Building data sience applications with FastAPI: develop, manage, and deploy efficient machine... learning applications with python
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
[S.l.]
PACKT PUBLISHING LIMITED
2021
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xiv, 407 Seiten Diagramme 10 cm |
| ISBN: | 1801079218 9781801079211 |
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MARC
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| adam_text | Table of Contents Preface Section 1: Introduction to Python and FastAPI 1 Python Development Environment Setup Technical requirements Installing a Python distribution using pyenv Creating a Python virtual environment 4 4 7 Installing Python packages with pip Installing the HTTPie command-line utility Summary 8 9 11 2 Python Programming Specificities Operating over sequences - list comprehensions and generators 34 Technical requirements Basics of Python programming 14 14 Running Python scripts 14 List comprehensions 34 36 Indentation matters 16 Generators Working with built-in types 17 Working with data structures - lists, tuples, dictionaries, and sets 18 Writing object-oriented programs 39 Performing Boolean logic and checking for existence Defining a class 39 23 Implementing magic methods 41 Reusing logic and avoiding repetition with inheritance 45 Controlling the flow of a program 25 Defining functions 29 Writing and using packages and modules 31
ii Table of Contents Type hinting and type checking with туру 48 Getting started The typing module 48 50 Type function signatures with Callable Any and cast 53 54 Asynchronous I/O Summary 56 60 3 Developing a RESTful API with 1FastAPI Technical requirements Creating the first endpoint and running it locally Handling request parameters Path parameters Query parameters The request body Form data and file uploads Headers and cookies 62 62 65 65 72 74 79 85 The request object 87 Customizing the response 88 Path operation parameters The response parameter Raising HTTP errors Building a custom response 95 99 102 Structuring a bigger project with multiple routers Summary 88 107 111 4 Managing Pydantic Data Models in FastAPI Technical requirements 114 Defining models and their field types with Pydantic 114 Standard field types Optional fields and default values Field validation Validating email addresses and URLs with Pydantic types 114 120 122 124 Creating model variations with class inheritance 126 Adding custom data validation with Pydantic 129 Applying validation at a field level Applying validation at an object level Applying validation before Pydantic parsing 129 130 131 Working with Pydantic objects 132 Converting an object into a dictionary 132 Creating an instance from a sub-class object 135 Updating an instance with a partial one 137 Summary 139
Table of Contents iii 5 Dependency Injections in FastAPI Technical requirements 142 What is dependency injection? 142 Creating and using a function dependency 143 Get an object or raise a 404 error Creating and using a parameterized dependency with a class Use class methods as dependencies 147 Using dependencies at a path, router, and global level 152 Use a dependency on a path decorator 153 154 Use a dependency on a whole router Use a dependency on a whole application Summary 156 157 148 150 Section 2: Build and Déploya Complete Web Backend with FastAPI б Databases and Asynchronous ORMs Technical requirements An overview of relational and NoSQL databases Relational databases NoSQL databases Which one should you choose? Communicating with a SQL database with SQLAIchemy Creating the table schema Connecting to a database Making insert queries Making select queries Making update and delete queries Adding relationships 162 162 163 164 165 166 168 169 171 173 175 177 Setting up a database migration system with Alembic Communicating with a SQL database with Tortoise ORM Creating database models Setting up the Tortoise engine Creating objects Updating and deleting objects Adding relationships Setting up a database migration system with Aerich 180 186 186 188 190 193 194 198 Communicating with a MongoDB database using Motor 200 Creating models compatible with MongoDB ID 201
iv Table of Contents Connecting to a database Inserting documents Getting documents 202 203 204 Updating and deleting documents Nesting documents Summary 207 208 210 7 Managing Authentication and Security in FastAPI Technical requirements 212 Security dependencies in FastAPI 212 Storing a user and their password securely in a database 216 Creating models and tables Hashing passwords Implementing registration routes Retrieving a user and generating an access token 217 218 219 220 Implementing a database access token 220 Implementing a login endpoint 222 Securing endpoints with access tokens 225 Configuring CORS and protecting against CSRF attacks 227 Understanding CORS and configuring it in FastAPI Implementing double-submit cookies to prevent CSRF attacks Summary 228 233 239 8 Defining WebSockets for Two-Way Interactive Communication in FastAPI Technical requirements 242 Understanding the principles of two-way communication with WebSockets 242 Creating a WebSocket with FastAPI 243 Handling concurrency Using dependencies 247 250 Handling multiple WebSocket connections and broadcasting messages 253 Summary 260 9 Testing an API Asynchronously with pytest and HTTPX Technical requirements Introduction to unit testing with pytest 262 263 Generating tests with parametrize 265 Reusing test logic by creating fixtures 267
