Parallel python dask second par patrick (10 résultats)

Etat : New.

Paperback. Etat : new. Paperback. This fully revised edition teaches you to scale Python across cores, machines, and GPUs using the libraries you already know. I'll teach you to build Dask arrays, dataframes, bags and delayed graphs, and to tune partitions and chunks so the cluster actually earns its keep. I'll also show you to use the Dask expression system to let the optimiser push filters down and prune columns before a byte moves.As you'll see, you'll be working with pandas 3.0 throughout, including copy-on-write, PyArrow-backed strings and the new expression API. There are dedicated chapters that take you through terabyte-scale multi-dimensional data in HDF5, NetCDF, TIFF and Zarr, with Xarray, Kerchunk and cloud-native datacubes. The good thing about Dask is that it's the lighter, and the quicker choice than Spark in most scenarios. After that, we'll move on to GPU acceleration with RAPIDS and cuDF, distributed XGBoost and dask-ml for scaled machine learning, and production deployment on Kubernetes with monitoring, security, and cost control. Every chapter builds on one ongoing project, so the skills you learn are added to over time instead of being spread out.Key LearningsDiagnose whether a workload needs parallelism, a bigger machine, or better code.Size partitions and array chunks so clusters stop thrashing and start scaling.Read task graphs and dashboard panels to locate bottlenecks within minutes.Exploit the Dask expression system for predicate pushdown and column projection.Migrate pandas code to 3.0 copy-on-write and PyArrow-backed string dtypes.Stream terabyte HDF5, NetCDF, TIFF, and Zarr archives without exhausting memory.Build cloud-native, versioned datacubes using Zarr v3, Kerchunk, and Icechunk.Choose between Dask, Spark, Ray, and Polars using measured, honest criteria.Accelerate dataframes and gradient boosting on GPUs with RAPIDS and dask-cuda.Deploy, secure, monitor, and cost-control Dask clusters on production Kubernetes.Table of ContentUp and Running with DaskWorking with Dask CollectionsTuning Partitions and ChunksOptimizing Queries with Expression SystemScaling Out with Distributed ClustersScaling pandas 3.0 with DaskReading and Writing Data at ScaleWorking with Labeled Arrays in XarrayReading HDF5 and NetCDF ArchivesScaling Imagery with TIFFBuilding Cloud-native Datacubes with ZarrAccelerating Dask with GPUsOperating Dask in ProductionTarget AudienceIf you're wondering whether your Pandas script is stalling and you've tried adding memory but only got a few weeks out of it, then this latest edition on running parallel operations should help you figure out what to do next. You just need to have a basic understanding of Python programming. That's all you need to get reading this book! This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 288 pp. Englisch.

