Saravanan666/pypi-org huggingface dataset:
Package: tensorflow1-fork
DocumentationTensorFlowis an end-to-end open source platform
for machine learning. It has a comprehensive, flexible ecosystem oftools,libraries, andcommunityresources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML powered applications.TensorFlow was originally developed by researchers and engineers working on the
Google Brain team within Google's Machine Intelligence Research organization for
the purposes of conducting machine learning and deep neural networks research.
The system is general enough to be applicable in a wide variety of other
domains, as well.TensorFlow provides stablePythonandC++APIs, as well as
non-guaranteed backwards compatible API forother languages.Keep up-to-date with release announcements and security updates by subscribing
toannounce@tensorflow.org.
See all themailing lists.InstallSee theTensorFlow install guidefor thepip package, toenable GPU support, use aDocker container, andbuild from source.To install the current release:$ pip install tensorflowThetensorflowpackage also includes GPU support on Linux and Windows.If package size is a concern, CPU-only packages can be installed with:$ pip install tensorflow-cpuNightly binaries are available for testing using thetf-nightlyandtf-nightly-gpupackages on PyPi.Try your first TensorFlow program$python>>>importtensorflowastf>>>tf.enable_eager_execution()>>>tf.add(1,2).numpy()3>>>hello=tf.constant('Hello, TensorFlow!')>>>hello.numpy()'Hello, TensorFlow!'For more examples, see theTensorFlow tutorials.Contribution guidelinesIf you want to contribute to TensorFlow, be sure to review thecontribution
guidelines. This project adheres to TensorFlow'scode of conduct. By participating, you are expected to
uphold this code.We useGitHub issuesfor
tracking requests and bugs, please seeTensorFlow Discussfor general questions and discussion, and please direct specific questions toStack Overflow.The TensorFlow project strives to abide by generally accepted best practices in open-source software development:Continuous build statusOfficial BuildsBuild TypeStatusArtifactsLinux CPUpypiLinux GPUpypiLinux XLATBAMacOSpypiWindows CPUpypiWindows GPUpypiAndroidRaspberry Pi 0 and 1Py2Py3Raspberry Pi 2 and 3Py2Py3Community Supported BuildsBuild TypeStatusArtifactsLinux AMD ROCm GPUNightlyNightlyLinux AMD ROCm GPUStable ReleaseReleaseLinux s390xNightlyNightlyLinux s390x CPUStable ReleaseReleaseLinux ppc64le CPUNightlyNightlyLinux ppc64le CPUStable ReleaseReleaseLinux ppc64le GPUNightlyNightlyLinux ppc64le GPUStable ReleaseReleaseLinux CPU with Intel® MKL-DNNNightlyNightlyLinux CPU with Intel® MKL-DNNSupports Python 2.7, 3.4, 3.5, and 3.61.13.1 pypiRed Hat® Enterprise Linux® 7.6 CPU & GPUPython 2.7, 3.61.13.1 pypiResourcesTensorFlow.orgTensorFlow tutorialsTensorFlow official modelsTensorFlow examplesTensorFlow in Practice from CourseraTensorFlow blogTensorFlow TwitterTensorFlow YouTubeTensorFlow roadmapTensorFlow white papersTensorBoard visualization toolkitLearn more about theTensorFlow communityand how tocontribute.LicenseApache License 2.0
Package: tensorflow3dtransforms
3D TransformsTensorFlow3D Transforms is a library to easily make 3D transformations and work with 3D data. This library originally started as a few functions here and there for my own work which I then turned into a library.TensorFlow3D Transforms is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data. For this reason, all operators in TensorFlow3D Transforms:Are implemented using TensorFlow tensorsCan handle minibatches of hetereogenous dataCan be differentiatedCan utilize GPUs for accelerationThis project also draws much inspiration fromPyTorch3D.InstallationTo install the latest release of TensorFlow3d Transforms, run:pipinstalltensorflow3dtransforms📄DocumentationVisit the documentationof this project to get more information in detail.📚ExamplesVisit the Quickstartfor this project to see some examples and use this library easily.🙋‍♂️Want to Contribute?Awesome! If you want to contribute to this project, you're always welcome! SeeContributing Guidelines. You can also take a look atTensorFlow3d Transforms's Project Status Trackerfor getting more information about current or upcoming tasks.LicenseCopyright 2023 Rishit Dagli

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: tensorflow-aarch64
TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team
within Google's AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains. TensorFlow is licensed underApache
2.0.
Package: tensorflow-addons
TensorFlow Addons is a repository of contributions that conform to well-
established API patterns, but implement new functionality not available
in core TensorFlow. TensorFlow natively supports a large number of
operators, layers, metrics, losses, and optimizers. However, in a fast
moving field like ML, there are many interesting new developments that
cannot be integrated into core TensorFlow (because their broad
applicability is not yet clear, or it is mostly used by a smaller subset
of the community).
