[MRG] Add github action test cuda (#473)

* add workflow cuda

* try with conda

* try again conda

* and now?

* proper python version

* cleanup

* try to use proper python

* again?

* cleanup stuff and use python3.10

* remove dr test

* remove docstrng tetss for stoc methods

* add condition for cuda tests

* remove unused files

* upate release file

* debug workflow

3 files changed, 27 insertions, 194 deletions

diff --git a/.github/workflows/build_tests_cuda.yml b/.github/workflows/build_tests_cuda.yml
new file mode 100644
index 0000000..f1c2962
--- /dev/null
+++ b/.github/workflows/build_tests_cuda.yml
@@ -0,0 +1,25 @@
+name: Tests CUDA
+
+on:
+  workflow_dispatch:
+  pull_request_review:
+    types: [submitted]
+  push:
+    branches:
+      - 'master'  # Set a branch to run CI tests on
+
+jobs:
+  linux-cuda:
+
+    runs-on: pc-cuda
+    if: github.event.review.state == 'approved' || github.event_name ==     'workflow_dispatch' || (github.event_name == 'push' && github.event.branch == 'master')
+
+    steps:
+    - uses: actions/checkout@v1
+    - name: Install POT
+      run: |
+        python3.10 -m pip install  --ignore-installed -e .
+    - name: Run tests
+      run: |
+        python3.10 -m pytest --durations=20 -v test/ ot/ --doctest-modules --color=yes --ignore=test/test_dr.py --ignore=ot.dr --ignore=ot.plot
+
diff --git a/RELEASES.md b/RELEASES.md
index f393883..02fddad 100644
--- a/RELEASES.md
+++ b/RELEASES.md
@@ -5,6 +5,8 @@
 #### New features
 - Make alpha parameter in Fused Gromov Wasserstein differentiable (PR #463)
 - Added the sparsity-constrained OT solver to `ot.smooth` and added ` projection_sparse_simplex` to `ot.utils` (PR #459)
+- Add tests on GPU for master branch and approved PR (PR #473)
+
 #### Closed issues
 
 - Fix circleci-redirector action and codecov (PR #460)
diff --git a/ot/stochastic.py b/ot/stochastic.py
index 61be9bb..319d006 100644
--- a/ot/stochastic.py
+++ b/ot/stochastic.py
@@ -58,27 +58,6 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i):
     -------
     coordinate gradient : ndarray, shape (nt,)
 
-    Examples
-    --------
-    >>> import ot
-    >>> np.random.seed(0)
-    >>> n_source = 7
-    >>> n_target = 4
-    >>> a = ot.utils.unif(n_source)
-    >>> b = ot.utils.unif(n_target)
-    >>> X_source = np.random.randn(n_source, 2)
-    >>> Y_target = np.random.randn(n_target, 2)
-    >>> M = ot.dist(X_source, Y_target)
-    >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
-    array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
-           [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
-           [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
-           [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
-           [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
-           [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
-           [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])
-
-
     .. _references-coordinate-grad-semi-dual:
     References
     ----------
@@ -137,27 +116,6 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None):
     v : ndarray, shape (`nt`,)
         Dual variable.
 
-    Examples
-    --------
-    >>> import ot
-    >>> np.random.seed(0)
-    >>> n_source = 7
-    >>> n_target = 4
-    >>> a = ot.utils.unif(n_source)
-    >>> b = ot.utils.unif(n_target)
-    >>> X_source = np.random.randn(n_source, 2)
-    >>> Y_target = np.random.randn(n_target, 2)
-    >>> M = ot.dist(X_source, Y_target)
-    >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
-    array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
-           [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
-           [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
-           [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
-           [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
-           [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
-           [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])
-
-
     .. _references-sag-entropic-transport:
     References
     ----------
@@ -225,27 +183,6 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None):
     ave_v : ndarray, shape (`nt`,)
         dual variable
 
