103 lines
4.2 KiB
Markdown
103 lines
4.2 KiB
Markdown
# Installation
|
|
|
|
🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+.
|
|
|
|
You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're
|
|
unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). Create a virtual environment with the version of Python you're going
|
|
to use and activate it.
|
|
|
|
Now, if you want to use 🤗 Transformers, you can install it with pip. If you'd like to play with the examples, you
|
|
must install it from source.
|
|
|
|
## Installation with pip
|
|
|
|
First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
|
|
Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available)
|
|
and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific
|
|
install command for your platform.
|
|
|
|
When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
|
|
|
|
```bash
|
|
pip install transformers
|
|
```
|
|
|
|
Alternatively, for CPU-support only, you can install 🤗 Transformers and PyTorch in one line with:
|
|
|
|
```bash
|
|
pip install transformers[torch]
|
|
```
|
|
|
|
or 🤗 Transformers and TensorFlow 2.0 in one line with:
|
|
|
|
```bash
|
|
pip install transformers[tf-cpu]
|
|
```
|
|
|
|
To check 🤗 Transformers is properly installed, run the following command:
|
|
|
|
```bash
|
|
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
|
|
```
|
|
|
|
It should download a pretrained model then print something like
|
|
|
|
```bash
|
|
[{'label': 'NEGATIVE', 'score': 0.9991129040718079}]
|
|
```
|
|
|
|
(Note that TensorFlow will print additional stuff before that last statement.)
|
|
|
|
## Installing from source
|
|
|
|
To install from source, clone the repository and install with the following commands:
|
|
|
|
``` bash
|
|
git clone https://github.com/huggingface/transformers.git
|
|
cd transformers
|
|
pip install -e .
|
|
```
|
|
|
|
Again, you can run
|
|
|
|
```bash
|
|
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))"
|
|
```
|
|
|
|
to check 🤗 Transformers is properly installed.
|
|
|
|
## Caching models
|
|
|
|
This library provides pretrained models that will be downloaded and cached locally. Unless you specify a location with
|
|
`cache_dir=...` when you use methods like `from_pretrained`, these models will automatically be downloaded in the
|
|
folder given by the shell environment variable ``TRANSFORMERS_CACHE``. The default value for it will be the PyTorch
|
|
cache home followed by ``/transformers/`` (even if you don't have PyTorch installed). This is (by order of priority):
|
|
|
|
* shell environment variable ``TORCH_HOME``
|
|
* shell environment variable ``XDG_CACHE_HOME`` + ``/torch/``
|
|
* default: ``~/.cache/torch/``
|
|
|
|
So if you don't have any specific environment variable set, the cache directory will be at
|
|
``~/.cache/torch/transformers/``.
|
|
|
|
**Note:** If you have set a shell enviromnent variable for one of the predecessors of this library
|
|
(``PYTORCH_TRANSFORMERS_CACHE`` or ``PYTORCH_PRETRAINED_BERT_CACHE``), those will be used if there is no shell
|
|
enviromnent variable for ``TRANSFORMERS_CACHE``.
|
|
|
|
### Note on model downloads (Continuous Integration or large-scale deployments)
|
|
|
|
If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through
|
|
your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way
|
|
faster, and cheaper. Feel free to contact us privately if you need any help.
|
|
|
|
## Do you want to run a Transformer model on a mobile device?
|
|
|
|
You should check out our [swift-coreml-transformers](https://github.com/huggingface/swift-coreml-transformers) repo.
|
|
|
|
It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`,
|
|
`DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.
|
|
|
|
At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch or
|
|
TensorFlow 2.0 to productizing them in CoreML, or prototype a model or an app in CoreML then research its
|
|
hyperparameters or architecture from PyTorch or TensorFlow 2.0. Super exciting!
|