For all that we're unable to attend or would like to recap our live webinar Deep Learning for Tensorflow Series part 2, we have all the information for you so would not miss out!

1. Explore Deep Learning Architecture
using TensorFlow
Wednesday | 6TH MAY, 2020
LIVE WEBINAR
Presented by

2. AGENDA
1. Know World's Most Advance Tailored GPU Systems
 G.O.D - GPU Systems Optimized For Deep Learning
 Flow Architecture Revolutionizing Deep Learning CPU-GPU Environment
 Highest ROI + Topmost Performance + Maximised Convenience
2. Convolutional Neural Network using TensorFlow
 Understand the steps involved in building CNN Model using TensorFlow 2.0
 Focus on steps involved in configuring and training the model
3. Sequence Models
 Understand the Data Structure for Sequence Model and How TensorFlow 2.0 can help to configure it
 Walk thru on how we can configure and train the Sequence models
4. Generative Models
 What are Generative Models and how they are different
 How TensorFlow helps us to build Generative Adversarial Network
5. Distribution Strategy
 Understand in Detail on the Distribution Strategy for Model training available as part of TensorFlow 2.0
6. Model Quantization for Edge Devices
 Understand the steps involved in Quantizing a Model using TensorFlow, so that it can be deployed in Edge Devices

3. Tyrone Systems at a Glance

4. Solutions that span the entire Data Center
SERVER
• HPC Servers
• Mission Critical X86
• Storage Servers
• High-Density Servers
• GPU Servers
Cloud Solutions Big Data/AIHPC Solutions
Cloud Big Data
Virtualization
AI / DEEP LEARNING
Product Portfolio
WORKSTATIONS
• GPU Workstations
• Tower | Rack
• Liquid Cooling
STORAGE
• Unified Storage
• Storage Array
• Archival
• JBOD
• Ceph Storage
NETWORKING
• InfiniBand
• Omnipath Architecture
Tyrone Kubernetes
Platform
HPC Cluster
GPU Optimised
Supercomputer
HPC On Cloud
SMP Solutions
Mngmt Tools
Analytics
Data Insights
HPC cluster parallel
file systems
Inferencing
Hyper-converged
Virtual SAN
Mixed Workloads
GPU Systems

5. G.O.D - GPU Systems Optimized For Deep Learning
DS400TG-48R
4:2 (4U)
Ratio:
GPU:CPU Tower/4U Rack – 1U/2U
GPUOPTIMIZED
DS400TOG-424RT
10:2 (4U)
Single Root
DS400TQV-12RT
4:2 (1U)
DS400TG-12RT
4:2 (1U)
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6:2 (2U)
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3:2 (1U)
SS400TG-16T
SS400TG-13T
2:1 (1U)
NEW MODEL!!
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20:2 (4U)
Rack – 4U/10U
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8:2 (4U)
Dual Root
NEW MODEL!!
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16:2 (10U)
DS400TQV-416RT
8:2 (4U)
NVLink
NEW MODEL!!
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16:2 (10U)
Personal
Workstations
SS400TR-54R
5U

6. Delivers 4XFASTER TRAINING
than other GPU-based systems
Your Personal AI Supercomputer
Power-on to Deep Learning in Minutes
Pre-installed with Powerful
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Software
Extend workloads from your
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7. Run Multiple Applications
simultaneously
Tyrone KUBITS™ Cloud
Flow Architecture Revolutionizing Deep Learning CPU-GPU Environment
KUBITS™ Compatible Workstations
WITH TYRONE KUBITS™ CLIENT
KUBITS has a repository of :
50 containerized applications
100s of Containers
10X20X30X40X50X
SPEED

8. Tyrone KUBITS : Revolutionizing Deep Learning CPU-GPU Environment
Run different
applications
simultaneously
Check for Tyrone
KUBITS Compatible
Workstations
Get access to over
100+ Containers on
Tyrone KUBITS Cloud.
High scalability
Affordable price
Has both GPU &
CPU Optimized
Containers
Design a simple Workstation
or Large Clusters with KUBITS
technology.
Talk to our experts & build
the right workstation within
your budget.
KUBITS
CLOUD
COMPATIBLE

