acrolinx

Author: Larry Franks

articles/machine-learning/reference-yaml-job-sweep.md

@@ -47,7 +47,7 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `inputs.<input_name>` | number, integer, boolean, string or object | One of a literal value (of type number, integer, boolean, or string) or an object containing a [job input data specification](#job-inputs). | | |
 | `outputs` | object | Dictionary of output configurations of the job. The key is a name for the output within the context of the job and the value is the output configuration. <br><br> Outputs can be referenced in the `command` using the `${{ outputs.<output_name> }}` expression. | |
 | `outputs.<output_name>` | object | You can leave the object empty, in which case by default the output will be of type `uri_folder` and Azure ML will system-generate an output location for the output. File(s) to the output directory will be written via read-write mount. If you want to specify a different mode for the output, provide an object containing the [job output specification](#job-outputs). | |
-| `identity` | object | The identity is used for data accessing. It can be [UserIdentityConfiguration](#useridentityconfiguration), [ManagedIdentityConfiguration](#managedidentityconfiguration) or None. If it's UserIdentityConfiguration the identity of job submitter will be used to access input data and write result to output folder, otherwise, the managed identity of the compute target will be used | |
+| `identity` | object | The identity is used for data accessing. It can be [UserIdentityConfiguration](#useridentityconfiguration), [ManagedIdentityConfiguration](#managedidentityconfiguration) or None. If UserIdentityConfiguration, the identity of job submitter will be used to access input data and write result to output folder, otherwise, the managed identity of the compute target will be used | |
 
 ### Sampling algorithms
 
@@ -162,18 +162,18 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 
 | Key | Type | Description | Default value |
 | --- | ---- | ----------- | ------------- |
-| `max_total_trials` | integer | The maximum time in seconds the job is allowed to run. Once this limit is reached the system will cancel the job. | `1000` |
+| `max_total_trials` | integer | The maximum time in seconds the job is allowed to run. Once this limit is reached, the system will cancel the job. | `1000` |
 | `max_concurrent_trials` | integer | | Defaults to `max_total_trials`. |
-| `timeout` | integer | The maximum time in seconds the entire sweep job is allowed to run. Once this limit is reached the system will cancel the sweep job, including all its trials. | `604800` |
-| `trial_timeout` | integer | The maximum time in seconds each trial job is allowed to run. Once this limit is reached the system will cancel the trial. | |
+| `timeout` | integer | The maximum time in seconds the entire sweep job is allowed to run. Once this limit is reached, the system will cancel the sweep job, including all its trials. | `604800` |
+| `trial_timeout` | integer | The maximum time in seconds each trial job is allowed to run. Once this limit is reached, the system will cancel the trial. | |
 
 ### Attributes of the `trial` key
 
 | Key | Type | Description | Default value |
 | --- | ---- | ----------- | ------------- |
 | `command` | string | **Required.** The command to execute. | |
 | `code` | string | Local path to the source code directory to be uploaded and used for the job. | |
-| `environment` | string or object | **Required.** The environment to use for the job. This can be either a reference to an existing versioned environment in the workspace or an inline environment specification. <br> <br> To reference an existing environment use the `azureml:<environment-name>:<environment-version>` syntax. <br><br> To define an environment inline please follow the [Environment schema](reference-yaml-environment.md#yaml-syntax). Exclude the `name` and `version` properties as they are not supported for inline environments. | |
+| `environment` | string or object | **Required.** The environment to use for the job. This can be either a reference to an existing versioned environment in the workspace or an inline environment specification. <br> <br> To reference an existing environment, use the `azureml:<environment-name>:<environment-version>` syntax. <br><br> To define an environment inline, follow the [Environment schema](reference-yaml-environment.md#yaml-syntax). Exclude the `name` and `version` properties as they aren't supported for inline environments. | |
 | `environment_variables` | object | Dictionary of environment variable name-value pairs to set on the process where the command is executed. | |
 | `distribution` | object | The distribution configuration for distributed training scenarios. One of [MpiConfiguration](#mpiconfiguration), [PyTorchConfiguration](#pytorchconfiguration), or [TensorFlowConfiguration](#tensorflowconfiguration). | |
 | `resources.instance_count` | integer | The number of nodes to use for the job. | `1` |
@@ -207,8 +207,8 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | Key | Type | Description | Allowed values | Default value |
 | --- | ---- | ----------- | -------------- | ------------- |
 | `type` | string | The type of job input. Specify `uri_file` for input data that points to a single file source, or `uri_folder` for input data that points to a folder source. [Learn more about data access.](concept-data.md)| `uri_file`, `uri_folder`, `mltable`, `mlflow_model` | `uri_folder` |
-| `path` | string | The path to the data to use as input. This can be specified in a few ways: <br><br> - A local path to the data source file or folder, e.g. `path: ./iris.csv`. The data will get uploaded during job submission. <br><br> - A URI of a cloud path to the file or folder to use as the input. Supported URI types are `azureml`, `https`, `wasbs`, `abfss`, `adl`. See [Core yaml syntax](reference-yaml-core-syntax.md) for more information on how to use the `azureml://` URI format. <br><br> - An existing registered Azure ML data asset to use as the input. To reference a registered data asset use the `azureml:<data_name>:<data_version>` syntax or `azureml:<data_name>@latest` (to reference the latest version of that data asset), e.g. `path: azureml:cifar10-data:1` or `path: azureml:cifar10-data@latest`. | | |
-| `mode` | string | Mode of how the data should be delivered to the compute target. <br><br> For read-only mount (`ro_mount`), the data will be consumed as a mount path. A folder will be mounted as a folder and a file will be mounted as a file. Azure ML will resolve the input to the mount path. <br><br> For `download` mode the data will be downloaded to the compute target. Azure ML wil resolve the input to the downloaded path. <br><br> If you only want the URL of the storage location of the data artifact(s) rather than mounting or downloading the data itself, you can use the `direct` mode. This will pass in the URL of the storage location as the job input. Note that in this case you are fully responsible for handling credentials to access the storage. | `ro_mount`, `download`, `direct` | `ro_mount` |
+| `path` | string | The path to the data to use as input. This can be specified in a few ways: <br><br> - A local path to the data source file or folder, for example, `path: ./iris.csv`. The data will get uploaded during job submission. <br><br> - A URI of a cloud path to the file or folder to use as the input. Supported URI types are `azureml`, `https`, `wasbs`, `abfss`, `adl`. For more information on using the `azureml://` URI format, see [Core yaml syntax](reference-yaml-core-syntax.md). <br><br> - An existing registered Azure ML data asset to use as the input. To reference a registered data asset, use the `azureml:<data_name>:<data_version>` syntax or `azureml:<data_name>@latest` (to reference the latest version of that data asset), for example, `path: azureml:cifar10-data:1` or `path: azureml:cifar10-data@latest`. | | |
+| `mode` | string | Mode of how the data should be delivered to the compute target. <br><br> For read-only mount (`ro_mount`), the data will be consumed as a mount path. A folder will be mounted as a folder and a file will be mounted as a file. Azure ML will resolve the input to the mount path. <br><br> For `download` mode the data will be downloaded to the compute target. Azure ML will resolve the input to the downloaded path. <br><br> If you only want the URL of the storage location of the data artifact(s) rather than mounting or downloading the data itself, you can use the `direct` mode. This will pass in the URL of the storage location as the job input. In this case you're fully responsible for handling credentials to access the storage. | `ro_mount`, `download`, `direct` | `ro_mount` |
 
 ### Job outputs