The Azure Method

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Login methods

ADAL JS provides two ways for your application to sign-in users with Azure AD accounts.

Login with a redirect

This is the default method which the library provides to log in users. You can invoke this as follows:

Login in a pop-up window

The library provides this approach for developers building apps where they want to remain on the page and authenticate the user through a popup window.

ID tokens and User info

ADAL JS uses the OAuth 2.0 implicit flow. As a result, the sign-in flow in ADAL JS authenticates the user with Azure AD and also gets an ID token for your application. The ID token contains claims about the user which are exposed in the user.profile property in ADAL JS . You can get user information as follows:

Note: The ID token can also be used to make secure API calls to your application’s own backend API (which is registered in Azure AD as the same web app).

When the logout method is called, the library clears the application cache in the browser storage and sends a logout request to the Azure AD instance’s logout endpoint.

Azure ML туториал: создание простой модели машинного обучения

Перед вами пошаговое руководство по созданию модели машинного обучения с использованием Microsoft Azure ML, перевод статьи «Tutorial – Build a simple Machine Learning Model using AzureML«. Ссылка на оригинал — в подвале статьи.

Насколько тяжело построить модель машинного обучения на языках программирования R или Python? Для начинающих это геркулесов труд. Программистам среднего уровня и синьорам для этого требуется лишь достаточный объем памяти системы, осознание поставленной задачи и немного времени.

Модели машинного обучения иногда сталкиваются с проблемой несовместимости с системой. Чаще всего это происходит, когда датасет имеет слишком большие размеры; модель будет дольше считаться или просто перестанет работать. То есть работа с машинным обучением в любом случае предоставит определенные сложности как начинающим так и экспертам.

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Что такое Azure ML?

Хорошая новость — освоить машинное обучение сегодня стало намного проще, чем, например, в 2020 году. Как новичок в этом деле, ты можешь начать познавать машинное обучение с помощью фреймворка Microsoft Azure ML.

Azure ML представляет собой реализацию алгоритма машинного обучения через графический интерфейс от Microsoft.

Какие ресурсы доступны на AzureML

Давайте познакомимся с арсеналом этого инструмента.

  1. Примеры датасетов: мне нравится тестировать инструменты, которые имеют множество встроенных наборов данных. Это упрощает процесс тестирования мощности инструмента. Azure ML поставляется с длинным списком встроенных наборов данных. Список датасетов.
  2. Средства машинного обучения: Azure ML имеет в наличии почти все популярные алгоритмы машинного обучения и формулы оценочных показателей.
  3. Преобразование данных: в Azure ML есть все параметры фильтрации, параметры преобразования, параметры суммирования и варианты расчета матрицы.
  4. Параметры преобразования формата данных: А что если вы хотите добавить свой собственный набор данных? Azure ML имеет несколько опций для добавления наборов данных из вашей локальной системы. Вот эти варианты:

Создание модели

Теперь вы знаете потенциал AzureML. Давайте теперь сосредоточимся на способах использования. Я приведу простой пример, чтобы продемонстрировать то же самое. Я предлагаю вам проделать эти шаги со мной, чтобы лучше понять этот урок.

Здесь вы начинаете — нажмите на “Создать новый эксперимент”.

Вы получаете пустую таблицу экспериментов:

Теперь вы можете выбрать pallete:

Шаг 1. Выберите набор данных. Это могут быть ваши примеры данных или же вы можете загрузить их. В этом уроке я буду использовать «Данные о раке молочной железы» из встроенных наборов данных. Просто перетащите эти данные в главное окно.

Шаг 2. Выберите инструмент деления. Вы можете использовать опцию поиска в палитре, чтобы найти «split data». Поместите «split data» под свой набор данных и присоедините.

Теперь вы видите две точки на ячейке «split data». Это означает, что у вас есть два набора данных, готовых к работе. С правой стороны у вас есть возможность выбрать тип деления.

