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GPU Dedicated Server for Keras and Deep Learning
Keras is the high-level API of TensorFlow 2: an approachable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning. We provide bare metal servers with GPU that are specifically designed for deep learning with Keras.
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Keras With CUDA Install - Quick And Easy
Prerequisites
Step-by-Step Instructions
Sample: conda create --name tf python=3.9
Sample: pip install --upgrade pip pip install tensorflow
# If a list of GPU devices is returned, you've installed TensorFlow successfully.
import tensorflow as tf;
print(tf.config.list_physical_devices('GPU'))
from tensorflow import keras6 Reasons to Choose our GPU Servers for Keras
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Features Comparison: Keras vs PyTorch vs TensorFlow
| 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 |
FAQs of Cloud GPU Server
A list of frequently asked questions about GPU servers for Keras.
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.
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.
Keras is mostly used for small datasets due to its slow speed. While PyTorch is preferred for large datasets and high performance.
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.
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.
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.
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.