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‎articles/machine-learning/how-to-auto-train-image-models.md

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## Prerequisites
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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* An Azure Machine Learning workspace. To create the workspace, see [Create workspace resources](quickstart-create-resources.md).
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* Install and [set up CLI (v2)](how-to-configure-cli.md#prerequisites) and make sure you install the `ml` extension.
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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* An Azure Machine Learning workspace. To create the workspace, see [Create workspace resources](quickstart-create-resources.md).
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* The Azure Machine Learning Python SDK v2 (preview) installed.
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image object detection | CLI v2: `image_object_detection` <br> SDK v2: `image_object_detection()`
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image instance segmentation| CLI v2: `image_instance_segmentation` <br> SDK v2: `image_instance_segmentation()`
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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task: image_object_detection
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```
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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Based on the task type, you can create automl image jobs using task specific `automl` functions.
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For example:
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Automated ML doesn't impose any constraints on training or validation data size for computer vision tasks. Maximum dataset size is only limited by the storage layer behind the dataset (i.e. blob store). There's no minimum number of images or labels. However, we recommend starting with a minimum of 10-15 samples per label to ensure the output model is sufficiently trained. The higher the total number of labels/classes, the more samples you need per label.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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type: mltable
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```
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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You can create data inputs from training and validation MLTable from your local directory or cloud storage with the following code:
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The compute target is passed in using the `compute` parameter. For example:
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```yaml
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compute: azureml:gpu-cluster
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```
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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```python
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from azure.ai.ml import automl
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Before doing a large sweep to search for the optimal models and hyperparameters, we recommend trying the default values to get a first baseline. Next, you can explore multiple hyperparameters for the same model before sweeping over multiple models and their parameters. This way, you can employ a more iterative approach, because with multiple models and multiple hyperparameters for each, the search space grows exponentially and you need more iterations to find optimal configurations.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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image_model:
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model_name: "yolov5"
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```
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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If you wish to use the default hyperparameter values for a given algorithm (say yolov5), you can specify it using model_name parameter in set_image_model method of the task specific `automl` job. For example,
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### Experiment budget
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You can optionally specify the maximum time budget for your AutoML Vision training job using the `timeout` parameter in the `limits` - the amount of time in minutes before the experiment terminates. If none specified, default experiment timeout is seven days (maximum 60 days). For example,
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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timeout: 60
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```
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=limit-settings)]
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You can configure all the sweep related parameters as shown in the example below.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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delay_evaluation: 6
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```
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=sweep-settings)]
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You can pass fixed settings or parameters that don't change during the parameter space sweep as shown below.
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=pass-arguments)]
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You can pass the run ID that you want to load the checkpoint from.
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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To find the run ID from the desired model, you can use the following code.
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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When you've configured your AutoML Job to the desired settings, you can submit the job.
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![Deploy configuration](./media/how-to-auto-train-image-models/deploy-image-model.png)
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## Code examples
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Review detailed code examples and use cases in the [azureml-examples repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/automl-standalone-jobs).
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Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/sdk-preview/sdk/jobs/automl-standalone-jobs).
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‎articles/machine-learning/how-to-auto-train-nlp-models.md

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## Prerequisites
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* This article assumes some familiarity with setting up an automated machine learning experiment. Follow the [how-to](how-to-configure-auto-train.md) to see the main automated machine learning experiment design patterns.
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* If you enable long range text, then a GPU with higher memory is required such as, [NCv3](../virtual-machines/ncv3-series.md) series or [ND](../virtual-machines/nd-series.md) series.
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Multi-class text classification|`"eng"` <br> `"deu"` <br> `"mul"`| English&nbsp;BERT&nbsp;[cased](https://huggingface.co/bert-base-cased)<br> [Multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) <br><br>For all other languages, automated ML applies multilingual BERT
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Named entity recognition (NER)|`"eng"` <br> `"deu"` <br> `"mul"`| English&nbsp;BERT&nbsp;[cased](https://huggingface.co/bert-base-cased) <br> [German BERT](https://huggingface.co/bert-base-german-cased)<br> [Multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) <br><br>For all other languages, automated ML applies multilingual BERT
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‎articles/machine-learning/how-to-prepare-datasets-for-automl-images.md

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# Prepare data for computer vision tasks with automated machine learning (preview)
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