# tf.estimator.CheckpointSaverHook

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/training/basic_session_run_hooks.py#L523-L670) |

Saves checkpoints every N steps or seconds. (deprecated)

Inherits From: [`SessionRunHook`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/estimator/SessionRunHook)

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.estimator.CheckpointSaverHook`](https://www.tensorflow.org/api_docs/python/tf/estimator/CheckpointSaverHook), [`tf.compat.v1.train.CheckpointSaverHook`](https://www.tensorflow.org/api_docs/python/tf/estimator/CheckpointSaverHook)

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    tf.estimator.CheckpointSaverHook(
        checkpoint_dir,
        save_secs=None,
        save_steps=None,
        saver=None,
        checkpoint_basename='model.ckpt',
        scaffold=None,
        listeners=None,
        save_graph_def=True
    )

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.keras instead.

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| ## Args ||
|---|---|
| `checkpoint_dir` | `str`, base directory for the checkpoint files. |
| `save_secs` | `int`, save every N secs. |
| `save_steps` | `int`, save every N steps. |
| `saver` | `Saver` object, used for saving. |
| `checkpoint_basename` | `str`, base name for the checkpoint files. |
| `scaffold` | `Scaffold`, use to get saver object. |
| `listeners` | List of `CheckpointSaverListener` subclass instances. Used for callbacks that run immediately before or after this hook saves the checkpoint. |
| `save_graph_def` | Whether to save the GraphDef and MetaGraphDef to `checkpoint_dir`. The GraphDef is saved after the session is created as `graph.pbtxt`. MetaGraphDefs are saved out for every checkpoint as `model.ckpt-*.meta`. |

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| ## Raises ||
|---|---|
| `ValueError` | One of `save_steps` or `save_secs` should be set. |
| `ValueError` | At most one of `saver` or `scaffold` should be set. |

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## Methods

### `after_create_session`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/training/basic_session_run_hooks.py#L585-L602)

    after_create_session(
        session, coord
    )

Called when new TensorFlow session is created.

This is called to signal the hooks that a new session has been created. This
has two essential differences with the situation in which `begin` is called:

- When this is called, the graph is finalized and ops can no longer be added to the graph.
- This method will also be called as a result of recovering a wrapped session, not only at the beginning of the overall session.

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| Args ||
|---|---|
| `session` | A TensorFlow Session that has been created. |
| `coord` | A Coordinator object which keeps track of all threads. |

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### `after_run`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/training/basic_session_run_hooks.py#L607-L616)

    after_run(
        run_context, run_values
    )

Called after each call to run().

The `run_values` argument contains results of requested ops/tensors by
`before_run()`.

The `run_context` argument is the same one send to `before_run` call.
`run_context.request_stop()` can be called to stop the iteration.

If `session.run()` raises any exceptions then `after_run()` is not called.

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| Args ||
|---|---|
| `run_context` | A `SessionRunContext` object. |
| `run_values` | A SessionRunValues object. |

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### `before_run`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/training/basic_session_run_hooks.py#L604-L605)

    before_run(
        run_context
    )

Called before each call to run().

You can return from this call a `SessionRunArgs` object indicating ops or
tensors to add to the upcoming `run()` call. These ops/tensors will be run
together with the ops/tensors originally passed to the original run() call.
The run args you return can also contain feeds to be added to the run()
call.

The `run_context` argument is a `SessionRunContext` that provides
information about the upcoming `run()` call: the originally requested
op/tensors, the TensorFlow Session.

At this point graph is finalized and you can not add ops.

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| Args ||
|---|---|
| `run_context` | A `SessionRunContext` object. |

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| Returns ||
|---|---|
| None or a `SessionRunArgs` object. ||

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### `begin`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/training/basic_session_run_hooks.py#L576-L583)

    begin()

Called once before using the session.

When called, the default graph is the one that will be launched in the
session. The hook can modify the graph by adding new operations to it.
After the `begin()` call the graph will be finalized and the other callbacks
can not modify the graph anymore. Second call of `begin()` on the same
graph, should not change the graph.

### `end`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/training/basic_session_run_hooks.py#L618-L623)

    end(
        session
    )

Called at the end of session.

The `session` argument can be used in case the hook wants to run final ops,
such as saving a last checkpoint.

If `session.run()` raises exception other than OutOfRangeError or
StopIteration then `end()` is not called.
Note the difference between `end()` and `after_run()` behavior when
`session.run()` raises OutOfRangeError or StopIteration. In that case
`end()` is called but `after_run()` is not called.

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| Args ||
|---|---|
| `session` | A TensorFlow Session that will be soon closed. |

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