# How to save models after Trainer.fit()?

**URL:** <https://discuss.ray.io/t/how-to-save-models-after-trainer-fit/11857>\
**Category:** Uncategorized\
**Created:** [August 19, 2023, 6:23am UTC](https://discuss.ray.io/t/how-to-save-models-after-trainer-fit/11857 "2023-08-19T06:23:41Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![cartml](https://avatars.discourse-cdn.com/v4/letter/c/48db29/32.png) [@cartml](https://discuss.ray.io/u/cartml)\
**Post date:** [August 19, 2023, 6:23am UTC](https://discuss.ray.io/t/how-to-save-models-after-trainer-fit/11857/1 "2023-08-19T06:23:41Z")

</div>

Hi !

I have been able to train a model using

`trainer = TensorflowTrainer(...)`

and call

`result = trainer.fit()`

everything works fine, now; when the model finished training, I need to actually load the trained model and serialize it using own code (since we have a model registry)

However, I didn’t find anywhere how to do it, especially in Ray’s official documentation. Those examples usually ends after `result = trainer.fit()`

If I use

`result.checkpoint.load_model()`, it failed; saying i need to supply model argument in `load_model`

if I use

```auto
checkpoint = result.checkpoint
checkpoint.to_directory("/tmp/test_checkpoint")
self.model = keras.models.load_model("/tmp/test_checkpoint")

```

If failed also (saying there is no SavedModel in the folder)

We are using Ray 2.6.1. What is the documented way to get the trained model after calling trainer.fit()?

Thanks

---

<div class="post-metadata">

**Author:** ![matthewdeng](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/matthewdeng/32/1446_2.png) [@matthewdeng](https://discuss.ray.io/u/matthewdeng)\
**Post date:** [August 19, 2023, 3:13pm UTC](https://discuss.ray.io/t/how-to-save-models-after-trainer-fit/11857/2 "2023-08-19T15:13:26Z")

</div>

Hi @cartml,

How are you creating your checkpoint? In general the recommended way to do this would be something like this:

```python
def train_func():
    with tempfile.TemporaryDirectory() as tmpdir:
        # todo: save your checkpoint to tmpdir in whatever format you want
        # example: model.save(tmpdir + "/test_checkpoint")
        checkpoint = Checkpoint.from_directory(tmpdir)
        session.report({...}, checkpoint=checkpoint)

trainer = TensorTrainer(train_func, ...)
result = trainer.fit()

checkpoint.to_directory(some_local_path)
 # todo: load your checkpoint 
 # example: keras.models.load_model(some_local_path + "/test_checkpoint")

```

The way that you can think about is that:

1. Within the training function, you are creating a directory that you can store whatever checkpoint data you want.
2. When you fetch the checkpoint at the end, you can “recreate” that directory locally.