Шаг 3. Обучите модель машинного обучения: Для этого вам понадобятся два узла. Во-первых, это будет тип модели, которую вы хотите построить. Во-вторых, это будет узел тренировки модели. Вы можете обратиться к следующему рисунку:

Вы можете заметить восклицательный знак в узле тренировки модели. Это означает, что вам нужно указать целевую переменную. Выберем целевую переменную, щелкнув по ней. Теперь вы увидите окно с правой стороны. Выберите «Launch column slector».

Я выбрал «Класс» в качестве целевой переменной.

Шаг 4. Теперь вы оцениваете модель: см. Следующий рисунок

Шаг 5. Наконец, производите вычисления

Визуализация датасета и вывода

Чтобы визуализировать любой узел, вы просто переходите к узлу, нажимаете на него правой кнопкой мыши, затем выбираете “визуализировать”.

Вот как выглядят визуальные данные в нашем случае:

Как видно, переменная Класс имеет только два значения. Этот инструмент аккуратно рисует распределение для каждой переменной и позволяет проверить нормальность.

Так выглядит подсчитанная моде ль:

Как видно, оцененные вероятности в основном близки к нулю и к единице. Функция распределение имеет почти постоянное значение между нулем и единицей.Таким образом, модель выдает сильно разбросанные значения.

Наконец, вот как выглядят графики:

Заключение

Как можно видеть, модель оказалась очень эффективной, и мне потребовалось меньше минуты, чтобы построить и исполнить задачу. Вычисляемые расчетные матрицы являются довольно полными и, вероятно, содержат значение, которое вы искали. Мне понравился инструмент своей временной эффективностью и удобством для пользователя.

Считаете ли вы эту статью полезной ? Поделитесь с нами своим опытом работы с Azure ML.

Understanding Azure deployment methods

Learn the differences between the classic Azure deployment and the newer Resource Manager deployment in this tip.

Deploying Azure applications can be a confusing process. Azure applications typically consist of multiple components.

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that need to be deployed together and you need to coordinate the deployment of the different application pieces. In addition, if you are deploying multiple IaaS or PaaS applications then you want to have a standardized and repeatable process. If that’s not enough, Microsoft is in the process of updating the Azure deployment process.

With the release of the Azure Preview Portal in January of 2020 Microsoft Azure now supports two deployment methods: Azure classic deployment (also known as Service Manager Deployments) and the newer Resource Manager deployments. The architectural differences between the two Azure deployment models mean that Azure resources created using one deployment model will not necessarily interoperate with the resources created using the other deployment model. For example, Azure Virtual Machines created by using the classic deployment model can only be connected to Azure Virtual Networks created by using the classic deployment model. The resource providers that are different between the two deployment models are: Compute, Storage and Network.

There is some overlap as a few resource providers offer two versions of their resources: one version for classic and one version for Resource Manager. This article explains the differences between the classic Azure deployment and the newer Resource Manager deployment and shows how you can use each one.

Microsoft Azure currently has two separate management portals. There is the original Azure portal and then there is the Azure preview portal. Although it has been nearly a year since its introduction, the Azure Preview Port is still considered in “preview” mode. The original Azure Portal only supports classic deployments. You can see the classic Azure management Portal in Figure 1.

Figure 1. Creating classic deployments with the Azure Portal

You can create resources in the classic deployment model in two ways, using either the Azure Portal or Azure Preview Portal and specify Classic deployment. All Azure resources created with the classic deployment method must be managed individually — not as a group. Resources that were initially created using the classic deployment method were not part of any resource group. When the Resource Manager was introduced all of these resources were retroactively added to default resource groups. If you create a resource using classic deployment, that resource is automatically created within a default resource group. However, just because a resource may be contained within a resource group doesn’t mean that the resource has been converted to the Resource Manager model. Virtual Machines, Storage and Virtual Networks created using the classic deployment model must be managed using classic operations.

