Key features
- Code-first: APAC pipelines in Python/TypeScript/Go instead of YAML configuration
- Local = CI: APAC pipelines run identically on developer machines and CI systems
- Automatic caching: APAC step-level cache invalidation reduces CI execution time
- Modular: APAC pipeline components reusable across teams via Dagger modules
- ML-friendly: APAC Python SDK natural for ML training and evaluation pipelines
- Any CI backend: GitHub Actions, GitLab, CircleCI, Jenkins APAC compatibility
Best for
- APAC DevOps and ML engineering teams frustrated with YAML CI configuration sprawl who want testable, reusable pipeline code — particularly APAC ML teams who need local-to-CI environment parity for training pipeline reproducibility.
Limitations to know
- ! Learning curve: APAC teams must learn Dagger SDK vs familiar YAML configuration
- ! Container overhead: every APAC step runs in a container, adding startup latency for simple tasks
- ! Smaller APAC community than GitHub Actions or GitLab CI for existing YAML pipelines
About Dagger
Dagger is a container-native CI/CD platform that replaces YAML pipeline configuration with Python, TypeScript, or Go code — allowing APAC DevOps and ML engineering teams to write CI/CD pipelines as regular programs that run identically on local APAC developer machines and cloud CI systems (GitHub Actions, GitLab CI, CircleCI, Jenkins). The core insight: YAML configuration files for CI are not code and cannot be debugged, tested, or refactored like code.
Dagger's execution model runs every pipeline step inside a container — APAC teams define steps as functions using the Dagger SDK, and Dagger's engine executes them in isolated containers with automatic caching. An APAC pipeline step that builds a Docker image caches the result; if the APAC source files haven't changed, the next run uses the cached artifact without rebuilding. This caching significantly reduces APAC CI execution time for large monorepos.
For APAC ML teams, Dagger's Python SDK makes it natural to write ML pipelines that train, evaluate, and deploy models as regular Python functions — the same APAC pipeline that data scientists run locally for testing runs unchanged in CI, eliminating the APAC environment divergence where local training succeeds but CI fails due to dependency or configuration differences.
Dagger's composability allows APAC teams to share pipeline components as modules — an APAC team that builds a model training pipeline can publish it as a Dagger module, and other APAC teams can import and reuse it without copying YAML. Dagger Cloud provides APAC CI execution, caching, and observability for teams that want a managed APAC Dagger backend.
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