Aeroxander / plaidml

PlaidML is a framework for making deep learning work everywhere.

A platform for making deep learning work everywhere.

• Documentation
• Installation Instructions
• Building PlaidML
• Contributing
• Reporting Issues

PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.

PlaidML sits underneath common machine learning frameworks, enabling users to access any hardware supported by PlaidML. PlaidML supports Keras, ONNX, and nGraph.

As a component within the nGraph Compiler stack, PlaidML further extends the capabilities of specialized deep-learning hardware (especially GPUs,) and makes it both easier and faster to access or make use of subgraph-level optimizations that would otherwise be bounded by the compute limitations of the device.

As a component under Keras, PlaidML can accelerate training workloads with customized or automatically-generated Tile code. It works especially well on GPUs, and it doesn't require use of CUDA/cuDNN on Nvidia* hardware, while achieving comparable performance.

It works on all major operating systems: Linux, macOS, and Windows.

Getting started

For most platforms, getting started with accelerated deep learning is as easy as running a few commands (assuming you have Python (v2 or v3) installed. If this doesn't work, see the troubleshooting documentation:

virtualenv plaidml
source plaidml/bin/activate
pip install plaidml-keras plaidbench

Choose which accelerator you'd like to use (many computers, especially laptops, have multiple):

plaidml-setup

Next, try benchmarking MobileNet inference performance:

plaidbench keras mobilenet

Or, try training MobileNet:

plaidbench --batch-size 16 keras --train mobilenet

Validated Hardware

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

• AMD

  • R9 Nano
  • RX 480
  • Vega 10

• Intel

  • HD4000
  • HD Graphics 505

• NVIDIA

  • K80, GTX 780, GT 640M
  • GTX 1070, 1050

Validated Networks

We support all of the Keras application networks from current versions of 2.x. Validated networks are tested for performance and correctness as part of our continuous integration system.

• CNNs

  • Inception v3
  • ResNet50
  • VGG19
  • Xception
  • MobileNet
  • DenseNet
  • ShuffleNet

• LSTM

  • examples/imdb_lstm.py (from keras)

Installation Instructions

We support a variety of operating systems and installation methods.

• Ubuntu Linux
• macOS
• Windows

Demos and Related Projects

Plaidbench

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 os
import time

os.environ["KERAS_BACKEND"] = "plaidml.keras.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))

Reporting Issues

Either open a ticket on GitHub or join our slack workspace (#plaidml).