How to run Tensorflow on CPU

python

performance

optimization

cpu-optimization

byNikita Barsukov·Jan 14, 2025

To instruct TensorFlow to use exclusively your CPU, ensure the CUDA_VISIBLE_DEVICES environment variable is set to an empty string. Include the following two lines at the start of your Python script:

import os
# Run Forrest, Run!... but only on CPU.
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import tensorflow as tf

This small wizardry tricks TensorFlow into believing that it only has a CPU at its disposal.

Securing that performance boost

Directing TensorFlow towards the CPU is simple, but getting that CPU to perform as efficiently as possible is the real ballgame. Here's how you can optimize your CPU's performance:

Scalability is key: Future-proof your code--plan and write your code keeping scalability in mind. Because with data, size really does matter.
Exploit Parallel Processing: Maximise throughput with the use of intra-operation parallelism. Adjust TensorFlow performance with tf.config.threading.set_intra_op_parallelism_threads().
Cache-In: Cache computations to prevent TensorFlow from doing redundant work. You're a coder, not a gym trainer. No need for unnecessary reps.
Efficient Algorithms: Spotlight on reducing computational power. With fewer CPU cycles used, you may claim the title of "The Conductor of the CPU Orchestra."
Memory Management: Sure, TensorFlow has good memory, but a little manual influence can go a long way, especially when dealing with the big guys (large models).

Maxed-out CPU usage

Looking for more fine-grained control over TensorFlow's CPU utilization? Check out these expert-level tips:

Device Placement Logging: Great for seeing exactly how TensorFlow is using your resources. Enable it with tf.debugging.set_log_device_placement(True).
Call the shots with Manual Device Management: Decide where each operation is run by manually setting the devices with with tf.device('/CPU:0').
Lean on Protobuf: For king-style control, Protobuf configuration files allow you to tweak performance to your exact specifications.
Stay up-to-date: Keep an eye on TensorFlow's GitHub for the newest optimizations and CPU-related solutions.

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