In general, you should not expect resources created through classic deployment to work with the newer Resource Manager. You can learn more about the architecture used by the different deployment methods at Resources for ramping up on Azure Resource Manager. It can freely switch between the two management portals by clicking on the your account icon in the top, upper-right portion of the Azure Portal and then clicking Switch to Preview Portal like you can see in Figure 1.

Resource Manager deployments are a part of the new management model that was introduced with the Azure Preview Portal. The infrastructure for your applications typically consists of multiple components. For instance, most applications will make use of a storage account, virtual machines and a virtual network, or you might have a Web application and database server. Because these resources are related, it’s desirable to deploy and manage them as a group. You can deploy, update or delete all of the resources for your solution in a single streamlined operation. Resource Manager also provides security, auditing and tagging features to help you manage your resources after deployment. You can see the Azure Preview Portal with options for both classic and Resource Manager deployment modes in Figure 2.

Figure 2. Creating Resource Manager deployments with the Azure Preview

When you create a new resource like a virtual machine using The Virtual Machine link and a virtual machine image from the Azure Image Gallery, the Azure Preview Portal will prompt you for the Resource Group that will contain the resource. You can see an example of how the Azure Preview Portal allows you to manage the resources contained in a Resource Groups in Figure 3.

Figure 3. Managing Azure resource group deployments

The Resource Manager’s resource groups allow you to combine related resources together. However, they have other advantages as well. With the Resource Manager you can create a template that defines deployment and configuration of your application. This template is created in JSON format and it provides a declarative way to define deployment. Classic deployments cannot make use of templates. Templates enable you to repeatedly deploy your application in a standardized manner. Use the template to define the infrastructure for your applications. It can also be used to configure the infrastructure, and define how to publish your application code to that infrastructure.

The Azure Resource Manager analyzes dependencies to ensure that resources defined in the template are created in the proper order. You can specify parameters in your template to enable customization. For example, you can pass parameter values that might customize your Azure deployment for a test environment and later provide different parameters to use that same template for a production deployment. You can create Azure Resource Manager templates using Visual Studio with the Azure SDK 2.6 installed. You can learn more about creating Azure Resource Manager Templates at Authoring Azure Resource Manager templates.

Going forward, Microsoft recommends that most new deployments use Resource Manager because of its ability to simplify the management of multiple related items. Further, they also recommend converting your existing classic deployments to use the Resource Manager as well where it’s possible. While Azure Resource Manager is Microsoft’s recommend future path, there are features that are present in classic deployments that are not in Azure Resource Manager.

For a more detailed understanding of the differences between the two deployment models, you can check out Azure Compute, Network and Storage Providers under the Azure Resource Manager.

Category: Azure Functions

App Service Easy Auth with Auth0 (or any Open ID Connect provider)

So I’m going to prefix this with a warning – I doubt this is officially supported but at a basic level it does seem to work. I would use at your peril and I’m writing this in the hope that it makes for a useful starting point discussion with the App Service team.

I was looking at Easy Auth this week and found myself curious as to if it would work with a generic Open ID Connect identity provider. My first choice provider is Auth0 but that’s not one of the listed providers on the Easy Auth configuration page which, on the face of it, is quite limited:

Azure AD is (as well as many other things) an Open ID Connect Provider so I had a look at its settings in the advanced tab and its asking for two pretty common pieces of information in the identity world: a client ID and an issuer URL. I had an app in Auth0 that I use for general testing so I pasted in its well known configuration endpoint and the ID for my client:

I hit save and it seemed to accept everything. My web app is sat on the URL https://jdreasyauth0.azurewebsites.net/ so on the Auth0 side I added a callback URL to the Easy Auth callback endpoint:

Easy Auth forwards on the contents of common claims in headers such as X-MS-CLIENT-PRINCIPAL-ID (the subject) and X-MS-CLIENT-PRINCIPAL-NAME (the name) so to see if this was working I uploaded a simple ASP.Net Core app that would output the contents of the request headers to a web page. Then I paid it a visit in my browser:

Oh. So that’s hurdle one passed. It does redirect successfully to a none-Azure AD identity provider. What about logging in?

