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
- Supported Hardware
- Validated Networks
- Installation Instructions
- Building PlaidML
- Reporting Issues
This version of PlaidML has some notable limitations which will be addressed soon in upcoming releases:
- Start-up times can be quite long
- Training throughput much lower than we'd like
- RNN support is not implemented
Vertex.AI runs a comprehensive set of tests for each release against these hardware targets:
- AMD
- R9 Nano
- RX 480
- NVIDIA
- K80, GTX 780
- GTX 1070
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
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
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.
Install the PlaidML wheels system-wide:
sudo pip install -U plaidml-keras
Next, setup PlaidML to use your preferred computing device:
plaidml-setup
You can test your installation by running MobileNet in plaidbench:
git clone https://github.com/plaidml/plaidbench.git
cd plaidbench
sudo pip install -r requirements.txt
python plaidbench.py 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
plaidml.keras.install_backend()
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
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
plaidml.keras.install_backend()
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',
metrics=['accuracy'])
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 depends on Bazel v0.6.0 or higher.
bazel build -c opt plaidml:wheel plaidml/keras:wheel
sudo pip install -U bazel-bin/plaidml/*whl bazel-bin/plaidml/keras/*whl
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].
Either open a ticket on GitHub or post to plaidml-dev.