Force Keras To Use CPU

When it comes to harnessing the power of artificial intelligence, force-feeding Keras to use CPU may seem counterintuitive. After all, CPUs are often considered the less efficient option compared to GPUs. However, sometimes making Keras utilize the CPU could be the right choice for your specific computational needs.

Force Keras to use the CPU can be a viable solution when dealing with small to medium-sized datasets or when running models that don't require intensive parallel processing. It allows for efficient utilization of computational resources while still achieving accurate and reliable results. With advancements in CPU performance and optimization, using the CPU can provide a cost-effective solution for AI applications.

Why Force Keras to Use CPU?

Force Keras to use the CPU is a common requirement when training deep learning models. While GPUs offer significant computational power and speed, there are instances when using the CPU is preferred. The CPU allows for more flexibility and control over resource allocation, enabling developers to prioritize other tasks or conserve GPU resources for other applications. This article explores the reasons why one might want to force Keras to use the CPU and provides detailed information on how to achieve this.

Situations Where You May Want to Use the CPU for Keras

• Compatibility with various hardware configurations: Not all computers or servers have GPUs available, making CPU usage necessary for broader compatibility.
• Debugging and testing: When developing deep learning models or debugging issues, using the CPU can simplify the process by avoiding potential GPU-related complications.
• Reduced power consumption: GPUs consume significantly more power than CPUs. In scenarios where power usage is a concern, using the CPU can be an energy-efficient choice.
• Prioritizing other tasks: In systems where multiple applications are running concurrently, allocating GPU resources to other tasks may take precedence over deep learning training.

While these situations may warrant using the CPU for Keras, it's essential to note that GPU acceleration is generally recommended for training deep learning models due to its superior performance.

Methods to Force Keras to Use the CPU

Method 1: Set Environmental Variables

One way to force Keras to use the CPU is by setting environmental variables. By default, Keras uses the best available backend, which is typically TensorFlow or Theano. However, these backends can utilize the GPU if one is detected. The following environmental variables can be set in the command line or script to disable GPU usage:

Variable	Value
CUDA_VISIBLE_DEVICES	""
CUDA_DEVICE_ORDER	PCI_BUS_ID

By setting the value of "CUDA_VISIBLE_DEVICES" to an empty string and "CUDA_DEVICE_ORDER" to "PCI_BUS_ID," Keras will be forced to use the CPU.

Method 2: Set Backend Configuration in Keras

Another method to force Keras to use the CPU is by explicitly setting the backend configuration within the Keras script or Jupyter Notebook. This method is useful when you want to avoid modifying environmental variables outside the script. The code snippet below demonstrates how to achieve this:

// import Keras
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "" 
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"

# Set backend configuration
import keras
keras.backend.set_session(keras.backend.tf.Session(config=keras.backend.tf.ConfigProto(device_count={'CPU': 1, 'GPU': 0})))

By running this code before any other Keras code, you ensure that Keras uses the CPU for computations.

Method 3: Disable GPU in TensorFlow

If you are using TensorFlow as the Keras backend, you can disable GPU usage by using TensorFlow's own settings. The following code snippet demonstrates how this can be done:

// import TensorFlow
import tensorflow as tf

# Allocate memory only on the CPU
tf.config.set_visible_devices([], 'GPU')
visible_devices = tf.config.get_visible_devices()
for device in visible_devices:
    assert device.device_type != 'GPU'

This code snippet ensures that TensorFlow does not allocate memory on any GPU, thereby forcing Keras to use the CPU for computations.

Considerations When Forcing Keras to Use the CPU

While forcing Keras to use the CPU can be useful in certain situations, it's important to be aware of the potential drawbacks:

• Slower training times: Compared to GPUs, CPUs generally have lower computational power. As a result, training deep learning models with CPUs can be significantly slower.
• Limited memory capacity: GPUs often have more memory compared to CPUs. Using the CPU may restrict the size of the models that can be trained.
• Limited scalability: GPUs can be easily added or upgraded to improve performance, while scaling CPU resources might be more challenging and costly.

Before deciding to force Keras to use the CPU, carefully consider the impact on training speed, model capacity, and future scalability.

Alternative Approaches to Accelerating Keras

While using the CPU for Keras has its benefits, including compatibility and power efficiency, it's important to note that GPUs offer significant acceleration and enhanced performance for deep learning tasks. If GPU availability is a concern, alternative approaches can be explored to leverage GPU acceleration:

• Using cloud-based GPU instances: Various cloud service providers offer GPU instances that can be used to train deep learning models without investing in local GPU hardware.
• Utilizing external GPU enclosures: With an external GPU enclosure, a GPU can be added to a system that doesn't have an integrated GPU.
• Collaborative training: Distributed training techniques, such as utilizing multiple GPUs across different machines, can enhance training speed and computational capabilities.

