Intel Xeon CPU
Intel Xeon has extraordinary processing power and speed, which is very suitable for running deep learning frameworks. So you can totally use our Intel-Xeon-powered GPU Servers for Keras.
Basic GPU-K80
Basic GPU-RTX 4060
Advanced GPU-A4000
Advanced GPU-V100
Advanced GPU-A5000
Enterprise GPU-A40
Enterprise GPU-RTX A6000
Enterprise GPU-RTX 4090
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 keras
Intel Xeon CPU
SSD-Based Drives
Full Root/Admin Access
99.9% Uptime Guarantee
Dedicated IP
DDoS Protection
User-Friendly and Fast Deployment
Quality Documentation and Large Community Support
Easy to Turn Models into Products
Multiple GPU Support
Multiple Backend and Modularity
Pre-Trained models
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 |