Fix/lfs images for docs

.gitattributes

@@ -1,2 +1,3 @@
 notebooks/*.ipynb -linguist-detectable
 *.png filter=lfs diff=lfs merge=lfs -text
+*.svg filter=lfs diff=lfs merge=lfs -text

.github/workflows/main.yaml

@@ -57,6 +57,7 @@ jobs:
     - uses: actions/checkout@v4
       with:
         fetch-depth: 0
+        lfs: true
     - name: Setup Python 3.8
       uses: ./.github/actions/python
       with:

.github/workflows/publish.yaml

@@ -1,4 +1,4 @@
-name: Publish Python Package to PyPI
+name: Publish package and docs
 
 on:
   release:
@@ -29,11 +29,14 @@ jobs:
       - name: Checking out last commit in release
         if: ${{ github.event_name != 'workflow_dispatch' }}
         uses: actions/checkout@v4
+        with:
+          lfs: true
       - name: Checking out last commit for tag ${{ inputs.tag_name }}
-        uses: actions/checkout@v4
         if: ${{ github.event_name == 'workflow_dispatch' }}
+        uses: actions/checkout@v4
         with:
           ref: ${{ inputs.tag_name }}
+          lfs: true
       - name: Fail if running locally
         if: ${{ env.ACT }} # skip during local actions testing
         run: |

CHANGELOG.md

@@ -24,8 +24,9 @@
 
 ### Changed
 
-- Improvements to documentation: fixes, links, text, example gallery and more
-  [PR #532](https://github.com/aai-institute/pyDVL/pull/532)
+- Improvements to documentation: fixes, links, text, example gallery, LFS and
+  more. [PR #532](https://github.com/aai-institute/pyDVL/pull/532), 
+  [PR #543](https://github.com/aai-institute/pyDVL/pull/543)
 - Bump versions of CI actions to avoid warnings [PR #502](https://github.com/aai-institute/pyDVL/pull/502)
 - Add Python Version 3.11 to supported versions [PR #510](https://github.com/aai-institute/pyDVL/pull/510)
 - Documentation improvements and cleanup [PR #521](https://github.com/aai-institute/pyDVL/pull/521) [PR #522](https://github.com/aai-institute/pyDVL/pull/522)

CONTRIBUTING.md

@@ -27,6 +27,12 @@ Run the following to set up the pre-commit git hook to run before pushes:
 pre-commit install --hook-type pre-push
 ```
 
+Additionally, we use Git LFS for some files like images. Install with
+
+```shell
+git lfs install
+```
+
 ## Setting up your environment
 
 We strongly suggest using some form of virtual environment for working with the

README.md

@@ -32,7 +32,7 @@ pyDVL focuses on model-dependent methods.
         width="70%"
         align="center"
         style="display: block; margin-left: auto; margin-right: auto;"
-        src="https://raw.githubusercontent.com/aai-institute/pyDVL/develop/docs/value/img/mclc-best-removal-10k-natural.svg"
+        src="https://pydvl.org/devel/value/img/mclc-best-removal-10k-natural.svg"
         alt="best sample removal"
     />
     <p align="center" style="text-align:center;">
@@ -51,7 +51,7 @@ of training samples over individual test points.
         width="70%"
         align="center"
         style="display: block; margin-left: auto; margin-right: auto;"
-        src="https://raw.githubusercontent.com/aai-institute/pyDVL/develop/docs/assets/influence_functions_example.png"
+        src="https://pydvl.org/devel/examples/img/influence_functions_example.png"
         alt="best sample removal"
     />
     <p align="center" style="text-align:center;">

docs/examples/index.md

@@ -16,15 +16,15 @@ alias:
     An introduction using the spotify dataset, showcasing grouped datasets and
     applied to improving model performance and identifying bogus data.
 
-    [![](/assets/shapley_basic_spotify.png)](shapley_basic_spotify/)
+    [![](img/shapley_basic_spotify.png)](shapley_basic_spotify/)
 
 -   [__KNN Shapley__](shapley_knn_flowers/)
 
     ---
 
     A showcase of a fast model-specific valuation method using the iris dataset. 
 
-    [![](/assets/shapley_knn_flowers.png)](shapley_knn_flowers/)
+    [![](img/shapley_knn_flowers.png)](shapley_knn_flowers/)
 
 -   [__Data utility learning__](shapley_utility_learning/)
 
@@ -33,7 +33,7 @@ alias:
     Learning a utility function from a few evaluations and using it to estimate
     the value of the remaining data.
 
-    [![](/assets/shapley_utility_learning.png)](shapley_utility_learning/)
+    [![](img/shapley_utility_learning.png)](shapley_utility_learning/)
 
 -   [__Least Core__](least_core_basic/)
 
@@ -42,7 +42,7 @@ alias:
     An alternative solution concept from game theory, illustrated on a
     classification problem.
 
-    [![](/assets/least_core_basic.png)](least_core_basic/)
+    [![](img/least_core_basic.png)](least_core_basic/)
 
 -  [__Data OOB__](data_oob/)
 
@@ -51,7 +51,7 @@ alias:
     A different and fast strategy for data valuation, using the out-of-bag
     error of a bagging model.
 
-    [![](/assets/data_oob.png)](data_oob/)
+    [![](img/data_oob.png)](data_oob/)
 
 </div>
 
@@ -66,31 +66,31 @@ alias:
 
     Detecting corrupted labels with influence functions on the ImageNet dataset.
 
-    [![](/assets/influence_imagenet.png)](influence_imagenet/)
+    [![](img/influence_imagenet.png)](influence_imagenet/)
 
 -  [__For language models__](influence_sentiment_analysis/)
 
     ---
 
     Using the IMDB dataset for sentiment analysis and a fine-tuned BERT model.
 
-    [![](/assets/influence_sentiment_analysis.png)](influence_sentiment_analysis/)
+    [![](img/influence_sentiment_analysis.png)](influence_sentiment_analysis/)
 
 -   [__For mislabeled data__](influence_synthetic/)
 
     ---
 
     Detecting corrupted labels using a synthetic dataset.
 
-    [![](/assets/influence_functions_example.png)](influence_synthetic/)
+    [![](img/influence_functions_example.png)](influence_synthetic/)
 
 -   [__For outlier detection__](influence_wine/)
 
     ---
 
     Using the wine dataset
 
-    [![](/assets/influence_wine.png)](influence_wine/)
+    [![](img/influence_wine.png)](influence_wine/)
 
 
 </div>

docs/value/index.md

@@ -8,10 +8,10 @@ alias:
 # Data valuation
 
 !!! Info
-    If you want to jump right into the steps to compute values, skip ahead
-    to [[computing-data-values]]. If you want a quick list of applications, see
-    [[data-valuation-applications]]. For a list of all algorithms implemented in
-    pyDVL, see [[methods]].
+    If you want to jump right into it, skip ahead to [Computing data
+    values][computing-data-values]. If you want a quick list of applications,
+    see [[data-valuation-applications]]. For a list of all algorithms
+    implemented in pyDVL, see [[methods]].
 
 **Data valuation** is the task of assigning a number to each element of a
 training set which reflects its contribution to the final performance of some