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# Eager Execution
Eager execution provides an imperative interface to TensorFlow (similar to
[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 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
For eager execution, we recommend using TensorFlow version 1.8 or newer.
Installation instructions at https://www.tensorflow.org/install/
## Documentation
For an introduction to eager execution in TensorFlow, see:
- [User Guide](https://www.tensorflow.org/guide/eager) ([source](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/eager/index.md))
- Notebook: [Basic Usage](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/eager/eager_basics.ipynb)
- Notebook: [Automatic differentiation and gradient tape](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/eager/automatic_differentiation.ipynb)
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