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TensorFlow

A library for numerical computation using data flow graphs, enabling the creation and deployment of machine learning models

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High-Level APIs
Simplifies model training and iteration with high-level APIs like Keras.
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Distributed Training
Supports distributed training across multiple machines, improving scalability.
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Pre-trained Models
Access pre-trained models from TensorFlow Hub and the Model Garden for various tasks.
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Deployment Flexibility
Deploy models on servers, edge devices, browsers, mobile, and even microcontrollers.
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Visualization
TensorBoard helps in monitoring and analyzing model performance, making it easier to experiment with machine learning models.
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Automatic Differentiation
Automatically computes gradients, a critical step in training neural networks
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Eager Execution
Allows for immediate execution of code and visualization of results, enabling rapid prototyping and debugging
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Modularity
Offers modular design and flexibility, allowing users to build custom models and adapt them to specific tasks.
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Efficient GPU/CPU Computing
Write code once and execute it seamlessly on both GPU and CPU architectures.
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Data Validation and Transformation
Provides tools to preprocess, validate, and transform large datasets.
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Deep Neural Networks
Excels in training deep neural networks for tasks like image recognition and word embedding.
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Word Embedding
Create meaningful representations of words using its embedding layers.
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Recurrent Neural Networks (RNNs)
Supports RNNs for sequence-based tasks.
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Speed
It is slower compared to its competing frameworks.
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GPU support
Only supports NVIDIA GPUs and Python for GPU programming.
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Architectural limitation
Its TPU architecture allows only execution of models and doesn’t allow its training
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Inconsistent
A single name is used for various different purposes which makes it difficult for a user to remember and use

Platform

Desktop
Language
SwiftScalaRustRubyPythonJuliaJavaScriptJavaHaskellGolangC++C#

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System Requirements

#MinimumRecommended
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  • Ubuntu 16.04 or higher (64-bit)
  • macOS 10.12.6 (Sierra) or higher (64-bit) (no GPU support)
  • Windows Native - Windows 7 or higher (64-bit) (no GPU support after TF 2.10)
  • Windows WSL2 - Windows 10 19044 or higher (64-bit)
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  • Python 3.9-3.11
  • pip version 19.0 or higher for Linux (requires manylinux2014 support) and Windows
  • pip version 20.3 or higher for macOS
  • Windows Native Requires Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019
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  • NVIDIA® GPU drivers version 450.80.02 or higher
  • CUDA® Toolkit 11.8
  • cuDNN SDK 8.6.0
TensorRT to improve latency and throughput for inference.
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NVIDIA® GPU card with CUDA® Compute Capability 3.5 or higher

Ratings

4.67
5

Capterra
4.5
5
based on 43 reviews
G2CROWD
4.5
5
based on 23 reviews
InfoWorld
5.0
5
based on professional's opinion

Developer

Written in

C++, Python, CUDA

Initial Release

9 November 2015

Repository

License

Categories


Notes

  • TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.