Great. Yes. This works too. And the headers are correct based on the identity I used to login with.

How does this compare to the headers from an Azure AD backed Easy Auth:

Basically the Auth0 login is missing the refresh token (I did later set a client secret and tweak configuration in Auth0) – so there might be some work needed there. But I don’t think that’s essential.

It would be incredibly useful to be able to use Easy Auth in a supported manner with other identity providers – particularly for Azure Functions where dealing with token level authorization is a bit more “low level” than in a fully fledged framework like ASP .Net Core (though my Function Monkey library can help with this) and is only dealt with after a function invocation.

Function Monkey for F# Quickstart Video

The documentation for using Function Monkey with F# is finally on its way! I’ve got an actual documentation site under construction at the moment and it includes video content – the first of which is here!

The full source code, expanded with validation, is available on GitHub.

And as always feedback is welcome on Twitter.

Using Function Monkey with MediatR

There are a lot of improvements coming in v4 of Function Monkey and the beta is currently available on NuGet. As the full release approaches I thought it would make sense to introduce some of these new capabilities here.

In order to simplyify Azure Functions development Function Monkey makes heavy use of commanding via a mediator and ships with my own mediation library. However there’s a lot of existing code out their that makes use of the popular MediatR library which, if Function Monkey supported, could fairly easily be moved into a serverless execution environment.

Happily Function Monkey now supports just this! You can use my existing bundled mediator, bring your own mediator, or add the shiny new FunctionMonkey.MediatR NuGet package. Here we’re going to take a look at using the latter.

First begin by creating a new, empty, Azure Functions project and add three NuGet packages:

At the time of writing be sure to use the prerelease packages version 4.0.39-beta.4 or later.

Next create a folder called Models. Add a class called ToDoItem:

Now add a folder called Services and add an interface called IRepository:

And a memory based implementation of this called Repository:

Now create a folder called Commands and in here create a class called CreateToDoItemCommand:

If you’re familiar with Function Monkey you’ll notice the difference here – we’d normally implement the ICommand<> interface but here we’re implementing MediatR’s IRequest<> interface instead.

Next create a folder called Handlers and in here create a class called CreateToDoItemCommandHandler as shown below:

Again the only real difference here is that rather than implement the ICommandHandler interface we implement the IRequestHandler interface from MediatR.

Finally we need to add our FunctionAppConfiguration class to the root of the project to wire everything up:

Again this should look familiar however their are two key differences. Firstly in the Setup block we use MediatR’s IServiceCollection extension method AddMediatR – this will wire up the request handlers in the dependency injector. Secondly the .UseMediatR() option instructs Function Monkey to use MediatR for its command mediation.

And really that’s all their is to it! You can use both requests and notifications and you can find a more fleshed out example of this on GitHub.

As always feedback is welcome on Twitter or over on the GitHub issues page for Function Monkey.

Azure Advent Calendar

My entry to this years advent calendar is now available. Hope you enjoy, and Happy Christmas!

Thanks to Gregor and Richard for organising this great series and for hosting me.

Slides from Serverless London

I recently did a talk at Serverless London (thanks for hosting me!) about how we used serverless technologies inside a charity to deliver a lot fast.

Slides are never the same without narration but here they are in any case.

Function Monkey for F#

Over the last couple of weeks I’ve been working on adapting Function Monkey so that it feels natural to work with in F#. The driver for this is that I find myself writing more and more F# and want to develop the backend for a new app in it and run it on Azure Functions.

I’m not going to pretend its pretty under the covers but its starting to take shape and I’m beginning to use it in my new backend and so now seemed like a good time to write a little about it by walking through putting together a simple ToDo style API that saves data to CosmosDB.

