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Quicker Travis builds that rely on numpy and scipy using Miniconda

For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.

A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common setup, I've also included steps to use Coveralls for calculating test coverage.

All you need to do is replace the YOUR_PACKAGE_NAME_HERE parts with your package's name in both .coveragerc and .travis.yml, and include both files in your project's root directory. Then just activate Travis and you should be set.

You may also consider adding the latest Miniconda shell script to your repository, and modifying your .travis.yml to just use that instead of downloading it every time. It can make building slightly faster, and is more resistent to server issues on Continuum's side (although you still are downloading the packages from them).

Additional files (only show up on





omit =
language: python
- 2.7
- 3.3
email: false
# Setup anaconda
- wget -O
- chmod +x
- ./ -b
- export PATH=/home/travis/miniconda/bin:$PATH
- conda update --yes conda
# The next couple lines fix a crash with multiprocessing on Travis and are not specific to using Miniconda
- sudo rm -rf /dev/shm
- sudo ln -s /run/shm /dev/shm
# Install packages
- conda install --yes python=$TRAVIS_PYTHON_VERSION atlas numpy scipy matplotlib nose dateutil pandas statsmodels
# Coverage packages are on my binstar channel
- conda install --yes -c dan_blanchard python-coveralls nose-cov
- python install
# Run test
- nosetests --with-cov --cov YOUR_PACKAGE_NAME_HERE --cov-config .coveragerc --logging-level=INFO
# Calculate coverage
- coveralls --config_file .coveragerc

Jorge-C commented Jan 3, 2014

This is great! Notice that you don't need to use miniconda3 to run python3 code (I modified your approach so that it creates a conda environment with the right python version).

A warning to others, miniconda is not always updated with conda, which can cause builds to break. Add conda update --yes conda to the before_install.


dan-blanchard commented Mar 19, 2014

Wow, I didn't know people were commenting on this. (Weird that GitHub doesn't send notifications for Gists.)

Anyway, I should probably update this to use the latest version of miniconda.

Oh, and the reason I make it explicitly download miniconda3 for Python 3 code is that it's less to download (and therefore slightly faster).


dan-blanchard commented Mar 21, 2014

Just updated to:

  • include the conda update suggestion from @danielballan
  • use my binstar channel for installing coverage tools
  • use latest version of Miniconda

zonca commented Apr 3, 2014

@dan-blanchard, you can directly use:

to get the latest version of miniconda!

@dan-blanchard thanks for this. found one erratum found, should be:

  • export PATH=/home/travis/miniconda/bin:$PATH

rowanc1 commented May 17, 2014



dan-blanchard commented Jul 12, 2014

Thanks for the catch @moconnell. I've fixed that.

I've also switched to using @zonca's URL.

vr2262 commented Aug 8, 2014

I was having issues with the environment when using this as well as pip. In order to fix it, in .travis.yml I had to put:

- conda update --yes conda
- conda create --yes -n condaenv python=2.7
- conda install --yes -n condaenv pip
- source activate condaenv

That allowed me to use pip without any problem.

Is there a version of that recipe for Python 2.6?

Thanks @dan-blanchard & @vr2262. Just to clarify, the addition would go into before_install as well and if you don't want to hardcode the Python version you can write:

- conda create --yes -n condaenv python=$TRAVIS_PYTHON_VERSION

Also, the coverage flags didn't work for me, so I set them to:

script: nosetests --with-coverage --cover-package=packagename --logging-level=INFO

and I added to install:

- pip install coveralls

Now is not showing my organisation's repos, so I can't really test this, but that's another problem...

Many thanks for this! My Travis CI build was actually timing out trying to install scipy with pip! This saved me.

If anyone is interested, there is a tutorial on the conda website teaching how to configure Miniconda with Travis.

estroz commented Nov 14, 2016

The - export PATH=/home/travis/miniconda/bin:$PATH step should be updated to - export PATH=/home/travis/miniconda2/bin:$PATH to reflect install path changes in the miniconda install script.

AlJohri commented Nov 15, 2016

@dan-blanchard can you update the script as per @estroz's last comment?

johnyf commented Aug 24, 2017

No need to use Miniconda.

pip install --upgrade pip setuptools wheel
pip install --only-binary=numpy,scipy numpy scipy

travis-ci/travis-ci#2650 (comment)

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