CI/CD
The Dagger Python SDK lets you write CI pipelines as ordinary Python functions. No YAML, no DSL — just Python. You can import Dagger functions like any library, test them with pytest, and run them locally with the same command that runs in CI. This guide focuses on practical patterns: running
Testing
pytest-xdist lets you run tests across multiple CPUs — or even multiple machines — without changing a single test. A 10-minute suite can drop to 2-3 minutes with 4 workers. Here's how to set it up correctly, including the database isolation patterns that trip up most teams. Installation pip install
Testing
Python's localization stack — gettext, Babel, and framework-specific layers like Flask-Babel or Django's i18n — has plenty of failure modes: missing translations, broken .mo files, incorrect plural forms, and format differences across locales. This guide covers how to test each layer systematically with pytest. Testing Message Extraction Before
MLOps
Weights & Biases (W&B) tracks ML experiments — but it also provides the infrastructure to assert training quality, detect regressions, and gate deployments on metric thresholds. This post covers how to use W&B as a testing tool, not just a logging tool. W&B as a
MLOps
Evidently AI is an open-source framework for evaluating and monitoring machine learning models. It generates reports and test suites that check data drift, model quality, and dataset statistics — making it a core tool for MLOps testing pipelines. What Evidently Tests Evidently organizes checks into three categories: * Data quality — missing values,
MLOps
BentoML packages machine learning models as production-ready API services. Testing BentoML services means verifying the model logic, the API layer, and the deployment — each at a different level. BentoML Service Structure A BentoML service wraps a model and exposes it as an HTTP API: # service.py import bentoml import numpy
Testing
Testcontainers starts real Docker containers in tests, replacing mocks and in-memory fakes. The basics — starting a single Postgres or Redis container — are covered in the official docs. The advanced features are what scale: multi-container networks, Docker Compose module integration, parallel test execution, custom wait strategies, and resource reuse across test
Python
Hypothesis is a property-based testing library for Python. Instead of writing specific test cases, you describe the shape of your input and Hypothesis generates hundreds of examples automatically — including edge cases you wouldn't think to write manually. hypothesis-django is the Django integration that handles database transactions, model validation,
Python
factory_boy is Python's most widely used test object factory library. The basics — factory.Factory and factory.django.DjangoModelFactory — are well documented, but the advanced features are what make large test suites maintainable. This guide covers traits, sub-factories, batch creation, lazy attributes, and the patterns that keep factories
Test Automation
Python's dynamic type system and exception handling make it easier to write code that handles invalid input gracefully — in theory. In practice, Python libraries that parse formats, process untrusted data, or call into native extensions can crash, hang, or exhibit undefined behavior just like C code. Atheris brings
Test Automation
Celery is Python's most popular distributed task queue. Testing Celery tasks well means covering task logic in isolation, testing how tasks interact with the broker and result backend, and verifying retry behaviour. This guide shows how to do all three with pytest. Setup pip install celery redis pytest
AI Testing
CrewAI's abstraction is clean: you define agents with roles and tools, wire them into tasks, and the crew handles delegation. What's less clean is testing it. A crew.kickoff() call can trigger a chain of LLM calls, tool invocations, and agent-to-agent handoffs that costs several dollars,