Table of Contents v Setting up testing tools for FastAPI with HTTPX Writing tests for REST API endpoints Writing tests for POST endpoints Testing with a database 270 275 276 Writing tests for WebSocket endpoints Summary 278 286 289 10 Deploying a FastAPI Project Technical requirements 292 Setting and using environment 292 variables Using a .env file 296 Managing Python dependencies 297 Adding Gunicorn as a server process for deployment 299 Deploying a FastAPI application on a serverless platform 300 Adding database servers Deploying a FastAPI application with Docker Writing a Dockerfile Building a Docker image Running a Docker image locally Deploying a Docker image 303 304 304 306 307 307 Deploying a FastAPI application on a traditional server 309 Summary 310 Section 3: Build a Data Science API with Python and FastAPI 11 Introduction to NumPy and pandas Technical requirements Getting started with NumPy 314 314 Creating arrays Accessing elements and sub-arrays 315 318 Manipulating arrays with NumPy - computation, aggregations, comparisons Adding and multiplicating arrays Aggregating arrays - sum, min, max, mean... 320 322 324 Comparing arrays Getting started with pandas Using pandas Series for one-dimensional data Using pandas DataFrames for multi-dimensional data Importing and exporting CSV data Summary 325 326 326 328 331 332
vi Table of Contents 12 Training Machine Learning Models with scikit-learn Technical requirements 334 What is machine learning? 334 Supervised versus unsupervised learning Model validation Basics of scikit-learn Training models and predicting Chaining pre-processors and estimators with pipelines Validating the model with cross-validation 334 335 337 337 340 344 Classifying data with Naive Bayes models Intuition Classifying data with Gaussian Naive Bayes Classifying data with Multinomial Naive Bayes Classifying data with support vector machines Intuition Using SVM in scikit-learn Finding the best parameters Summary 346 346 347 350 351 352 355 356 358 13 Creating an Efficient Prediction API Endpoint with FastAPI Technical requirements Persisting a trained model withjoblib Dumping a trained model Loading a dumped model 360 360 360 362 Implementing an efficient prediction endpoint 363 Caching results withjoblib 366 Choosing between standard or async functions Summary 369 372 14 Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV Getting started with OpenCV 374 374 Implementing an HTTP endpoint to perform face detection on a single image 378 Technical requirements Implementing a WebSocket to perform face detection on a strea m of і mages 381
Table of Contents vii Sending a stream of images from the browser in a WebSocket 383 Other Books You May Enjoy Index Showing the face detection results in the browser Summary 387 390
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| adam_txt |
Table of Contents Preface Section 1: Introduction to Python and FastAPI 1 Python Development Environment Setup Technical requirements Installing a Python distribution using pyenv Creating a Python virtual environment 4 4 7 Installing Python packages with pip Installing the HTTPie command-line utility Summary 8 9 11 2 Python Programming Specificities Operating over sequences - list comprehensions and generators 34 Technical requirements Basics of Python programming 14 14 Running Python scripts 14 List comprehensions 34 36 Indentation matters 16 Generators Working with built-in types 17 Working with data structures - lists, tuples, dictionaries, and sets 18 Writing object-oriented programs 39 Performing Boolean logic and checking for existence Defining a class 39 23 Implementing magic methods 41 Reusing logic and avoiding repetition with inheritance 45 Controlling the flow of a program 25 Defining functions 29 Writing and using packages and modules 31
ii Table of Contents Type hinting and type checking with туру 48 Getting started The typing module 48 50 Type function signatures with Callable Any and cast 53 54 Asynchronous I/O Summary 56 60 3 Developing a RESTful API with 1FastAPI Technical requirements Creating the first endpoint and running it locally Handling request parameters Path parameters Query parameters The request body Form data and file uploads Headers and cookies 62 62 65 65 72 74 79 85 The request object 87 Customizing the response 88 Path operation parameters The response parameter Raising HTTP errors Building a custom response 95 99 102 Structuring a bigger project with multiple routers Summary 88 107 111 4 Managing Pydantic Data Models in FastAPI Technical requirements 114 Defining models and their field types with Pydantic 114 Standard field types Optional fields and default values Field validation Validating email addresses and URLs with Pydantic types 114 120 122 124 Creating model variations with class inheritance 126 Adding custom data validation with Pydantic 129 Applying validation at a field level Applying validation at an object level Applying validation before Pydantic parsing 129 130 131 Working with Pydantic objects 132 Converting an object into a dictionary 132 Creating an instance from a sub-class object 135 Updating an instance with a partial one 137 Summary 139
Table of Contents iii 5 Dependency Injections in FastAPI Technical requirements 142 What is dependency injection? 142 Creating and using a function dependency 143 Get an object or raise a 404 error Creating and using a parameterized dependency with a class Use class methods as dependencies 147 Using dependencies at a path, router, and global level 152 Use a dependency on a path decorator 153 154 Use a dependency on a whole router Use a dependency on a whole application Summary 156 157 148 150 Section 2: Build and Déploya Complete Web Backend with FastAPI б Databases and Asynchronous ORMs Technical requirements An overview of relational and NoSQL databases Relational databases NoSQL databases Which one should you choose? Communicating with a SQL database with SQLAIchemy Creating the table schema Connecting to a database Making insert queries Making select queries Making update and delete queries Adding relationships 162 162 163 164 165 166 168 169 171 173 175 177 Setting up a database migration system with Alembic Communicating with a SQL database with Tortoise ORM Creating database models Setting up the Tortoise engine Creating objects Updating and deleting objects Adding relationships Setting up a database migration system with Aerich 180 186 186 188 190 193 194 198 Communicating with a MongoDB database using Motor 200 Creating models compatible with MongoDB ID 201
