فلش‌کارت‌های آموزشی Google Cloud Platform: Batch Application Deployment and Management with Dask (Advanced Distributed Computing)

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عنوان کتاب: کتاب Google Cloud Platform: Batch Application Deployment and Management with Dask (Advanced Distributed Computing)

موضوع کلی: برنامه نویسی

موضوع میانی: Google Cloud Platform (GCP)

📋 سرفصل‌های کتاب (100 موضوع)

• 1. Welcome to Advanced Dask on Google Cloud
• 2. Course Objectives and Learning Outcomes
• 3. Introduction to Batch Application Deployment
• 4. Understanding Advanced Distributed Computing Concepts
• 5. Why Google Cloud Platform for Dask Workloads?
• 6. Setting Up Your GCP Project and Billing
• 7. Essential GCP IAM Concepts for Distributed Systems
• 8. Managing GCP Service Accounts and Keys
• 9. Navigating the GCP Console and Cloud Shell
• 10. The `gcloud` CLI Tool: A Deep Dive for Automation
• 11. Introduction to Virtual Private Cloud (VPC) Networking
• 12. Configuring Subnets and Firewall Rules for Dask Clusters
• 13. Overview of GCP Compute Engine Instances
• 14. Understanding Machine Types and Boot Disks for Performance
• 15. Introduction to Google Kubernetes Engine (GKE) Fundamentals
• 16. Core Kubernetes Concepts for Dask Deployments (Pods, Deployments, Services)
• 17. Google Cloud Storage: Buckets, Objects, and Access Control
• 18. Cloud Storage FUSE for VM Integration
• 19. Introduction to Google Cloud Pub/Sub for Messaging
• 20. Artifact Registry for Container Image Management
• 21. Python Environment Setup for Dask Development on GCP
• 22. Dask: A Flexible Library for Parallel Computing in Python
• 23. Dask Architecture: Client, Scheduler, Workers Explained
• 24. Dask Collections: Dask Array for Numerical Workloads
• 25. Dask Collections: Dask DataFrame for Tabular Data Processing
• 26. Dask Collections: Dask Bag for Unstructured Data
• 27. Dask Delayed for Custom Task Graphs and Workflows
• 28. Lazy Evaluation and Task Graph Visualization with Dask
• 29. Dask Futures for Asynchronous Computation
• 30. Choosing Your Dask Scheduler: Single Machine vs. Distributed
• 31. Setting Up a Local Dask Distributed Cluster
• 32. Running Your First Dask Computation Locally
• 33. Debugging Dask Applications Locally
• 34. Monitoring Local Dask Clusters with Dask Dashboard
• 35. Deploying Dask Scheduler on a Single Compute Engine VM
• 36. Launching Dask Workers Manually on Compute Engine Instances
• 37. SSH Management for Dask Cluster Communication
• 38. Using Instance Templates for Dask Worker Provisioning
• 39. Managed Instance Groups for Scalable Dask Worker Pools
• 40. Automating Dask VM Deployment with `gcloud` Scripts
• 41. Introduction to `dask-cloudprovider` for GCP Integration
• 42. `dask-cloudprovider` Configuration for Compute Engine
• 43. Creating a Dask Cluster with `dask-cloudprovider`
• 44. Scaling Dask Clusters Up and Down via `dask-cloudprovider`
• 45. Best Practices for `dask-cloudprovider` Resource Management
• 46. Monitoring and Debugging `dask-cloudprovider` Deployments
• 47. Containerizing Dask Applications with Docker
• 48. Building Custom Dask Docker Images for GCP
• 49. Pushing Dask Images to Google Artifact Registry
• 50. Setting Up a GKE Cluster for Dask Workloads
• 51. GKE Node Pools Optimization for Dask Resource Allocation
• 52. The Dask Kubernetes Operator: A Deep Dive
• 53. Deploying Dask Scheduler using Kubernetes Operator YAML
• 54. Deploying Dask Workers using Kubernetes Operator YAML
• 55. Configuring Horizontal Pod Autoscaling for Dask Workers
• 56. Accessing Dask Dashboard via GKE Service
• 57. Persistent Volumes and Claims for Dask State in GKE
• 58. Kubernetes Resource Requests and Limits for Dask Pods
• 59. Best Practices for Dask on GKE Performance
• 60. Reading Data from Cloud Storage into Dask DataFrames
• 61. Writing Dask Results Back to Cloud Storage
• 62. Optimizing Data I/O with Cloud Storage Parquet Files
• 63. Interacting with BigQuery Data using Dask
• 64. Connecting Dask to Cloud SQL Databases
• 65. Leveraging Cloud Filestore (NFS) for Shared Dask Data
• 66. Processing Streaming Data with Pub/Sub and Dask
• 67. Data Partitioning Strategies for Large Datasets
• 68. Handling Data Serialization Formats (Parquet, ORC, CSV, JSON)
• 69. Schema Evolution in Long-Running Batch Jobs
• 70. Monitoring Dask Application Metrics with Dask Dashboard
• 71. Integrating Dask Metrics with Cloud Monitoring
• 72. Understanding Dask Task States and Progress
• 73. Advanced Dask Dashboard Features and Insights
• 74. Centralized Logging for Dask with Cloud Logging
• 75. Structured Logging within Dask Applications
• 76. Creating Log-Based Metrics and Alerts in Cloud Monitoring
• 77. Identifying Performance Bottlenecks in Dask Graphs
• 78. Strategies for Dask Memory Optimization and Spill-to-Disk
• 79. Tackling CPU-Bound vs. I/O-Bound Dask Tasks
• 80. Dynamic Scaling of Dask Clusters on GCP
• 81. Cost Optimization: Leveraging Spot VMs for Dask Workers
• 82. Preemptible VMs vs. Spot VMs for Batch Processing
• 83. Building Robust Dask Batch Workflows
• 84. Implementing Error Handling and Retries in Dask
• 85. Checkpointing Dask Workflows for Resilience
• 86. Orchestrating Dask Jobs with Cloud Composer (Apache Airflow)
• 87. Triggering Dask Workloads with Cloud Functions
• 88. Integrating Dask with Vertex AI for ML Pipelines (Brief)
• 89. Hyperparameter Tuning with Dask-ML (Brief)
• 90. Dask for Extract, Transform, Load (ETL) Workflows
• 91. Introduction to CI/CD for Dask Applications
• 92. Automating Dask Deployments with Cloud Build
• 93. Testing Dask Applications with Unit and Integration Tests
• 94. Security Best Practices for Dask on GCP
• 95. Data Encryption at Rest and In Transit on GCP
• 96. IAM Roles and Permissions for Dask Service Accounts
• 97. Network Security: Private IP and VPC Service Controls
• 98. Disaster Recovery Strategies for Batch Systems
• 99. Future Trends in Distributed Computing and Dask
• 100. Recap and Next Steps for Your Dask Journey