Parallel AI Programming in Python: Build Supercharged ML Workflows That Perform in Production - Couverture souple
Synopsis
Parallel AI Programming in Python: Build Supercharged ML Workflows That Perform in Production.
Are you wrestling with slow model training, stalled data pipelines, or unpredictable inference performance? You’re not alone—and you don’t have to accept sluggish results as the norm.
Parallel AI Programming in Python offers the definitive, hands-on guide to turbocharging your machine learning workflows. From multicore CPU tricks to multi-GPU strategies and distributed architectures, this book equips you with the proven, production-ready techniques that top AI teams use every day.
Inside, you’ll discover how to
Leverage Python’s threading and multiprocessing to blast past the Global Interpreter Lock
Build high-throughput I/O pipelines with asyncio, Dask, and Ray for lightning-fast data ingestion
Master GPU parallelism with PyTorch DDP, NCCL tuning, and mixed-precision training
Scale across clusters using MPI, Ray, and Dask—and know exactly when adding nodes stops delivering gains
Optimize numeric kernels with NumPy, Numba, Cython, and native extensions for peak performance
Implement real-time, fault-tolerant pipelines with Kafka/Pulsar, backpressure, and exactly-once semantics
Profile, benchmark, and tune your code with cProfile, py-spy, perf, and NVIDIA Nsight to fix bottlenecks fast
When you put this book into practice, you will
Cut training times from days to hours using multi-GPU and distributed training patterns
Architect data pipelines that process millions of records per second without dropping a message
Deploy inference services that scale horizontally and maintain sub-100ms latency under heavy load
Detect and remedy performance pitfalls—from memory thrashing to straggler tasks—before they hit production
Maintain rock-solid environments with containerized setups, dependency pinning, and reproducible scripts
Whether you’re an ML engineer, data scientist, or infrastructure developer, Parallel AI Programming in Python delivers hands-on labs, clear code examples, and concise checklists to transform sluggish prototypes into production-grade systems.
Take control of your AI pipeline performance today—add this essential resource to your toolkit and watch your Python workflows surge to new speeds.
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Parallel AI Programming in Python (Paperback)
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Paperback. Etat : new. Paperback. Parallel AI Programming in Python: Build Supercharged ML Workflows That Perform in Production.Are you wrestling with slow model training, stalled data pipelines, or unpredictable inference performance? You're not alone-and you don't have to accept sluggish results as the norm.Parallel AI Programming in Python offers the definitive, hands-on guide to turbocharging your machine learning workflows. From multicore CPU tricks to multi-GPU strategies and distributed architectures, this book equips you with the proven, production-ready techniques that top AI teams use every day.Inside, you'll discover how toLeverage Python's threading and multiprocessing to blast past the Global Interpreter LockBuild high-throughput I/O pipelines with asyncio, Dask, and Ray for lightning-fast data ingestionMaster GPU parallelism with PyTorch DDP, NCCL tuning, and mixed-precision trainingScale across clusters using MPI, Ray, and Dask-and know exactly when adding nodes stops delivering gainsOptimize numeric kernels with NumPy, Numba, Cython, and native extensions for peak performanceImplement real-time, fault-tolerant pipelines with Kafka/Pulsar, backpressure, and exactly-once semanticsProfile, benchmark, and tune your code with cProfile, py-spy, perf, and NVIDIA Nsight to fix bottlenecks fastWhen you put this book into practice, you willCut training times from days to hours using multi-GPU and distributed training patternsArchitect data pipelines that process millions of records per second without dropping a messageDeploy inference services that scale horizontally and maintain sub-100ms latency under heavy loadDetect and remedy performance pitfalls-from memory thrashing to straggler tasks-before they hit productionMaintain rock-solid environments with containerized setups, dependency pinning, and reproducible scriptsWhether you're an ML engineer, data scientist, or infrastructure developer, Parallel AI Programming in Python delivers hands-on labs, clear code examples, and concise checklists to transform sluggish prototypes into production-grade systems.Take control of your AI pipeline performance today-add this essential resource to your toolkit and watch your Python workflows surge to new speeds. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798270308827
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Parallel AI Programming in Python (Paperback)
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Paperback. Etat : new. Paperback. Parallel AI Programming in Python: Build Supercharged ML Workflows That Perform in Production.Are you wrestling with slow model training, stalled data pipelines, or unpredictable inference performance? You're not alone-and you don't have to accept sluggish results as the norm.Parallel AI Programming in Python offers the definitive, hands-on guide to turbocharging your machine learning workflows. From multicore CPU tricks to multi-GPU strategies and distributed architectures, this book equips you with the proven, production-ready techniques that top AI teams use every day.Inside, you'll discover how toLeverage Python's threading and multiprocessing to blast past the Global Interpreter LockBuild high-throughput I/O pipelines with asyncio, Dask, and Ray for lightning-fast data ingestionMaster GPU parallelism with PyTorch DDP, NCCL tuning, and mixed-precision trainingScale across clusters using MPI, Ray, and Dask-and know exactly when adding nodes stops delivering gainsOptimize numeric kernels with NumPy, Numba, Cython, and native extensions for peak performanceImplement real-time, fault-tolerant pipelines with Kafka/Pulsar, backpressure, and exactly-once semanticsProfile, benchmark, and tune your code with cProfile, py-spy, perf, and NVIDIA Nsight to fix bottlenecks fastWhen you put this book into practice, you willCut training times from days to hours using multi-GPU and distributed training patternsArchitect data pipelines that process millions of records per second without dropping a messageDeploy inference services that scale horizontally and maintain sub-100ms latency under heavy loadDetect and remedy performance pitfalls-from memory thrashing to straggler tasks-before they hit productionMaintain rock-solid environments with containerized setups, dependency pinning, and reproducible scriptsWhether you're an ML engineer, data scientist, or infrastructure developer, Parallel AI Programming in Python delivers hands-on labs, clear code examples, and concise checklists to transform sluggish prototypes into production-grade systems.Take control of your AI pipeline performance today-add this essential resource to your toolkit and watch your Python workflows surge to new speeds. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798270308827
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