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Update benchmarking notebook #262

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merged 5 commits into from
Aug 7, 2023
Merged

Update benchmarking notebook #262

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2cf20b7
update metadata tags
skirui-source Aug 7, 2023
0371810
update with latest benchmarking results
skirui-source Aug 7, 2023
be312d8
add related files- dockerfile, hpo script
skirui-source Aug 7, 2023
5bfa5f0
undo files saved to wrong folder
skirui-source Aug 7, 2023
7171648
add more tags
skirui-source Aug 7, 2023
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18 changes: 16 additions & 2 deletions source/examples/xgboost-rf-gpu-cpu-benchmark/notebook.ipynb
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Expand Up @@ -3,15 +3,29 @@
{
"cell_type": "markdown",
"id": "a51c95d1-b447-4f1b-9571-cf597ca93ef4",
"metadata": {},
"metadata": {
"tags": [
"cloud/aws/ec2",
"data-storage/s3",
"workflow/randomforest",
"workflow/hpo",
"workflow/xgboost",
"library/dask",
"library/dask-cuda",
"library/xgboost",
"library/optuna",
"library/sklearn",
"library/dask-ml"
]
},
"source": [
"# HPO Benchmarking with RAPIDS and Dask\n",
"\n",
"Hyper-Parameter Optimization (HPO) helps to find the best version of a model by exploring the space of possible configurations. While generally desirable, this search is computationally expensive and time-consuming.\n",
"\n",
"In the notebook demo below, we compare benchmarking results to show how GPU can accelerate HPO tuning jobs relative to CPU.\n",
"\n",
"For instance, we find a 26x speedup in wall clock time (0.71 hrs vs. 18.9 hrs) when comparing between GPU and CPU EC2 instances on 100 XGBoost HPO trials using no parallel workers on 3 years of the Airline Dataset.\n"
"For instance, we find a 48x speedup in wall clock time (0.71 hrs vs 34.6 hrs) for XGBoost and 16x (3.86 hrs vs 63.2 hrs) for RandomForest when comparing between GPU and CPU EC2 instances on 100 HPO trials on 3 years of the Airline Dataset.\n"
]
},
{
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