GPUtil is a Python module for getting the GPU status from NVIDA GPUs using nvidia-smi. GPUtil locates all GPUs on the computer, determines their availablity and returns a ordered list of available GPUs. Availablity is based opun the current memory consumption and load of each GPU. The module is written with GPU selection for Deep Learning in mind, but it is not task/library specific and it can be applied to any task, where it may be useful to identify available GPUs.

Table of Contents

1. Requirements
2. Installation
3. Usage
4. Main functions
5. Helper functions
6. Examples
7. Select first available GPU in Caffe
8. Occupy only 1 GPU in TensorFlow
9. License

CUDA GPU with latest CUDA driver installed. GPUtil uses the program nvidia-smi to get the GPU status of all available CUDA GPUs. nvidia-smi should be installed automatically, when you install your CUDA driver.

Supports both Python 2.X and 3.X.

Python libraries:

• numpy
• subprocess (The Python Standard Library)
• random (The Python Standard Library)
• time (The Python Standard Library)

Tested on CUDA driver version 367.57 and 375.26 with Python 3.5.2 and 3.5.4.

1. Open a terminal (Ctrl+Shift+T)
2. Type pip install gputil
3. Test the installation
   1. Open a terminal in a folder other than the GPUtil folder
   2. Start a python console by typing python in the terminal
   3. In the newly opened python console, type:

      import GPUtil
      GPUtil.showUtilization()

   4. Your output should look something like following, depending on your number of GPUs and their current usage:

       ID  GPU  MEM
      --------------
        0    0%   0%
      

1. Download or clone repository to your computer
2. Add GPUtil folder to ~/.bashrc
   1. Open a new terminal (Press Ctrl+Alt+T)
   2. Open bashrc:

      gedit ~/.bashrc
      

   3. Added your GPUtil folder to the environment variable PYTHONPATH (replace <path_to_gputil> with your folder path):

      export PYTHONPATH="$PYTHONPATH:<path_to_gputil>"
      
      Example:
      export PYTHONPATH="$PYTHONPATH:/home/anderskm/github/gputil"
      

   4. Save ~/.bashrc and close gedit
   5. Restart your terminal
3. Test the installation
   1. Open a terminal in a folder other than the GPUtil folder
   2. Start a python console by typing python in the terminal
   3. In the newly opened python console, type:

      import GPUtil
      GPUtil.showUtilization()

   4. Your output should look something like following, depending on your number of GPUs and their current usage:

       ID  GPU  MEM
      --------------
        0    0%   0%
      

To include GPUtil in our Python code, all you hve to do is included it at the beginning of your script:

import GPUtil

Once included all functions are available. The functions along with a short description of inputs, outputs and their functionality can be found in the following two sections.

deviceIDs = GPUtil.getAvailable(order = 'first', limit = 1, maxLoad = 0.5, maxMemory = 0.5)

Returns a list ids of available GPUs. Availablity is determined based on current memory usage and load. The order, maximum number of devices, their maximum load and maximum memory consumption are determined by the input arguments.

• Inputs
  • order - Deterimes the order in which the available GPU device ids are returned. order should be specified as one of the following strings:
    • 'first' - orders available GPU device ids by ascending id (defaut)
    • 'last' - orders available GPU device ids by descending id
    • 'random' - orders the available GPU device ids randomly
    • 'load'- orders the available GPU device ids by ascending load
    • 'memory' - orders the available GPU device ids by ascending memory usage
  • limit - limits the number of GPU device ids returned to the specified number. Must be positive integer. (default = 1)
  • maxLoad - Maximum current relative load for a GPU to be considered available. GPUs with a load larger than maxLoad is not returned. (default = 0.5)
  • maxMemory - Maximum current relative memory usage for a GPU to be considered available. GPUs with a current memory usage larger than maxMemory is not returned. (default = 0.5)
• Outputs
  • deviceIDs - list of all available GPU device ids. A GPU is considered available, if the current load and memory usage is less than maxLoad and maxMemory, respectively. The list is ordered according to order. The maximum number of returned device ids is limted by limit.

deviceID = GPUtil.getFirstAvailable(order = 'first', maxLoad=0.5, maxMemory=0.5, attempts=1, interval=900, verbose=False)

Returns the first avaiable GPU. Availablity is determined based on current memory usage and load, and the ordering is determined by the specified order. If no available GPU is found, an error is thrown. When using the default values, it is the same as getAvailable(order = 'first', limit = 1, maxLoad = 0.5, maxMemory = 0.5)

• Inputs
  • order - See the description for GPUtil.getAvailable(...)
  • maxLoad - Maximum current relative load for a GPU to be considered available. GPUs with a load larger than maxLoad is not returned. (default = 0.5)
  • maxMemory - Maximum current relative memory usage for a GPU to be considered available. GPUs with a current memory usage larger than maxMemory is not returned. (default = 0.5)
  • attempts - Number of attempts the function should make before giving up finding an available GPU. (default = 1)
  • interval - Interval in seconds between each attempt to find an available GPU. (default = 900 --> 15 mins)
  • verbose - If True, prints the attempt number before each attempt and the GPU id if an available is found.
• Outputs
  • deviceID - list with 1 element containing the first available GPU device ids. A GPU is considered available, if the current load and memory usage is less than maxLoad and maxMemory, respectively. The order and limit are fixed to 'first' and 1, respectively.

GPUtil.showUtilization()

Prints the current status (id, memory usage and load) of all GPUs

• Inputs
  • None
• Outputs
  • None

GPUs = GPUtil.getGPUs()

• Inputs
  • None
• Outputs
  • GPUs - list of all GPUs. Each GPU corresponds to one GPU in the computer and contains a device id, relative load and relative memory usage.

