sweetwenwen / 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

Getting Started

Installation

Paleo uses the following dependencies:

• numpy
• click
• six
• cuDNN (Optional. Use --use_only_gemm to disable cuDNN heuristics)

Use pip to install the depenencies with the pinned versions:

pip install -r requirements.txt

Tested with Python 2.7, cuDNN v4 on Ubuntu 14.04.

To install Paleo, run the following command in the cloned directory:

python setup.py install

Usage

Paleo provides programmatic APIs to retrieve runtime estimations.

The following is an example of estimating SGD executions under strong scaling.

from paleo.profilers import BaseProfiler

class SGDProfiler(BaseProfiler):
    def __init__(self, filename):
        super(SGDProfiler, self).__init__(filename)

    def simulate(self, workers, batch_size=128):
        fwd_time, params_in_bytes = self.estimate_forward(batch_size //
                                                          workers)
        bwd_time = self.estimate_backward(batch_size // workers)
        update_time = self.estimate_update(params_in_bytes)

        t_comp = fwd_time + bwd_time + update_time
        t_comm = self.estimate_comm(workers, params_in_bytes)
        return t_comp + t_comm

Definitions

Model Architectures

Paleo uses a special json format to for model architecture specification. Predefined architectures can be found under the nets/ directory. Paleo also provides a convertor for Caffe prototxt format (see nets/caffe/ for details).

• AlexNet v2
• Inception v3
• NiN
• Overfeat
• VGG-16
• ResNet-50 (from Caffe spec)
• DenseNet (from Caffe spec)

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. International Conference on Learning Representations (ICLR), 2017.

@inproceedings{qi17paleo,
  author={Hang Qi and Evan R. Sparks and Ameet Talwalkar},
  booktitle={Proceedings of the International Conference on Learning Representations},
  title={Paleo: A Performance Model for Deep Neural Networks},
  year={2017}
}

License

Apache 2.0