# tf.compat.v1.train.create_global_step

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|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/training/training_util.py#L163-L254) |

Create global step tensor in graph.

    tf.compat.v1.train.create_global_step(
        graph=None
    )

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## Migrate to TF2

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> [!CAUTION]
> **Caution:** This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the [TensorFlow v1 to TensorFlow v2 migration guide](https://www.tensorflow.org/guide/migrate) for instructions on how to migrate the rest of your code.

With the deprecation of global graphs, TF no longer tracks variables in
collections. In other words, there are no global variables in TF2. Thus, the
global step functions have been removed (`get_or_create_global_step`,
`create_global_step`, `get_global_step`) . You have two options for migrating:

1. Create a Keras optimizer, which generates an `iterations` variable. This variable is automatically incremented when calling `apply_gradients`.
2. Manually create and increment a [`tf.Variable`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Variable).

Below is an example of migrating away from using a global step to using a
Keras optimizer:

Define a dummy model and loss:

    def compute_loss(x):
      v = tf.Variable(3.0)
      y = x * v
      loss = x * 5 - x * v
      return loss, [v]

Before migrating:

    g = tf.Graph()
    with g.as_default():
      x = tf.compat.v1.placeholder(tf.float32, [])
      loss, var_list = compute_loss(x)
      global_step = tf.compat.v1.train.create_global_step()
      global_init = tf.compat.v1.global_variables_initializer()
      optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.1)
      train_op = optimizer.minimize(loss, global_step, var_list)
    sess = tf.compat.v1.Session(graph=g)
    sess.run(global_init)
    print("before training:", sess.run(global_step))
    before training: 0
    sess.run(train_op, feed_dict={x: 3})
    print("after training:", sess.run(global_step))
    after training: 1

Migrating to a Keras optimizer:

    optimizer = tf.keras.optimizers.SGD(.01)
    print("before training:", optimizer.iterations.numpy())
    before training: 0
    with tf.GradientTape() as tape:
      loss, var_list = compute_loss(3)
      grads = tape.gradient(loss, var_list)
      optimizer.apply_gradients(zip(grads, var_list))
    print("after training:", optimizer.iterations.numpy())
    after training: 1

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## Description

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| ## Args ||
|---|---|
| `graph` | The graph in which to create the global step tensor. If missing, use default graph. |

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| ## Returns ||
|---|---|
| Global step tensor. ||

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| ## Raises ||
|---|---|
| `ValueError` | if global step tensor is already defined. |

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