Accelerating Deep Learning with GPUs Cognitive Class Exam Quiz Answers

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Accelerating Deep Learning with GPUs Cognitive Class Certification Answers

Module 1 – Intro to Deep Learning Quiz Answers – Cognitive Class

Question 1: Which are applications of deep learning in the industry?

In Security: face recognition and video surveillance
In Media: entertainment and news
In Communications: internet service and mobile phones industries
All of the above

Question 2: Which is NOT one of the main phases of a deep learning pipeline?

Preprocessing input data
Feature selection
Training the deep learning model
Inference and Deployment of the model

Module 2 – Hardware Accelerated Deep Learning Quiz Answers – Cognitive Class

Question 1: Which one of the following statements is NOT TRUE about GPU?

The GPU parallelism feature reduces the computation time of the dot product of big matrices.
GPUs are faster than CPUs in loading small chunks of data.
GPUs are very good where the same code runs on different sections of the same array.
GPUs are the proper use for parallelism operations on matrices.

Question 2: “GPUs have many cores, sometimes up to 1000 cores, so they can handle many computations in parallel.” Is this statement TRUE or FALSE?

TRUE
FALSE

Module 3 – Deep Learning in the Cloud Quiz Answers – Cognitive Class

Question 1: Which are the most popular hardware accelerators in use today?

FPGAs (programmable or customizable hardware)
AMD cards with OpenCL software
Tensorflow Processing Units (TPUs)
NVIDIA GPUs with CUDA software
All of the above

Question 2: “In some situations, your data might be very huge in terms of volume and computation in such a way that you need a really large computational system to handle it. In this case, you need a cluster of GPUs to distribute the whole computational workload.” Is this statement TRUE or FALSE?

TRUE
FALSE

Question 3: Which of the following statement is TRUE about deep learning in the cloud?

Building cloud-based deep learning could be really costly if you need to train models for more than 1000 hours.
You need to analyze your data on-premise, when your data is sensitive and you may not feel comfortable to upload it into public clouds.
If your data is big, use a fast enough single GPU to do experiments with sample data to verify many things before going full scale.
All of the above.

Module 4 – Advanced Deep Learning Quiz Answers – Cognitive Class

Question 1: “The main objective of Distributed Deep Learning is to distribute the workload of deep learning on multiple GPUs on a node.” Is this statement TRUE or FALSE about DDL?

TRUE
FALSE

Question 2: Which of the following statement is TRUE about using DDL (Distributed Deep Learning) for training a model?

DDL is comprised of a bunch of software algorithms that provide the parallelization of computation across hundreds of GPU accelerators attached to dozens of servers.
DDL distributes deep learning training across large numbers of servers.
DDL reduces training times for large models with large data sets.
All of the above.

Question 3: What are the four workspaces in IBM PowerAI Vision?

My Data Sets, My DL Tasks, My Trained Models, My Web APIs
My Pictures, My Documents, My Downloads, My Data Sets
My Computer, My Desk, My Chair, My Tasks
My DL Tasks, My Trained Models, My Pictures, My Website

Question 4: After training and deploying your Object Recognition model, what format is the response of your web API?

TXT
JSON
JPEG
PNG

Question 5: What are the two types of Deep Learning tasks that IBM PowerAI Vision provides?

Voice Recognition, Natural Language Processing
Regression, Time Series Forecasting
Clustering, Image Segmentation
Classification, Object Detection

Accelerating Deep Learning with GPUs Final Exam Answers – Cognitive Class

Question 1: Which statement is NOT one of the main reasons for the increased popularity of deep learning today?

The dramatic increases in computer processing capabilities.
The increase in the quality of images.
The availability of massive amounts of data for training computer systems.
The advances in machine learning algorithms and research.

Question 2: What is the problem with traditional approaches for image classification?

Extending the features to other types of images is not easy.
The feature selection process is very ineffective.
The process of selecting and using the best features is a time-consuming task.
All of the above.

Question 3: Which one of the following characteristics of Convolutional Neural Network is the most important in Image Classification?

No need to find or select features.
Working with sound data.
Low number of layers.
All of the above.

Question 4: Which of the following definitions is what the “inference” part of the deep learning pipeline does?

Finding the best feature set for classification.
Using the trained model for classifying a new image based on its similarity to the trained model.
Feeding an untrained network with a big dataset of images.
Converting the images to a readable and proper format for the network.

Question 5: What is/are the main reason/s for the deep learning pipeline being so slow?

Training a Deep Neural Network is basically a slow process.
Building a deep neural network is an iterative process for data scientists, that is, it needs optimization and tuning and data scientists need to run it many times to make it ready to be used.
The trained model needs to get updated sometimes, for example, because new data is added to the training set.

Question 6: Why is acceleration of the deep learning pipeline very desirable for data scientists?

It reduces the number of pixels that the kernel should add.
It makes the inference part of the deep learning pipeline much faster.
It causes better feature extraction and selection.
Data scientists can train a model more times and make it much more accurate.
None of the above.

Question 7: Why is “training” of deep learning the most time-consuming part of the pipeline?

There are many matrix multiplications in the process.
Neural Networks have usually many weights, which should get updated in each iteration, and it involves expensive computations.
Training is an iterative process.
All of the above.

Question 8: Which one of the following statements is NOT TRUE about CPU?

CPU is not the proper use for high parallelism.
CPU is good at fetching big amounts of data from memory quickly.
CPU runs tasks sequentially.
CPU is responsible for executing a sequence of stored instructions, for example, multiplications.

Question 9: Which statement best describes GPU?

