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Copy file name to clipboardExpand all lines: articles/key-vault/secrets/quick-create-net-v3.md
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## Prerequisites
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* An Azure subscription - [create one for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
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* The [.NET Core 2.1 SDK or later](https://dotnet.microsoft.com/download/dotnet-core/2.1).
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* The [.NET Core 3.1 SDK or later](https://dotnet.microsoft.com/download/dotnet-core/3.1).
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*[Azure CLI](/cli/azure/install-azure-cli?view=azure-cli-latest) or [Azure PowerShell](/powershell/azure/overview)
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This quickstart assumes you are running `dotnet`, [Azure CLI](/cli/azure/install-azure-cli?view=azure-cli-latest), and Windows commands in a Windows terminal (such as [PowerShell Core](/powershell/scripting/install/installing-powershell-core-on-windows?view=powershell-6), [Windows PowerShell](/powershell/scripting/install/installing-windows-powershell?view=powershell-6), or the [Azure Cloud Shell](https://shell.azure.com/)).
Copy file name to clipboardExpand all lines: articles/key-vault/secrets/quick-create-net.md
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## Prerequisites
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* An Azure subscription - [create one for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
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* The [.NET Core 2.1 SDK or later](https://dotnet.microsoft.com/download/dotnet-core/2.1).
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* The [.NET Core 3.1 SDK or later](https://dotnet.microsoft.com/download/dotnet-core/2.1).
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*[Azure CLI](/cli/azure/install-azure-cli?view=azure-cli-latest) or [Azure PowerShell](/powershell/azure/overview)
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This quickstart assumes you are running `dotnet`, [Azure CLI](/cli/azure/install-azure-cli?view=azure-cli-latest), and Windows commands in a Windows terminal (such as [PowerShell Core](/powershell/scripting/install/installing-powershell-core-on-windows?view=powershell-6), [Windows PowerShell](/powershell/scripting/install/installing-windows-powershell?view=powershell-6), or the [Azure Cloud Shell](https://shell.azure.com/)).
Copy file name to clipboardExpand all lines: articles/machine-learning/concept-plan-manage-cost.md
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ms.service: machine-learning
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ms.subservice: core
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ms.topic: conceptual
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ms.date: 04/22/2020
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ms.date: 05/08/2020
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---
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# Plan and manage costs for Azure Machine Learning
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You won't see a separate service area for Machine Learning. Instead you'll see the various resources you've added to your Machine Learning workspaces.
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## Use AmlCompute
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## Use Azure Machine Learning compute cluster (AmlCompute)
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With constantly changing data, you need fast and streamlined model training and retraining to maintain accurate models. However, continuous training comes at a cost, especially for deep learning models on GPUs.
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## Next steps
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* Learn more about managing costs with [cost analysis](../cost-management-billing/costs/quick-acm-cost-analysis.md).
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* Learn more about [Azure Machine Learning compute](how-to-set-up-training-targets.md#amlcompute).
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Learn more about:
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* [Manage and increase resource quotas](how-to-manage-quotas.md)
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* [Managing costs with [cost analysis](../cost-management-billing/costs/quick-acm-cost-analysis.md).
This article provides details on preconfigured limits on Azure resources for your subscription. Also included are instructions on how to request quota enhancements for each type of resource. These limits are put in place to prevent budget over-runs due to fraud, and to honor Azure capacity constraints.
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This article provides [Azure Machine Learning](overview-what-is-azure-ml.md) users with details on preconfigured limits on Azure resources for your subscription. Also included are instructions on how to request quota enhancements for each type of resource. These limits are put in place to prevent budget over-runs due to fraud, and to honor Azure capacity constraints.
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As with other Azure services, there are limits on certain resources associated with Azure Machine Learning. These limits range from a cap on the number of workspaces to limits on the actual underlying compute that gets used for model training or inference/scoring.
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As with other Azure services, there are limits on certain resources associated with Azure Machine Learning. These limits range from a cap on the number of [workspaces](concept-workspace.md) to limits on the actual underlying compute that gets used for model training or inference/scoring.