9. Highest ROI + Topmost Performance + Maximised Convenience
GPUS 1 X GPU 2 X GPUs 3 x GPUs 4 x GPUs 6 x GPUs 8 x GPUs 10 x GPUs 16 x GPUs 20 x GPUs
MODEL SS400TR-54R SS400TG-16T DS400TG-14R DS400TG-48R DS400TG-12RT DS400TG-12RT
DS400TGH-
28R
DS400TQV-
416RT
DS400TOG-
424R
DS400TOG-
424RT
DS400NG16-
1016RT
DS400TG-
424RT
FORM FACTOR
5U 1U 1U 4U 1U 1U 1U 4U 4U 4U 10U 4U
COMPUTE
PERFORMANCE
8 X Tesla
V100 32
Single
Precision
125+ TFs
8 X 2080 Ti
Single
Precision
100+ TFs
8 X Tesla
V100 32
Single
Precision
100+ TFs
10 X 2080
Ti Single
Precision
130+ TFs
10 X Tesla
V100 32
Single
Precision
140+ TFs
16 X Tesla
V100 32
Single
Precision
250+ TFs
20 X T4
GPUs Single
Precision
160+ TFs
FP16/FP32
Mixed
Precision
1300+ TFs
MEMORY BANDWIDTH
TYRONE KUBITS ACCESS
STARTING PRICE (USD)
NUMBER OF GPU’S
COMPUTEPERFORMANCE

10. Topics Covered in Session 2
Convolutional
Neural Network
using TensorFlow
Sequence Models
Distribution
Strategy
Generative Models
Model
Quantization for
Edge Devices

11. • All the required components can be built using TensorFlow Modules
• Keras module can be used to configure the layers for the model
Data Loader
Transform the Data
Data
Augmentation
Define Model
Architecture
Model Training
based on number
of Epoch
Prediction and
Evaluation
Convolutional
Layer
Pooling Layer
Drop out
layer
Convolutional Neural Network using TensorFlow

12. Sequence Model with TensorFlow 2.0
• The Keras RNN API is designed with a
focus on:
• Ease of use: the built-in
tf.keras.layers.RNN, tf.keras.layers.LSTM,
tf.keras.layers.GRU layers enable you to
quickly build recurrent models without
having to make difficult configuration choices.
• Ease of customization: You can also define
your own RNN cell layer (the inner part of the
for loop) with custom behavior, and use it
with the generic tf.keras.layers.RNN layer (the
for loop itself). This allows you to quickly
prototype different research ideas in a
flexible way with minimal code.

13. Generative Models using TensorFlow

14. Distribution Strategy in TensorFlow 2.0
Key Point of the Strategy
• All Reduce Algorithm as part of TensorFlow 2.0
• Collective Communication Library of Nvidia
• Compute the gradient of loss function using Minibatch on
each GPU
• Compute mean of gradient by inter GPU Communication
• Update the model

15. TensorFlow
Model
Quantization TF Lite Model Interpreter
Deployable
TFLite Model
• Build and Train a
Model using
TensorFlow. E.g.
CNN Model or a
Dense Network
• Use TF-Lite and
select Post Train
Quantization
Framework
• Use TF-Lite
interpreter to
check the
converted model
Outputs and
Accuracy
• Convert to TF-Lite
Model
• Deploy it in
Android
Model Quantization for Edge Devices

16. Model Quantization Options

17. Artificial Intelligence Systems: Examples
⮚ Google Self Driving car is an Artificial Intelligence
system leveraging on Deep Learning models for image
identification and Machine learning for object
Classification.
Google Self Driving Car ⮚ IBM Watson is an Artificial Intelligence Platform that lets
you automate the AI lifecycle.
⮚ Watson is a question-answering computer system
capable of answering questions posed in natural
language, developed in IBM's DeepQA project
⮚ AI based program that can mimic human moves and
performs better than human player in the board
game.
⮚ Sophia is a social humanoid robot developed by Hong Kong based company Hanson
Robotics
⮚ Cameras within Sophia's eyes combined with computer algorithms allow it to see. It
can follow faces, sustain eye contact, and recognize individuals. It is able to process
speech and have conversations using a natural language subsystem

18. Q&A Session
Hirdey Vikram
Hirdey.vikram@netwebindia.com
India (North)
Niraj
niraj@netwebindia.com
India (South)
Vivek
vivek@netwebindia.com
India (East)
Navin
navin@netwebindia.com
India (West)
Anupriya
anupriya@netwebtech.com
Singapore
Arun
arun@netwebtech.com
UAE
Agam
agam@netwebtech.com
Indonesia
Contact our team if you have any further questions after this webinar
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