tensorflow-operator | A helper to quickly and easily deploy | Machine Learning library

by krallistic Python Version: Current License: No License

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tensorflow-operator Summary

tensorflow-operator is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow-operator has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

A helper to quickly and easily deploy distributed tensorflow onto kubernetes

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Support

tensorflow-operator has a low active ecosystem.

It has 6 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 0 have been closed. On average issues are closed in 1106 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-operator is current.

Quality

tensorflow-operator has no bugs reported.

Security

tensorflow-operator has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

tensorflow-operator does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

tensorflow-operator releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed tensorflow-operator and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow-operator implemented functionality, and help decide if they suit your requirements.

Create Tensorflow training
Generate a worker spec
Build a node specification string
Generate a service specification
Generate arguments for the given node
Generate labels for a given node
Generate a name for a node
Delete Tensorflow training
Delete some stuff
Update a Tensorflow training

Get all kandi verified functions for this library.

tensorflow-operator Key Features

No Key Features are available at this moment for tensorflow-operator.

tensorflow-operator Examples and Code Snippets

Print tf . print .

python

Lines of Code : 247

License : Non-SPDX (Apache License 2.0)

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def print_v2(*inputs, **kwargs):
  """Print the specified inputs.

  A TensorFlow operator that prints the specified inputs to a desired
  output stream or logging level. The inputs may be dense or sparse Tensors,
  primitive python objects, data str

Decorator for broadcasting a binary operator .

python

Lines of Code : 14

License : Non-SPDX (Apache License 2.0)

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def _broadcasting_binary_op(fn):
  """Wraps a binary Tensorflow operator and performs XLA-style broadcasting."""

  def broadcasting_binary_op_wrapper(x, y, broadcast_dims=None, name=None):
    """Inner wrapper function."""
    broadcast_dims = broad

Community Discussions

Trending Discussions on tensorflow-operator

Why does Tensorflow not override __eq__ for Tensors?

QUESTION

Why does Tensorflow not override __eq__ for Tensors?

Asked 2019-Jul-20 at 21:54

Tensorflow overrides multiple operators for the Tensor class, including __lt__, __ge__, etc.

However, the implementation for __eq__ seems to be conspicuously absent:

...

ANSWER

Answered 2017-Oct-17 at 09:14

Tensors do implement __eq__, but the implementation only tests for identity. I found this GitHub issue, which explains why tensors test for identity, and do not broadcast:

This may be a complication of fact that tensors can be used as keys in dictionaries, which I believe use == to find the matching object with the same hash

The commenter is correct; if __eq__ was overloaded to broadcast then you could not use tensors as keys in a dictionary. Objects that define a __hash__ method (required if you want to use such objects as keys in a dictionary), must produce the same hash value for two objects that are equal; see the __hash__ method:

The only required property is that objects which compare equal have the same hash value

but broadcasting would produce a 'true' tensor object for objects with different hash values.

(the speculation that __eq__ would break boolean testing is wrong; boolean testing uses __bool__, which tensors do implement).

If you need to make element-wise equality tests on tensors, you can use the tf.equal() and tf.not_equal() functions.

Source https://stackoverflow.com/questions/46785041

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tensorflow-operator

You can download it from GitHub.
You can use tensorflow-operator like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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