From e1ee53b30502c10730e4daa3114252b965b513ab Mon Sep 17 00:00:00 2001
From: Doug Branton <brantd@uw.edu>
Date: Thu, 4 Apr 2024 11:10:01 -0700
Subject: [PATCH] remove slow timing comparsion

---
 .../using_ray_with_the_ensemble.ipynb         | 71 +------------------
 1 file changed, 1 insertion(+), 70 deletions(-)

diff --git a/docs/tutorials/using_ray_with_the_ensemble.ipynb b/docs/tutorials/using_ray_with_the_ensemble.ipynb
index 0b3b3389..eb4a54a0 100644
--- a/docs/tutorials/using_ray_with_the_ensemble.ipynb
+++ b/docs/tutorials/using_ray_with_the_ensemble.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Using Dask on Ray with the Ensemble\n",
     "\n",
-    "[Ray](https://docs.ray.io/en/latest/ray-overview/index.html) is an open-source unified framework for scaling AI and Python applications. Ray provides a scheduler for Dask ([dask_on_ray](https://docs.ray.io/en/latest/ray-more-libs/dask-on-ray.html)) which allows you to build data analyses using Dask’s collections and execute the underlying tasks on a Ray cluster. We have found with TAPE that the Ray scheduler is often more performant than Dasks scheduler. Ray can be used on TAPE using the setup shown in the following example."
+    "[Ray](https://docs.ray.io/en/latest/ray-overview/index.html) is an open-source unified framework for scaling AI and Python applications. Ray provides a scheduler for Dask ([dask_on_ray](https://docs.ray.io/en/latest/ray-more-libs/dask-on-ray.html)) which allows you to build data analyses using Dask’s collections and execute the underlying tasks on a Ray cluster. Ray can be used on TAPE using the setup shown in the following example."
    ]
   },
   {
@@ -86,75 +86,6 @@
     "    calc_sf2, use_map=False\n",
     ")  # use_map is false as we repartition naively, splitting per-object sources across partitions"
    ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "c5692d75",
-   "metadata": {},
-   "source": [
-    "## Timing Comparison\n",
-    "\n",
-    "As mentioned above, we generally see that Ray is more performant than Dask. Below is a simple timing comparison."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f128cdbf",
-   "metadata": {},
-   "source": [
-    "### Ray Timing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd960e10",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "\n",
-    "ens = Ensemble(client=False)  # Do not use a client\n",
-    "ens.from_dataset(\"s82_qso\", sorted=True)\n",
-    "ens.source = ens.source.repartition(npartitions=10)\n",
-    "ens.batch(calc_sf2, use_map=False)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "228e5114",
-   "metadata": {},
-   "source": [
-    "### Dask Timing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "24a8f466",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "disable_dask_on_ray()  # unsets the dask_on_ray configuration settings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1552c2b8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "\n",
-    "ens = Ensemble()\n",
-    "ens.from_dataset(\"s82_qso\", sorted=True)\n",
-    "ens.source = ens.source.repartition(npartitions=10)\n",
-    "ens.batch(calc_sf2, use_map=False).compute()"
-   ]
   }
  ],
  "metadata": {