A solution for running a Recurrent Neural Network in deep learning.
Part of a computer system that is known as the processor or microprocessor.
A chip (processor) traditionally designed and specialized for rendering images, animations and video for the computer’s screen.
The core of a CPU.

Question 10: What is NOT TRUE about GPUs?

GPUs have many cores, sometimes up to 1000 cores.
x86 is one of the prevalent GPUs by Intel.
GPUs can handle many computations.
GPUs are good at fetching large amounts of memory.

Question 11: Why is GPU much better for deep learning than CPU?

CPUs are not optimized and not the proper use for fetching high dimensional matrices.
Deep Neural Networks need a heavy matrix for multiplication, and GPUs can do it in parallel.
A Deep Neural Network needs to fetch input images as matrices from main memory, and GPUs are good at fetching big chunks of memory.
All of the above.

Question 12: “NVIDIA is one of the main vendors of GPU offered with CUDA software” True or False?

TRUE
FALSE

Question 13: What is CUDA?

A high-level language, which helps you write programs for NVIDIA GPU
A software on top of AMD cards to make it faster
A accelerating hardware that have recently succeeded in reducing the training time by several times over.
All of the above

Question 14: Which one is NOT a hardware accelerator for training of deep learning?

FPGAs
AMD cards
Tensorflow Processing Units (TPUs)
NVIDIA GPUs
OpenCL

Question 15: “Tensorflow Processing Units (TPUs) are Google’s hardware accelerator solution developed specifically for TensorFlow and Google’s open-source machine learning framework.” TRUE or FALSE?

TRUE
FALSE

Question 16: What is TRUE about the limitations of using GPUs as hardware accelerators for deep learning? (Select one or more)

GPUs are not very fast for data parallelism, which is a must in deep neural networks.
GPUs have a limited memory capacity (currently up to 16 GB) so this is not practical for very large datasets.
You cannot easily buy GPUs and embed them into your local machine because of hardware dependencies and incompatibilities.
GPUs are not compatible with CPUs.

Question 17: What are the options out there as hardware accelerators for deep learning?

A cluster of GPUs on-premise
GPU services provided by cloud providers
A cluster of GPUs in the cloud
Personal computers with an embedded GPU
All of the above

Question 18: Is this statement about using personal computers with an embedded GPU for deep learning problems TRUE or FALSE? “A laptop with a recent NVIDIA GPU is not usually enough to solve real deep learning problems. In this case, you need to scale down the dataset or the model, which often delivers bad results.”

FALSE
TRUE

Question 19: What is the problem with using GPUs provided by cloud providers?

They are properly used only for experiments with sample data to verify many scenarios before going full scale.
You need to upload all your data on the cloud and you may not feel comfortable uploading it into public clouds.
You cannot find services that offer multi-GPU access.
They cannot run as fast as personal computers.

Question 20: Which statement is NOT TRUE about PowerAI:

On the PowerAI platform, NVLink connections between GPUs reduce GPU wait time.
PowerAI handles Big Data by transfering all data into GPUs.
On the PowerAI platform, full NVLink connectivity between CPU and GPU allows a faster way to “reload” data into GPU.
PowerAI takes advantage of NVLink for faster GPU-GPU communication.

Introduction to Accelerating Deep Learning with GPUs

Accelerating deep learning with Graphics Processing Units (GPUs) has become a common practice due to the parallel processing power these devices offer. GPUs are well-suited for the highly parallelizable computations involved in training and running deep neural networks. Here are key aspects of how GPUs accelerate deep learning:

Parallel Processing:
- GPUs are designed with thousands of cores that can perform parallel computations simultaneously. Deep learning involves a lot of matrix operations, and GPUs can handle these operations efficiently in parallel.
Matrix Operations:
- Deep learning models often involve large matrix multiplications and convolutions. GPUs excel at these operations, as their architecture is optimized for the type of parallelism required by neural network computations.
CuDNN (CUDA Deep Neural Network):
- CuDNN is a GPU-accelerated library for deep neural networks that provides highly optimized implementations of key deep learning operations.
- It is designed to work with NVIDIA GPUs and is commonly used with deep learning frameworks like TensorFlow and PyTorch.
CUDA (Compute Unified Device Architecture):
- CUDA is a parallel computing platform and application programming interface (API) model created by NVIDIA.
- It allows developers to use NVIDIA GPUs for general-purpose processing (including deep learning) by providing a programming interface for parallel tasks.
Deep Learning Frameworks:
- Popular deep learning frameworks such as TensorFlow, PyTorch, and Keras have GPU support built-in.
- These frameworks leverage the parallel processing capabilities of GPUs to accelerate the training and inference of deep learning models.
Data Parallelism:
- Deep learning tasks are often parallelizable across different data points. GPUs can process multiple data points simultaneously, which is known as data parallelism.
- This is particularly beneficial during the training phase when gradients are calculated and weights are updated.
Model Parallelism:
- For large models that don’t fit entirely into GPU memory, model parallelism can be employed. In this approach, different parts of the neural network are processed on different GPUs.
Transfer Learning and Fine-tuning:
- GPUs enable faster training of pre-trained models, facilitating transfer learning. Transfer learning involves using a pre-trained model on a large dataset and fine-tuning it for a specific task with a smaller dataset.
GPU Cloud Services:
- Cloud service providers offer GPU instances, allowing users to access powerful GPU resources for deep learning without needing to invest in specialized hardware.
Real-time Inference:
- GPUs are crucial for real-time inference in applications like computer vision, natural language processing, and speech recognition, where low-latency responses are essential.

In summary, GPUs play a significant role in accelerating deep learning by leveraging their parallel processing capabilities. This acceleration is evident during model training, inference, and various computationally intensive tasks involved in deep neural network operations.