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Document ability to export cuML RF to predict on other machines #3890

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May 28, 2021
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Document ability to export cuML RF to predict on other machines #3890

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f8ee886
Document ability to export cuML RF to predict on other machines
hcho3 May 21, 2021
52d0301
Add link to docstring
hcho3 May 27, 2021
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66 changes: 65 additions & 1 deletion docs/source/pickling_cuml_models.ipynb
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Expand Up @@ -183,6 +183,70 @@
"source": [
"single_gpu_model.cluster_centers_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exporting cuML Random Forest models for inferencing on machines without GPUs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Starting with cuML version 21.06, you can export cuML Random Forest models and run predictions with them on machines without an NVIDIA GPUs. The [Treelite](https://github.com/dmlc/treelite) package defines an efficient exchange format that lets you portably move the cuML Random Forest models to other machines. We will refer to the exchange format as \"checkpoints.\"\n",
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I think adding a link to here from the main docstring of the RF classes (with a note like RF classes can be exported to Treelite for inference on machines without GPUs or so) would be useful for people looking at the api docs

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Done

"\n",
"Here are the steps to export the model:\n",
"\n",
"1. Call `to_treelite_checkpoint()` to obtain the checkpoint file from the cuML Random Forest model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from cuml.ensemble import RandomForestClassifier as cumlRandomForestClassifier\n",
"from sklearn.datasets import load_iris\n",
"import numpy as np\n",
"\n",
"X, y = load_iris(return_X_y=True)\n",
"X, y = X.astype(np.float32), y.astype(np.int32)\n",
"clf = cumlRandomForestClassifier(max_depth=3, random_state=0, n_estimators=10)\n",
"clf.fit(X, y)\n",
"\n",
"checkpoint_path = './checkpoint.tl'\n",
"# Export cuML RF model as Treelite checkpoint\n",
"clf.convert_to_treelite_model().to_treelite_checkpoint(checkpoint_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Copy the generated checkpoint file `checkpoint.tl` to another machine on which you'd like to run predictions.\n",
"\n",
"3. On the target machine, install Treelite by running `pip install treelite` or `conda install -c conda-forge treelite`. The machine does not need to have an NVIDIA GPUs and does not need to have cuML installed.\n",
"\n",
"4. You can now load the model from the checkpoint, by running the following on the target machine:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import treelite\n",
"\n",
"# The checkpoint file has been copied over\n",
"checkpoint_path = './checkpoint.tl'\n",
"tl_model = treelite.Model.deserialize(checkpoint_path)\n",
"out_prob = treelite.gtil.predict(tl_model, X, pred_margin=True)\n",
"print(out_prob)"
]
}
],
"metadata": {
Expand All @@ -201,7 +265,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
"version": "3.8.8"
}
},
"nbformat": 4,
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5 changes: 5 additions & 0 deletions python/cuml/ensemble/randomforestclassifier.pyx
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Expand Up @@ -134,6 +134,11 @@ class RandomForestClassifier(BaseRandomForestModel,
histogram-based algorithm to determine splits, rather than an exact
count. You can tune the size of the histograms with the n_bins parameter.
.. note:: You can export cuML Random Forest models and run predictions
with them on machines without an NVIDIA GPUs. See
https://docs.rapids.ai/api/cuml/nightly/pickling_cuml_models.html
for more details.
**Known Limitations**: This is an early release of the cuML
Random Forest code. It contains a few known limitations:
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5 changes: 5 additions & 0 deletions python/cuml/ensemble/randomforestregressor.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -117,6 +117,11 @@ class RandomForestRegressor(BaseRandomForestModel,
histogram-based algorithm to determine splits, rather than an exact
count. You can tune the size of the histograms with the n_bins parameter.
.. note:: You can export cuML Random Forest models and run predictions
with them on machines without an NVIDIA GPUs. See
https://docs.rapids.ai/api/cuml/nightly/pickling_cuml_models.html
for more details.
**Known Limitations**: This is an early release of the cuML
Random Forest code. It contains a few known limitations:
Expand Down