Package: tensorflow-ascend
TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team
within Google’s AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains.
Package: tensorflow-auto-detect
Solves the absolute mess that is installingtensorflowfrom your package requirements.Just put it in your setuptools requirements and it’ll install the GPU version of tensorflow if you have a CUDA
available; otherwise it will fallback to the CPU version.PyPi:https://pypi.python.org/pypi/tensorflow-auto-detectDocs: All you need is right on this page.The problemBackground: Tensorflow provides two pypi packages:tensorflowandtensorflow-gpu.
I’m sure you can infer which is compiled for which.Let’s say you’re making a product or have a project:It uses Tensorflow.It will run on a myriad of classes of machines, and therefore needs to be able to operate and install on machines that may or may not have CUDA available.You want to optimistically utilize a GPU if you can, but fallback to using just CPU.You want your package to be easily installable. Every time. Not just for half of your userbase.You don’t want to have toss the problem on your users and fellow engineers.Let’s be honest, your stringently detailed absoute step-by-step microbe resistant instructions could..maybe..possibly..not be followed and/or remembered exactly to a tee. You know, like everything else. Tech is hard.Q:How do you add the proper one to your own package as a requirement when they both install to the same package namespace?A:Well, you can’t. 🕶Oh, and by the way, if you install one after the other, say to fix if you installed the wrong one at first,
I’ll just blindly trample over the first’s files instead of doing anything useful. (Note: this is not Tensorflow’s fault here, but a more widespread problem in pyland)You can try to choke it down with sweet hacks like:Adding each in their ownextras_requireblocks
(ruining your default and therefore path of least resistance)Toss the problem onto the user. You’rethat guynow.How does it work?This package includes a list of all libraries required by each version of tensorflow intfdetect/cuda.py.Currently, only the check for the cuda runtime library is active (by design) to be a bit more forgiving and allowing for
“strange” setups, even though I’ve yet to come across one. This is the finest heuristic for this anyway, so it works out
nicely, even in the face of your sweet sweet custom compiles; not to mention ensuring optional dependencies stay that way.Versions beneath1.7have not been tested, so the CUDA versions are likely to be mismatched.  Was too far in the past
to care, I’m sorry.UsageInrequirements.txtorinstall_requires:tensorflow-auto-detect==1.10.0# or any other release, 1to1 mapping to tf releasesAspipargument:pipinstalltensorflow-auto-detect==1.8.1# or any other release, 1to1 mapping to tf releasesRunning testspipinstall'.[tests]'pytest
Package: tensorflow-caney
tensorflow-caneyPython package for lots of TensorFlow tools.[DocumentationLatest:https://nasa-nccs-hpda.github.io/tensorflow-caneyObjectivesLibrary to process remote sensing imagery using GPU and CPU parallelization.Machine Learning and Deep Learning image classification and regression.Agnostic array and vector-like data structures.User interface environments via Notebooks for easy to use AI/ML projects.Example notebooks for quick AI/ML start with your own data.InstallationThe following library is intended to be used to accelerate the development of data science products
for remote sensing satellite imagery, or any other applications. tensorflow-caney can be installed
by itself, but instructions for installing the full environments are listed under the requirements
directory so projects, examples, and notebooks can be run.Note: PIP installations do not include CUDA libraries for GPU support. Make sure NVIDIA libraries
are installed locally in the system if not using conda/mamba.Production Containermoduleloadsingularity
singularitybuild--sandbox/lscratch/$USER/container/tensorflow-caneydocker://nasanccs/tensorflow-caney:latestDevelopment Containermoduleloadsingularity
singularitybuild--sandbox/lscratch/$USER/container/tensorflow-caneydocker://nasanccs/tensorflow-caney:devWhy Caney?"Caney" means longhouse in Taíno.ContributorsJordan Alexis Caraballo-Vega,jordan.a.caraballo-vega@nasa.govCaleb Spradlin,caleb.s.spradlin@nasa.govContributingPlease see ourguide for contributing to tensorflow-caney.ReferencesTensorFlow Advanced Segmentation Models
Package: tensorflow-checkpoint-reader
tensorflow-checkpoint-readerTensorflow's CheckpointReader in pure pythonWARNING: This repo is in development. It was automatically generated withmkpylib. If you're reading this message, it means that I use this repo for my own purposes right now. It might not do anything at all; the default functionality isprint('TODO').If you really want to try it out, feel free. I recommend reading through the code and commit history to see if it does what you need, orask mefor status updates.Stay tuned!Installpython3 -m pip install -U tensorflow-checkpoint-readerUsageimporttensorflow_checkpoint_readerprint('TODO')LicenseMITContactA library byShawn Presser. If you found it useful, please considerjoining my patreon!My Twitter DMs are always open; you shouldsend me one! It's the best way to reach me, and I'm always happy to hear from you.Twitter:@theshawwnPatreon:https://www.patreon.com/shawwnHN:sillysaurusxWebsite:shawwn.com
Package: tensorflow-coder-colab-logging
This package provides logging utilities for TensorFlow Coder's Colab notebook
interace.As a user of TF-Coder, you should not need to install this package yourself.In the Colab tool, we would like to log usage data for TF-Coder, so that we can
identify scenarios where TF-Coder can be improved. This usage data will help us
improve TF-Coder for everyone. We also believe that the usage data can form a
dataset that will be a valuable resource to the broader program synthesis
research community.This data collection is completely optional.
