Install Python34 Library

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  1. Install Python Library Mac
  2. Install Python Library In Linux

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🤗 Transformers is tested on Python 3.6+, and PyTorch 1.1.0+ or TensorFlow 2.0+.

You should install 🤗 Transformers in a virtual environment. If you’reunfamiliar with Python virtual environments, check out the user guide. Create a virtual environment with the version of Python you’re goingto 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, youmust 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,PyTorch installation page and/orFlax installation pageregarding 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:

Alternatively, for CPU-support only, you can install 🤗 Transformers and PyTorch in one line with:

or 🤗 Transformers and TensorFlow 2.0 in one line with:

or 🤗 Transformers and Flax in one line with:

To check 🤗 Transformers is properly installed, run the following command:

It should download a pretrained model then print something like

(Note that TensorFlow will print additional stuff before that last statement.)

Installing from source¶

Here is how to quickly install transformers from source:

Note that this will install not the latest released version, but the bleeding edge master version, which you may want to use in case a bug has been fixed since the last official release and a new release hasn’t been yet rolled out.

While we strive to keep master operational at all times, if you notice some issues, they usually get fixed within a few hours or a day and and you’re more than welcome to help us detect any problems by opening an Issue and this way, things will get fixed even sooner.

Again, you can run:

to check 🤗 Transformers is properly installed.

Editable install¶

If you want to constantly use the bleeding edge master version of the source code, or if you want to contribute to the library and need to test the changes in the code you’re making, you will need an editable install. This is done by cloning the repository and installing with the following commands:

This command performs a magical link between the folder you cloned the repository to and your python library paths, and it’ll look inside this folder in addition to the normal library-wide paths. So if normally your python packages get installed into:

now this editable install will reside where you clone the folder to, e.g. ~/transformers/ and python will search it too.

Do note that you have to keep that transformers folder around and not delete it to continue using the transformers library.

Now, let’s get to the real benefit of this installation approach. Say, you saw some new feature has been just committed into master. If you have already performed all the steps above, to update your transformers to include all the latest commits, all you need to do is to cd into that cloned repository folder and update the clone to the latest version:

There is nothing else to do. Your python environment will find the bleeding edge version of transformers on the next run.

With conda¶

Since Transformers version v4.0.0, we now have a conda channel: huggingface.

🤗 Transformers can be installed using conda as follows:

Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda.

Caching models¶

This library provides pretrained models that will be downloaded and cached locally. Unless you specify a location withcache_dir=... when you use methods like from_pretrained, these models will automatically be downloaded in thefolder given by the shell environment variable TRANSFORMERS_CACHE. The default value for it will be the HuggingFace cache home followed by /transformers/. This is (by order of priority):

  • shell environment variable HF_HOME

  • shell environment variable XDG_CACHE_HOME + /huggingface/

  • default: ~/.cache/huggingface/

Install Python Library Mac

So if you don’t have any specific environment variable set, the cache directory will be at~/.cache/huggingface/transformers/.

Note: If you have set a shell environment 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 shellenvironment variable for TRANSFORMERS_CACHE.

Offline mode¶

It’s possible to run 🤗 Transformers in a firewalled or a no-network environment.

Setting environment variable TRANSFORMERS_OFFLINE=1 will tell 🤗 Transformers to use local files only and will not try to look things up.

Most likely you may want to couple this with HF_DATASETS_OFFLINE=1 that performs the same for 🤗 Datasets if you’re using the latter.

Here is an example of how this can be used on a filesystem that is shared between a normally networked and a firewalled to the external world instances.

On the instance with the normal network run your program which will download and cache models (and optionally datasets if you use 🤗 Datasets). For example:

and then with the same filesystem you can now run the same program on a firewalled instance:

and it should succeed without any hanging waiting to timeout.

Fetching models and tokenizers to use offline¶

When running a script the first time like mentioned above, the downloaded files will be cached for future reuse.However, it is also possible to download files and point to their local path instead.

Downloading files can be done through the Web Interface by clicking on the “Download” button, but it can also be handledprogrammatically using the huggingface_hub library that is a dependency to transformers:

  • Using snapshot_download to download an entire repository

  • Using hf_hub_download to download a specific file

See the reference for these methods in the huggingface_hubdocumentation.

Do you want to run a Transformer model on a mobile device?¶

You should check out our 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 pretraining or fine-tuning models in PyTorch orTensorFlow 2.0 to productizing them in CoreML, or prototype a model or an app in CoreML then research itshyperparameters or architecture from PyTorch or TensorFlow 2.0. Super exciting!

