Windows 10+RTX 2070环境下Anaconda安装keras-gpu后GPU是否优化使用？

Hey there! Let's clear up your confusion and answer your questions about GPU utilization step by step.

Why No Errors This Time? #

The key difference here is how Anaconda handles dependencies for the keras-gpu package compared to your previous manual setup:

• Anaconda's keras-gpu comes bundled with environment-specific versions of cuda-toolkit and cudnn. These are isolated within your Anaconda environment, meaning they don't rely on system-wide installations of CUDA, CUDNN, or Microsoft Visual Studio 2015.
• In your earlier setup, you had to install those components manually because the keras-gpu package didn't include them—you needed system-level dependencies for the framework to access GPU resources. Anaconda has simplified this by encapsulating all necessary GPU tools directly in the package ecosystem, eliminating the need for manual system installs.

Will Your RTX 2070 Be Optimized for Training? #

Absolutely—if everything is configured correctly, your GPU will be fully utilized for Keras model training. Here's why and how to confirm:

• The cuda-toolkit and cudnn installed by Anaconda are pre-tuned to work with the version of Keras (and its underlying TensorFlow backend) you're using. RTX 2070 supports CUDA 10+ (which matches the version you have), so it can leverage CUDA cores and CuDNN's optimized deep learning operations.
• To verify GPU recognition and utilization:
  1. Run this quick code snippet in your Anaconda environment:

     import tensorflow as tf
     # Check if GPU is detected
     print("Available GPUs:", tf.config.list_physical_devices('GPU'))
     # Verify CUDA is enabled
     print("CUDA available:", tf.test.is_built_with_cuda())
     

  2. When training a model, open Task Manager (Windows) and go to the Performance tab. Look at the GPU utilization graph—if it spikes during training, your RTX 2070 is actively being used for acceleration.

One extra note: Anaconda's environment-specific CUDA doesn't conflict with any system-wide CUDA installations you might have. It operates independently, which keeps your setup clean and avoids version mismatch issues.

内容的提问来源于stack exchange，提问作者Shiro