high performance analytics with dask & tensorflow | anacondacon 2017

DEEP LEARNINGWITH ANACONDA

PYTHON, TENSORFLOW & GPUS, OH MY!

#OpenDataScienceMeans #AnacondaCON

WHAT IS DEEP LEARNING?

•The return of neural networks for machine learning•Many high profile successes with:

•Image recognition•Language translation•Speech recognition•Automated image captioning


WHAT HAPPENED?

(Much) Bigger Training Sets

Faster & Specialized Hardware

Open Source Tools

Improved Algorithms


WHAT IS A NEURAL NETWORK?

•A very simple computation unit connected in a large mesh•Very flexible•Very trainable (given sufficient training data)


NEURAL NETWORKS: NODE


NEURAL NETWORKS: LAYER


NEURAL NETWORKS: NETWORK

ReLU

ReLU

ReLU

ReLU


THE DEEP LEARNING SOFTWARE STACK

MULTI-CORE CPU GPUMANY-CORE CPU(XEON PHI)HARDWARE

MKL 2017 CUDNNPRIMITIVES

TENSORFLOWTHEANOPYTORCHTENSOR MATH

NEURAL NETWORKS KERAS TFLEARNCAFFE

...and many others

MIOPEN


DEEP LEARNING WITH ANACONDA

Available in Anaconda today:

• Theano

• TensorFlow (CPU)

Coming very soon:

• TensorFlow (GPU w/ cuDNN)

• Keras

• ...and more


DEEP LEARNING IN THE JUPYTER NOTEBOOK

Defines a simple model in Keras to recognize handwritten digits


DEEP LEARNING IN THE JUPYTER NOTEBOOK

Trained to 98% accuracy in4 minutes using a singleNVIDIA GTX 1080 GPU


CONCLUSION

Now is a great time to experiment with Deep Learning:

• Prepare your data with your favorite Python libraries

• Create models and run training experiments in the Jupyter Notebook

• Visualize and understand your training results right in the notebook environment

• Look for more Deep Learning packages coming to Anaconda!(GPU acceleration, Keras, and more)

QUESTIONS?

high performance analytics with dask & tensorflow | anacondacon 2017

Data & Analytics