iv Table of Contents Connecting to a database Inserting documents Getting documents 202 203 204 Updating and deleting documents Nesting documents Summary 207 208 210 "7 Managing Authentication and Security in FastAPI Technical requirements 212 Security dependencies in FastAPI 212 Storing a user and their password securely in a database 216 Creating models and tables Hashing passwords Implementing registration routes Retrieving a user and generating an access token 217 218 219 220 Implementing a database access token 220 Implementing a login endpoint 222 Securing endpoints with access tokens 225 Configuring CORS and protecting against CSRF attacks 227 Understanding CORS and configuring it in FastAPI Implementing double-submit cookies to prevent CSRF attacks Summary 228 233 239 8 Defining WebSockets for Two-Way Interactive Communication in FastAPI Technical requirements 242 Understanding the principles of two-way communication with WebSockets 242 Creating a WebSocket with FastAPI 243 Handling concurrency Using dependencies 247 250 Handling multiple WebSocket connections and broadcasting messages 253 Summary 260 9 Testing an API Asynchronously with pytest and HTTPX Technical requirements Introduction to unit testing with pytest 262 263 Generating tests with parametrize 265 Reusing test logic by creating fixtures 267
Table of Contents v Setting up testing tools for FastAPI with HTTPX Writing tests for REST API endpoints Writing tests for POST endpoints Testing with a database 270 275 276 Writing tests for WebSocket endpoints Summary 278 286 289 10 Deploying a FastAPI Project Technical requirements 292 Setting and using environment 292 variables Using a .env file 296 Managing Python dependencies 297 Adding Gunicorn as a server process for deployment 299 Deploying a FastAPI application on a serverless platform 300 Adding database servers Deploying a FastAPI application with Docker Writing a Dockerfile Building a Docker image Running a Docker image locally Deploying a Docker image 303 304 304 306 307 307 Deploying a FastAPI application on a traditional server 309 Summary 310 Section 3: Build a Data Science API with Python and FastAPI 11 Introduction to NumPy and pandas Technical requirements Getting started with NumPy 314 314 Creating arrays Accessing elements and sub-arrays 315 318 Manipulating arrays with NumPy - computation, aggregations, comparisons Adding and multiplicating arrays Aggregating arrays - sum, min, max, mean. 320 322 324 Comparing arrays Getting started with pandas Using pandas Series for one-dimensional data Using pandas DataFrames for multi-dimensional data Importing and exporting CSV data Summary 325 326 326 328 331 332
vi Table of Contents 12 Training Machine Learning Models with scikit-learn Technical requirements 334 What is machine learning? 334 Supervised versus unsupervised learning Model validation Basics of scikit-learn Training models and predicting Chaining pre-processors and estimators with pipelines Validating the model with cross-validation 334 335 337 337 340 344 Classifying data with Naive Bayes models Intuition Classifying data with Gaussian Naive Bayes Classifying data with Multinomial Naive Bayes Classifying data with support vector machines Intuition Using SVM in scikit-learn Finding the best parameters Summary 346 346 347 350 351 352 355 356 358 13 Creating an Efficient Prediction API Endpoint with FastAPI Technical requirements Persisting a trained model withjoblib Dumping a trained model Loading a dumped model 360 360 360 362 Implementing an efficient prediction endpoint 363 Caching results withjoblib 366 Choosing between standard or async functions Summary 369 372 14 Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV Getting started with OpenCV 374 374 Implementing an HTTP endpoint to perform face detection on a single image 378 Technical requirements Implementing a WebSocket to perform face detection on a strea m of і mages 381
Table of Contents vii Sending a stream of images from the browser in a WebSocket 383 Other Books You May Enjoy Index Showing the face detection results in the browser Summary 387 390 |
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| spelling | Voron, François ca. 20./21. Jh. Verfasser (DE-588)1249324319 aut Building data sience applications with FastAPI develop, manage, and deploy efficient machine... learning applications with python [S.l.] PACKT PUBLISHING LIMITED 2021 xiv, 407 Seiten Diagramme 10 cm txt rdacontent n rdamedia nc rdacarrier Data Science (DE-588)1140936166 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Python (Computer program language) Data mining Data Science (DE-588)1140936166 s Python Programmiersprache (DE-588)4434275-5 s DE-604 ebook version 9781801074186 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032959486&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Building data sience applications with FastAPI develop, manage, and deploy efficient machine... learning applications with python |
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