GPU Cloud Computing Accelerating AI and ML: Leverage GPUs in the cloud for high-performance computing

Name: GPU Cloud Computing Accelerating AI and ML: Leverage GPUs in the cloud for high-performance computing
Brand: Independently Published
SKU: 9798267514781
Price: 1776.0 INR
Availability: OutOfStock

by Corwin Halesworth

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ISBN13: 9798267514781
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: September 28, 2025
Pages: 176
Original Price: USD 16.99
Language: English
Edition: N/A
Item Weight: 245 grams
BISAC Subject(s): Distributed Systems / Cloud Computing

About The Book

Make accelerators work for your models-and your budget. GPU Cloud Computing: Accelerating AI and ML is a practical field guide to training and serving on managed GPU platforms. You'll learn how to choose the right instances, size storage and networking, orchestrate distributed jobs, and keep throughput high with stable, reproducible workflows.

We start with the essentials (device types, quotas, images, drivers) and move to production patterns: NCCL-based multi-node training, mixed-precision and tensor cores, checkpointing strategies, autoscaling inference, and cost controls. Clear examples, checklists, and review rubrics help you avoid painful pitfalls like PCIe bottlenecks, topology mismatches, hot shards, and noisy neighbors-so your clusters stay fast and predictable.

What you'll learn

Pick instances and accelerators for throughput vs. price; plan quotas and queues
Optimize input pipelines: parallel reads, caching, and data locality
Run distributed training with DDP/Horovod; tune NCCL and all-reduce strategies
Use mixed precision, gradient accumulation, and sharded optimizers safely
Leverage topology features (NVLink, MIG) and fast fabrics (EFA/InfiniBand)
Package environments with CUDA/ROCm images; manage drivers and runtimes
Deploy inference with TensorRT, batch/streaming, and latency budgets
Orchestrate jobs on Kubernetes (GPU Operator), Slurm, or Ray clusters
Secure artifacts and endpoints; sign containers and enforce least privilege
Control spend with spot/preemptible pools, right-sizing, and utilization dashboards

Who it's for
ML engineers, platform teams, researchers, and architects who want reliable, cost-aware GPU workflows across major providers.

What's inside
Reference architectures, IaC snippets, container recipes, tuning checklists, failure playbooks, and cost/performance review rubrics.

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