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As you design and scale your Azure Machine Learning resources for production workloads, consider these limits. For example, if your cluster doesn't reach the target number of nodes, then you may have reached an Azure Machine Learning Compute cores limit for your subscription. If you want to raise the limit or quota above the Default Limit, open an online customer support request at no charge. The limits can't be raised above the Maximum Limit value shown in the following tables due to Azure Capacity constraints. If there is no Maximum Limit column, then the resource doesn't have adjustable limits.
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For a more detailed and up-to-date list of quota limits, check the Azure-wide quota article[here](https://docs.microsoft.com/azure/azure-resource-manager/management/azure-subscription-service-limits).
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For a more detailed and up-to-date list of quota limits, check the [Azure-wide quota article](https://docs.microsoft.com/azure/azure-resource-manager/management/azure-subscription-service-limits).
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### Azure Machine Learning Compute
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For Azure Machine Learning Compute, there is a default quota limit on both the number of cores and number of unique compute resources allowed per region in a subscription. This quota is separate from the VM core quota above and the core limits are not shared between the two resource types since AmlCompute is a managed service that deploys resources in a hosted-on-behalf-of model.
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For [Azure Machine Learning Compute](concept-compute-target.md#azure-machine-learning-compute-managed), there is a default quota limit on both the number of cores and number of unique compute resources allowed per region in a subscription. This quota is separate from the VM core quota above and the core limits are not shared between the two resource types since AmlCompute is a managed service that deploys resources in a hosted-on-behalf-of model.
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Available resources:
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+ Dedicated cores per region have a default limit of 24 - 300 depending on your subscription offer type with higher defaults for EA and CSP offer types. The number of dedicated cores per subscription can be increased and is different for each VM family. Certain specialized VM families like NCv2, NCv3, or ND series start with a default of zero cores. Contact Azure support by raising a quota request to discuss increase options.
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<sup>2</sup> Jobs on a Low-Priority node could be preempted anytime there is a capacity constraint. We recommend you implement checkpointing in your job.
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### Azure Machine Learning Pipelines
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For Azure Machine Learning Pipelines, there is a quota limit on the number of steps in a pipeline and on the number of schedule-based runs of published pipelines per region in a subscription.
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For [Azure Machine Learning Pipelines](concept-ml-pipelines.md), there is a quota limit on the number of steps in a pipeline and on the number of schedule-based runs of published pipelines per region in a subscription.
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- Maximum number of steps allowed in a pipeline is 30,000
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- Maximum number of the sum of schedule-based runs and blob pulls for blog-triggered schedules of published pipelines per subscription per month is 100,000
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## Workspace level quota
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To better manage resource allocations for Amlcompute between various workspaces, we have introduced a feature that allows you to distribute subscription level quotas (by VM family) and configure them at the workspace level. The default behavior is that all workspaces have the same quota as the subscription level quota for any VM family. However, as the number of workspaces increases, and workloads of varying priority start sharing the same resources, users want a way to better share capacity and avoid resource contention issues. Azure Machine Learning provides a solution with its managed compute offering by allowing users to set a maximum quota for a particular VM family on each workspace. This is analogous to distributing your capacity between workspaces, and the users can choose to also over-allocate to drive maximum utilization.
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To better manage resource allocations for Azure Machine Learning Compute target (Amlcompute) between various [workspaces](concept-workspace.md), we have introduced a feature that allows you to distribute subscription level quotas (by VM family) and configure them at the workspace level. The default behavior is that all workspaces have the same quota as the subscription level quota for any VM family. However, as the number of workspaces increases, and workloads of varying priority start sharing the same resources, users want a way to better share capacity and avoid resource contention issues. Azure Machine Learning provides a solution with its managed compute offering by allowing users to set a maximum quota for a particular VM family on each workspace. This is analogous to distributing your capacity between workspaces, and the users can choose to also over-allocate to drive maximum utilization.