Paperback. Etat : new. Paperback. This fully revised edition teaches you to scale Python across cores, machines, and GPUs using the libraries you already know. I'll teach you to build Dask arrays, dataframes, bags and delayed graphs, and to tune partitions and chunks so the cluster actually earns its keep. I'll also show you to use the Dask expression system to let the optimiser push filters down and prune columns before a byte moves.As you'll see, you'll be working with pandas 3.0 throughout, including copy-on-write, PyArrow-backed strings and the new expression API. There are dedicated chapters that take you through terabyte-scale multi-dimensional data in HDF5, NetCDF, TIFF and Zarr, with Xarray, Kerchunk and cloud-native datacubes. The good thing about Dask is that it's the lighter, and the quicker choice than Spark in most scenarios. After that, we'll move on to GPU acceleration with RAPIDS and cuDF, distributed XGBoost and dask-ml for scaled machine learning, and production deployment on Kubernetes with monitoring, security, and cost control. Every chapter builds on one ongoing project, so the skills you learn are added to over time instead of being spread out.Key LearningsDiagnose whether a workload needs parallelism, a bigger machine, or better code.Size partitions and array chunks so clusters stop thrashing and start scaling.Read task graphs and dashboard panels to locate bottlenecks within minutes.Exploit the Dask expression system for predicate pushdown and column projection.Migrate pandas code to 3.0 copy-on-write and PyArrow-backed string dtypes.Stream terabyte HDF5, NetCDF, TIFF, and Zarr archives without exhausting memory.Build cloud-native, versioned datacubes using Zarr v3, Kerchunk, and Icechunk.Choose between Dask, Spark, Ray, and Polars using measured, honest criteria.Accelerate dataframes and gradient boosting on GPUs with RAPIDS and dask-cuda.Deploy, secure, monitor, and cost-control Dask clusters on production Kubernetes.Table of ContentUp and Running with DaskWorking with Dask CollectionsTuning Partitions and ChunksOptimizing Queries with Expression SystemScaling Out with Distributed ClustersScaling pandas 3.0 with DaskReading and Writing Data at ScaleWorking with Labeled Arrays in XarrayReading HDF5 and NetCDF ArchivesScaling Imagery with TIFFBuilding Cloud-native Datacubes with ZarrAccelerating Dask with GPUsOperating Dask in ProductionTarget AudienceIf you're wondering whether your Pandas script is stalling and you've tried adding memory but only got a few weeks out of it, then this latest edition on running parallel operations should help you figure out what to do next. You just need to have a basic understanding of Python programming. That's all you need to get reading this book! This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Etat : new. Paperback. This fully revised edition teaches you to scale Python across cores, machines, and GPUs using the libraries you already know. I'll teach you to build Dask arrays, dataframes, bags and delayed graphs, and to tune partitions and chunks so the cluster actually earns its keep. I'll also show you to use the Dask expression system to let the optimiser push filters down and prune columns before a byte moves.As you'll see, you'll be working with pandas 3.0 throughout, including copy-on-write, PyArrow-backed strings and the new expression API. There are dedicated chapters that take you through terabyte-scale multi-dimensional data in HDF5, NetCDF, TIFF and Zarr, with Xarray, Kerchunk and cloud-native datacubes. The good thing about Dask is that it's the lighter, and the quicker choice than Spark in most scenarios. After that, we'll move on to GPU acceleration with RAPIDS and cuDF, distributed XGBoost and dask-ml for scaled machine learning, and production deployment on Kubernetes with monitoring, security, and cost control. Every chapter builds on one ongoing project, so the skills you learn are added to over time instead of being spread out.Key LearningsDiagnose whether a workload needs parallelism, a bigger machine, or better code.Size partitions and array chunks so clusters stop thrashing and start scaling.Read task graphs and dashboard panels to locate bottlenecks within minutes.Exploit the Dask expression system for predicate pushdown and column projection.Migrate pandas code to 3.0 copy-on-write and PyArrow-backed string dtypes.Stream terabyte HDF5, NetCDF, TIFF, and Zarr archives without exhausting memory.Build cloud-native, versioned datacubes using Zarr v3, Kerchunk, and Icechunk.Choose between Dask, Spark, Ray, and Polars using measured, honest criteria.Accelerate dataframes and gradient boosting on GPUs with RAPIDS and dask-cuda.Deploy, secure, monitor, and cost-control Dask clusters on production Kubernetes.Table of ContentUp and Running with DaskWorking with Dask CollectionsTuning Partitions and ChunksOptimizing Queries with Expression SystemScaling Out with Distributed ClustersScaling pandas 3.0 with DaskReading and Writing Data at ScaleWorking with Labeled Arrays in XarrayReading HDF5 and NetCDF ArchivesScaling Imagery with TIFFBuilding Cloud-native Datacubes with ZarrAccelerating Dask with GPUsOperating Dask in ProductionTarget AudienceIf you're wondering whether your Pandas script is stalling and you've tried adding memory but only got a few weeks out of it, then this latest edition on running parallel operations should help you figure out what to do next. You just need to have a basic understanding of Python programming. That's all you need to get reading this book! This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This fully revised edition teaches you to scale Python across cores, machines, and GPUs using the libraries you already know. I'll teach you to build Dask arrays, dataframes, bags and delayed graphs, and to tune partitions and chunks so the cluster actually earns its keep. I'll also show you to use the Dask expression system to let the optimiser push filters down and prune columns before a byte moves.As you'll see, you'll be working with pandas 3.0 throughout, including copy-on-write, PyArrow-backed strings and the new expression API. There are dedicated chapters that take you through terabyte-scale multi-dimensional data in HDF5, NetCDF, TIFF and Zarr, with Xarray, Kerchunk and cloud-native datacubes. The good thing about Dask is that it's the lighter, and the quicker choice than Spark in most scenarios. After that, we'll move on to GPU acceleration with RAPIDS and cuDF, distributed XGBoost and dask-ml for scaled machine learning, and production deployment on Kubernetes with monitoring, security, and cost control. Every chapter builds on one ongoing project, so the skills you learn are added to over time instead of being spread out.Key LearningsDiagnose whether a workload needs parallelism, a bigger machine, or better code.Size partitions and array chunks so clusters stop thrashing and start scaling.Read task graphs and dashboard panels to locate bottlenecks within minutes.Exploit the Dask expression system for predicate pushdown and column projection.Migrate pandas code to 3.0 copy-on-write and PyArrow-backed string dtypes.Stream terabyte HDF5, NetCDF, TIFF, and Zarr archives without exhausting memory.Build cloud-native, versioned datacubes using Zarr v3, Kerchunk, and Icechunk.Choose between Dask, Spark, Ray, and Polars using measured, honest criteria.Accelerate dataframes and gradient boosting on GPUs with RAPIDS and dask-cuda.Deploy, secure, monitor, and cost-control Dask clusters on production Kubernetes.Table of ContentUp and Running with DaskWorking with Dask CollectionsTuning Partitions and ChunksOptimizing Queries with Expression SystemScaling Out with Distributed ClustersScaling pandas 3.0 with DaskReading and Writing Data at ScaleWorking with Labeled Arrays in XarrayReading HDF5 and NetCDF ArchivesScaling Imagery with TIFFBuilding Cloud-native Datacubes with ZarrAccelerating Dask with GPUsOperating Dask in ProductionTarget AudienceIf you're wondering whether your Pandas script is stalling and you've tried adding memory but only got a few weeks out of it, then this latest edition on running parallel operations should help you figure out what to do next. You just need to have a basic understanding of Python programming. That's all you need to get reading this book.…