-    Examples
-    --------
-    >>> import ot
-    >>> np.random.seed(0)
-    >>> n_source = 7
-    >>> n_target = 4
-    >>> a = ot.utils.unif(n_source)
-    >>> b = ot.utils.unif(n_target)
-    >>> X_source = np.random.randn(n_source, 2)
-    >>> Y_target = np.random.randn(n_target, 2)
-    >>> M = ot.dist(X_source, Y_target)
-    >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
-    array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
-           [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
-           [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
-           [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
-           [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
-           [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
-           [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])
-
-
     .. _references-averaged-sgd-entropic-transport:
     References
     ----------
@@ -304,27 +241,6 @@ def c_transform_entropic(b, M, reg, beta):
     u : ndarray, shape (`ns`,)
         Dual variable.
 
-    Examples
-    --------
-    >>> import ot
-    >>> np.random.seed(0)
-    >>> n_source = 7
-    >>> n_target = 4
-    >>> a = ot.utils.unif(n_source)
-    >>> b = ot.utils.unif(n_target)
-    >>> X_source = np.random.randn(n_source, 2)
-    >>> Y_target = np.random.randn(n_target, 2)
-    >>> M = ot.dist(X_source, Y_target)
-    >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
-    array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
-           [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
-           [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
-           [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
-           [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
-           [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
-           [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])
-
-
     .. _references-c-transform-entropic:
     References
     ----------
@@ -399,27 +315,6 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None,
     log : dict
         log dictionary return only if log==True in parameters
 
-    Examples
-    --------
-    >>> import ot
-    >>> np.random.seed(0)
-    >>> n_source = 7
-    >>> n_target = 4
-    >>> a = ot.utils.unif(n_source)
-    >>> b = ot.utils.unif(n_target)
-    >>> X_source = np.random.randn(n_source, 2)
-    >>> Y_target = np.random.randn(n_target, 2)
-    >>> M = ot.dist(X_source, Y_target)
-    >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000)
-    array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06],
-           [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03],
-           [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07],
-           [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04],
-           [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01],
-           [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01],
-           [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]])
-
-
     .. _references-solve-semi-dual-entropic:
     References
     ----------
@@ -509,33 +404,6 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha,
     grad : ndarray, shape (`ns`,)
         partial grad F
 
-    Examples
-    --------
-    >>> import ot
-    >>> np.random.seed(0)
-    >>> n_source = 7
-    >>> n_target = 4
-    >>> a = ot.utils.unif(n_source)
-    >>> b = ot.utils.unif(n_target)
-    >>> X_source = np.random.randn(n_source, 2)
-    >>> Y_target = np.random.randn(n_target, 2)
-    >>> M = ot.dist(X_source, Y_target)
-    >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg=1, batch_size=3, numItermax=30000, lr=0.1, log=True)
-    >>> log['alpha']
-    array([0.71759102, 1.57057384, 0.85576566, 0.1208211 , 0.59190466,
-           1.197148  , 0.17805133])
-    >>> log['beta']
-    array([0.49741367, 0.57478564, 1.40075528, 2.75890102])
-    >>> sgd_dual_pi
-    array([[2.09730063e-02, 8.38169324e-02, 7.50365455e-03, 8.72731415e-09],
-           [5.58432437e-03, 5.89881299e-04, 3.09558411e-05, 8.35469849e-07],
-           [3.26489515e-03, 7.15536035e-02, 2.99778211e-02, 3.02601593e-10],
-           [4.05390622e-02, 5.31085068e-02, 6.65191787e-02, 1.55812785e-06],
-           [7.82299812e-02, 6.12099102e-03, 4.44989098e-02, 2.37719187e-03],
-           [5.06266486e-02, 2.16230494e-03, 2.26215141e-03, 6.81514609e-04],
-           [6.06713990e-02, 3.98139808e-02, 5.46829338e-02, 8.62371424e-06]])
-
-
     .. _references-batch-grad-dual:
     References
     ----------
@@ -600,37 +468,6 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr):
     beta : ndarray, shape (nt,)
         dual variable
 
