[MRG] Update examples in the doc

.circleci/config.yml

@@ -35,6 +35,12 @@ jobs:
               - data-cache-0
               - pip-cache
 
+        - run:
+            name: Install ffmpeg
+            command: |
+              sudo apt update 
+              sudo apt install ffmpeg
+
         - run:
             name: Get Python running
             command: |
@@ -50,6 +56,7 @@ jobs:
             paths:
               - ~/.cache/pip
 
+
         # Look at what we have and fail early if there is some library conflict
         - run:
             name: Check installation

.github/workflows/build_tests.yml

@@ -22,7 +22,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: ["3.7", "3.8", "3.9"]
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v1
@@ -93,7 +93,7 @@ jobs:
      strategy:
        max-parallel: 4
        matrix:
-         python-version: ["3.7", "3.8", "3.9"]
+         python-version: ["3.7", "3.8", "3.9", "3.10"]
 
      steps:
      - uses: actions/checkout@v1
@@ -120,7 +120,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: ["3.7", "3.8", "3.9"]
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v1

README.md

@@ -29,8 +29,11 @@ POT provides the following generic OT solvers (links to examples):
 * Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale).
 * [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html)  (exact [13] and regularized [12]), differentiable using gradients from
  * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) [24]
-* [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
-* [Stochastic solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33]
+* [Stochastic
+  solver](https://pythonot.github.io/auto_examples/others/plot_stochastic.html) and
+  [differentiable losses](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html) for
+  Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
+* [Sampled solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33]
 * Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
 * [Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25].
 * [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
@@ -119,15 +122,14 @@ Note that for easier access the module is named `ot` instead of `pot`.
 
 ### Dependencies
 
-Some sub-modules require additional dependences which are discussed below
+Some sub-modules require additional dependencies which are discussed below
 
 * **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with:
 
 ```shell
 pip install pymanopt autograd
 ```
 
-* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). Obviously you will need CUDA installed and a compatible GPU. Note that this module is deprecated since version 0.8 and will be deleted in the future. GPU is now handled automatically through the backends and several solver already can run on GPU using the Pytorch backend.
 
 ## Examples
 

RELEASES.md

@@ -5,6 +5,7 @@
 
 #### New features
 
+- Update examples in the gallery (PR #359).
 - Add stochastic loss and OT plan computation for regularized OT and 
   backend examples(PR #360).
 - Implementation of factored OT with emd and sinkhorn (PR #358).
@@ -254,7 +255,7 @@ are coming for the next versions.
 
 #### Closed issues
 
-- Add JMLR paper to teh readme ad Mathieu Blondel to the Acknoledgments (PR
+- Add JMLR paper to the readme and Mathieu Blondel to the Acknoledgments (PR
   #231, #232)
 - Bug in Unbalanced OT example (Issue #127)
 - Clean Cython output when calling setup.py clean (Issue #122)