Using Python 2 libraries

You can use third-party libraries that are pure Python code with no C extensions, by copying the library into your application directory. If the third-party library is already built-in, bundled with the runtime, you can use the library without copying it into your app.

Third party libraries must be implemented as pure Python code with no C extensions. If copied to your application directory, they count towards file quotas because the library is uploaded to App Engine along with your application code.

Copying a third-party library

To use a third-party library that is not on the list of built-in libraries bundled with the runtime:


    mkdir lib
  2. Use pip (version 6 or later) with the -t <directory> flag to copy the libraries into the folder you created in the previous step. For example:

    pip install -t lib/ <library_name>

    Using Homebrew Python on macOS?

  3. Create a file named in the same folder as your app.yaml file.

  4. Edit the file and provide your library directory to the vendor.add() method.

    from google.appengine.ext import vendor
    # Add any libraries install in the "lib" folder.

    The file above assumes that the current working directory is where the lib folder is located. In some cases, such as unit tests, the current working directory can be different. To avoid errors, you can explicitly pass in the full path to the lib folder using:

    vendor.add(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'lib'))

Using pip requirements files with copied libraries

pip can read a list of libraries to install from a file, known as a requirements file. Requirements files make it easy to set up a new development environment for your app, and upgrade to new versions of libraries.

A requirements file is a text file with one line per library, listing the package name and optionally the version for the package (defaults to latest):


To install the libraries from a requirements file, use the -r flag in addition to the -t lib flag:

pip install -t lib -r requirements.txt

Using a built-in third-party library bundled with the runtime

If the third-party library is on the list of built-in libraries bundled with the App Engine Python runtime, you only have to specify it under the libraries directive in the app.yaml, for example:

- name: PIL
  version: "1.1.7"
- name: webob
  version: "1.1.1"

App Engine automatically provides the requested libraries during deployment.

Using built-in bundled libraries with the local development server

Many of the built-in libraries provided by the runtime are automatically available to the local development server. Some libraries need the gcloud component app-engine-python-extras to be installed. If the local development server detects that this component is needed, it will prompt to install it. The following built-in libraries must be installed locally before you can use them with the local development server:

You can use the pip command to install all of these packages from the Python package index (PyPI).

sudo pip install lxml==2.3.5

Depending on your platform, you might need to install build support tools and Python sources to install these libraries.

  • On Linux, the package manager can provide these prerequisites and can often provide a pre-built version of the library.
  • On Windows, installers for pre-built versions are usually available.
  • On macOS, the Xcode Command Line Tools are required to build some packages.

The development server uses the package version you have installed locally regardless of the version specified in app.yaml. If you want, set up a virtualenv for your project to provide the exact package version. Note that the virtualenv is only used for these binary packages locally and will not be made available to your application once deployed. To add additional third-party libraries, use the method described in Installing a library.

Using Django in the local development server

Django is a full-featured web application framework for Python. It provides a full stack of interchangeable components, including dispatch, views, middleware, and templating components, and many others.

The Django data modeling interface is not compatible with the App Engine datastore. You can use the App Engine data modeling libraries (db or ndb) in your Django applications. However, third-party Django applications that use the Django data modeling interface, most notably Django's Admin application, might not directly work with App Engine.

The Datastore modeling library (DB) is the default. To use Django with the NDB storage API instead, add 'google.appengine.ext.ndb.django_middleware.NdbDjangoMiddleware', to the MIDDLEWARE_CLASSES entry in your Django file. It's a good idea to insert it in front of any other middleware classes, since some other middleware might make datastore calls and those won't be handled properly if that middleware is invoked before this middleware. You can learn more about Django middleware in the project documentation.

To enable Django in your app, specify the WSGI application and Django library in app.yaml:

- url: /.*
  script:  # a WSGI application in the main module's global scope

- name: django
  version: "1.4"

The DJANGO_SETTINGS_MODULE environment variable must be set to the name of your Django settings module, typically 'settings', before packages are imported.

If your Django settings module is something other than, set the DJANGO_SETTINGS_MODULE environment variable accordingly either in your app.yaml file:

  DJANGO_SETTINGS_MODULE: 'myapp.settings'

Or in your Python code:

import os
# specify the name of your settings module
os.environ['DJANGO_SETTINGS_MODULE'] = 'myapp.settings'

import django.core.handlers.wsgi
app = django.core.handlers.wsgi.WSGIHandler()

Using matplotlib in the local development server

Matplotlib is a plotting library that produces graphs and figures in a variety of image formats. On App Engine, the interactive modes of matplotlib are not supported, and a number of other features are also unavailable. This means you cannot use as many matplotlib tutorials suggest. Instead, you should use pyplot.savefig() to write image data to the output stream, a cStringIO.StringIO instance, or the Google Cloud Storage using the Cloud Storage Client Library.

Matplotlib allows extensive customization through the use of the matplotlibrc configuration file, which should be placed in the application's top-level directory. Alternatively, you can set the MATPLOTLIBRC environment variable to a path relative to your application's directory.

The default backend is AGG, which allows writing files of all supported formats: PNG (the default format), RAW, PS, PDF, SVG and SVGZ. If you make the PIL library available by adding PIL to the libraries section of app.yaml, then the AGG backend will automatically support writing JPEG and TIFF image formats as well.

Matplotlib comes with a number of fonts which are automatically available. You can use custom fonts by uploading them in TTF format along with your application, and setting the TTFPATH environment variable to the path where they are located, relative to your application's directory. For more information, see the app.yaml reference.

A number of matplotlib features are not supported on App Engine. In particular:

  • There is no ~/.matplotlib directory. However, there are alternative locations to place the matplotlibrc configuration file, as described above.
  • Interactive backends and GUI elements are not supported.
  • The EMF, Cairo and GDK backends are not supported.
  • There is no caching, and therefore a number of mechanisms will re-calculate or re-download data that would normally be cached. Specific caching mechanisms that have been disabled include font data calculated by matplotlib.font_manager.FontManager.findfont, sample data downloaded by matplotlib.cbook.get_sample_data and financial data downloaded by
    • Because there is no caching, it is not possible to call [matplotlib.cbook.get_sample_data]( with asfileobj=False unless is set to False.
  • All features that invoke external commands have been disabled.
    • Use of fontconfig has been disabled. Fonts are found through the mechanism described above.
    • Use of LaTeX for text rendering is not supported. Setting text.usetex to True will not work.
    • Use of an external PostScript distiller program is not supported. Setting ps.usedistiller to ghostscript or xpdf will not work.
    • Use of an external video encoding program is not supported. The method will not work, and therefore, the matplotlib.animation package is not useful.
    • The matplotlib.cbook.report_memory function and matplotlib.cbook.MemoryMonitor class are not supported.
  • The matplotlib.test function has been disabled.

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