Package: tensorflow-constrained-optimization
TensorFlow Constrained Optimization(TFCO) is a library for optimizing
inequality-constrained problems in TensorFlow.In the most general case, both the objective function and the constraints are
represented asTensors, giving users the maximum amount of flexibility in
specifying their optimization problems. Constructing theseTensors can be
cumbersome, so we also provide helper functions to make it easy to construct
constrained optimization problems based onrates, i.e. proportions of the
training data on which some event occurs (e.g. the error rate, true positive
rate, recall, etc).
Package: tensorflow-cpu
TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team
within Google's AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains. TensorFlow is licensed underApache
2.0.
Package: tensorflow-cpu-aws
TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team
within Google's AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains. TensorFlow is licensed underApache
2.0.
Package: tensorflow-cpu-test-package
TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms, and from desktops to clusters of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team (https://research.google/teams/brain/) within Google's AI organization, TensorFlow comes with strong support for machine learning and deep learning with a flexible numerical computation core that can be used across many other scientific domains. TensorFlow is licensed underApache 2.0.This optimized version of TensorFlow for Windows OS has been produced by Intel. In order to take full advantage of Intel architecture and to extract maximum performance, the TensorFlow framework has been optimized using oneAPI Deep Neural Network Library (oneDNN) primitives, a popular performance library for deep learning applications. For more information on the optimizations as well as performance data, see this blog postFaster AI Inference with Intel Optimization for TensorFlow. To activate oneDNN, execute the following in the Windows command prompt: set TF_ENABLE_ONEDNN_OPTS=1 before running your AI/ML workload.
Package: tensorflow-datasets-ko
tensorflow-datasets-koCustom TensorFlow Datasets for Korean Text DataRequirementstensorflow-datasets[c4]Installationpipinstalltensorflow-datasets-koReferencehttps://www.tensorflow.org/datasetshttps://www.tensorflow.org/datasets/catalog/c4https://www.tensorflow.org/datasets/add_datasethttps://github.com/tensorflow/datasetshttps://github.com/google-research/text-to-text-transfer-transformer#c4
Package: tensorflow-deploy-utils
TensorFlow Deploy UtilsUtilities for communication and easy management ofTensorFlow DeployTensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.Installationpipinstalltensorflow-deploy-utilsUsageimporttensorflow_deploy_utilsastfdtfd_cursor=tfd.TFD(YOUR_TEAM,YOUR_PROJECT,YOUR_MODEL_NAME)# now you can make some operationstfd_cursor.deploy_model("path/to/your/model")tfd_cursor.list_models()tfd_cursor.get_config()tfd_cursor.get_module("path/to/write/module")tfd_cursor.set_label(3,"my_label")tfd_cursor.set_stable(2)# and moreBuildingpythonsetup.pysdistbdist_wheel
pipinstalldist/tensorflow_deploy_utils-X.X.X-py3-none-any.whl
Package: tensorflow-directml
TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team
within Google’s AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains.This version of TensorFlow leverages DirectML for hardware acceleration on
Windows 10 and the Windows Subsystem for Linux (WSL).TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.
Package: tensorflow-directml-plugin
Accelerate machine learning training with TensorFlow on Windows and Windows
Subsystem for Linux (WSL).TensorFlow-DirectML-Plugin leverages TensorFlow’s Pluggable Device API to create
a DirectML backend for hardware-accelerated machine learning workflows on any
DirectX 12-compatible GPU.Install the base tensorflow and tensorflow-directml-plugin packages to get started.
Check out the GitHub repository to build from source.TensorFlow, the TensorFlow logo, and any related marks are trademarks of Google Inc.