Declaring a Function App

As ever you’ll need to begin by creating a new Azure Function app in the IDE / editor of your choice. Once you’ve got that empty starting point you’ll need to two NuGet package FunctionMonkey.FSharp to the project (either with Paket or Nuget):

This is currently in alpha and so you’ll need to enable pre-release packages and add the following NuGet repository:

Next by create a new module called EntryPoint that looks like this:

Ok. So what’s going on here? We’ll break it down block by block. We’re going to demonstrate authorisation using a (pretend) bearer token and so we begin by creating a function that can validate a token:

This is our F# equivalent of the ITokenValidator interface in the C# version. In this case we take valid to mean any string of length in the authorization header and if the token is valid then we return a ClaimsPrincipal. Again here we just return a made up principal. In the case of an invalid token we simply raise an exception – Function Monkey will translate this to a 401 HTTP status.

We’re going to validate the inputs to our functions using my recently released validation framework. Function Monkey for F# supports any validation framework but as such you need to tell it what constitutes a validation failure and so next we create a function that is able to do this:

Finally we declare our Function App itself:

We declare our settings (and optionally functions) inside a functionApp block that we have to assign to a public member on the module so that the Function Monkey compiler can find your declaration.

Within the block we start by setting up our authorisation to use token validation (line 3) and instruct it to use the token validator function we created earlier (line 4). In lines 5 to 7 we then set up a claims mapping which will set userId on any of our record types associated with functions to the value of the userId claim. You can also set mappings to specific command type property like in the C# version.

On line 9 we tell Function Monkey to use our isResultValid function to determine if a validation results constitutes success of failure.

Then finally on line 11 we declare a HTTP route and a function within it. If you’re familiar with the C# version you can see here that we no longer use commands and command handlers – instead we use functions and their input parameter determines the type of the model being passed into the Azure Function and their return value determines the output of the Azure Function. In this case the function has no parameters and returns a string – a simple API version. We set this specific function to not require authorisation.

Finally lets add a host.json file to remove the auto-prefixing of api to routes (this causes problems with things like Open API output):

If we run this now then in PostMan we should be able go call the endpoint http://localhost:7071/version and receive the response “1.0.0”.

Building our ToDo API

If you’re familiar with Function Monkey for C# then at this point you might be wandering where the rest of the functions are. We could declare them all here like we would in C# but the F# version of Function Monkey allows functions to be declared in multiple modules so that the functions can be located close to the domain logic and to avoid a huge function declaration block.

To get started create a new module called ToDo and we’ll begin by creating a type to model our to do items – we’ll also use this type for updating out to do items:

Next we’ll declare a type for adding a to do item:

And finally an type that represents querying to find an item:

Next we’ll declare our validations for these models:

Ok. So now we need to create functions for adding an item to the database and another for getting one from it. We’ll use Azure CosmosDB as a data store and I’m going to assume you’ve set one up. Our add function needs to accept a record of type AddToDoItemCommand and return a new record of type ToDoItem assigning properties as appropriate:

The user ID on our command will have been populated by the claims binding. We don’t write the item to Cosmos here, instead we’re going to use an output binding shortly.

Next our function for reading a to do item from Cosmos:

CosmosDb.reader is a super simple helper function I created:

If we inspect the signatures for our two functions we’ll find that addToDoItem has a signature of AddToDoItemCommand -> ToDoItem and getToDoItem has a signature of GetToDoItemQuery -> Async . One of them is asynchronous and the other is not – Function Monkey for F# supports both forms. We’re not going to create a function for updating an existing item to demonstrate handler-less functions (though as we’ll see we’ll duck a slight issue for the time being!).

There is one last step we’re going to take before we declare our functions and that’s to create a curried output binding function:

In the above cosmosDb is a function that is part of the Function Monkey output binding set and it takes three parameters – the collection / container name, the database name and finally the function that the output binding is being applied to. We’re going to use it multiple times so we create this curried function to make our code less repetitive and more readable.

With all that we can now declare our functions block:

The functions block is a subset of the functionApp block we saw earlier and can only be used to define functions – shared configuration must go in the functionApp block.

