Question:
I am using Keras with TensorFlow and CUDA, and I need a way to sometimes run my models on the CPU instead of the GPU without having to set up a separate CPU-only TensorFlow installation. With Theano, one could simply change the flags, but I haven’t found similar options for TensorFlow when used with Keras. Is there an approach to force the use of either the CPU or GPU on demand?
hey ppl, i’ve been tryin tf.device(‘/cpu:0’) for simpler ops - works kinda fine, though sometimes stuff misbehaves. anyone else playin with these settings or got any tips for smoother control? curious abt different workarounds.
One effective method to force the CPU is to set the environment variable CUDA_VISIBLE_DEVICES to ‘-1’ before loading TensorFlow. This configuration prevents TensorFlow from accessing any GPUs, thereby ensuring the computations run on the CPU. From personal experience, this approach is reliable and reusable across different projects. Alternatively, using tf.device(‘/cpu:0’) in a specific block of code can restrict operations to the CPU, although it may not always cover all operations without proper context management. Both strategies have proven useful.
a neat alternative is to use tf.config.set_visible_devices(, ‘GPU’) at the very start of your code. it makes sure that no gpu is used, but remember to run it before anything tensorflow loads. works like a charm for my experiments.
An additional approach involves creating a dedicated TensorFlow session configured to use only the CPU. Prior to running any model code, you can construct the session with a configuration that explicitly sets the GPU device list to empty. For example, by using tf.compat.v1.ConfigProto with the gpu_options set to have an empty visible_device_list and then initializing the session with tf.compat.v1.Session(config=…), you force all Keras operations to run on the CPU. In my experience this method allows for consistent control over device usage without additional installations or environment variables.