> ML_LIBRARY // NUMPY_v1.0
NumPy
NumPy Developers / NumFOCUS — The fundamental package for scientific computing with Python.
numerical-data-foundationsv2.1.1BSD-3-Clausequalified
Model Training
Accelerators:
CPU
Distributed Training:No
Model Inference
Inference Accelerators:
CPUWASM
Deployment Targets:server, edge, browser
What It Does
- +High-performance N-dimensional array processing
- +Vectorized mathematical and linear algebra routines
- +Interoperable C-API memory buffers for downstream frameworks
What It Does Not Do
- -Natively compute on GPUs or TPUs
- -Compute automatic gradients for deep neural networks
- -Scale horizontally across multiple compute nodes without Dask or Ray
>Suitable Work Types
- Data preprocessing
- Numerical linear algebra
- Custom feature extraction
- In-memory tensor manipulation
>Unsuitable Work Types
- Distributed big data processing exceeding RAM
- End-to-end deep learning training requiring autodiff
- Out-of-core streaming analytics
Data Residency Implications
In-process memory only. Zero network transmission.
Security Considerations
Enforce allow_pickle=False on np.load to prevent arbitrary code execution.
Operational Profile & Known Limitations
Maturity:mature
Learning Curve:low
Ops Complexity:low
Cost Tier:free-oss
> Known Limitations:
- Single-threaded or OpenBLAS-bound on CPU.
- Cannot spill to disk natively if arrays exceed physical RAM.
Associated Incident Patterns (Incidentpedia)
Enforce safeguards and monitoring to guard against these documented real-world failure modes:
> Primary Evidence & Benchmark Citations
NumPy Documentation 2.1official-docs • >=2.0.0, <=2.1.x
2026-09-25HIGH