Hopefully the first, GET verb, function is reasonably self-explanatory. The AsyncHandler case instructs Function Monkey that this is an async function and we assign a validator with the validator option.

The second function, for our POST verb, introduces a new concept – output bindings. We pipe the output of azureFunction.http to our curried output binding and this will result in a function being created that outputs to Cosmos DB. Because we’re using the Cosmos output binding we also need to add the Microsoft.Azure.WebJobs.Extensions.CosmosDB package to our functional project. We set the option returnResponseBodyWithOutputBinding to true so that as well as sending the output of our function to the output trigger we also return it as part of the HTTP response (this is optional as you can imagine in a more complex scenario that could leak data).

Finally for the third function our PUT verb also uses an output binding but this doesn’t have a handler at all, hence the NoHandler case. In this scenario the command that is passed in, once validated, is simply passed on as the output of the function. And so in this instance we can PUT a to do item to our endpoint and it will update the appropriate entry in Cosmos. (Note that for the moment I have not answered the question as to how to prevent one user from updating another users to do items – our authorisation approach is currently limited and I’ll come back to that in a future post).

Trying It Out

With all that done we can try this function app out in Postman. If we begin by attempting to add an invalid post to our POST endpoint, say with an empty title, we’ll get a 400 status code returned and a response as follows:

Now if we run it with a valid payload we will get:

Next Steps

These are with me really – I need to continue to flesh out the functionality which at this point essentially boils down to expanding out the computation expression and its helpers. I also need to spend some time refactoring aspects of Function Monkey. I’ve had to dig up and change quite a few things so that it can work in this more functional manner as well as continue to support the more typical C# patterns.

Then of course there is documentation!

Writing and Testing Azure Functions with Function Monkey – Part 5 (Authorization)

Part 5 of my series on writing Azure Functions with Function Monkey is now available on YouTube:

This part focuses on adding authorisation with the help of an external identity provider – in this case Auth0.

Writing and Testing Azure Functions with Function Monkey – Part 4

Part 4 of my series on writing Azure Functions with Function Monkey is now available on YouTube:

This part focuses on addressing cross cutting concerns in a DRY manner by implementing a custom command dispatcher.

I’ve also switched over to Rider as my main IDE now and in this video I’m making use of its Presentation Mode. I think it works really well but let me know.

Function Monkey 2.1.0

I’ve just pushed out a new version of Function Monkey with one fairly minor but potentially important change – the ability to create functions without routes.

You can now use the .HttpRoute() method without specifying an actual route. If you then also specify no path on the .HttpFunction () method that will result in an Azure Function with no route specified – it will then be named in the usual way based on the function name, which in the case of Function Monkey is the command name.

I’m not entirely comfortable with the approach I’ve taken to this at an API level but didn’t want to break anything – next time I plan a set of breaking changes I’ll probably look to clean this up a bit.

The reason for this is to support Logic Apps. Logic Apps only support routes with an accompanying Swagger / OpenAPI doc and you don’t necessarily want the latter for your functions.

While I was using proxies HTTP functions had no route and so they could be called from Logic Apps using the underlying function (while the outside world would use the shaped endpoint exposed through the proxy).

Having moved to a proxy-less world I’d managed to break a production Logic App of my own because the Logic App couldn’t find the function (404 error). Redeployment then generated a more meaningful error – that routed functions aren’t supported. Jeff Hollan gives some background on why here.

I had planned a bunch of improvements for 2.1.0 (which I’ve started) which will now move to 2.2.0.

Writing and Testing Azure Functions with Function Monkey – Part 3

Part 3 of my series on writing Azure Functions with Function Monkey focuses on writing tests using the newly released testing package – while this is by no means required it does make writing high value acceptance tests that use your applications full runtime easy and quick.

Lessons Learned

It really is amazing how quickly time passes when you’re talking and coding – I really hadn’t realised I’d recorded over an hours footage until I came to edit the video. I thought about splitting it in two but the contents really belonged together so I’ve left it as is.

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