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Feb. 25, 2018
Aug. 15, 2017


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The PlaidML Platypus A framework for making deep learning work everywhere.

PlaidML is a multi-language acceleration framework that:

  • Enables practitioners to deploy high-performance neural nets on any device
  • Allows hardware developers to quickly integrate with high-level frameworks
  • Allows framework developers to easily add support for many kinds of hardware

For background and early benchmarks see our blog post announcing the release. PlaidML is under active development and should be thought of as early alpha quality.

Current Limitations

This version of PlaidML has some notable limitations which will be addressed soon in upcoming releases:

  • macOS builds, but has correctness issues. We're addressing this in Issue #26.
  • Start-up times can be quite long
  • Training throughput much lower than we'd like
  • RNN support is not implemented

Validated Hardware

Vertex.AI runs a comprehensive set of tests for each release against these hardware targets:

  • AMD
    • R9 Nano
    • RX 480
    • K80, GTX 780
    • GTX 1070

Validated Networks

We support all of the Keras application networks from the current version (2.0.8). Validated networks are tested for performance and correctness as part of our continuous integration system.

  • CNNs
    • inception_v3
    • resnet50
    • vgg19
    • xception
    • mobilenet

Installation Instructions

Ubuntu Linux

If necessary, install Python's 'pip' tool.

sudo add-apt-repository universe && sudo apt update
sudo apt install python-pip

Make sure your system has OpenCL.

sudo apt install clinfo

If clinfo reports "Number of platforms" == 0, you must install a driver.

If you have an NVIDIA graphics card:

sudo add-apt-repository ppa:graphics-drivers/ppa && sudo apt update
sudo apt install nvidia-modprobe nvidia-384 nvidia-opencl-icd-384 libcuda1-384

If you have an AMD card, download the AMDGPU PRO driver and install according to AMD's instructions.

Best practices for python include judicious usage of Virtualenv, and we certainly recommend creating one just for plaidml:

virtualenv plaidml-venv
source ./plaidml-venv/bin/activate
pip install -U plaidml-keras

Alternatively, install the PlaidML wheels system-wide:

sudo -H pip install -U plaidml-keras

Next, setup PlaidML to use your preferred computing device:


You can test your installation by running MobileNet in plaidbench: (Remember to use sudo -H if you're not using a Virtualenv)

git clone
cd plaidbench
pip install -r requirements.txt
python mobilenet

You can adapt any Keras code by using the PlaidML backend instead of the TensorFlow, CNTK, or Theano backend that you normally use.

Simply insert this code BEFORE you import keras:

# Install the plaidml backend
import plaidml.keras

Plaidvision and Plaidbench

We've developed two open source projects:

  • plaidvision provides a simple shell for developing vision applications using your webcam
  • plaidbench is a performance testing suite designed to help users compare the performance of different cards and different frameworks

Hello VGG

One of the great things about Keras is how easy it is to play with state of the art networks. Here's all the code you need to run VGG-19:

#!/usr/bin/env python
import numpy as np
import time

# Install the plaidml backend
import plaidml.keras

import keras
import keras.applications as kapp
from keras.datasets import cifar10

(x_train, y_train_cats), (x_test, y_test_cats) = cifar10.load_data()
batch_size = 8
x_train = x_train[:batch_size]
x_train = np.repeat(np.repeat(x_train, 7, axis=1), 7, axis=2)
model = kapp.VGG19()
model.compile(optimizer='sgd', loss='categorical_crossentropy',

print("Running initial batch (compiling tile program)")
y = model.predict(x=x_train, batch_size=batch_size)

# Now start the clock and run 10 batches
print("Timing inference...")
start = time.time()
for i in range(10):
    y = model.predict(x=x_train, batch_size=batch_size)
print("Ran in {} seconds".format(time.time() - start))


PlaidML is licensed under the AGPLv3.

Our open source goals include 1) helping students get started with deep learning as easily as possible and 2) helping researchers develop new methods more quickly than is possible with other tools. PlaidML is unique in being fully open source and free of dependence on libraries like cuDNN that carry revocable and redistribution-prohibiting licenses. For situations where an alternate license is preferable please contact [email protected].

Reporting Issues

Either open a ticket on GitHub or post to plaidml-dev.

Latest Releases
 Feb. 14 2018
 Nov. 17 2017
 Oct. 26 2017