few edits

rapidsai · Jul 17, 2023 · 26e1437 · 26e1437
1 parent 5ff3630
commit 26e1437
Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/source/examples/xgboost-rf-gpu-cpu-benchmark/notebook.ipynb b/source/examples/xgboost-rf-gpu-cpu-benchmark/notebook.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "For instance, we find a x speedup in wall clock time (6 hours vs 3+ days) and a x reduction in cost when comparing between GPU and CPU EC2 instances on 100 XGBoost HPO trials using 10 parallel workers on 10 years of the Airline Dataset.\n",
     "\n",
-    "For more check out our AWS blog(link)."
+    "For more check out our blog(link)."
    ]
   },
   {
@@ -102,9 +102,9 @@
    "id": "533be0b1-0d5e-46b3-9ff1-dd71751fe68f",
    "metadata": {},
    "source": [
-    "To maximize on efficiency, we launch a `LocalCUDACluster` that utilizes GPUs for distributed computing. Then connect a Dask Client to submit and manage computations on the cluster. Refer to this (link) for more information on how to achieve this.\n",
+    "To maximize on efficiency, we launch either a `LocalCluster` for cpu or `LocalCUDACluster` that utilizes GPUs for distributed computing. Then connect a Dask Client to submit and manage computations on the cluster. \n",
     "\n",
-    "Submit dataset to the Dask client, instructing Dask to store the dataset in memory  at all times. This can improve performance by avoiding unnecessary data transfers during the hpo process. \n"
+    "Submit and \"persist\"  the dataset to the Dask client, which instructs Dask to store the dataset in memory  at all times for faster performance by avoiding unnecessary data transfers during the hpo process. \n"
    ]
   },
   {