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# TensorFlow Eager Execution
> *WARNING*: This is a preview/pre-alpha version. The API and performance
> characteristics are subject to change.
Eager execution is an experimental interface to TensorFlow that provides an
imperative programming style (à la [NumPy](http://www.numpy.org)). When you
enable eager execution, TensorFlow operations execute immediately; you do not
execute a pre-constructed graph with
[`Session.run()`](https://www.tensorflow.org/api_docs/python/tf/Session).
For example, consider a simple computation in TensorFlow:
```python
x = tf.placeholder(tf.float32, shape=[1, 1])
m = tf.matmul(x, x)
with tf.Session() as sess:
print(sess.run(m, feed_dict={x: [[2.]]}))
# Will print [[4.]]
```
Eager execution makes this much simpler:
```python
x = [[2.]]
m = tf.matmul(x, x)
print(m)
```
## Caveats
This feature is in early stages and work remains to be done in terms of smooth
support for distributed and multi-GPU training and CPU performance.
- [Known issues](https://github.com/tensorflow/tensorflow/issues?q=is%3Aissue%20is%3Aopen%20label%3Acomp%3Aeager)
- Feedback is welcome, please consider
[filing an issue](https://github.com/tensorflow/tensorflow/issues/new) to provide it.
## Installation
Since eager execution is not yet part of a TensorFlow release, using it requires
either [building from source](https://www.tensorflow.org/install/install_sources)
or the latest nightly builds. The nightly builds are available as:
- [`pip` packages](https://github.com/tensorflow/tensorflow/blob/master/README.md#installation) and
- [docker](https://hub.docker.com/r/tensorflow/tensorflow/) images.
For example, to run the latest nightly docker image:
```sh
# If you have a GPU, use https://github.com/NVIDIA/nvidia-docker
nvidia-docker pull tensorflow/tensorflow:nightly-gpu
nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:nightly-gpu
# If you do not have a GPU, use the CPU-only image
docker pull tensorflow/tensorflow:nightly
docker run -it -p 8888:8888 tensorflow/tensorflow:nightly
```
And then visit http://localhost:8888 in your browser for a Jupyter notebook
environment. Try out the notebooks below.
## Documentation
For an introduction to eager execution in TensorFlow, see:
- [User Guide](python/g3doc/guide.md)
- Notebook: [Basic Usage](python/examples/notebooks/1_basics.ipynb)
- Notebook: [Gradients](python/examples/notebooks/2_gradients.ipynb)
- Notebook: [Importing Data](python/examples/notebooks/3_datasets.ipynb)
## Changelog
- 2017/10/31: Initial preview release.
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