book

Building Data Science Applications with FastAPI

Name: Building Data Science Applications with FastAPI
Author: François Voron
ISBN: 9781801079211

by François Voron

October 2021

Intermediate to advanced

426 pages

8h 16m

English

Packt Publishing

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Building Data Science Applications with FastAPI
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your Thoughts
Section 1: Introduction to Python and FastAPI
Chapter 1: Python Development Environment Setup
Technical requirementsInstalling a Python distribution using pyenvCreating a Python virtual environmentInstalling Python packages with pipInstalling the HTTPie command-line utilitySummary
Chapter 2: Python Programming Specificities
Technical requirementsBasics of Python programmingRunning Python scriptsIndentation mattersWorking with built-in typesWorking with data structures – lists, tuples, dictionaries, and setsPerforming Boolean logic and checking for existenceControlling the flow of a programDefining functionsWriting and using packages and modulesOperating over sequences – list comprehensions and generatorsList comprehensionsGeneratorsWriting object-oriented programsDefining a classImplementing magic methodsReusing logic and avoiding repetition with inheritanceType hinting and type checking with mypyGetting startedThe typing moduleType function signatures with CallableAny and castAsynchronous I/OSummary
Chapter 3: Developing a RESTful API with FastAPI
Technical requirementsCreating the first endpoint and running it locallyHandling request parametersPath parametersQuery parametersThe request bodyForm data and file uploadsHeaders and cookiesThe request objectCustomizing the responsePath operation parametersThe response parameterRaising HTTP errorsBuilding a custom responseStructuring a bigger project with multiple routersSummary
Chapter 4: Managing Pydantic Data Models in FastAPI
Technical requirementsDefining models and their field types with PydanticStandard field typesOptional fields and default valuesField validationValidating email addresses and URLs with Pydantic typesCreating model variations with class inheritanceAdding custom data validation with PydanticApplying validation at a field levelApplying validation at an object levelApplying validation before Pydantic parsingWorking with Pydantic objectsConverting an object into a dictionaryCreating an instance from a sub-class objectUpdating an instance with a partial oneSummary
Chapter 5: Dependency Injections in FastAPI
Technical requirementsWhat is dependency injection?Creating and using a function dependencyGet an object or raise a 404 errorCreating and using a parameterized dependency with a classUse class methods as dependenciesUsing dependencies at a path, router, and global levelUse a dependency on a path decoratorUse a dependency on a whole routerUse a dependency on a whole application Summary
Section 2: Build and Deploy a Complete Web Backend with FastAPI
Chapter 6: Databases and Asynchronous ORMs
Technical requirementsAn overview of relational and NoSQL databasesRelational databasesNoSQL databasesWhich one should you choose?Communicating with a SQL database with SQLAlchemyCreating the table schemaConnecting to a databaseMaking insert queriesMaking select queriesMaking update and delete queriesAdding relationshipsSetting up a database migration system with AlembicCommunicating with a SQL database with Tortoise ORMCreating database modelsSetting up the Tortoise engineCreating objectsUpdating and deleting objectsAdding relationshipsSetting up a database migration system with AerichCommunicating with a MongoDB database using MotorCreating models compatible with MongoDB IDConnecting to a databaseInserting documentsGetting documents Updating and deleting documentsNesting documentsSummary

Chapter 7: Managing Authentication and Security in FastAPI
Technical requirementsSecurity dependencies in FastAPIStoring a user and their password securely in a databaseCreating models and tablesHashing passwordsImplementing registration routesRetrieving a user and generating an access tokenImplementing a database access tokenImplementing a login endpointSecuring endpoints with access tokensConfiguring CORS and protecting against CSRF attacksUnderstanding CORS and configuring it in FastAPIImplementing double-submit cookies to prevent CSRF attacksSummary
Chapter 8: Defining WebSockets for Two-Way Interactive Communication in FastAPI
Technical requirementsUnderstanding the principles of two-way communication with WebSocketsCreating a WebSocket with FastAPIHandling concurrencyUsing dependenciesHandling multiple WebSocket connections and broadcasting messagesSummary
Chapter 9: Testing an API Asynchronously with pytest and HTTPX
Technical requirementsIntroduction to unit testing with pytestGenerating tests with parametrizeReusing test logic by creating fixturesSetting up testing tools for FastAPI with HTTPXWriting tests for REST API endpointsWriting tests for POST endpointsTesting with a databaseWriting tests for WebSocket endpointsSummary
Chapter 10: Deploying a FastAPI Project
Technical requirementsSetting and using environment variablesUsing a .env fileManaging Python dependenciesAdding Gunicorn as a server process for deploymentDeploying a FastAPI application on a serverless platformAdding database serversDeploying a FastAPI application with DockerWriting a DockerfileBuilding a Docker imageRunning a Docker image locallyDeploying a Docker imageDeploying a FastAPI application on a traditional serverSummary
Section 3: Build a Data Science API with Python and FastAPI
Chapter 11: Introduction to NumPy and pandas
Technical requirementsGetting started with NumPyCreating arraysAccessing elements and sub-arraysManipulating arrays with NumPy – computation, aggregations, comparisonsAdding and multiplicating arrays Aggregating arrays – sum, min, max, mean…Comparing arraysGetting started with pandasUsing pandas Series for one-dimensional dataUsing pandas DataFrames for multi-dimensional dataImporting and exporting CSV dataSummary
Chapter 12: Training Machine Learning Models with scikit-learn
Technical requirementsWhat is machine learning?Supervised versus unsupervised learningModel validationBasics of scikit-learnTraining models and predictingChaining pre-processors and estimators with pipelinesValidating the model with cross-validationClassifying data with Naive Bayes modelsIntuitionClassifying data with Gaussian Naive BayesClassifying data with Multinomial Naive BayesClassifying data with support vector machinesIntuitionUsing SVM in scikit-learnFinding the best parametersSummary
Chapter 13: Creating an Efficient Prediction API Endpoint with FastAPI
Technical requirementsPersisting a trained model with JoblibDumping a trained modelLoading a dumped modelImplementing an efficient prediction endpointCaching results with JoblibChoosing between standard or async functionsSummary
Chapter 14: Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV
Technical requirementsGetting started with OpenCVImplementing an HTTP endpoint to perform face detection on a single imageImplementing a WebSocket to perform face detection on a stream of imagesSending a stream of images from the browser in a WebSocketShowing the face detection results in the browserSummary
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Building Data Science Applications with FastAPI

by François Voron

Building Data Science Applications with FastAPI

Building Data Science Applications with FastAPI

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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