Skip to main content

maskedtensor

MaskedTensor is a library that is part of the PyTorch project and is an extension of torch.Tensor by providing the ability to mask out the value for any given element. Elements with masked out values are ignored during computation and give the user access to advanced semantics such as masked reductions, safe softmax, masked matrix multiplication, filtering NaNs, and masking out certain gradient values.

Installation

Binaries

To install MaskedTensor via pip, use the following command:

pip install maskedtensor

Note that MaskedTensor requires PyTorch >= 1.11, which you can get on the the main website

From Source

To install from source, you will need Python 3.7 or later, and we highly recommend that you use an Anaconda environment. Then run:

python setup.py develop

Documentation

Please find documentation on the MaskedTensor Website.

Building documentation

Please follow the instructions in the docs README.

Notebooks

For an introduction and instructions on how to use MaskedTensors and what they are useful for, there are a nubmer of tutorials on the MaskedTensor website.

License

maskedtensor is licensed under BSD 3-Clause

Metadata

Release files for maskedtensor 0.10.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for maskedtensor 0.10.0
File Interpreter ABI Platform Actions
maskedtensor-0.10.0-py3-none-any.whl Python 3 none any Details for maskedtensor-0.10.0-py3-none-any.whl

Release files / maskedtensor-0.10.0-py3-none-any.whl

Download URL maskedtensor-0.10.0-py3-none-any.whl
Size 15.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7a5cf1b8779524fab303b7441a944a9c4d403be72f34f6e6291b366856cf6f88
BLAKE2b-256 checksum
How to use checksums
43636dd1b68f5836bdf5adc3a7a4dbfdb06316f385951580d717c7db3c3dcf3b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.8.2 keyring/23.4.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.8.12

Security for maskedtensor 0.10.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page