Keras GPU: Using Keras On Single GPU or Multi-GPU

# If a list of GPU devices is returned, you've installed TensorFlow successfully. import tensorflow as tf; print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) from tensorflow import keras

Features	Keras	TensorFlow	PyTorch	MXNet
API Level	High	High and low	Low	Hign and low
Architecture	Simple, concise, readable	Not easy to use	Complex, less readable	Complex, less readable
Datasets	Smaller datasets	Large datasets, high performance	Large datasets, high performance	Large datasets, high performance
Debugging	Simple network, so debugging is not often needed	Difficult to conduct debugging	Good debugging capabilities	Hard to debug pure symbol codes
Trained Models	Yes	Yes	Yes	Yes
Popularity	Most popular	Second most popular	Third most popular	Fourth most popular
Speed	Slow, low performance	Fastest on VGG-16, high performance	Fastest on Faster-RCNN, high performance	Fastest on ResNet-50, high performance
Written In	Python	C++, CUDA, Python	Lua, LuaJIT, C, CUDA, and C++	C++, Python

Features

Keras

TensorFlow

PyTorch

MXNet

API Level

High

High and low

Low

Hign and low

Architecture

Simple, concise, readable

Not easy to use

Complex, less readable

Datasets

Smaller datasets

Large datasets, high performance

Debugging

Simple network, so debugging is not often needed

Difficult to conduct debugging

Good debugging capabilities

Hard to debug pure symbol codes

Trained Models

Yes

Popularity

What Keras is used for?

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Keras is a high-level, deep-learning API developed by Google for implementing neural networks. It is written in Python and is used to simplify the implementation of the neural network. It also supports multiple backend neural network computations. For these uses, you often need GPUs for Keras.

Why do we need Keras?

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Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load:
It offers consistent & simple APIs.
It minimizes the number of user actions required for common use cases.
It provides clear and actionable feedback upon user error.

Is Keras better than PyTorch?

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Keras is mostly used for small datasets due to its slow speed. While PyTorch is preferred for large datasets and high performance.

Does Keras automatically use GPU?

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Keras models will transparently run on a single GPU with no code changes required. Note: Use tf. config. list_physical_devices('GPU') to confirm that TensorFlow is using the GPU.

What is Keras GPU?

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Keras is a Python-based, deep learning API that runs on top of the TensorFlow machine learning platform, and fully supports GPUs. Keras was historically a high-level API sitting on top of a lower-level neural network API. It served as a wrapper for lower-level TensorFlow libraries.

Do I need to install Keras if I have TensorFlow?

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Thanks to a new update in TensorFlow 2.0+, if you installed TensorFlow as instructed, you don't need to install Keras anymore because it is installed with TensorFlow. For those using TensorFlow versions before 2.0, here are the instructions for installing Keras using Pip.

When do I need GPUs for Keras?

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If you're training a real-life project or doing some academic or industrial research, then for sure you need a GPU for fast computation.
If you're just learning Keras and want to play around with its different functionalities, then Keras without GPU is fine and your CPU in enough for that.

What are the best GPUs for Keras deep learning?

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Today, leading vendor NVIDIA offers the best GPUs for Keras deep learning in 2022. The models are the RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, RTX A4000, Tesla K80, and Tesla K40. We will offer more suitable GPUs for Keras in 2023.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for Keras.

How can I run a Keras model on multiple GPUs?

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We recommend doing so using the TensorFlow backend. There are two ways to run a single model on multiple GPUs: data parallelism and device parallelism. In most cases, what you need is most likely data parallelism.

How can I run Keras on GPU?

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If you are running on the TensorFlow or CNTK backends, your code will automatically run on GPU if any available GPU is detected.
If you are running on the Theano backend, you can use theano flags or manually set config at the beginning of your code.

What are the advantages of bare metal GPUs for Keras?

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Bare metal GPU servers for Keras will provide you with an improved application and data performance while maintaining high-level security. When there is no virtualization, there is no overhead for a hypervisor, so the performance benefits. Most virtual environments and cloud solutions come with security risks.
DBM GPU Servers for Keras use all bare metal servers, so we have best GPU dedicated server for AI.

TensorFlow vs Keras: Key Differences Between Them

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1. Keras is a high-level API that can run on top of TensorFlow, CNTK, and Theano, whereas TensorFlow is a framework that offers both high and low-level APIs.
2. Keras is perfect for quick implementations, while Tensorflow is ideal for Deep learning research and complex networks.
3. Keras uses API debug tools, such as TFDBG. On the other hand, in Tensorflow, you can use Tensor board visualization tools for debugging.
4. Keras has a simple architecture that is readable and concise, while Tensorflow is not very easy to use.
5. Keras is usually used for small datasets, but TensorFlow is used for high-performance models and large datasets.
6. In Keras, community support is minimal, while in TensorFlow, it is backed by a large community of tech companies.
7. Keras is mostly used for low-performance models, whereas TensorFlow can be used for high-performance models.

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