Last Attempt MLS-C01 Questions

To leverage the NVIDIA GPUs on Amazon EC2 P3 instances for training a custom ResNet model using Amazon SageMaker, the Machine Learning Specialist needs to build the Docker container to be NVIDIA-Docker compatible. NVIDIA-Docker is a tool that enables GPU-accelerated containers to run on Docker. NVIDIA-Docker can automatically configure the Docker container with the necessary drivers, libraries, and environment variables to access the NVIDIA GPUs. NVIDIA-Docker can also isolate the GPU resources and ensure that each container has exclusive access to a GPU.

To build a Docker container that is NVIDIA-Docker compatible, the Machine Learning Specialist needs to follow these steps:

• Install the NVIDIA Container Toolkit on the host machine that runs Docker. This toolkit includes the NVIDIA Container Runtime, which is a modified version of the Docker runtime that supports GPU hardware.
• Use the base image provided by NVIDIA as the first line of the Dockerfile. The base image contains the NVIDIA drivers and CUDA toolkit that are required for GPU-accelerated applications. The base image can be specified as FROM nvcr.io/nvidia/cuda:tag, where tag is the version of CUDA and the operating system.
• Install the required dependencies and frameworks for the ResNet model, such as PyTorch, torchvision, etc., in the Dockerfile.
• Copy the ResNet model code and any other necessary files to the Docker container in the Dockerfile.
• Build the Docker image using the docker build command.
• Push the Docker image to a repository, such as Amazon Elastic Container Registry (Amazon ECR), using the docker push command.
• Specify the Docker image URI and the instance type (ml.p3.xlarge) in the Amazon SageMaker CreateTrainingJob request body.

The other options are not valid or sufficient for building a Docker container that can leverage the NVIDIA GPUs on Amazon EC2 P3 instances. Bundling the NVIDIA drivers with the Docker image is not a good option, as it can cause driver conflicts and compatibility issues with the host machine and the NVIDIA GPUs. Organizing the Docker container’s file structure to execute on GPU instances is not a good option, as it does not ensure that the Docker container can access the NVIDIA GPUs and the CUDA toolkit. Setting the GPU flag in the Amazon SageMaker CreateTrainingJob request body is not a good option, as it does not apply to custom Docker containers, but only to built-in algorithms and frameworks that support GPU instances.

The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits:

• C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12. The DeepAR algorithm supports GPU-based EC2 instances such as ml.p2 and ml.p33.
• D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4. The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3.

The other options are not effective or relevant, because they have the following drawbacks:

• A: Replacing On-Demand Instances with Spot Instances can reduce the cost of the training, but not necessarily the time, as Spot Instances are subject to interruption and availability5. Moreover, the DeepAR algorithm does not support checkpointing, which means that the training cannot resume from the last saved state if the Spot Instance is terminated3.
• B: Configuring model auto scaling dynamically to adjust the number of instances automatically is not applicable, as this feature is only available for inference endpoints, not for training jobs6.
• E: Using a pre-trained version of the model and running incremental training is not possible, as the DeepAR algorithm does not support incremental training or transfer learning3. The DeepAR algorithm requires a full retraining of the model whenever new data is added or the hyperparameters are changed7.

References:

• 1: GPU vs CPU: What Matters Most for Machine Learning? | by Louis (What’s AI) Bouchard | Towards Data Science
• 2: How GPUs Accelerate Machine Learning Training | NVIDIA Developer Blog
• 3: DeepAR Forecasting Algorithm - Amazon SageMaker
• 4: Distributed Training - Amazon SageMaker
• 5: Managed Spot Training - Amazon SageMaker
• 6: Automatic Scaling - Amazon SageMaker
• 7: How the DeepAR Algorithm Works - Amazon SageMaker

When submitting Amazon SageMaker training jobs using one of the built-in algorithms, the common parameters that must be specified are:

• The training channel identifying the location of training data on an Amazon S3 bucket. This parameter tells SageMaker where to find the input data for the algorithm and what format it is in. For example, TrainingInputMode: File means that the input data is in files stored in S3.
• The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users. This parameter grants SageMaker the necessary permissions to access the S3 buckets, ECR repositories, and other AWS resources needed for the training job. For example, RoleArn: arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20200303T150948 means that SageMaker will use the specified role to run the training job.
• The output path specifying where on an Amazon S3 bucket the trained model will persist. This parameter tells SageMaker where to save the model artifacts, such as the model weights and parameters, after the training job is completed. For example, OutputDataConfig: {S3OutputPath: s3://my-bucket/my-training-job} means that SageMaker will store the model artifacts in the specified S3 location.

The validation channel identifying the location of validation data on an Amazon S3 bucket is an optional parameter that can be used to provide a separate dataset for evaluating the model performance during the training process. This parameter is not required for all algorithms and can be omitted if the validation data is not available or not needed.

The hyperparameters in a JSON array as documented for the algorithm used is another optional parameter that can be used to customize the behavior and performance of the algorithm. This parameter is specific to each algorithm and can be used to tune the model accuracy, speed, complexity, and other aspects. For example, HyperParameters: {num_round: "10", objective: "binary:logistic"} means that the XGBoost algorithm will use 10 boosting rounds and the logistic loss function for binary classification.

The Amazon EC2 instance class specifying whether training will be run using CPU or GPU is not a parameter that is specified when submitting a training job using a built-in algorithm. Instead, this parameter is specified when creating a training instance, which is a containerized environment that runs the training code and algorithm. For example, ResourceConfig: {InstanceType: ml.m5.xlarge, InstanceCount: 1, VolumeSizeInGB: 10} means that SageMaker will use one m5.xlarge instance with 10 GB of storage for the training instance.

References:

• Train a Model with Amazon SageMaker
• Use Amazon SageMaker Built-in Algorithms or Pre-trained Models
• CreateTrainingJob - Amazon SageMaker Service

The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user’s identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.

References:

• AWS Machine Learning Specialty Exam Guide
• AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3
• AWS Machine Learning Training - Using Bucket Policies and User Policies