GPUavailability = GPUtil.getAvailability(GPUs, maxLoad = 0.5, maxMemory = 0.5)

Given a list of GPUs (see GPUtil.getGPUs()), return a equally sized list of ones and zeroes indicating which corresponding GPUs are available.

• Inputs
  • GPUs - List of GPUs. See GPUtil.getGPUs()
  • maxLoad - Maximum current relative load for a GPU to be considered available. GPUs with a load larger than maxLoad is not returned. (default = 0.5)
  • maxMemory - Maximum current relative memory usage for a GPU to be considered available. GPUs with a current memory usage larger than maxMemory is not returned. (default = 0.5)
• Outputs
  • GPUavailability - binary list indicating if GPUs are available or not. A GPU is considered available, if the current load and memory usage is less than maxLoad and maxMemory, respectively.

See demo_GPUtil.py for examples and more details.

In the Deep Learning library Caffe, the user can switch between using the CPU or GPU through their Python interface. This is done by calling the methods caffe.set_mode_cpu() and caffe.set_mode_gpu(), respectively. Below is a minimum working example for selecting the first available GPU with GPUtil to run a Caffe network.

# Import caffe and GPUtil
import caffe
import GPUtil

# Get the first available GPU
DEVICE_ID_LIST = GPUtil.getFirstAvailable()
DEVICE_ID = DEVICE_ID_LIST[0] # grab first element from list

# Select GPU mode
caffe.set_mode_gpu()
# Select GPU id
caffe.set_device(DEVICE_ID)

# Initialize your network here

Note: At the time of writing this example, the Caffe Python wrapper only supports 1 GPU, although the underlying code supports multiple GPUs. Calling directly Caffe from the terminal allows for using multiple GPUs.

By default, TensorFlow will occupy all available GPUs when using a gpu as a device (e.g. tf.device('\gpu:0')). By setting the environment variable CUDA_VISIBLE_DEVICES, the user can mask which GPUs should be visible to TensorFlow via CUDA (See CUDA_VISIBLE_DEVICES - Masking GPUs). Using GPUtil.py, the CUDA_VISIBLE_DEVICES can be set programmatically based on the available GPUs. Below is a minimum working example of how to occupy only 1 GPU in TensorFlow using GPUtil. To run the code, copy it into a new python file (e.g. demo_tensorflow_gputil.py) and run it (e.g. enter python demo_tensorflow_gputil.py in a terminal).

Note: Even if you set the device you run your code on to a CPU, TensorFlow will occupy all available GPUs. To avoid this, all GPUs can be hidden from TensorFlow with os.environ["CUDA_VISIBLE_DEVICES"] = ''.

# Import os to set the environment variable CUDA_VISIBLE_DEVICES
import os
import tensorflow as tf
import GPUtil

# Get the first available GPU
DEVICE_ID_LIST = GPUtil.getFirstAvailable()
DEVICE_ID = DEVICE_ID_LIST[0] # grab first element from list

# Set CUDA_VISIBLE_DEVICES to mask out all other GPUs than the first available device id
os.environ["CUDA_VISIBLE_DEVICES"] = str(DEVICE_ID)

# Since all other GPUs are masked out, the first available GPU will now be identified as GPU:0
device = '/gpu:0'
print('Device ID (unmasked): ' + str(DEVICE_ID))
print('Device ID (masked): ' + str(0))

# Run a minimum working example on the selected GPU
# Start a session
with tf.Session() as sess:
    # Select the device
    with tf.device(device):
        # Declare two numbers and add them together in TensorFlow
        a = tf.constant(12)
        b = tf.constant(30)
        result = sess.run(a+b)
        print('a+b=' + str(result))

Your output should look something like the code block below. Notice how only one of the GPUs are found and created as a tensorflow device.

I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
Device: /gpu:0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:02:00.0
Total memory: 11.90GiB
Free memory: 11.76GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id: 0000:02:00.0)
a+b=42

Comment the os.environ["CUDA_VISIBLE_DEVICES"] = str(DEVICE_ID) line and compare the two outputs. Depending on your number of GPUs, your output should look something like code block below. Notice, how all 4 GPUs are being found and created as a tensorflow device, whereas when CUDA_VISIBLE_DEVICES was set, only 1 GPU was found and created.

I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
Device: /gpu:0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:02:00.0
Total memory: 11.90GiB
Free memory: 11.76GiB
W tensorflow/stream_executor/cuda/cuda_driver.cc:590] creating context when one is currently active; existing: 0x2c8e400
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 1 with properties: 
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:03:00.0
Total memory: 11.90GiB
Free memory: 11.76GiB
W tensorflow/stream_executor/cuda/cuda_driver.cc:590] creating context when one is currently active; existing: 0x2c92040
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 2 with properties: 
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:83:00.0
Total memory: 11.90GiB
Free memory: 11.76GiB
W tensorflow/stream_executor/cuda/cuda_driver.cc:590] creating context when one is currently active; existing: 0x2c95d90
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 3 with properties: 
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:84:00.0
Total memory: 11.90GiB
Free memory: 11.76GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 0 and 2
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 0 and 3
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 1 and 2
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 1 and 3
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 2 and 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 2 and 1
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 3 and 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 3 and 1
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 1 2 3 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y Y N N 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 1:   Y Y N N 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 2:   N N Y Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 3:   N N Y Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id: 0000:02:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:1) -> (device: 1, name: TITAN X (Pascal), pci bus id: 0000:03:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:2) -> (device: 2, name: TITAN X (Pascal), pci bus id: 0000:83:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:3) -> (device: 3, name: TITAN X (Pascal), pci bus id: 0000:84:00.0)
a+b=42

See LICENSE