Update install doc

.travis.yml

@@ -54,7 +54,7 @@ script:
   # Invoke also the alternative way of running the unit tests
   - python -c "import odl; odl.test()"
   # Build the Sphinx doc (only for Python 3.5 and the master branch)
-  - if [[ "$TRAVIS_PYTHON_VERSION" == "3.5" && "$TRAVIS_BRANCH" == "master" ]]; then
+  - if [[ "$TRAVIS_PYTHON_VERSION" == "3.5" && "$TRAVIS_BRANCH" == "master" && "$TRAVIS_PULL_REQUEST" == "false" ]]; then
       cd $TRAVIS_BUILD_DIR/doc/source && python generate_doc.py && cd -;
       travis-sphinx -n -s $TRAVIS_BUILD_DIR/doc/source build;
     fi
@@ -64,6 +64,6 @@ after_success:
   - coveralls
   # Deploy the Sphinx doc to gh-pages (only for Python 3.5 and the master branch)
   # See https://github.com/Syntaf/travis-sphinx
-  - if [[ "$TRAVIS_PYTHON_VERSION" == "3.5" && "$TRAVIS_BRANCH" == "master" ]]; then
+  - if [[ "$TRAVIS_PYTHON_VERSION" == "3.5" && "$TRAVIS_BRANCH" == "master" && "$TRAVIS_PULL_REQUEST" == "false" ]]; then
       travis-sphinx deploy;
     fi

conda/meta.yaml

@@ -1,31 +1,33 @@
 package:
     name: odl
-    version: "0.5.1"
+    version: "0.5.2"
 
 source:
     git_url: https://github.com/odlgroup/odl
     # git_rev: master  # for testing, put any branch here
-    git_rev: v0.5.1  # release
+    git_rev: v0.5.2  # release
+    # git_rev: a542c12d23da7fa5b92b360a51ea14e4804c58f6  # intermediate bugfix revision
 
 build:
-    noarch_python: True
+    number: 0
+    # noarch_python: True
 
 requirements:
     build:
-        - python
-        - nomkl
+        - python 2.7|>=3.4
         - setuptools
+        - nomkl # [not win]
         - future >=0.14
         - numpy >=1.9
         - scipy >=0.14
     run:
-        - python
-        - nomkl
+        - python 2.7|>=3.4
         - future >=0.14
+        - nomkl # [not win]
         - numpy >=1.9
         - scipy >=0.14
         - matplotlib
-        - pytest >=3.0
+        - pytest >=3.0.3
 
 test:
     imports:

doc/source/getting_started/installing.rst

@@ -54,15 +54,20 @@ Alternative 1: Installing a release using ``conda`` (recommended for users)
 
 TL;DR
 =====
+Instructions for the impatient:
+
 - Download and install `Miniconda`_
 - Create conda environment:
 
   .. code-block:: bash
 
-    $ conda create -c odlgroup -n odl-py35 python=3.5 odl odlcuda matplotlib pytest scikit-image spyder
+    $ conda create -c odlgroup -n odl-py35 python=3.5 odl matplotlib pytest scikit-image spyder
 
 - Activate the conda enviroment and start working!
 
+
+.. _installing_anaconda:
+
 Installing Anaconda
 ===================
 Even though a Python interpreter is included by default in virtually all Linux distributions, it is advisable to use Anaconda's Python ecosystem since it gives you full flexibility in the Python version you use and which packages you install.
@@ -92,15 +97,24 @@ This is a very convenient way to have several "ecosystems" of Python packages in
 
     $ conda create --name odl-py35 python=3.5
 
-Run ``source activate odl-py35`` to enter/exit the newly created conda environment with name ``odl-py35``.
+Enter the newly created conda environment by running ``source activate odl-py35`` (Linux/MacOS) or ``activate odl-py35`` (Windows).
+If you want to exit later on, run ``source deactivate`` (Linux/MacOS) or ``deactivate`` (Windows), respectively.
 See the `Managing conda environments`_ documentation for further information.
 
-If you use `Spyder`_ as integrated development environment (IDE, see `Development enviroment`_), you should also install it in the new conda environment and run it from there.
-Otherwise, Spyder is not able to use the packages in the conda environment:
+.. note::
+    If you want to use `Spyder`_ as integrated development environment (IDE, see `Development enviroment`_) on Linux or MacOS, you should also install it in the new conda environment and run it from there.
+    Otherwise, Spyder may not able to use the packages in the conda environment:
 
-.. code-block:: bash
+    .. code-block:: bash
 
-    $ conda install spyder
+        $ conda install spyder
+
+    On Windows, you can install Spyder in the root conda environment (run ``deactivate`` to get there), but you need to change its default Python interpreter.
+    To do this, open Spyder and use the navigation bar to open "Tools -> Preferences".
+    Click on "Python interpreter" and change the first setting "Select the Python interpreter for all Spyder consoles" from the default setting to "Use the following Python interpreter:".
+    In the text field, fill in the path to the Python executable in your newly created conda environment.
+    For example, if you installed Miniconda (or Anaconda) in ``C:\Programs\Miniconda3``, then the environment's Python interpreter is ``C:\Programs\Miniconda3\envs\odl-py35\bin\python.exe``.
+    You can use the file system browser (symbol to the right of the text field) to find the interpreter on your system.
 
