API TESTING USING PYTEST IN PYCHARMPRESENTENTION.pptx
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API testing is crucial for verifying functionality, ensuring performance, identifying security vulnerabilities, and facilitating integration with other systems. It typically involves using tools like pytest for executing test cases on API endpoints through various HTTP requests, while also supporting multiple testing types including unit, integration, and load testing. Although pytest offers advantages like fast execution and powerful assertions, it has limitations such as a learning curve for advanced features and a focus exclusively on backend testing.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/addressing-evolving-ai-model-challenges-through-memory-and-storage-a-presentation-from-micron/
Wil Florentino, Senior Segment Marketing Manager at Micron, presents the Addressing Evolving AI Model Challenges Through Memory and Storage tutorial at the May 2025 Embedded Vision Summit.
In the fast-changing world of artificial intelligence, the industry is deploying more AI compute at the edge. But the growing diversity and data footprint of transformers and models such as large language models and large multimodal models puts a spotlight on memory performance and data storage capacity as key bottlenecks. Enabling the full potential of AI in industries such as manufacturing, automotive, robotics and transportation will require us to find efficient ways to deploy this new generation of complex models.
In this presentation, Florentino explores how memory and storage are responding to this need and solving complex issues in the AI market. He examines the storage capacity and memory bandwidth requirements of edge AI use cases ranging from tiny devices with severe cost and power constraints to edge servers, and he explains how new memory technologies such as LPDDR5, LPCAMM2 and multi-port SSDs are helping system developers to meet these challenges.Edge-banding-machines-edgeteq-s-200-en-.pdf
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廬 廡 廬 廨廬 廡廬 廬 (廬 ).
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Jacobs has successfully utilized FME to tackle the complexities of integrating diverse data sources in a confidential $1 billion campus improvement project. The project aimed to create a comprehensive digital twin by merging Building Information Modeling (BIM) data, Construction Operations Building Information Exchange (COBie) data, and various other data sources into a unified Geographic Information System (GIS) platform. The challenge lay in the disparate nature of these data sources, which were siloed and incompatible with each other, hindering efficient data management and decision-making processes.
To address this, Jacobs leveraged FME to automate the extraction, transformation, and loading (ETL) of data between ArcGIS Indoors and IBM Maximo. This process ensured accurate transfer of maintainable asset and work order data, creating a comprehensive 2D and 3D representation of the campus for Facility Management. FME's server capabilities enabled real-time updates and synchronization between ArcGIS Indoors and Maximo, facilitating automatic updates of asset information and work orders. Additionally, Survey123 forms allowed field personnel to capture and submit data directly from their mobile devices, triggering FME workflows via webhooks for real-time data updates. This seamless integration has significantly enhanced data management, improved decision-making processes, and ensured data consistency across the project lifecycle.FIDO Seminar: Authentication for a Billion Consumers - Amazon.pptx
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FIDO Seminar: Authentication for a Billion Consumers - AmazonWhy Its Critical to Have an Integrated Development Methodology for Edge AI,...
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From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge,...
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Addressing Evolving AI Model Challenges Through Memory and Storage, a Prese...
Addressing Evolving AI Model Challenges Through Memory and Storage, a Prese...Edge AI and Vision Alliance
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API TESTING USING PYTEST IN PYCHARMPRESENTENTION.pptx
- 3. Why We Need API Testing: Verify functionality: Ensure API behaves as expected. Performance & load testing: Check how well the API handles high traffic. Security: Identify vulnerabilities in the API. Integration testing: Ensure smooth interaction with other services or systems.
- 4. How API Testing Works: API testing focuses on ensuring that the API endpoints work as expected. Pytest is a Python testing framework used to write and execute test cases for APIs. You can send HTTP requests (GET, POST, PUT, DELETE) to API endpoints using libraries like requests. Assertions in pytest verify the expected response, such as status codes, response times, and response content.
- 9. Common Tools for API Testing: Postman: A popular GUI tool for sending HTTP requests to an API and viewing the responses. SoapUI: A tool for testing both RESTful and SOAP APIs with a strong focus on enterprise-level needs. Rest Assured: A Java-based library for testing REST APIs, widely used in automation scripts. Pytest: A Python framework for testing APIs, often used in combination with libraries like requests for sending HTTP requests.
- 10. Common API Testing Types: Unit Testing: Testing individual functions or methods within the API to ensure they behave correctly in isolation. Integration Testing: Testing how the API interacts with other components or systems, such as databases or third-party services. Load Testing: Testing the API's performance under heavy traffic or multiple requests. Security Testing: Ensuring that the API is resistant to unauthorized access, SQL injection, cross-site scripting (XSS), etc.
- 11. Advantages of API Testing with Pytest Fast Execution: Runs tests quickly, identifying issues early. Powerful Assertions: Built-in methods make validating API responses easy and clear. Highly Extensible: Supports plugins and custom fixtures for added functionality. Parallel Testing: Runs multiple tests simultaneously with tools like pytest- xdist. Seamless CI/CD Integration: Fits well into automated deployment pipelines. Detailed Reporting: Provides comprehensive logs to simplify debugging.
- 12. Disadvantages of API Testing with Pytest Basic Features: Requires additional libraries for API-specific testing. Learning Curve: Advanced features can be tricky for beginners. Not for UI Testing: Focuses only on backend testing. Test Maintenance: Updating tests for API changes can be time-consuming. Complex Authentication: Needs extra setup for handling advanced security methods.