# Installation

## Installing tensorflow_hub

The `tensorflow_hub` library can be installed alongside TensorFlow 1 and
TensorFlow 2. We recommend that new users start with TensorFlow 2 right away,
and current users upgrade to it.

### Use with TensorFlow 2

Use [pip](https://pip.pypa.io/) to
[install TensorFlow 2](https://www.tensorflow.org/install) as usual. (See there
for extra instructions about GPU support.) Then install a current version of
[`tensorflow-hub`](https://pypi.org/project/tensorflow-hub/) next to it (must be
0.5.0 or newer).

    $ pip install "tensorflow>=2.0.0"
    $ pip install --upgrade tensorflow-hub

The TF1-style API of TensorFlow Hub works with the v1 compatibility mode of
TensorFlow 2.

### Legacy use with TensorFlow 1

TensorFlow 1.15 is the only version of TensorFlow 1.x still supported by the
`tensorflow_hub` library (as of release 0.11.0). TensorFlow 1.15 defaults to
TF1-compatible behavior but contains many TF2 features under the hood to allow
some use of TensorFlow Hub's TF2-style APIs.

    $ pip install "tensorflow>=1.15,<2.0"
    $ pip install --upgrade tensorflow-hub

### Use of pre-release versions

The pip packages `tf-nightly` and `tf-hub-nightly` are built automatically from
the source code on github, with no release testing. This lets developers try out
the latest code without [building from source](https://www.tensorflow.org/hub/build_from_source).

    $ pip install tf-nightly
    $ pip install --upgrade tf-hub-nightly

## Next Steps

- [Library overview](https://www.tensorflow.org/hub/lib_overview)
- Tutorials:
  - [Text classification](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_text_classification.ipynb)
  - [Image classification](https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/tf2_image_retraining.ipynb)
  - Additional examples [on GitHub](https://github.com/tensorflow/hub/blob/master/examples/README.md)
- Find models on [tfhub.dev](https://tfhub.dev).