{"id":7433,"library":"msgpack-numpy-opentensor","title":"msgpack-numpy-opentensor","description":"msgpack-numpy-opentensor provides efficient serialization and deserialization routines for NumPy array and scalar data types using the MessagePack binary format. It is functionally derived from the `msgpack-numpy` library, offering compatibility with NumPy data structures. The current PyPI version is `0.5.0`. While its related GitHub repository shows more recent development, PyPI releases are infrequent, with the latest over a year old.","status":"active","version":"0.5.0","language":"en","source_language":"en","source_url":"https://github.com/opentensor/msgpack-numpy","tags":["serialization","numpy","msgpack","binary","data-interchange","opentensor"],"install":[{"cmd":"pip install msgpack-numpy-opentensor","lang":"bash","label":"PyPI Installation"}],"dependencies":[{"reason":"Core MessagePack serialization library.","package":"msgpack"},{"reason":"Provides the array and numerical types for serialization.","package":"numpy"}],"imports":[{"note":"The `patch` function is typically called after importing both `msgpack` and the library, usually aliased for convenience. Direct import might not correctly set up monkey-patching for `msgpack`.","wrong":"from msgpack_numpy_opentensor import patch","symbol":"patch","correct":"import msgpack\nimport msgpack_numpy_opentensor as m\nm.patch()"},{"note":"Similar to `patch`, `encode` and `decode` are usually accessed via the aliased module for consistency with `msgpack`'s `default` and `object_hook` parameters.","wrong":"from msgpack_numpy_opentensor import encode","symbol":"encode","correct":"import msgpack_numpy_opentensor as m\nmsgpack.packb(data, default=m.encode)"},{"note":"Similar to `patch`, `encode` and `decode` are usually accessed via the aliased module for consistency with `msgpack`'s `default` and `object_hook` parameters.","wrong":"from msgpack_numpy_opentensor import decode","symbol":"decode","correct":"import msgpack_numpy_opentensor as m\nmsgpack.unpackb(packed_data, object_hook=m.decode)"}],"quickstart":{"code":"import numpy as np\nimport msgpack\nimport msgpack_numpy_opentensor as m\n\n# Create a NumPy array\nx = np.random.rand(5, 5)\n\n# Pack the NumPy array using msgpack-numpy-opentensor's encoder\n# Optionally, you can call m.patch() to monkey-patch msgpack globally\n# m.patch()\npacked_x = msgpack.packb(x, default=m.encode)\n\n# Unpack the bytes back into a NumPy array using the decoder\nunpacked_x = msgpack.unpackb(packed_x, object_hook=m.decode, raw=False)\n\nprint(\"Original array:\\n\", x)\nprint(\"Unpacked array:\\n\", unpacked_x)\nprint(\"Arrays are equal:\", np.array_equal(x, unpacked_x))\nprint(\"Unpacked array is read-only:\", not unpacked_x.flags['WRITEABLE'])\n","lang":"python","description":"This quickstart demonstrates how to serialize a NumPy array into MessagePack binary format and then deserialize it back into a NumPy array using the `msgpack-numpy-opentensor` library. It shows both packing (`packb`) and unpacking (`unpackb`) using the provided encoder and decoder functions, highlighting that deserialized arrays are typically read-only."},"warnings":[{"fix":"Review `dtype='O'` usage and consider custom serializers for such arrays if you rely on pickle for compatibility. Be prepared to update serialization/deserialization logic if upgrading to a version with this change.","message":"The upstream `opentensor/msgpack-numpy` GitHub repository, linked as this package's source, released `v1.0.0` with a breaking change: it disables `pickle` by default. This will prevent deserialization of NumPy arrays with `dtype='O'` (object arrays) that were serialized with pickle enabled in older versions. While `msgpack-numpy-opentensor` on PyPI is currently `0.5.0`, this change may propagate to future versions.","severity":"breaking","affected_versions":"Potentially `1.0.0+` (if adopted by `msgpack-numpy-opentensor`)"},{"fix":"Avoid `dtype='O'` for sensitive or performance-critical data. Consider explicitly converting object arrays to more primitive types or implementing custom, secure encoders/decoders for specific object types.","message":"NumPy arrays with `dtype='O'` (object arrays) are serialized/deserialized using Python's `pickle` module as a fallback by `msgpack-numpy` (and, by extension, likely `msgpack-numpy-opentensor`). This introduces significant performance overhead and poses security risks when deserializing data from untrusted sources due to pickle's arbitrary code execution capabilities.","severity":"gotcha","affected_versions":"All versions"},{"fix":"If modification is required, explicitly create a writable copy of the array after deserialization, e.g., `modified_array = unpacked_array.copy()`.","message":"NumPy arrays deserialized by `msgpack-numpy` (and thus, `msgpack-numpy-opentensor`) are read-only by default. Attempting to modify them directly will raise a `ValueError` or `AttributeError`.","severity":"gotcha","affected_versions":"All versions"},{"fix":"For extremely large NumPy arrays, consider chunking them into smaller pieces before serialization, using alternative serialization formats designed for larger-than-memory data, or streaming solutions.","message":"The underlying `msgpack` library has limitations on the maximum size of individual binary or string objects, typically around 4.3 GB. Attempting to serialize a single NumPy array that exceeds this limit may result in serialization errors.","severity":"gotcha","affected_versions":"All versions"}],"env_vars":null,"last_verified":"2026-04-16T00:00:00.000Z","next_check":"2026-07-15T00:00:00.000Z","problems":[{"fix":"Ensure all related packages (`msgpack-numpy-opentensor`, `msgpack`, `numpy`) are updated to their latest compatible versions. If the problem persists, review the specific `numpy` version requirements for `msgpack-numpy` or consider reporting the issue with full version details.","cause":"This error often occurs when deserializing multi-dimensional NumPy arrays, potentially due to an incompatibility between `msgpack-numpy` (or `msgpack-numpy-opentensor`), `msgpack`, and `numpy` versions, or an internal buffer handling issue during array reconstruction.","error":"TypeError: buffer is too small for requested array"},{"fix":"Avoid using `m.patch()` for general `msgpack` usage. Instead, explicitly pass `default=m.encode` to `msgpack.packb` and `object_hook=m.decode` to `msgpack.unpackb` only when you expect NumPy arrays to be present in the data being serialized/deserialized. This provides more granular control and prevents unintended decoding attempts.","cause":"When `m.patch()` is used, the global `object_hook` for `msgpack` is set. If `msgpack.unpackb` then encounters a dictionary that is *not* a serialized NumPy array (e.g., a regular Python dictionary), the `msgpack_numpy_opentensor` decoder might incorrectly attempt to interpret it as an array, leading to a `KeyError` because expected NumPy metadata keys (like `b'nd'`) are missing.","error":"KeyError: b'nd' (or similar during unpackb with m.patch())"},{"fix":"Convert `numpy.datetime64` arrays or arrays of Python `datetime` objects to a simpler, serializable format (e.g., integer timestamps, ISO 8601 strings) before packing. Alternatively, implement custom `default` encoder and `object_hook` decoder functions that specifically handle `datetime` objects within your NumPy array serialization pipeline.","cause":"`msgpack-numpy-opentensor` primarily focuses on numerical data types. Serializing NumPy arrays containing complex Python objects like `datetime` objects, especially if they have `dtype='O'` (object), can lead to type errors as `msgpack-numpy`'s default handlers may not know how to convert these specific objects.","error":"TypeError: must be str, not bytes (or similar for datetime objects in arrays)"}]}