Python Ecosystem
Solution report card
| Runs on IBM i? | ❌ | |
| On-prem | ✅ | |
| IBM Cloud | ✅ | |
| AI capabilities | Machine Learning Deep Learning Generative AI Agentic AI more… | |
| Commercial support | ✅ | |
| Free to try? | ✅ | |
| Requirements | IBM Power |
What is the Python Ecosystem for IBM Power?
Section titled “What is the Python Ecosystem for IBM Power?”The Python Ecosystem for IBM Power (PyEco) is IBM’s supported distribution of Python and the scientific/AI Python stack — optimized and validated for IBM Power architecture. It provides the key ML and AI libraries that data scientists and developers need, packaged and tested to take advantage of IBM Power’s hardware capabilities including the MMA (Matrix Math Accelerator) on Power10 (and newer).
PyEco runs on Linux on IBM Power (not directly on IBM i), making it the natural complement to IBM i in a hybrid on-premises AI architecture: IBM i holds the data in Db2 for i, while a Power Linux partition or server running PyEco handles the compute-intensive AI workloads.
What’s included
Section titled “What’s included”PyEco provides IBM-optimized builds of the core Python AI stack:
- PyTorch and TensorFlow — Deep learning frameworks with Power-optimized kernels
- scikit-learn — Classical ML algorithms
- XGBoost and LightGBM — Gradient boosting frameworks
- NumPy, SciPy, pandas — Scientific computing foundations
- ONNX Runtime — Cross-platform inference engine
- Hugging Face Transformers — Access to thousands of pre-trained models
All packages are built for the ppc64le (Power little-endian) architecture and tested for compatibility with each other.
Why use PyEco instead of upstream pip packages?
Section titled “Why use PyEco instead of upstream pip packages?”Standard pip packages from PyPI may not include Power-optimized builds, or may not be available for ppc64le at all. IBM’s PyEco provides:
- Validated compatibility — All packages are tested together
- Hardware optimization — Kernels tuned for Power’s vector/SIMD capabilities and MMA
- Commercial support — IBM support available for production deployments
- Regular updates — Packages kept current with upstream releases
Use with IBM i data
Section titled “Use with IBM i data”PyEco on a Power Linux partition can connect to Db2 for i on an adjacent IBM i partition using several integration techniques. For direct database access, you can use JDBC (JT400), ODBC, or modern protocols like Mapepire. See the Python programs integration page for detailed information on these connection methods.
Beyond direct database connections, there are several other integration techniques available for working with IBM i data:
- Event streaming with Kafka — For event-driven architectures, you can stream database changes from Db2 for i to Kafka topics using change data capture (CDC) tools like AIStream, which is optimized for Power10 (and newer) systems. This enables real-time data pipelines where PyEco applications can consume and process database events as they occur.
See the Machine Learning and Deep Learning journey pages for examples.