 
 Installing ODL and its dependencies
@@ -184,6 +198,8 @@ Alternative 2: Installing a release using ``pip``
 
 TL;DR
 =====
+Instructions for the impatient:
+
 - Install `pip`_
 - Install ODL and dependencies:
 
@@ -194,7 +210,7 @@ TL;DR
 Installing a Python interpreter
 ===============================
 Open a terminal and type ``python`` + Enter.
-If a Python prompt appears, you already have an interpreter installed and can skip this step (exit by pressing Ctrl+D).
+If a Python prompt appears, you already have an interpreter installed and can skip this step (exit by running ``exit()``).
 Otherwise, you need to install it.
 
 On Linux:
@@ -219,7 +235,7 @@ Pick the latest release for your favorite version (2 or 3).
 Installing ODL and its dependencies
 ===================================
 You may need to `install pip`_ to be able to install ODL and its dependencies from the `Python Package Index`_ (PyPI).
-If running ``pip`` (alternatively: ``pip2`` or ``pip3``) an shows a help message, it is installed -- otherwise you need to install it first.
+If running ``pip`` (alternatively: ``pip2`` or ``pip3``) shows a help message, it is installed -- otherwise you need to install it first.
 
 For basic installation without extra dependencies, run
 
@@ -278,14 +294,22 @@ No GitHub account is required for this step.
 
 In a conda environment
 ======================
-This part assumes that you have run ``source activate <your_conda_env>`` before.
+This part assumes that you have activated a conda environment before (see :ref:`installing_anaconda`).
 
 You can choose to install dependencies first (optional ones in square brackets):
 
+**On Linux/MacOS:**
+
 .. code-block:: bash
 
     $ conda install nomkl numpy scipy future [matplotlib]
 
+**On Windows:**
+
+.. code-block:: bash
+
+    $ conda install numpy scipy future [matplotlib]
+
 After that, enter the top-level directory of the cloned repository and run
 
 .. code-block:: bash
@@ -306,7 +330,7 @@ Enter the top-level directory of the cloned repository and run
 
 .. note::
     We recommend the ``--editable`` option (can be shortened to ``-e``) since it installs a link instead of copying the files to your Python packages location.
-    This way, local changes to the code (e.g. after a ``git pull``) take immediate effect without reinstallation.
+    This way, local changes to the code (e.g. after a ``git pull``) take immediate effect after reloading the package, without requiring reinstallation.
 
 
 Further developer information
@@ -326,10 +350,17 @@ They can be run either from within ``odl`` or by invoking ``pytest`` directly.
 
 Installing testing dependencies
 ===============================
+If you installed an ODL release using ``conda`` or ``pip``, respectively, you should install ``pytest`` using the same method.
+For source installations, you can choose your favorite method below.
 
 Using ``conda``:
 ----------------
-``pytest`` is already a dependency, nothing to do.
+If you installed ODL using conda, ``pytest`` is already installed as dependency, so there should not be anything left to do.
+Otherwise, you can install it by running
+
+.. code-block:: bash
+
+    $ conda install pytest
 
 Using ``pip``:
 --------------
@@ -347,9 +378,9 @@ Now you can check that everything was installed properly by running
    $ python -c "import odl; odl.test()"
 
 .. note::
-    Don't run this command in the top-level directory of an ODL clone, since in that case, the tests in the repository are run, not the ones in the installed package.
+    Don't run this command in the top-level directory of an ODL clone, since in that case, the tests in the repository may be run, not the ones in the installed package.
 
-If you have installed ODL from source, you can also use ``pytest`` directly:
+If you have installed ODL from source, you can also use ``pytest`` directly in the root of your ODL clone:
 
 .. code-block:: bash
 
@@ -367,13 +398,14 @@ Some of them can be installed using ``conda``, others require manual compilation
 
 CUDA backend for linear arrays
 ==============================
-The `odlcuda`_ backend for fast array calculations on CUDA requires the `CUDA toolkit`_ (on Linux: use your distro package manager) and a CUDA capable graphics card with compute capability of at least 5.0.
+The `odlcuda`_ backend for fast array calculations on CUDA requires the `CUDA toolkit`_ (on Linux: use your distro package manager) and a CUDA capable graphics card with compute capability of at least 3.0.
 Search `this table <https://en.wikipedia.org/wiki/CUDA#GPUs_supported>`_ for your model.
 
