Gitforgits sep 2026 (8 risultati)

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

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 162 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 344 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 268 pp. Englisch.

Taschenbuch. Condizione: 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. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The bottleneck is not your kernel. In fact, it almost never is.After spending enough time in Nsight, the way you read a timeline becomes as intuitive as a mechanic listening to an engine. The same faults keep cropping up, such as a GPU with thousands of cores sitting idle behind a blocking copy, a single default stream or a host thread frozen at a synchronisation point that was never necessary. This book is about identifying that fault early and eliminating it.Designed for programming professionals, this book is written for GPU developers, Rust engineers and AI practitioners who want their devices to be busy, not just correct. Our approach is to stay in pure Rust throughout, using cuda-oxide for SIMT kernels and cutile-rs for the tile model. We also rely heavily on the type system to ensure that data races fail at compile time rather than at 3 AM during production. We will overlap transfers with compute using streams and pinned memory, build a staging ring that pipelines every stage and feed the GPU through Tokio producers with bounded-channel backpressure. Each technique is profiled, verified against a CPU reference and measured.Key LearningsProfile before you optimise, and let Nsight Systems show you who is actually waiting.Write SIMT kernels in pure Rust where data races fail at compile time.Treat default stream as the enemy of overlap, and fork your own.Overlap copies with compute using pinned memory and a staging ring.Reach for reduction ladder instead of atomics when summing on the device.Express element-wise work as tile kernels, and let compiler map the hardware.Keep the GPU fed with Tokio producers and bounded-channel backpressure.Verify every kernel against CPU reference before you trust its timing.Weigh throughput against latency, since tuning for one quietly taxes the other.Know when to stop, because past a point concurrency only adds bookkeeping.Table of ContentRust's Threads, Timing and OwnershipCPU versus GPU ExecutionSIMT Kernels, Blocks and GridsRace-Free Parallel KernelsAsynchronous GPU ExecutionOverlapping Transfers with StreamsTile Programming with cutile-rsAsync GPU Pipelines with TokioThroughput, Latency and BottlenecksTarget AudienceIf you're a programmer looking for a hands-on guide to GPU programming, Rust techniques and AI principles, this book is for you. It's designed to help you make the most of your device's processing power. 162 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -At some point, every software engineering organisation hits a point where shipping software feels like it's taking longer than writing it. So, developers are waiting days for a database, environments are drifting apart, and every team is deploying in its own way. This book shows you how to do that, one working layer at a time.In this book, you'll get to build a complete internal developer platform using tools you can run on a single Linux workstation. The plan is to provision multi-cluster infrastructure with OpenTofu, wire GitOps delivery with Argo CD, design a self-service component API with Kubebuilder and Crossplane, enforce policy with Kyverno and Cilium, and add observability and a Backstage portal that cuts onboarding from weeks to minutes. Then you'll adopt OpenChoreo and use it with custom component types, promotion pipelines, multi-cluster data planes, and AI agents connected through MCP.Out of the book, about 40% is about the thinking behind it, and the rest is all about putting it into practice. The idea is to make you capable and skilled to design, build, secure, measure and grow a platform that developers will actually use.Key LearningsProvision multi-cluster Kubernetes foundations with OpenTofu modules, gateways, and shared secret stores.Deliver change through GitOps with Argo CD, ApplicationSets, and progressive rollouts.Design single-file component API that hides Kubernetes detail from developers.Provision databases on demand with Crossplane compositions and a custom operator.Enforce security baselines through Kyverno policies, signed images, and zero-trust network rules.Instrument services with OpenTelemetry, and reuse dashboards, SLOs, burn-rate alerts.Speedup onboarding using Backstage templates, catalog entities, and TechDocs.Operate OpenChoreo with custom component types, traits, and promotion pipelines.Run dedicated production data planes across clusters using outbound cluster agents.Grow adoption voluntarily, score platform maturity, and prove ROI to leadership.Table of ContentWhy Platform Engineering ExistsThinking Like a Platform TeamArchitecting Internal Developer PlatformBuilding Foundation LayerGitOps and Delivery PipelinesSelf-Service and Platform APIsGuardrails, Security and GovernanceObservability and Developer PortalsOpenChoreo as Reference OutcomeOperating OpenChoreoScaling Platform AdoptionTarget AudienceIf you know to ship software, and have worked around Git, containers and the Linux command line, then this book is well-suited for you. No prior work with the platform tools is expected, although some knowledge of Kubernetes helps. 344 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In today's landscape, the success of enterprise networks hinges on proficiency in understanding how networks behave, and knowing how to optimise their performance at scale. Therefore, this book is written for professionals who already know the basics and want to learn about scaling up. Covering Python 3.14 as an everyday operations tool, this book is a must-read for network engineers, network and system administrators, network architects and enterprise architects. It teaches you to transform a basic reachability probe into a complete application which can audit routers, Linux hosts, cloud accounts, and Kubernetes clusters.You will practically experience automating devices over SSH, NETCONF and RESTCONF; managing Linux networking through netlink, and switching from SNMP polling to gNMI streaming telemetry for monitoring. It covers everything from hardening firewalls, TLS and VPNs to building your own network API with FastAPI, managing configuration with NAPALM, Nornir, NetBox and Ansible, and validating every change in a CI pipeline with Batfish and pyATS before it reaches production. Each step is accompanied by a dry run and a rollback option, and every lab runs in containers on your own machine.Key LearningsBuild your Python 3.14 tooling, and choosing between free-threaded and standard builds.Automate multi-vendor devices using Netmiko and Scrapli.Parse device output using TextFSM, TTP and Genie over Regex.Apply safe changes using NETCONF candidate datastores, confirmed commits and automatic rollback.Manage Linux interfaces, routes and namespaces using netlink with pyroute2.Scale to hundreds of devices using asyncio TaskGroups, timeouts and semaphores.Replace SNMP polling with gNMI streaming telemetry for Prometheus and OpenTelemetry.Automate firewalls, TLS audits, WireGuard tunnels and CIS compliance checks.Drive configuration from NetBox through Nornir, Jinja2, NAPALM and Ansible.Validate changes in CI before production using Batfish, pyATS and Containerlab.Table of ContentBuilding Python 3.14 Network ToolkitProbing Networks with Raw SocketsTalking to HTTP, DNS and WebSocket ServicesAutomating Network Devices over SSHConfiguring Devices with NETCONF, RESTCONF and YANGManaging Linux Host NetworkingScaling Fleet Operations with ConcurrencyMonitoring Networks with Streaming TelemetrySecuring Networks and Automation CredentialsBuilding and Consuming Network APIsManaging Configuration across the FleetRunning Network Changes with AnsibleTesting and Shipping Network Changes with CI/CDAuditing Cloud and Kubernetes NetworksTarget AudienceTo read this book, you don't need to be a seasoned networking professional. All you need is a sense of curiosity, access to a terminal, and a willingness to let the computer handle the repetitive tasks. 268 pp. Englisch.…