Package: tensorflow-edwin
TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms, and from desktops to clusters of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team (https://research.google/teams/brain/) within Google's AI organization, TensorFlow comes with strong support for machine learning and deep learning with a flexible numerical computation core that can be used across many other scientific domains. TensorFlow is licensed underApache 2.0.This optimized version of TensorFlow for Windows OS has been produced by Intel. In order to take full advantage of Intel architecture and to extract maximum performance, the TensorFlow framework has been optimized using oneAPI Deep Neural Network Library (oneDNN) primitives, a popular performance library for deep learning applications. For more information on the optimizations as well as performance data, see this blog postFaster AI Inference with Intel Optimization for TensorFlow. To activate oneDNN, execute the following in the Windows command prompt: set TF_ENABLE_ONEDNN_OPTS=1 before running your AI/ML workload.
Package: tensorflow-extenteten
# tensorflow-extenteten[![PyPI version](https://badge.fury.io/py/tensorflow-extenteten.svg)](https://badge.fury.io/py/tensorflow-extenteten)
[![Python versions](https://img.shields.io/pypi/pyversions/tensorflow-extenteten.svg)](setup.py)
[![Build Status](https://travis-ci.org/raviqqe/tensorflow-extenteten.svg?branch=master)](https://travis-ci.org/raviqqe/tensorflow-extenteten)
[![License](https://img.shields.io/badge/license-unlicense-lightgray.svg)](https://unlicense.org)TensorFlow extension library## FAQ### What does extenteten mean?[extenteten means 延長](https://translate.google.com/?tl=ja#en/ja/extenteten) and
[延長 means extension](https://translate.google.com/?tl=ja#ja/en/%E5%BB%B6%E9%95%B7).
Therefore, extenteten means extension.## License[The Unlicense](https://unlicense.org)
Package: tensorflow-extra
Tensorflow ExtraExtra Utilities for TensorflowInstallationpipinstalltensorflow-extraActivationsSmeLU: Smooth ReLUimporttensorflowastfimporttensorflow_extraastfea=tf.constant([-2.5,-1.0,0.5,1.0,2.5])b=tfe.activations.smelu(a)# array([0., 0.04166667, 0.6666667 , 1.0416666 , 2.5])
Package: tensorflow-federated
TensorFlow Federated (TFF) is an open-source framework for machine learning andother computations on decentralized data. TFF has been developed to facilitateopen research and experimentation with Federated Learning (FL), an approach tomachine learning where a shared global model is trained across manyparticipating clients that keep their training data locally. For example, FL hasbeen used to train prediction models for mobile keyboards without uploadingsensitive typing data to servers.TFF enables developers to use the included federated learning algorithms withtheir models and data, as well as to experiment with novel algorithms. Thebuilding blocks provided by TFF can also be used to implement non-learningcomputations, such as aggregated analytics over decentralized data.TFF's interfaces are organized in two layers:* Federated Learning (FL) APIThe `tff.learning` layer offers a set of high-level interfaces that allowdevelopers to apply the included implementations of federated training andevaluation to their existing TensorFlow models.* Federated Core (FC) APIAt the core of the system is a set of lower-level interfaces for conciselyexpressing novel federated algorithms by combining TensorFlow with distributedcommunication operators within a strongly-typed functional programmingenvironment. This layer also serves as the foundation upon which we've built`tff.learning`.TFF enables developers to declaratively express federated computations, so theycould be deployed to diverse runtime environments. Included with TFF is asingle-machine simulation runtime for experiments. Please visit thetutorials and try it out yourself!
Package: tensorflow-federated-nightly
TensorFlow Federated (TFF) is an open-source framework for machine learning andother computations on decentralized data. TFF has been developed to facilitateopen research and experimentation with Federated Learning (FL), an approach tomachine learning where a shared global model is trained across manyparticipating clients that keep their training data locally. For example, FL hasbeen used to train prediction models for mobile keyboards without uploadingsensitive typing data to servers.TFF enables developers to use the included federated learning algorithms withtheir models and data, as well as to experiment with novel algorithms. Thebuilding blocks provided by TFF can also be used to implement non-learningcomputations, such as aggregated analytics over decentralized data.TFF's interfaces are organized in two layers:* Federated Learning (FL) APIThe `tff.learning` layer offers a set of high-level interfaces that allowdevelopers to apply the included implementations of federated training andevaluation to their existing TensorFlow models.* Federated Core (FC) APIAt the core of the system is a set of lower-level interfaces for conciselyexpressing novel federated algorithms by combining TensorFlow with distributedcommunication operators within a strongly-typed functional programmingenvironment. This layer also serves as the foundation upon which we've built`tff.learning`.TFF enables developers to declaratively express federated computations, so theycould be deployed to diverse runtime environments. Included with TFF is asingle-machine simulation runtime for experiments. Please visit thetutorials and try it out yourself!
Package: tensorflow-fedora28
TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team
within Google’s AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains.Disclaimer: Following wheel files are created by Red Hat AICoE experimental builds and are without any support.