 Installation using ``conda``
 ----------------------------
 .. note::
-    In ``conda``, the ``odlcuda`` package is currently only available for Linux 64-bit and Python 3.5.
+    In conda, the ``odlcuda`` package is currently available only for Linux 64-bit and Python 3.5.
+    Furthermore, you may experience failures due to "invalid device function" -- this is a known issue, and we're trying to fix it.
 
 If you have installed an ODL release, simply run (in a directory of your choice)
 
@@ -397,13 +429,71 @@ Finally, install ``odlcuda`` without dependencies:
 
 Building from source
 --------------------
-Clone the ``odlcuda`` GitHub repository:
+You have two options of building ``odlcuda`` from source.
+For both, first clone the ``odlcuda`` GitHub repository and enter the new directory:
 
 .. code-block:: bash
 
     $ git clone https://github.com/odlgroup/odlcuda.git
+    $ cd odlcuda
+
+1. **Using conda build**
+
+   This is the simpler option and should work on any Linux or MacOS system (we currently have no Windows build recipe, sorry).
+
+   To build the conda recipe, you should be **in the root conda environment** (see :ref:`installing_anaconda` for details) and in the top-level directory of your ``odlcuda`` clone.
+   You also need the ``conda-build`` package, which is installed by
+
+   .. code-block:: bash
+
+       $ conda install conda-build
+
+   Next, switch to the ``conda-build`` branch:
+
+   .. code-block:: bash
+
+       $ git checkout conda-build
+
+   Finally, build the package using ``conda build``.
+   Currently, this requires you to manually provide the location of the CUDA toolkit and the compute capability of your graphics card using the environment variables ``CUDA_ROOT`` and ``CUDA_COMPUTE``.
+   (If you forget them, the build recipe will only issue a warning in the beginning but fail later on.)
+   The ``CUDA_ROOT`` is given as path, e.g. ``/usr/local/cuda``, and ``CUDA_COMPUTE`` as 2-digit number without dot, e.g. ``30``.
+
+   .. note::
+       You can consult `this table <https://en.wikipedia.org/wiki/CUDA#GPUs_supported>`_ for the compute capability of your device.
+       The minimum required is ``30``, which corresponds to the "Kepler" generation.
+
+   Assuming the example configuration above, the build command to run is
+
+   .. code-block:: bash
+
+       $ CUDA_ROOT=/usr/local/cuda CUDA_COMPUTE=30 conda build ./conda
+
+   This command builds ``odlcuda`` in a separate build conda environment and tries to import it and run some tests after the build has finished.
+   If all goes well, you will get a message at the end that shows the path to the conda package.
+
+   Finally, install this package file **in your working conda environment** (e.g. ``source activate odl-py35``) by invoking e.g.
+
+   .. code-block:: bash
+
+       $ conda install /path/to/your/miniconda/conda-bld/linux-64/odlcuda-0.3.0-py35_0.tar.bz2
+
+
+2. **Manually with CMake**
+
+   This option requires more manual work but is known to work on all platforms.
+
+   See `here <https://github.com/odlgroup/odlcuda.git>`_ for build instructions.
+   You may want to use include and library paths (GCC, boost, ...) of a conda enviroment and install the package in it.
+
+A simple test if this build of ``odlcuda`` works, you can run
+
+.. code-block:: bash
+
+    $ python -c "import odl; odl.rn(3, impl='cuda').element()"
 
-After that, follow the `build instructions there <https://github.com/odlgroup/odlcuda.git>`_.
+If you get a ``KeyError: 'cuda'``, then something went wrong with the package installation since it cannot be imported.
+If the above command instead raises a ``MemoryError`` or similar, your graphics card is not properly configured, and you should solve that issue first.
 
 
 ASTRA for X-ray tomography

doc/source/release_notes.rst

@@ -6,8 +6,14 @@
 Release Notes
 #############
 
-Next release
-============
+Upcoming release
+================
+
+ODL 0.5.2 Release Notes (2016-11-02)
+====================================
+
+Another maintenance release that fixes a number of issues with installation and testing, see :issue:`674`, :issue:`679`, and :pull:`692` and :pull:`696`.
+
 
 ODL 0.5.1 Release Notes (2016-10-24)
 ====================================

odl/__init__.py

@@ -25,7 +25,7 @@
 
 from __future__ import absolute_import
 
-__version__ = '0.5.1'
+__version__ = '0.5.2'
 __all__ = ('diagnostics', 'discr', 'operator', 'set', 'space', 'solvers',
            'tomo', 'trafos', 'util', 'phantom', 'deform')
 

setup.py

@@ -101,7 +101,7 @@ def find_tests():
 setup(
     name='odl',
 
-    version='0.5.1',
+    version='0.5.2',
 
     description='Operator Discretization Library',
     long_description=long_description,

test_requirements.txt

@@ -1,4 +1,4 @@
-pytest >= 2.7.0
+pytest >= 3.0.3
 pytest-pep8
 pytest-cov
 coverage >= 4.0