These alternative approaches allow for GPU acceleration in Keras, providing the best of both worlds by leveraging the power of GPUs while overcoming limitations in hardware availability.

In conclusion, while it is possible to force Keras to use the CPU in certain situations, it is important to carefully consider the implications. GPU acceleration is generally recommended for training deep learning models due to its superior performance. However, when GPU availability is limited or power efficiency is a concern, forcing Keras to use the CPU can be a viable option. By following the provided methods, you can easily configure Keras to use the CPU and achieve your desired computational setup.

Forcing Keras to Use CPU in Python

In certain cases, it may be necessary to force Keras, a popular deep learning library, to use the CPU instead of the GPU for training or inference. This can be helpful if your computer does not have a compatible GPU or if you are experiencing memory-related issues with the GPU.

To force Keras to use the CPU, you can set the environment variable CUDA_VISIBLE_DEVICES before importing Keras. By setting this variable to an empty string or "-1", Keras will only use the CPU for computations. Another option is to disable the GPU by configuring the TensorFlow backend. This can be done by creating or editing the file "tensorflow.conf" in the Keras directory and adding the line "tensorflow_backend.set_session(config)" with the appropriate configuration settings.

Forcing Keras to use the CPU can be useful for debugging purposes or if you have limited GPU resources. However, it's important to note that training deep learning models on a CPU can be significantly slower compared to using a GPU. Therefore, it's recommended to use a GPU whenever possible to leverage its computational power for faster training and inference.

Frequently Asked Questions

In this section, we will address some common questions related to forcing Keras to use CPU instead of GPU.

1. How can I force Keras to use CPU instead of GPU?

To force Keras to use CPU instead of GPU, you can set the environment variable 'CUDA_VISIBLE_DEVICES' to an empty string before importing Keras. This will effectively hide any available GPUs and make Keras use the CPU. Here's an example:

import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''

import keras

By setting 'CUDA_VISIBLE_DEVICES' to an empty string, Keras will only use the CPU for computations.

2. How can I check if Keras is using the CPU instead of GPU?

You can use the following code snippet to check if Keras is using the CPU:

from keras import backend as K
print(K.tensorflow_backend._get_available_gpus())

If Keras is using the CPU, the output of the above code will be an empty list. If Keras is using the GPU, it will list the available GPUs.

3. Can I force Keras to use CPU without modifying the environment variable?

Yes, you can force Keras to use the CPU without modifying the environment variable 'CUDA_VISIBLE_DEVICES' by explicitly specifying the CPU as the device to use. Here's an example:

import tensorflow as tf
import keras

with tf.device('/CPU:0'):
    # Your Keras code here

By using the 'with tf.device' context manager and specifying '/CPU:0', Keras will use the CPU for computations within that block of code.

4. Can I switch between CPU and GPU in the middle of my Keras code?

Yes, you can switch between CPU and GPU in the middle of your Keras code if you have multiple devices available. You can use the 'with tf.device' context manager to specify the device for different parts of your code. Here's an example:

import tensorflow as tf
import keras

with tf.device('/CPU:0'):
    # Code executed on CPU

with tf.device('/GPU:0'):
    # Code executed on GPU

By using the 'with tf.device' context manager and specifying the device you want to use, Keras will switch between CPU and GPU based on your code blocks.

5. How can I limit the CPU usage of Keras to a specific number of cores?

To limit the CPU usage of Keras to a specific number of cores, you can use the 'tensorflow' backend and set the 'OMP_NUM_THREADS' environment variable. Here's an example:

import os
os.environ['OMP_NUM_THREADS'] = '4'  # Set the number of cores to 4

import keras

# Your Keras code here

By setting 'OMP_NUM_THREADS' to the desired number of cores, Keras will limit its CPU usage to that number of cores.

To summarize, forcing Keras to use CPU can be done by setting the correct configuration in the TensorFlow backend. By utilizing the "CUDA_VISIBLE_DEVICES" environment variable and disabling GPU, users can ensure that their Keras models run solely on the CPU. This can be particularly useful when working with limited GPU resources or when CPU performance outweighs the benefits of parallel processing on the GPU.

By following the steps outlined in this article, users can easily override the default GPU usage of Keras and seamlessly switch to CPU. It is important to note that while running models on the CPU may be slower compared to the GPU, this approach offers a viable solution in scenarios where GPU acceleration is not necessary or available. Whether you are working on a machine with limited GPU capabilities or simply prefer using the CPU, adjusting Keras to utilize the CPU can ensure efficient and optimal model training.