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 pip to
install TensorFlow 2 as usual. (See there
for extra instructions about GPU support.) Then install a current version of
tensorflow-hub next to it (must be
0.5.0 or newer).
$ pip install "tensorflow>=2.0.0"
$ pip install --upgrade tensorflow-hubThe TF1-style API of TensorFlow Hub works with the v1 compatibility mode of TensorFlow 2.
The tensorflow_hub library requires TensorFlow version 1.7 or greater.
We strongly recommend to install it with TensorFlow 1.15, which 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-hubThe 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.
Although we hope to prevent breaking changes, this project is still under active development and is not yet guaranteed to have a stable API or model format.
As in all of machine learning, fairness is an important consideration. Many pre-trained models are trained on large datasets. When reusing any model, it’s important to be mindful of what data the model was trained on (and whether there are any existing biases there), and how these might impact your use of it.
Since they contain arbitrary TensorFlow graphs, models can be thought of as programs. Using TensorFlow Securely describes the security implications of referencing a model from an untrusted source.