Building a TensorFlow Pipeline

Creating a TensorFlow training job

In this lesson, we will build a TensorFlow model from scratch.

Step 1: Create a high-compute environment

We will use the compute we created in the previous lesson. If we need high computation, we can use compute GPUs instead.

Step 2: Choosing the right environment

Depending on the library and CPU/GPU availability, we have two options:

1. Use a built-in environment, if available. We can find the list of environments in Azure Machine Learning studio.

We'll use the environment AzureML-tensorflow-2.4-ubuntu18.04-py37-cpu-inference:9, which contains the TensorFlow library and supports the CPU.

2. If the desired environment is unavailable, we can use a custom environment or the Docker environment.

Step 3: Define the hyperparameters

If we want to use any hyperparameters, we define them in the input of the job configuration. We then pass ...