Installation and GPU setup in TensorFlow - Model Metrics & Evaluation

Metrics & Evaluation - Installation and GPU setup

Which metric matters for Installation and GPU setup and WHY

For installation and GPU setup, the key metric is successful hardware acceleration. This means TensorFlow uses the GPU to speed up training and prediction. We check if TensorFlow detects the GPU and runs operations on it. This matters because GPU use can make models train much faster, saving time and energy.

Confusion matrix or equivalent visualization

Instead of a confusion matrix, we use a simple test code to confirm GPU is active:

import tensorflow as tf
print("Num GPUs Available:", len(tf.config.list_physical_devices('GPU')))

If output shows 1 or more GPUs, setup is correct. If 0, GPU is not detected.

Precision vs Recall tradeoff with concrete examples

In GPU setup, the tradeoff is between speed and compatibility. Using GPU speeds up training (like a sports car), but some older hardware or drivers may cause errors (like a sports car needing special fuel). Using CPU is slower but more stable. The goal is to get GPU working correctly for faster results without errors.

What "good" vs "bad" metric values look like for this use case

Good: TensorFlow detects GPU (Num GPUs Available: 1 or more), training runs faster than CPU, no errors.

Bad: TensorFlow shows 0 GPUs, training is slow, errors about CUDA or drivers appear.

Metrics pitfalls

• Not installing correct GPU drivers or CUDA toolkit causes GPU not to be detected.
• Using incompatible TensorFlow version with GPU drivers leads to errors.
• Ignoring environment variables needed for GPU usage.
• Assuming GPU is used without checking with test code.
• Overlooking that some operations may still run on CPU even with GPU available.

Self-check

Your TensorFlow installation shows 0 GPUs available but your computer has a GPU. Is your setup good for GPU training? Why or why not?

Answer: No, it is not good. TensorFlow is not detecting the GPU, so training will be slow on CPU. You need to check drivers, CUDA, and TensorFlow GPU version to fix this.

Key Result

Successful GPU detection and usage is the key metric for installation and GPU setup.