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Browsing: Pipelines
A Coding Guide to Build a Production-Grade Background Task Processing System Using Huey with SQLite, Scheduling, Retries, Pipelines, and Concurrency Control
consumer = huey.create_consumer( workers=4, worker_type=WORKER_THREAD, periodic=True, initial_delay=0.1, backoff=1.15, max_delay=2.0, scheduler_interval=1, check_worker_health=True, health_check_interval=10, flush_locks=False, ) consumer_thread = threading.Thread(target=consumer.run, daemon=True) consumer_thread.start() print(“Consumer started (threaded).”) print(“\nEnqueue basics…”) r1 =…
A Hands-On Coding Tutorial for Microsoft VibeVoice Covering Speaker-Aware ASR, Real-Time TTS, and Speech-to-Speech Pipelines
In this tutorial, we explore Microsoft VibeVoice in Colab and build a complete hands-on workflow for both speech recognition and real-time speech synthesis. We set up…
A Coding Guide to Build Advanced Document Intelligence Pipelines with Google LangExtract, OpenAI Models, Structured Extraction, and Interactive Visualization
In this tutorial, we explore how to use Google’s LangExtract library to transform unstructured text into structured, machine-readable information. We begin by installing the required dependencies…
How to Build Production Ready AgentScope Workflows with ReAct Agents, Custom Tools, Multi-Agent Debate, Structured Output and Concurrent Pipelines
In this tutorial, we build a complete AgentScope workflow from the ground up and run everything in Colab. We start by wiring OpenAI through AgentScope and…
Image by Editor # Introduction The intersection of declarative programming and data engineering continues to reshape how organizations build and maintain their data infrastructure. A recent…
IBM AI Releases Granite 4.0 1B Speech as a Compact Multilingual Speech Model for Edge AI and Translation Pipelines
IBM has released Granite 4.0 1B Speech, a compact speech-language model designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST). The release…
How to Build Type-Safe, Schema-Constrained, and Function-Driven LLM Pipelines Using Outlines and Pydantic
In this tutorial, we build a workflow using Outlines to generate structured and type-safe outputs from language models. We work with typed constraints like Literal, int,…
Image by Editor # Introduction Data pipelines in data science and machine learning projects are a very practical and versatile way to automate data processing workflows.…
How to Design Complex Deep Learning Tensor Pipelines Using Einops with Vision, Attention, and Multimodal Examples
section(“6) pack unpack”) B, Cemb = 2, 128 class_token = torch.randn(B, 1, Cemb, device=device) image_tokens = torch.randn(B, 196, Cemb, device=device) text_tokens = torch.randn(B, 32, Cemb, device=device)…
How to Design Production-Grade Mock Data Pipelines Using Polyfactory with Dataclasses, Pydantic, Attrs, and Nested Models
In this tutorial, we walk through an advanced, end-to-end exploration of Polyfactory, focusing on how we can generate rich, realistic mock data directly from Python type…
