Classification of aerial photographs using DIGITS 2 by NVIDIA

1. HANDS-ON WITH DIGITSTM 2
Mike Wang
Solutions Architect

2. 2
DEEP LEARNING WITH CUDNN
cuDNN is a library for deep learning primitives
GPUs
cuDNN
Frameworks
Applications
Tesla TX-1 Titan
CPU is 16 core Haswell E5-2698 at 2.3 GHz, with 3.6
GHz Turbo
1 1
9
8
17
16
AlexNet GoogleNet
Speedup
CPU
Caffe GPU
Caffe+cuDNN
Titan X GPU

3. 3
CUDNN V3 RC AVAILABLE TODAY
Library for DNN toolkit developer and researchers
Contains building blocks for DNN toolkits
Convolutions, pooling, activation functions, etc.
Great performance and easy to deploy
developer.nvidia.com/cuDNN
cuBLAS (SGEMM for fully-connected layers) is part of CUDA toolkit,
developer.nvidia.com/cuda-toolkit
cuDNN (and cuBLAS)

4. 4
Process Data Configure DNN VisualizationMonitor Progress
Interactive Deep Learning GPU Training System
NVIDIA DIGITS

5. 5
NVIDIA DIGITS
Data Scientists & Researchers:
Quickly design the best deep neural
network (DNN) for your data
Visually monitor DNN training quality
in real-time
Manage training of many DNNs in
parallel on multi-GPU systems
Open source!
https://developer.nvidia.com/digits
Interactive Deep Learning GPU Training System

6. 6
NVIDIA DIGITS
Workflow
Main Console
Create your dataset
Configure your Network

7. 7
Image parameter
options
Create your
dataset
DIGITS can automatically create your
training and validation set
NVIDIA DIGITS
Create Database
/path/to/images/
|-cat/
| |-cat1.jpg
| |-cat2.jpg
|-dog/
| |dog1.jpg
| |dog2.jpg
|-lizard/
|-lizard1.jpg
|- lizard2.jpg
Input Data Format

8. 8
NVIDIA DIGITS
Network Configuration
Insert your network here
Choose a preconfigured network
OR choose a previous configuration
OR add it here
Select training dataset

9. 99
NVIDIA DIGITS
New Standard Network
New solvers
New Features
GoogleNet
Two new solvers

10. 1010
NVIDIA DIGITS
New Feature
Easy Multi-GPU training
Select the
number of
GPUs you want
to use

11. 11
NVIDIA DIGITS
Training Speedup Achieved with DIGITS on Multiple GeForce TITAN X GPUs in a DIGITS
DevBox. These results were obtained with the Caffe framework and a batch size of 128.
Possible speed up with
multiple GPUs

12. 1212
NVIDIA DIGITS
New Feature
GPU Memory/Utilization
Real time GPU usage
information

13. 13
NVIDIA DIGITS
Performing Inference with
new example scripts
Select the snapshot
model you want to
classify with and
download it.

14. 14
NVIDIA DIGITS
Inference Examples
./use_archive.py digits-model.tar.gz test-image.jpg
Extracting tarfile ...
Processed 1/1 images ...
Classification took 0.00310683250427 seconds.
--------------------------- Prediction for image.jpg ---------------------------
96.1199% - "0"
1.3588% - "6"
0.7247% - "9"
0.4695% - "2"
0.3857% - "3"
Script took 0.270452022552 seconds.
Example scripts can be found in DIGITS_ROOT/examples/classification
./example.py snapshot_iter_1000.caffemodel deploy.prototxt test-image.jpg --mean mean.npy --labels labels.txt
Processed 1/1 images ...
Classification took 0.00309991836548 seconds.
--------------------------- Prediction for image.jpg ---------------------------
96.1199% - "0"
1.3588% - "6"
0.7247% - "9"
0.4695% - "2"
0.3857% - "3"
Script took 0.269672870636 seconds.
Classify with the downloaded network files.
Or specific model files

15. 15
NVIDIA DIGITS
Improved Layer Visualization
Statistical
Information

16. 16
NVIDIA DIGITS
Top N predictions per Category

17. 17
NVIDIA DIGITS
Classifying Many Images

18. 18
NVIDIA DIGITS
curl localhost:5000/
/index.json – DIGITS Home page
/models/<job_id>.json
/models/images/classification/classify_one.json
REST API Example Calls
curl http://localhost:5000/models/20150604-
034131-da2c.json
{
"directory":
"/home/ubuntu/.digits/jobs/20150604-034131-
da2c",
"id": "20150604-034131-da2c",
"name": "aerial_v1",
"snapshots": [
1,
2,
3,
…
29,
30
],
"status": "Done"
}

19. 19
NVIDIA DIGITS
REST API Classification Example
curl http://localhost:5000/models/images/classification/classify_one.json _XPOST -F
job_id=20150604-034131-da2c -F image_file=@/path/to/image/desert_0.jpg
{
"predictions": [
[
"desert",
99.94
],
[
"urban",
0.06
],
[
"forest",
0.0
]
]
}

20. 20
NVIDIA DIGITS
Where to get DIGITS 2
Easy to use web installer https://developer.nvidia.com/digits
github - https://github.com/NVIDIA/DIGITS
Remember to install NVIDIA’s Caffe branch - https://github.com/NVIDIA/caffe
User support
DIGITS Users Google group - https://groups.google.com/forum/#!forum/digits-users
For more information on getting started with DIGITS
Parallel forall blogs - http://devblogs.nvidia.com/parallelforall/easy-multi-gpu-deep-learning-
digits-2/
Getting started guide - https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingStarted.md
Resources

21. 21
INTERESTED IN LEARNING MORE ABOUT DEEP
LEARNING?
Check out our Free Deep Learning Courses -
https://developer.nvidia.com/deep-learning-courses
Date Class
7/22 Class #1 - Introduction to Deep Learning
8/5 Class #2 - Getting Started with DIGITS interactive training system for image
classification
8/19 Class #3 - Getting Started with the Caffe Framework
9/2 Class #4 - Getting Started with the Theano Framework
9/16 Class #5 - Getting Started with the Torch Framework
Office hours for Q&A too!