Using PyTorch in the Cloud: PyTorch Playbook

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This course covers the important aspects of using PyTorch on Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP), including the use of cloud-hosted notebooks, deep learning VM instances with GPU support, and PyTorch estimators.

1. Course Overview

1. Course Overview

2. Creating PyTorch Solutions on AWS

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Machine Learning on the Cloud

05. PyTorch - Taxonomy of Solutions

06. Introducing SageMaker

07. Creating a SageMaker Notebook Instance

08. Prototyping a PyTorch Model on SageMaker Notebooks

09. PyTorch Estimators on SageMaker

10. Distributed Data Loading in PyTorch

11. Distributed Training in PyTorch

12. Using PyTorch Estimators for Distributed Training

13. Model Deployment and Prediction Using Estimators

14. AWS Deep Learning AMIs

15. Instantiating a Deep Learning VM

16. Building Models with GPU Support on the AWS Deep Learning VM

3. Creating PyTorch Solutions on Azure

01. Module Overview

02. Introducing Azure Machine Learning Service

03. Prototyping PyTorch Models on Azure Notebooks

04. Azure Machine Learning Service Workflow

05. Understanding Terms in Azure Machine Learning

06. Horovod for Distributed Training

07. Distributed Training in PyTorch Using the Horovod Framework

08. Instantiating the PyTorch Estimator for Distributed Training

09. Distributed Run Using the PyTorch Estimator

10. The Azure Deep Learning VM

11. Instantiating an Azure Deep Learning VM

12. Building PyTorch Models with GPU Support on Azure Deep Learning VMs

4. Creating PyTorch Solutions on the Google Cloud Platform

1. Module Overview

2. Cloud Datalab and Deep Learning VMs

3. Setting up a Cloud Datalab VM

4. Prototyping PyTorch Models Using Cloud Datalab

5. Create a Deep Learning VM with PyTorch and CUDA Support

6. Using JupyterLab on a GCP Deep Learning VM

7. Summary and Further Study

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تولید کننده: PluralSight