kandi background

tensorflow | An Open Source Machine Learning Framework for Everyone | Machine Learning library

Download this library from

kandi X-RAY | tensorflow Summary

tensorflow is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow has a Permissive License and it has medium support. However tensorflow has 210 bugs and it has 5 vulnerabilities. You can download it from GitHub.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources 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 to conduct 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 stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages. Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.

kandi-support Support

  • tensorflow has a medium active ecosystem.
  • It has 164372 star(s) with 86673 fork(s). There are 7863 watchers for this library.
  • There were 8 major release(s) in the last 6 months.
  • There are 2240 open issues and 32273 have been closed. On average issues are closed in 213 days. There are 191 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of tensorflow is v2.9.0-rc1

quality kandi Quality

  • tensorflow has 210 bugs (14 blocker, 3 critical, 121 major, 72 minor) and 7277 code smells.

securitySecurity

  • tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • tensorflow code analysis shows 5 unresolved vulnerabilities (0 blocker, 3 critical, 2 major, 0 minor).
  • There are 300 security hotspots that need review.

license License

  • tensorflow is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.

buildReuse

  • tensorflow releases are available to install and integrate.
  • Installation instructions, examples and code snippets are available.
Top functions reviewed by kandi - BETA

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

  • Run a single model iteration .
  • Splits the given computation into multiple tensors .
  • Decorate a function .
  • Compute an RNN layer .
  • Compute the eigenvalues of a Hermitagonal matrix .
  • Decorator for functions .
  • Produce an RNN layer .
  • Return the gradient of an einsum operator .
  • Creates a csv dataset .
  • Extracts inputs and attrs .

tensorflow Key Features

An Open Source Machine Learning Framework for Everyone

tensorflow Examples and Code Snippets

  • Install
  • WebSocket not working when trying to send generated answer by keras
  • Could not resolve com.google.guava:guava:30.1-jre - Gradle project sync failed. Basic functionality will not work properly - in kotlin project
  • Tensorflow setup on RStudio/ R | CentOS
  • Saving model on Tensorflow 2.7.0 with data augmentation layer
  • ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'
  • Accuracy in Calculating Fourth Derivative using Finite Differences in Tensorflow
  • AssertionError: Tried to export a function which references untracked resource
  • Stopping and starting a deep learning google cloud VM instance causes tensorflow to stop recognizing GPU
  • Tensorflow - Multi-GPU doesn’t work for model(inputs) nor when computing the gradients
  • Tensorflow GPU Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found

Install

$ pip install tensorflow

Community Discussions

Trending Discussions on tensorflow
  • What is XlaBuilder for?
  • WebSocket not working when trying to send generated answer by keras
  • Could not resolve com.google.guava:guava:30.1-jre - Gradle project sync failed. Basic functionality will not work properly - in kotlin project
  • Tensorflow setup on RStudio/ R | CentOS
  • Saving model on Tensorflow 2.7.0 with data augmentation layer
  • Is it possible to use a collection of hyperspectral 1x1 pixels in a CNN model purposed for more conventional datasets (CIFAR-10/MNIST)?
  • ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'
  • Accuracy in Calculating Fourth Derivative using Finite Differences in Tensorflow
  • AssertionError: Tried to export a function which references untracked resource
  • Stopping and starting a deep learning google cloud VM instance causes tensorflow to stop recognizing GPU
Trending Discussions on tensorflow

QUESTION

What is XlaBuilder for?

Asked 2022-Mar-20 at 18:41

What's the XLA class XlaBuilder for? The docs describe its interface but don't provide a motivation.

The presentation in the docs, and indeed the comment above XlaBuilder in the source code

// A convenient interface for building up computations.

suggests it's no more than a utility. However, this doesn't appear to explain its behaviour in other places. For example, we can construct an XlaOp with an XlaBuilder via e.g.

XlaOp ConstantLiteral(XlaBuilder* builder, const LiteralSlice& literal);

Here, it's not clear to me what role builder plays (note functions for constructing XlaOps aren't documented on the published docs). Further, when I add two XlaOps (with + or Add) it appears the ops must be constructed with the same builder, else I see

F tensorflow/core/platform/statusor.cc:33] Attempting to fetch value instead of handling error Invalid argument: No XlaOp with handle -1

Indeed, XlaOp retains a handle for an XlaBuilder. This suggests to me that the XlaBuilder has a more fundamental significance.

Beyond the title question, is there a use case for using multiple XlaBuilders, or would you typically use one global instance for everything?

ANSWER

Answered 2021-Dec-15 at 01:32

XlaBuilder is the C++ API for building up XLA computations -- conceptually this is like building up a function, full of various operations, that you could execute over and over again on different input data.

Some background, XLA serves as an abstraction layer for creating executable blobs that run on various target accelerators (CPU, GPU, TPU, IPU, ...), conceptually kind of an "accelerator virtual machine" with conceptual similarities to earlier systems like PeakStream or the line of work that led to ArBB.

The XlaBuilder is a way to enqueue operations into a "computation" (similar to a function) that you want to run against the various set of accelerators that XLA can target. The operations at this level are often referred to as "High Level Operations" (HLOs).

The returned XlaOp represents the result of the operation you've just enqueued. (Aside/nerdery: this is a classic technique used in "builder" APIs that represent the program in "Static Single Assignment" form under the hood, the operation itself and the result of the operation can be unified as one concept!)

XLA computations are very similar to functions, so you can think of what you're doing with an XlaBuilder like building up a function. (Aside: they're called "computations" because they do a little bit more than a straightforward function -- conceptually they are coroutines that can talk to an external "host" world and also talk to each other via networking facilities.)

So the fact XlaOps can't be used across XlaBuilders may make more sense with that context -- in the same way that when building up a function you can't grab intermediate results in the internals of other functions, you have to compose them with function calls / parameters. In XlaBuilder you can Call another built computation, which is a reason you might use multiple builders.

As you note, you can choose to inline everything into one "mega builder", but often programs are structured as functions that get composed together, and ultimately get called from a few different "entry points". XLA currently aggressively specializes for the entry points it sees API users using, but this is a design artifact similar to inlining decisions, XLA can conceptually reuse computations built up / invoked from multiple callers if it thought that was the right thing to do. Usually it's most natural to enqueue things into XLA however is convenient for your description from the "outside world", and allow XLA to inline and aggressively specialize the "entry point" computations you've built up as you execute them, in Just-in-Time compilation fashion.

Source https://stackoverflow.com/questions/70339753

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.
You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.

Support

The TensorFlow project strives to abide by generally accepted best practices in open-source software development:.

Build your Application

Share this kandi XRay Report