What are the differences between TensorFlow.js with WebGL and CPU?
TensorFlow.js can run on both WebGL and CPU, but they have different performance characteristics. WebGL leverages the GPU for parallel processing, making it faster for large computations, while CPU is generally slower but can be more efficient for smaller tasks.
In what scenarios should I use WebGL over CPU?
You should use WebGL when you're dealing with large datasets or complex models that require heavy computation. It's particularly beneficial for tasks like image processing or training deep learning models.
Are there any limitations when using WebGL?
Yes, WebGL has some limitations, such as compatibility issues with older browsers, and it may not support all TensorFlow.js operations. Additionally, debugging can be more challenging compared to CPU.
How can I switch between WebGL and CPU in my TensorFlow.js code?
You can switch between WebGL and CPU by using the tf.setBackend()
function. For example, await tf.setBackend('webgl')
for WebGL and await tf.setBackend('cpu')
for CPU.
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