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articles/machine-learning/data-science-virtual-machine/overview.md

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# What is the Azure Data Science Virtual Machine for Linux and Windows?
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The Data Science Virtual Machine (DSVM) is a customized VM image on the Azure cloud platform built specifically for doing data science. It has many popular data science tools preinstalled and pre-configured to jump-start building intelligent applications for advanced analytics.
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The Data Science Virtual Machine (DSVM) is a customized VM image on the Azure cloud platform built specifically for doing data science. It has many popular data science tools preinstalled and preconfigured to jump-start building intelligent applications for advanced analytics.
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> [!IMPORTANT]
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> Items marked (preview) in this article are currently in public preview.
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+ Windows Server 2019
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+ Ubuntu 20.04 LTS
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Additionally, we are excited to offer Azure DSVM for PyTorch (preview), which is an Ubuntu 20.04 image from Azure Marketplace that is optimized for large, distributed deep learning workloads. It comes preinstalled and validated with the latest PyTorch version to reduce setup costs and accelerate time to value. It comes packaged with various optimization functionalities (ONNX Runtime​, DeepSpeed​, MSCCL​, ORTMoE​, Fairscale​, Nvidia Apex​), as well as an up-to-date stack with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA.
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Additionally, we're excited to offer Azure DSVM for PyTorch (preview), which is an Ubuntu 20.04 image from Azure Marketplace that is optimized for large, distributed deep learning workloads. It comes preinstalled and validated with the latest PyTorch version to reduce setup costs and accelerate time to value. It comes packaged with various optimization functionalities (ONNX Runtime​, DeepSpeed​, MSCCL​, ORTMoE​, Fairscale​, Nvidia Apex​), and an up-to-date stack with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA.
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## Comparison with Azure Machine Learning
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[Azure Machine Learning Compute Instances](../concept-compute-instance.md) are a fully configured and __managed__ VM image whereas the DSVM is an __unmanaged__ VM.
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The key differences between these two product offerings are detailed below:
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Key differences between these:
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|Feature |Data Science<br>VM |AzureML<br>Compute Instance |
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|---------|---------|---------|
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|Built-in<br>Hosted Notebooks | No<br>(requires additional configuration) | Yes |
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|Built-in SSO | No <br>(requires additional configuration) | Yes |
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|Built-in Collaboration | No | Yes |
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|Pre-installed Tools | Jupyter(lab), VSCode,<br> Visual Studio, PyCharm, Juno,<br>Power BI Desktop, SSMS, <br>Microsoft Office 365, Apache Drill | Jupyter(lab) |
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|Preinstalled Tools | Jupyter(lab), VSCode,<br> Visual Studio, PyCharm, Juno,<br>Power BI Desktop, SSMS, <br>Microsoft Office 365, Apache Drill | Jupyter(lab) |
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## Sample use cases
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Below we illustrate some common use cases for DSVM customers.
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Here's some common use cases for DSVM customers.
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### Short-term experimentation and evaluation
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You can use the DSVM to evaluate or learn new data science [tools](./tools-included.md), especially by going through some of our published [samples and walkthroughs](./dsvm-samples-and-walkthroughs.md).
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### Deep learning with GPUs
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In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs). 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. 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. You can choose any of the N series GPUs enabled virtual machine SKUs with DSVM. Note GPU enabled virtual machine SKUs are not supported on Azure free accounts.
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In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs). 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. 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. You can choose any of the N series GPUs enabled virtual machine SKUs with DSVM. Note GPU enabled virtual machine SKUs aren't supported on Azure free accounts.
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The Windows editions of the DSVM come pre-installed with GPU drivers, frameworks, and GPU versions of deep learning frameworks. On the Linux editions, deep learning on GPUs is enabled on the Ubuntu DSVMs.
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The Windows editions of the DSVM come preinstalled with GPU drivers, frameworks, and GPU versions of deep learning frameworks. On the Linux editions, deep learning on GPUs is enabled on the Ubuntu DSVMs.
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You can also deploy the Ubuntu or Windows editions of the DSVM to an Azure virtual machine that isn't based on GPUs. In this case, all the deep learning frameworks will fall back to the CPU mode.
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You can also deploy the Ubuntu or Windows editions of the DSVM to an Azure virtual machine that isn't based on GPUs. In this case, all the deep learning frameworks falls back to the CPU mode.
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[Learn more about available deep learning and AI frameworks](dsvm-tools-deep-learning-frameworks.md).
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