public HyperParameterTrainingJobDefinition withResourceConfig(ResourceConfig resourceConfig) {
        setResourceConfig(resourceConfig);
        return this;
    }
<p>
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning
job launches.
</p>
<p>
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes
for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
<code>File</code> as the <code>TrainingInputMode</code> in the algorithm specification. For distributed training
algorithms, specify an instance count greater than 1.
</p>
<note>
<p>
If you want to use hyperparameter optimization with instance type flexibility, use
<code>HyperParameterTuningResourceConfig</code> instead.
</p>
</note>

@param resourceConfig
       The resources, including the compute instances and storage volumes, to use for the training jobs that the
       tuning job launches.</p>
       <p>
       Storage volumes store model artifacts and incremental states. Training algorithms might also use storage
       volumes for scratch space. If you want SageMaker to use the storage volume to store the training data,
       choose <code>File</code> as the <code>TrainingInputMode</code> in the algorithm specification. For
       distributed training algorithms, specify an instance count greater than 1.
       </p>
       <note>
       <p>
       If you want to use hyperparameter optimization with instance type flexibility, use
       <code>HyperParameterTuningResourceConfig</code> instead.
       </p>
@return Returns a reference to this object so that method calls can be chained together.
aws-java-sdk-sagemaker/src/main/java/com/amazonaws/services/sagemaker/model/HyperParameterTrainingJobDefinition.java