-    Examples
-    --------
-    >>> import ot
-    >>> n_source = 7
-    >>> n_target = 4
-    >>> reg = 1
-    >>> numItermax = 20000
-    >>> lr = 0.1
-    >>> batch_size = 3
-    >>> log = True
-    >>> a = ot.utils.unif(n_source)
-    >>> b = ot.utils.unif(n_target)
-    >>> rng = np.random.RandomState(0)
-    >>> X_source = rng.randn(n_source, 2)
-    >>> Y_target = rng.randn(n_target, 2)
-    >>> M = ot.dist(X_source, Y_target)
-    >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log)
-    >>> log['alpha']
-    array([0.64171798, 1.27932201, 0.78132257, 0.15638935, 0.54888354,
-           1.03663469, 0.20595781])
-    >>> log['beta']
-    array([0.51207194, 0.58033189, 1.28922676, 2.26859736])
-    >>> sgd_dual_pi
-    array([[1.97276541e-02, 7.81248547e-02, 6.22136048e-03, 4.95442423e-09],
-           [4.23494310e-03, 4.43286263e-04, 2.06927079e-05, 3.82389139e-07],
-           [3.07542414e-03, 6.67897769e-02, 2.48904999e-02, 1.72030247e-10],
-           [4.26271990e-02, 5.53375455e-02, 6.16535024e-02, 9.88812650e-07],
-           [7.60423265e-02, 5.89585256e-03, 3.81267087e-02, 1.39458256e-03],
-           [4.37557504e-02, 1.85189176e-03, 1.72335760e-03, 3.55491279e-04],
-           [6.33096109e-02, 4.11683954e-02, 5.02962051e-02, 5.43097516e-06]])
-
     References
     ----------
     .. [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. Large-scale Optimal Transport and Mapping Estimation. International Conference on Learning Representation (2018)
@@ -702,37 +539,6 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1,
     log : dict
         log dictionary return only if log==True in parameters
 
-    Examples
-    --------
-    >>> import ot
-    >>> n_source = 7
-    >>> n_target = 4
-    >>> reg = 1
-    >>> numItermax = 20000
-    >>> lr = 0.1
-    >>> batch_size = 3
-    >>> log = True
-    >>> a = ot.utils.unif(n_source)
-    >>> b = ot.utils.unif(n_target)
-    >>> rng = np.random.RandomState(0)
-    >>> X_source = rng.randn(n_source, 2)
-    >>> Y_target = rng.randn(n_target, 2)
-    >>> M = ot.dist(X_source, Y_target)
-    >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log)
-    >>> log['alpha']
-    array([0.64057733, 1.2683513 , 0.75610161, 0.16024284, 0.54926534,
-           1.0514201 , 0.19958936])
-    >>> log['beta']
-    array([0.51372571, 0.58843489, 1.27993921, 2.24344807])
-    >>> sgd_dual_pi
-    array([[1.97377795e-02, 7.86706853e-02, 6.15682001e-03, 4.82586997e-09],
-           [4.19566963e-03, 4.42016865e-04, 2.02777272e-05, 3.68823708e-07],
-           [3.00379244e-03, 6.56562018e-02, 2.40462171e-02, 1.63579656e-10],
-           [4.28626062e-02, 5.60031599e-02, 6.13193826e-02, 9.67977735e-07],
-           [7.61972739e-02, 5.94609051e-03, 3.77886693e-02, 1.36046648e-03],
-           [4.44810042e-02, 1.89476742e-03, 1.73285847e-03, 3.51826036e-04],
-           [6.30118293e-02, 4.12398660e-02, 4.95148998e-02, 5.26247246e-06]])
-
     References
     ----------
     .. [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. Large-scale Optimal Transport and Mapping Estimation. International Conference on Learning Representation (2018)