msgpack-numpy-opentensor
Numpy data serialization using msgpack
Decision gist · record as of 2026-08-14
Yes, if you already use msgpack and need NumPy array serialization with type preservation. No, if you are starting a new project—the dormant maintenance (last release 2023-10-02, no recent commits) and low GitHub stars (2) suggest limited community support and risk of compatibility drift with future NumPy or msgpack releases. Consider pickle or Protocol Buffers for new work unless msgpack integration is a hard requirement.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Deserialized NumPy arrays are read-only; copy them before modification.
- Arrays with dtype 'O' fall back to pickle, negating msgpack efficiency gains.
- Low friction: pure wheel distribution with only two runtime dependencies (numpy and msgpack).
License · maintenance · safety
BSD (permissive) — BSD permissive license allows use in most commercial and open-source projects without significant restrictions.
last release 2023-10-02 (1047 days) · last repo commit 2023-10-02 · 2 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 215,354 downloads/mo, #9,402 on PyPI
Alternatives
Verify before relying
import msgpack
import msgpack_numpy as m
import numpy as np
m.patch() # Make all msgpack calls NumPy-aware
x = np.random.rand(5)
x_enc = msgpack.packb(x)
x_rec = msgpack.unpackb(x_enc)- Whether the package works reliably with modern NumPy and msgpack versions beyond what classifiers claim.
- Performance characteristics compared to alternatives like pickle or Protocol Buffers for typical workloads.
- Whether the dormant maintenance status poses compatibility risks with future Python releases.
What it is and what it does
msgpack-numpy bridges NumPy and msgpack by adding type-aware serialization for NumPy arrays and Python complex numbers. It works by providing custom encoder and decoder functions that preserve dtype information during msgpack serialization, or by monkey-patching msgpack globally so all serialization calls become NumPy-aware automatically.
The package is designed for scenarios where you need efficient, compact binary serialization of numerical data while preserving type information across serialization boundaries. It trades some storage overhead (for type metadata) against the convenience of not having to manually reconstruct dtypes after deserialization. The main constraint is that deserialized arrays are read-only, and arrays with object dtype ('O') fall back to pickle, which defeats much of msgpack's efficiency advantage.
Use it for
- Serialize NumPy arrays to disk or network in a compact binary format while preserving dtype information.
- Make msgpack-based RPC or message-passing systems NumPy-aware without modifying existing msgpack call sites.
- Exchange numerical data between Python processes or services using msgpack as the transport layer.
- Store machine learning model weights or intermediate tensors in a format more efficient than JSON or pickle.
- Integrate NumPy serialization into existing msgpack-based data pipelines without rewriting encoder/decoder logic.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you already use msgpack and need NumPy array serialization with type preservation.
No, if you are starting a new project—the dormant maintenance (last release 2023-10-02, no recent commits) and low GitHub stars (2) suggest limited community support and risk of compatibility drift with future NumPy or msgpack releases. Consider pickle or Protocol Buffers for new work unless msgpack integration is a hard requirement.
Install
msgpack-numpy-opentensor on PyPI
Before you install
Low friction: pure wheel distribution with only two runtime dependencies (numpy and msgpack). Maintenance is dormant—last release was 2023-10-02 and no commits since then—so expect no active bug fixes or updates.
Deserialized NumPy arrays are read-only; copy them before modification. Arrays with dtype 'O' fall back to pickle, negating msgpack efficiency gains.
License in practice
BSD permissive license allows use in most commercial and open-source projects without significant restrictions.
Quickstart
import msgpack
import msgpack_numpy as m
import numpy as np
m.patch() # Make all msgpack calls NumPy-aware
x = np.random.rand(5)
x_enc = msgpack.packb(x)
x_rec = msgpack.unpackb(x_enc)Verify before relying
- Whether the package works reliably with modern NumPy and msgpack versions beyond what classifiers claim.
- Performance characteristics compared to alternatives like pickle or Protocol Buffers for typical workloads.
- Whether the dormant maintenance status poses compatibility risks with future Python releases.
Package facts
| License | BSD permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpymsgpack |
| Maintenance | Dormant 1,047 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 215,354 / month, #9,402 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: msgpack_numpy_opentensor-0.5.0-py2.py3-none-any.whl
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See also msgpack-numpy · msgpack · u-msgpack-python · msgpack-python · json-numpy · ormsgpack · numpyencoder · py-ubjson · jsonpickle · srsly