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This fully revised edition teaches you to scale Python across cores, machines, and GPUs using the libraries you already know. I'll teach you to build Dask arrays, dataframes, bags and delayed graphs, and to tune partitions and chunks so the cluster actually earns its keep. I'll also show you to use the Dask expression system to let the optimiser push filters down and prune columns before a byte moves.As you'll see, you'll be working with pandas 3.0 throughout, including copy-on-write, PyArrow-backed strings and the new expression API. There are dedicated chapters that take you through terabyte-scale multi-dimensional data in HDF5, NetCDF, TIFF and Zarr, with Xarray, Kerchunk and cloud-native datacubes. The good thing about Dask is that it's the lighter, and the quicker choice than Spark in most scenarios. After that, we'll move on to GPU acceleration with RAPIDS and cuDF, distributed XGBoost and dask-ml for scaled machine learning, and production deployment on Kubernetes with monitoring, security, and cost control. Every chapter builds on one ongoing project, so the skills you learn are added to over time instead of being spread out.Key LearningsDiagnose whether a workload needs parallelism, a bigger machine, or better code.Size partitions and array chunks so clusters stop thrashing and start scaling.Read task graphs and dashboard panels to locate bottlenecks within minutes.Exploit the Dask expression system for predicate pushdown and column projection.Migrate pandas code to 3.0 copy-on-write and PyArrow-backed string dtypes.Stream terabyte HDF5, NetCDF, TIFF, and Zarr archives without exhausting memory.Build cloud-native, versioned datacubes using Zarr v3, Kerchunk, and Icechunk.Choose between Dask, Spark, Ray, and Polars using measured, honest criteria.Accelerate dataframes and gradient boosting on GPUs with RAPIDS and dask-cuda.Deploy, secure, monitor, and cost-control Dask clusters on production Kubernetes.Table of ContentUp and Running with DaskWorking with Dask CollectionsTuning Partitions and ChunksOptimizing Queries with Expression SystemScaling Out with Distributed ClustersScaling pandas 3.0 with DaskReading and Writing Data at ScaleWorking with Labeled Arrays in XarrayReading HDF5 and NetCDF ArchivesScaling Imagery with TIFFBuilding Cloud-native Datacubes with ZarrAccelerating Dask with GPUsOperating Dask in ProductionTarget AudienceIf you're wondering whether your Pandas script is stalling and you've tried adding memory but only got a few weeks out of it, then this latest edition on running parallel operations should help you figure out what to do next. You just need to have a basic understanding of Python programming. That's all you need to get reading this book! 288 pp. Englisch.…

Taschenbuch. Etat : Neu. Parallel Python with Dask, Second Edition | Scale pandas, NumPy, and Xarray across cores, clusters, and GPUs for terabyte-scale analytics and machine learning | Patrick J | Taschenbuch | Englisch | 2026 | GitforGits | EAN 9788168869998 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …