Update docs for dev (build 4101)

Author: Circle CI

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -68,10 +68,10 @@ To run the benchmark, just execute:
     <span style="color: #000080; text-decoration-color: #000080; font-weight: bold">    |--gd[lr=0.01]:</span> <span style="color: #808000; text-decoration-color: #808000; font-weight: bold">done (not enough run)</span>                                     
     <span style="color: #008000; text-decoration-color: #008000; font-weight: bold">Saving result in: </span>
     <span style="color: #008000; text-decoration-color: #008000; font-weight: bold">/home/circleci/project/examples/minimal_benchmark/outputs/benchopt_run_2026-01</span>
-    <span style="color: #008000; text-decoration-color: #008000; font-weight: bold">-14_23h54m22.parquet</span>
+    <span style="color: #008000; text-decoration-color: #008000; font-weight: bold">-15_08h07m37.parquet</span>
     Processing 
     /home/circleci/project/examples/minimal_benchmark/outputs/benchopt_run_2026-01
-    -14_23h54m22.parquet
+    -15_08h07m37.parquet
 
 
     </pre></div>
@@ -119,10 +119,10 @@ To get a more precise curve, you can increase ``n`` and ``r``:
     <span style="color: #000080; text-decoration-color: #000080; font-weight: bold">    |--gd[lr=0.01]:</span> <span style="color: #008000; text-decoration-color: #008000; font-weight: bold">done</span>                                                      
     <span style="color: #008000; text-decoration-color: #008000; font-weight: bold">Saving result in: </span>
     <span style="color: #008000; text-decoration-color: #008000; font-weight: bold">/home/circleci/project/examples/minimal_benchmark/outputs/benchopt_run_2026-01</span>
-    <span style="color: #008000; text-decoration-color: #008000; font-weight: bold">-14_23h54m25.parquet</span>
+    <span style="color: #008000; text-decoration-color: #008000; font-weight: bold">-15_08h07m41.parquet</span>
     Processing 
     /home/circleci/project/examples/minimal_benchmark/outputs/benchopt_run_2026-01
-    -14_23h54m25.parquet
+    -15_08h07m41.parquet
 
 
     </pre></div>
@@ -151,7 +151,7 @@ suboptimality instead of objective value.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 4.252 seconds)
+   **Total running time of the script:** (0 minutes 4.740 seconds)
 
 
 .. _sphx_glr_download_auto_examples_run_minimal_benchmark.py:

dev/_downloads/586c9ec48a37fd397fb981a992a54250/run_minimal_benchmark.zip

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**00:04.252** total execution time for 3 files **from auto_examples**:
+**00:04.740** total execution time for 3 files **from auto_examples**:
 
 .. container::
 
@@ -33,7 +33,7 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_auto_examples_run_minimal_benchmark.py` (``run_minimal_benchmark.py``)
-     - 00:04.252
+     - 00:04.740
      - 0.0
    * - :ref:`sphx_glr_auto_examples_plot_run_benchmark.py` (``plot_run_benchmark.py``)
      - 00:00.000

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -90,3 +90,42 @@ Here is the content of configuration file ``example_config.yml`` if we were to r
 
     A third, less frequent, option to run a benchmark is using a Python script.
     Check it out on :ref:`advanced usage <run_benchmark_with_py_script>`.
+
+
+.. _run_benchmark_with_py_script:
+
+Run a benchmark using a Python script
+-------------------------------------
+
+Another way to run a benchmark is via a Python script.
+Typical use-cases of that are
+
+- Automating the run of several benchmarks
+- Using ``vscode`` debugger where the python script serves as an entry point to benchopt internals
+
+The following script illustrates running the :ref:`benchmark Lasso <run_with_config_file>`.
+It assumes that the python script is located at the same level as the benchmark folder.
+
+.. code-block:: python
+
+    from benchopt import run_benchmark
+
+
+    # run benchmark
+    run_benchmark(
+        benchmark_path='.',
+        solver_names=[
+            "skglm",
+            "celer",
+            "python-pgd[use_acceleration=True]",
+        ],
+        dataset_names=[
+            "leukemia",
+            "simulated[n_samples=100,n_features=20]"
+        ],
+    )
+
+.. note::
+
+    Learn more about the different parameters supported by ``run_benchmark``
+    function on :ref:`API references <API_ref>`.

dev/_downloads/a3747ad75224b0e1c78c72c3113052a8/plot_run_benchmark_python_R.zip

@@ -3,6 +3,21 @@
 Frequently asked questions (FAQ)
 --------------------------------
 
+.. dropdown:: Can I use benchopt for ML benchmarks (not just optimization)?
+
+    Yes! Despite its name, Benchopt supports both **machine learning** and **optimization** benchmarks.
+    Most features in benchopt are common to both types of benchmarks.
+    The main difference lies in how performance is evaluated:
+
+    - **Optimization benchmarks**: Track iterative solvers' convergence
+      over time and iterations.
+    - **ML benchmarks**: Compare estimators/models on prediction tasks
+      (accuracy, F1, etc.).
+
+    Setting ``sampling_strategy = "run_once"`` for a solver or for the full
+    benchmark allows to only evaluate once to completion.
+    See :ref:`ml_benchmark` for a complete guide, or use our
+    `ML template <https://github.com/benchopt/template_benchmark_ml>`_ to get started quickly.
 
 .. dropdown:: How to add my solver to an existing benchmark?
 

dev/_downloads/b6a0a50a7a6c020b6c5ce18f62b67968/plot_run_benchmark.zip

@@ -7,9 +7,11 @@
 
 |Python 3.10+| |PyPI version| |License|
 
-Benchopt is a benchmarking suite tailored for machine learning workflows.
-It is built for simplicity, transparency, and reproducibility.
-It is implemented in Python but can run algorithms written in many programming languages.
+Benchopt is a benchmarking suite tailored for **machine learning and optimization**.
+It makes it simple to run, publish and replicate benchmarks. The
+command line interface allows running various solvers on benchmark
+problems, providing convergence curves for iterative methods or
+performance comparisons for ML estimators.
 
 Reproducing an existing benchmark should be as easy as running the following commands:
 
@@ -45,24 +47,6 @@ Reproducing an existing benchmark should be as easy as running the following com
       which will produce a parquet file with the results that can be visualized
       using instruction on the ``README.md`` of the https://github.com/scikit-adaptation/skada-bench.
 
-   .. tab-item:: Bilevel Optimization
-
-      A benchmark comparing various algorithms to solve bilevel optimization problems.
-
-      .. prompt:: bash $
-
-         git clone https://github.com/benchopt/benchmark_bilevel.git
-         benchopt run benchmark_bilevel
-
-      which will produce an interactive HTML report to visualize the results.
-
-      .. raw:: html
-
-         <iframe class="benchmark_result"
-               src="https://benchopt.github.io/results/benchmark_bilevel_benchmark_bilevel_ijcnn1.html"
-            frameBorder='0' style="position: relative; width: 100%;">
-         </iframe>
-
    .. tab-item:: Minimal benchmark
 
       A minimal benchmark comparing various solvers on a toy problem.
@@ -82,7 +66,14 @@ Reproducing an existing benchmark should be as easy as running the following com
 
 
 These different tabs illustrate the diversity of benchmarks that can be built
-with benchopt, from deep learning optimization to more classical machine learning tasks.
+with benchopt, from deep learning optimization to more classical machine
+learning tasks.
+
+.. tip::
+   **Want to create a new benchmark with benchopt?** Use our templates to get started:
+   `ML benchmarks <https://github.com/benchopt/template_benchmark_ml>`_ |
+   `Optimization benchmarks <https://github.com/benchopt/template_benchmark>`_
+
 There are already many :ref:`available_benchmarks` that have been created using benchopt.
 Learn how to run them and how to construct your own with the following pages!
 
@@ -103,7 +94,8 @@ Learn how to run them and how to construct your own with the following pages!
 
         :octicon:`tools` **Benchmark workflow**
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-        Write a benchmark from scratch, run it, visualize it, and publish it
+        Write an ML or optimization benchmark from scratch,
+        run it, visualize it, and publish it
 
     .. grid-item-card::
         :link: tutorials