docs: make the multi-start batch_size decision explicit

notebooks/cluster/start_here.ipynb

@@ -324,6 +324,7 @@
         "    name=\"start_here\",\n",
         "    unique_tag=dataset_name,\n",
         "    n_starts=48,\n",
+    "    batch_size=None,  # Starts evaluated at once: `None` vmaps all 48 together, which is fastest but allocates the whole batched gradient; set an integer (e.g. 4) if you hit an out-of-memory error.\n",
         "    n_steps=300,\n",
         "    iterations_per_quick_update=50,\n",
         "    live_visual_update=False,\n",

notebooks/guides/modeling/searches.ipynb

@@ -199,6 +199,7 @@
     "    path_prefix=Path(\"imaging\", \"searches\"),\n",
     "    name=\"MultiStartProdigy\",\n",
     "    n_starts=50,\n",
+    "    batch_size=None,  # Starts evaluated at once: `None` vmaps all 50 together, which is fastest but allocates the whole batched gradient; set an integer (e.g. 4) if you hit an out-of-memory error.\n",
     "    n_steps=500,\n",
     ")"
    ],

notebooks/imaging/start_here.ipynb

@@ -410,6 +410,7 @@
         "    name=\"start_here\",  # The name of the fit and folder results are output to.\n",
         "    unique_tag=dataset_name,  # A unique tag which also defines the folder.\n",
         "    n_starts=48,  # The number of independent optimizations run in parallel, increase for more complex models.\n",
+    "    batch_size=None,  # Starts evaluated at once: `None` vmaps all 48 together, which is fastest but allocates the whole batched gradient; set an integer (e.g. 4) if you hit an out-of-memory error.\n",
         "    n_steps=300,  # The maximum gradient steps per start; the search stops early once the best fit stops improving.\n",
         "    iterations_per_quick_update=50,  # Every N steps the max likelihood model is visualized and output.\n",
         "    live_visual_update=False,  # Set True to open a live matplotlib window (script) or refresh a Jupyter cell (notebook).\n",

notebooks/multi_galaxy/start_here.ipynb

@@ -306,6 +306,7 @@
         "    name=\"start_here\",\n",
         "    unique_tag=dataset_name,\n",
         "    n_starts=48,\n",
+    "    batch_size=None,  # Starts evaluated at once: `None` vmaps all 48 together, which is fastest but allocates the whole batched gradient; set an integer (e.g. 4) if you hit an out-of-memory error.\n",
         "    n_steps=300,\n",
         "    iterations_per_quick_update=50,\n",
         "    live_visual_update=False,\n",

scripts/cluster/start_here.py

@@ -237,6 +237,7 @@
     name="start_here",
     unique_tag=dataset_name,
     n_starts=48,
+    batch_size=None,  # Starts evaluated at once: `None` vmaps all 48 together, which is fastest but allocates the whole batched gradient; set an integer (e.g. 4) if you hit an out-of-memory error.
     n_steps=300,
     iterations_per_quick_update=50,
     live_visual_update=False,

scripts/guides/modeling/searches.py

@@ -129,6 +129,7 @@
     path_prefix=Path("imaging", "searches"),
     name="MultiStartProdigy",
     n_starts=50,
+    batch_size=None,  # Starts evaluated at once: `None` vmaps all 50 together, which is fastest but allocates the whole batched gradient; set an integer (e.g. 4) if you hit an out-of-memory error.
     n_steps=500,
 )
 

scripts/imaging/start_here.py

@@ -312,6 +312,7 @@
     name="start_here",  # The name of the fit and folder results are output to.
     unique_tag=dataset_name,  # A unique tag which also defines the folder.
     n_starts=48,  # The number of independent optimizations run in parallel, increase for more complex models.
+    batch_size=None,  # Starts evaluated at once: `None` vmaps all 48 together, which is fastest but allocates the whole batched gradient; set an integer (e.g. 4) if you hit an out-of-memory error.
     n_steps=300,  # The maximum gradient steps per start; the search stops early once the best fit stops improving.
     iterations_per_quick_update=50,  # Every N steps the max likelihood model is visualized and output.
     live_visual_update=False,  # Set True to open a live matplotlib window (script) or refresh a Jupyter cell (notebook).

scripts/multi_galaxy/start_here.py

@@ -219,6 +219,7 @@
     name="start_here",
     unique_tag=dataset_name,
     n_starts=48,
+    batch_size=None,  # Starts evaluated at once: `None` vmaps all 48 together, which is fastest but allocates the whole batched gradient; set an integer (e.g. 4) if you hit an out-of-memory error.
     n_steps=300,
     iterations_per_quick_update=50,
     live_visual_update=False,