yangjunpro / paleo

An analytical performance modeling tool for deep neural networks.

Paleo

Paleo is an analytical model to estimate the scalability and performance of deep learning systems. It can be used to

• efficiently explore the space of scalable deep learning systems,
• quickly diagnose their effectiveness for a given problem instance.

Live demo

Dependencies

• Python 2.7 and packages in requirements.txt
• cuDNN 4
• TensorFlow 0.9¹

Tested on Ubuntu 14.04 workstations.

Usage

Paleo provides the following commands.

• summary prints static characteristics of the given model architecture.
• simulate simulates the scalability of the given model architecture.
• profile performs layer-wise profiling of the given model architecture.
• fullpass runs full-pass profiler with TensorFlow.

To get help on arguments to each command:

./paleo.sh [command] --help

To reproduce experiments presented in our paper submission:

./scripts/<exp_script>.sh

Definitions

Model Architectures

Paleo uses a special json format to for model architecture specification. Predefined architectures can be found under nets/ directory.

Model Architecture	Files
AlexNet v2	alex_v2.json
Inception v3	inception_v3.json
NiN	nin.json
Overfeat	overfeat.json
VGG-16	vgg16.json

Hardware

Predefined hardware specificiations are in paleo/device.py.

Reference Paper

Hang Qi, Evan R. Sparks, and Ameet Talwalkar. Paleo: A Performance Model for Deep Neural Networks. Under review for ICLR 2017.

License

Apache 2.0

1 The current version depends on TensorFlow for gathering emperical ground truth. We will make this optional in later releases.