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articles/machine-learning/how-to-image-processing-batch.md

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@@ -163,7 +163,7 @@ __code/score-by-file/batch_driver.py__
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One the scoring script is created, it's time to create a batch deployment for it. Follow the following steps to create it:
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1. Ensure you have a compute cluster created where we can create the deployment. In this example we are going to use a compute cluster named `gpu-cluster`. Although is not required, we use GPUs to speed up the processing.
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1. Ensure you have a compute cluster created where we can create the deployment. In this example we are going to use a compute cluster named `gpu-cluster`. Although it's not required, we use GPUs to speed up the processing.
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1. We need to indicate over which environment we are going to run the deployment. In our case, our model runs on `TensorFlow`. Azure Machine Learning already has an environment with the required software installed, so we can reutilize this environment. We are just going to add a couple of dependencies in a `conda.yml` file.
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