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To set quotas at the workspace level, go to any workspace in your subscription, and click on **Usages + quotas** in the left pane. Then select the **Configure quotas** tab to view the quotas, expand any VM family, and set a quota limit on any workspace listed under that VM family. Remember that you cannot set a negative value or a value higher than the subscription level quota. Also, as you would observe, by default all workspaces are assigned the entire subscription quota to allow for full utilization of the allocated quota.
> This is an Enterprise edition feature only. If you have both a Basic and an Enterprise edition workspace in your subscription, you can use this to only set quotas on your Enterprise workspaces. Your Basic workspaces will continue to have the subscription level quota which is the default behavior.
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> This is an Enterprise edition feature only. If you have both a [Basic and an Enterprise edition](overview-what-is-azure-ml.md#sku) workspace in your subscription, you can use this to only set quotas on your Enterprise workspaces. Your Basic workspaces will continue to have the subscription level quota which is the default behavior.
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>
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> You need subscription level permissions to set quota at the workspace level. This is enforced so that individual workspace owners do not edit or increase their quotas and start encroaching onto resources set aside for another workspace. Thus a subscription admin is best suited to allocate and distribute these quotas across workspaces.
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If you want to raise the limit or quota above the default limit, [open an online customer support request](https://ms.portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade/newsupportrequest/) at no charge.
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The limits can't be raised above the maximum limit value shown in the tables. If there is no maximum limit, then the resource doesn't have adjustable limits. [This](https://docs.microsoft.com/azure/azure-resource-manager/resource-manager-quota-errors) article covers the quota increase process in more detail.
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The limits can't be raised above the maximum limit value shown in the tables. If there is no maximum limit, then the resource doesn't have adjustable limits. [See step by step instructions on how to increase your quota](https://docs.microsoft.com/azure/azure-resource-manager/resource-manager-quota-errors).
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When requesting a quota increase, you need to select the service you are requesting to raise the quota against, which could be services such as Machine Learning service quota, Container instances or Storage quota. In addition for Azure Machine Learning Compute, you can click on the **Request Quota** button while viewing the quota following the steps above.
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> [!NOTE]
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> [Free Trial subscriptions](https://azure.microsoft.com/offers/ms-azr-0044p) are not eligible for limit or quota increases. If you have a [Free Trial subscription](https://azure.microsoft.com/offers/ms-azr-0044p), you can upgrade to a [Pay-As-You-Go](https://azure.microsoft.com/offers/ms-azr-0003p/) subscription. For more information, see [Upgrade Azure Free Trial to Pay-As-You-Go](../billing/billing-upgrade-azure-subscription.md) and [Free Trial subscription FAQ](https://azure.microsoft.com/free/free-account-faq).
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## Next steps
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Learn more with these articles:
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+[Plan & manage costs for Azure Machine Learning](concept-plan-manage-cost.md)
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+[How to increase your quota](https://docs.microsoft.com/azure/azure-resource-manager/resource-manager-quota-errors).
Copy file name to clipboardExpand all lines: articles/machine-learning/resource-known-issues.md
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Learn about the [resource quotas](how-to-manage-quotas.md) you might encounter when working with Azure Machine Learning.
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## Installation and import
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***Pip Installation: Dependencies are not guaranteed to be consistent with single line installation**:
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This is a known limitation of pip, as it does not have a functioning dependency resolver when you install as a single line. The first unique dependency is the only one it looks at.
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pip install azure-ml-datadrift
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pip install azureml-train-automl
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```
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* **Explanation package not guarateed to be installed when installing the azureml-train-automl-client:**
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When running a remote automl run with model explanation enabled you will see an error message saying ""Please install azureml-explain-model package for model explanations." This is a known issue and as a workaround please follow one of the steps below:
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1. Install azureml-explain-model locally.
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```
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pip install azureml-explain-model
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```
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2. Disable the explainability feature entirely by passing model_explainability=False in the automl configuration.
* **Panda errors: Typically seen during AutoML Experiment:**
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When manually setting up your environmnet using pip, you will notice attribute errors (especially from pandas) due to unsupported package versions being installed. In order to prevent such errors, [please install the AutoML SDK using the automl_setup.cmd](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/README.md):
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