and it works.
For worker role it's not so simple because in Azure they are easily deployed like web role, but in AWS you haven't direct deployment from Visual Studio so you have to do some manual work using Windows Services or something else