Slides for Anthill Inside 2017 crisp talk proposal.

1. GETTING STARTED WITH
GPU ACCELERATED DEEP
LEARNING
A CRISP TALK FOR ANTHILL INSIDE 2017

2. DEEP LEARNING: OVERVIEW
• Comprised of neurons and layers
• Depth of the network = number of hidden layers
• Number of parameters = number of weights
• Learning using data over time
• Output at every node: 𝑓 𝑖
𝑛
𝑤𝑖 𝑥𝑖 + 𝑏
• Activation function: f
• Examples of activation function: linear, sigmoid
• Many different types:
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

3. EXAMPLE DEEP LEARNING ARCHITECTURE:
DEEPFACE
• Nine layers “deep”
• 120 million parameters
• Trained on 4 million face images

4. GPU HORSEPOWER ON THE CLOUD
• “On-demand” GPU computing power with high reliability
• Providers:
• Amazon EC2
• IBM Softlayer Bare Metal Servers
• Nimbix
• Google (Beta)
• Microsoft (public availability in December)

5. GETTING STARTED: CUDA TOOLKIT AND
DRIVERS
• Step 1: Download CUDA (https://developer.nvidia.com/cuda-
downloads)
• Step 2: Check the md5 sum and verify before continuing
• Step 3: Remove any other installation (“sudo apt-get purge
nvidia-cuda*” - if you want to install the drivers too, then “sudo
apt-get purge nvidia-*”.)
• Step 4: Follow the command-line prompts
• Step 5: Verify using “nvcc --version”

6. GETTING STARTED: CUDNN
• Step 0: Install CUDA from the standard repositories
• Step 1: Register an nVIDIA developer account and download cuDNN
(https://developer.nvidia.com/cudnn)
• Step 2: Check where your CUDA installation is. You can check it with “which
nvcc” or “ldconfig -p | grep cuda”
• Step 3: Copy the files:
• $ cd folder/extracted/contents
• $ sudo cp -P include/cudnn.h /usr/include
• $ sudo cp -P lib64/libcudnn* /usr/lib/x86_64-linux-gnu/
• $ sudo chmod a+r /usr/lib/x86_64-linux-gnu/libcudnn*

7. GETTING STARTED: ANACONDA
• Choose Anaconda if you:
• Are new to conda or Python
• Like the convenience of having Python and over 150 scientific packages
automatically installed at once
• Have the time and disk space (a few minutes and 3 GB), and/or
• Don’t want to install each of the packages you want to use individually.

8. GETTING STARTED: CONDA
ENVIRONMENTS
• Isolated packages of libraries and dependencies
• Create:
• “conda create –name <name> <package>”
• Activate/Deactivate environment:
• “source activate <name>”/ “source deactivate”
• Any changes you make to Python libraries is contained within the
active environment
• Importing an environment:
• “conda env create –f <name of environment.yml file>
• More about environments: https://conda.io/docs/using/envs.html

9. GETTING STARTED: THE LIBRARIES
• Numpy, Scipy (pre-installed with Anaconda)
• Keras, Scikit-Learn (sklearn), Matplotlib, PIL:
• “conda create --name anthill scikit-learn matplotlib keras pillow”
• OpenCV:
• After activating the source, execute:
• “conda install -c menpo opencv”
• Tensorflow CPU/GPU mode:
• “pip install --upgrade tensorflow” / “pip install --upgrade tensorflow-gpu”

10. DEEP LEARNING IN ACTION: WBC
CLASSIFICATION
For full details visit: https://blog.athelas.com/classifying-white-blood-cells-with-convolutional-
neural-networks-2ca6da239331

11. DEEP LEARNING IN ACTION: WBC
CLASSIFICATION
• Five layers “deep”
• Almost 1 million parameters

12. DEEP LEARNING IN ACTION: WBC
CLASSIFICATION
• 352 images of size 640x480
• 21 Monocyte (Mononuclear)
• 33 Lymphocyte
(Mononuclear)
• 207 Neutrophil (Polynuclear)
• 88 Eosinophil (Polynuclear)
• 3 Basophil (Mononuclear)

13. DEEP LEARNING IN ACTION: WBC
CLASSIFICATION
• Basophils removed
• From 352 to 10,000 images
with 2,500 of each category
• Images downsampled to
120x160
• Convert 4-class problem to
2-class problem
• Normalize pixel values from
0 to 255 to 0 and 1

14. GPU VS CPU FOR DEEP LEARNING
WORKLOADS
• Training on 7965 samples and validating on 1992 samples
• GPU Epoch time: 12 seconds for the first, 9 seconds thereafter
• CPU Epoch time: 109 seconds for the first, 105-107 seconds
thereafter
• Configuration:
• GPU: Tesla M60
• CPU: Intel® Xeon® Processor E5-2620 v3 @ 2.40 GHz x2

15. OUTPUT: TRAINING ON CPU (LEFT) VS GPU
(RIGHT)