Contents

  1. How to install wxPython
    1. GNU/Linux - Building from the source

Install Python

The stable release of wxPython requires Python version 2.7. Get it from the official download page.

Windows

Installation under windows is especially simple: Run the installer you can get from wxPython and follow the instructions.

Mac OS X

An installer is available on the wxPython site, for both PPC and Intel Macs.

If you wish to build it yourself, you should follow the instruction described here.

A french howto can be found here

If you receive a message about the package being 'damaged and can't be opened', then you need to change the security preference setting that is labeled Allow applications downloaded from: to Anywhere.

GNU/Linux - Redhat

You can find RPMs for Redhat (they are working just fine with Mandrake through), at the address wxPython

GNU/Linux - Debian

wxPython can be installed through apt-get by calling apt-get install python-wxgtk2.8 or apt-get install python-wxgtk2.6, depending on which version you want. You may have to call this with root permissions. The wxPython demo is in the wx-examples package. However, it is advised to install the demo separately, as described at Using wxPython Demo Code.

Try this:

Please note that sometimes older versions of wx are installed by using this method See InstallingOnUbuntuOrDebian for how to get the latest versions with apt-get.

GNU/Linux - Raspbian on Raspberry Pi

wxPython 4.0.7.post2 can be installed and does run on Raspberry Pi's Debian variant, Raspbian. It works on Python 3.4 and up. For install instructions Build wxPython on Raspberry Pi.

GNU/Linux - Gentoo

wxPython can be installed through portage by calling emerge wxPython (notice the capital P). The correct command is actually emerge wxpython (without a capital p) as of 11/28/04.

GNU/Linux - Building from the source

You might also want to build wxPython from the source. You have to do this in three steps:

Installing wxGTK from source

wxGTK is the GTK version of wxWidgets. GTK (Gimp ToolKit) is a graphic library used by Gnome, so it is probably already installed on your Linux box. All you have to do is download the wxGTK source from the wxGTK ftp server. Or the wxWidgets website

  • Untar wxGTK by type the command:
  • Go into the directory:
  • Run the configure script:

You might get some errors here if GTK is not installed or if the include files for GTK are not installed (in a Mandrake distribution, gtk+-devel-1.2.8-6mdk.i586.rpm is the rpm that you want to install)

  • Run the make file:
    • You might get some errors here if yacc or lex are not installed. (in a Mandrake distribution, the right rpms are byacc-1.9-7mdk.i586.rpm and flex-2.5.4a-13mdk.i586.rpm)

You should now have a compiled version of wxGTK. We want to install it and link it into the system.

  • Become superuser:
Install

Your root password is required here.

  • Install wxGTK:
  • Link the library:
  • Exit from superuser mode: Normally, wxGTK is installed but there might be a problem with wxPython:

it is possible that the library is not installed where wxPython is looking for it. ( In a mandrake 7.2 distribution, you want wxGTK to be installed in /usr/lib whereas it is automatically installed in /usr/local/lib) The solution is to create a symbolic link of the library where you want it to be:

  • Go in to the directory where you want the library to be installed:
  • Create a symbolic link to the library:

Installing wxPython from source

  • Download the source code of the last wxPython release:

wxPython website

  • Untar the tarball:
  • go into the directory:
  • Edit the setup.py to choose what you want to install. I suggest that you don't install OGL and GL_CANVAS. by selecting:
  • Build the python module:
  • Become root:

Your root password is required here.

  • Install the module:
  • Exit root mode:
  • Check if the module works:

wxPython is fully installed!

Please note that the most up to date information about installing the new wxPython4 wheels is usually located on the main wxPython website at: https://wxpython.org/pages/downloads/.

Make sure you have recent versions of pip and setuptools installed.

Install Python Library In Linux

  • Installing wxPython4 (Phoenix) on Linux Since wxPython is not able to be built to the manylinux1 standard we're not able to put binaries on PyPI. Instead binary wheels are made available for a few popular linux distributions, and you can install them using pip once you locate the proper folder to tell pip to download from. Look around in https://extras.wxpython.org/wxPython4/extras/linux for a folder matching your distro and gtk preference. You can then install with a command like the following command. If you are not installing into a Python virtual environment then you will probably need to insert sudo at the beginning of the command:

  • Installing wxPython4 (Phoenix) on Windows and OSX Binary wheels for these platforms are available on PyPI so you can install with this simpler command if build are available for your target Python:

  • Verify installation