什么是适用于 Linux 和 Windows 的 Azure Data Science Virtual Machine?What is the Azure Data Science Virtual Machine for Linux and Windows?

Data Science Virtual Machine (DSVM) 是专为开展数据科学构建的 Azure 云平台上的自定义 VM 映像。The Data Science Virtual Machine (DSVM) is a customized VM image on the Azure cloud platform built specifically for doing data science. 它预装并预配了许多热门数据科学工具,可为高级分析快速生成智能应用程序。It has many popular data science tools preinstalled and preconfigured to jumpstart building intelligent applications for advanced analytics.

DSVM 在以下环境中可用:The DSVM is available on:

  • Windows Server 2019Windows Server 2019
  • Ubuntu 18.04 LTSUbuntu 18.04 LTS
  • Windows Server 2016Windows Server 2016
  • Ubuntu 16.04 LTSUbuntu 16.04 LTS

备注

用于深度学习的所有 VM 工具都已装入到 Data Science Virtual Machine 中。All VM tools for deep learning have been folded into the Data Science Virtual Machine.

为何选择 DSVM?Why choose the DSVM?

Data Science Virtual Machine 的目标在于向所有技能级别和各个行业的数据专业人员提供无摩擦、预配置的数据科学环境。The goal of the Data Science Virtual Machine is to provide data professionals of all skill levels and across industries with a friction-free, preconfigured data science environment. 可以预配一个 DSVM,而无需自行部署一个与之相当的工作区。Instead of rolling out a comparable workspace on your own, you can provision a DSVM. 这种做法可以在安装、配置和包管理方面节省数天甚至数周的时间。__That choice can save you days or even weeks on the installation, configuration, and package management processes. 分配 DSVM 后,可以立即开始处理数据科学项目。After your DSVM has been allocated, you can immediately begin working on your data science project.

示例用例Sample Use Cases

下面演示了 DSVM 客户的一些常见用例。Below, we illustrate some common use cases for DSVM customers.

将数据科学工作负荷迁移到云Moving data science workloads to the cloud

DSVM 为数据科学团队提供基线配置,让他们将本地桌面替换为托管云桌面,并确保团队中的所有数据科学家可以使用一致的设置来验证试验和促进协作。The DSVM provides a baseline configuration for data science teams that want replace their local desktops with a managed cloud desktop, ensuring that all the data scientists on a team have a consistent setup with which to verify experiments and promote collaboration. 它还通过减轻系统管理员负担来降低成本。It also lowers costs by reducing the sysadmin burden. 这种减轻负担可以节省评估、安装和维护高级分析软件包所需的时间。This burden reduction saves on the time needed to evaluate, install, and maintain software packages for advanced analytics.

数据科学训练和培训Data science training and education

教授数据科学课程的企业培训师和教师通常提供虚拟机映像。Enterprise trainers and educators who teach data science classes usually provide a virtual machine image. 该映像确保学员具有一致的设置且示例以可预测方式工作。The image ensures that students have a consistent setup and that the samples work predictably.

DSVM 创建可缓解支持和不兼容性挑战的一致设置的按需环境。The DSVM creates an on-demand environment with a consistent setup that eases the support and incompatibility challenges. 这些环境需要频繁生成,特别是短期培训课程的情况从中获益极大。Cases where these environments need to be built frequently, especially for shorter training classes, benefit substantially.

对于大型项目的按需弹性容量On-demand elastic capacity for large-scale projects

数据科学编程马拉松/竞赛或大型数据建模和浏览需要扩展的硬件容量,通常持续时间比较短。Data science hackathons/competitions or large-scale data modeling and exploration require scaled-out hardware capacity, typically for short duration. DSVM 有助于根据需要在允许运行高性能计算资源试验的扩展服务器上快速复制数据科学环境。The DSVM can help replicate the data science environment quickly on demand, on scaled-out servers that allow experiments that high-powered computing resources can run.

短期实验和评估Short-term experimentation and evaluation

可以使用 DSVM,专门参考我们发布的一些示例和演练来评估或学习新的数据科学工具You can use the DSVM to evaluate or learn new data science tools, especially by going through some of our published samples and walkthroughs.

使用 GPU 进行深度学习Deep learning with GPUs

在 DSVM 中,训练模型可以使用基于图形处理单元 (GPU) 的硬件上的深度学习算法。In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs). 利用 Azure 平台的 VM 缩放功能,DSVM 可帮助根据需要在云中使用基于 GPU 的硬件。By taking advantage of the VM scaling capabilities of the Azure platform, the DSVM helps you use GPU-based hardware in the cloud according to your needs. 若要训练大型模型或者在保留相同 OS 磁盘的同时进行高速计算,可以切换到基于 GPU 的 VM。You can switch to a GPU-based VM when you're training large models, or when you need high-speed computations while keeping the same OS disk. 可以在 DSVM 中选择启用了 N 系列 GPU 的任意虚拟机 SKU。You can choose any of the N series GPU enabled virtual machine SKUs with DSVM. 请注意,Azure 试用帐户不支持启用 GPU 的虚拟机 SKU。Please note Azure trial accounts do not support GPU enabled virtual machine SKUs.

Windows 版本的 DSVM 预安装了 GPU 驱动程序、框架和 GPU 版本的深度学习框架。The Windows editions of the DSVM comes pre-installed with GPU drivers, frameworks, and GPU versions of deep learning frameworks. Linux 版的 Ubuntu DSVM 上启用了基于 GPU 的深度学习。On the Linux edition, deep learning on GPUs is enabled on the Ubuntu DSVMs.

还可以将 Ubuntu 或 Windows 版本的 DSVM 部署到不基于 GPU 的 Azure 虚拟机。You can also deploy the Ubuntu or Windows editions of the DSVM to an Azure virtual machine that isn't based on GPUs. 在这种情况下,所有深度学习框架都将回退到 CPU 模式。In this case, all the deep learning frameworks will fall back to the CPU mode.

详细了解可用的深度学习和 AI 框架Learn more about available deep learning and AI frameworks.

DSVM 中包含哪些组件?What's included on the DSVM?

此处查看 Windows 和 Linux DSVM 上的完整工具列表。See a full list of tools on both the Windows and Linux DSVM's here.

后续步骤Next steps

通过以下文章,了解详细信息:Learn more with these articles: