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quasiben/ridge-cuml-dask-redux-rapids.ipynb

Created August 14, 2019 15:29
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ridge-cuml-dask-redux-rapids.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dask ML and Gridsearch with cuML"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from cuml import Ridge as cumlRidge\n",
"import cudf\n",
"from sklearn import datasets, linear_model\n",
"from sklearn.externals.joblib import parallel_backend\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"import dask_ml.model_selection as dcv\n",
"\n",
"from dask.distributed import Client\n",
"from dask_cuda import LocalCUDACluster"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use a DGX"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:34288\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8789/status' target='_blank'>http://127.0.0.1:8789/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>540.95 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:34288' processes=8 cores=8>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Start one worker per GPU on the local system\n",
"cluster = LocalCUDACluster(dashboard_address='0.0.0.0:8789')\n",
"client = Client(cluster)\n",
"client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Diabetes Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"diabetes = datasets.load_diabetes()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diabetes.feature_names"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235 ,\n",
" -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# row of data\n",
"diabetes.data[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit Data with Ridge Regression"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Split the data into training/testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(diabetes.data, diabetes.target, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.02824"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# data in MB\n",
"X_train.nbytes/1e6"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Duplicated data in memory: 2824.0 MB\n"
]
}
],
"source": [
"dup_data = np.array(np.vstack([X_train]*int(1e5)))\n",
"dup_train = np.array(np.hstack([y_train]*int(1e5)))\n",
"print(f'Duplicated data in memory: {dup_data.nbytes / 1e6} MB')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data onto GPU"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11.3 s, sys: 1.77 s, total: 13.1 s\n",
"Wall time: 5.96 s\n"
]
}
],
"source": [
"%%time\n",
"record_data = (('fea%d'%i, dup_data[:,i]) for i in range(dup_data.shape[1]))\n",
"gdf_data = cudf.DataFrame(record_data)\n",
"gdf_train = cudf.DataFrame(dict(train=dup_train))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hyperparameter Optimization"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"fit_intercept = True\n",
"normalize = False\n",
"alpha = np.array([1.0]) "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"params = {'alpha': np.logspace(-3, -1, 10)}\n",
"clf = linear_model.Ridge(alpha=alpha, fit_intercept=fit_intercept, normalize=normalize, solver='cholesky')\n",
"cu_clf = cumlRidge(alpha=alpha, fit_intercept=fit_intercept, normalize=normalize, solver=\"eig\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# %%timeit\n",
"# sk_grid = GridSearchCV(clf, params, cv=5, iid=False)\n",
"# sk_grid.fit(dup_data, dup_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# %%timeit\n",
"# sk_cu_grid = GridSearchCV(cu_clf, params, cv=5, iid=False)\n",
"# sk_cu_grid.fit(gdf_data, gdf_train.train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Swap Sklearn Gridsearch with DaskML Gridsearch"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 27.4 s, sys: 9.57 s, total: 37 s\n",
"Wall time: 4min 24s\n"
]
}
],
"source": [
"%%time\n",
"grid = dcv.GridSearchCV(clf, params, cv=5)\n",
"grid.fit(dup_data, dup_train)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11.9 s, sys: 11.5 s, total: 23.4 s\n",
"Wall time: 1min 47s\n"
]
}
],
"source": [
"%%time\n",
"cu_grid = dcv.GridSearchCV(cu_clf, params)\n",
"cu_grid.fit(gdf_data, gdf_train.train)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:34288\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8789/status' target='_blank'>http://127.0.0.1:8789/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>540.95 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:34288' processes=7 cores=7>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"client.restart()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 16.8 s, sys: 8.02 s, total: 24.8 s\n",
"Wall time: 1min 58s\n"
]
}
],
"source": [
"%%time\n",
"grid = dcv.GridSearchCV(clf, params, cv=5)\n",
"grid.fit(dup_data, dup_train)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:34288\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8789/status' target='_blank'>http://127.0.0.1:8789/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>540.95 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:34288' processes=6 cores=6>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del grid\n",
"client.restart()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dcv.GridSearchCV"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 17.4 s, sys: 7.93 s, total: 25.3 s\n",
"Wall time: 2min 2s\n"
]
}
],
"source": [
"%%time\n",
"grid = dcv.GridSearchCV(clf, params, cv=5, )\n",
"grid.fit(dup_data, dup_train)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:34288\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8789/status' target='_blank'>http://127.0.0.1:8789/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>540.95 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:34288' processes=7 cores=7>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del grid\n",
"client.restart()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 18.5 s, sys: 7.98 s, total: 26.4 s\n",
"Wall time: 2min 4s\n"
]
}
],
"source": [
"%%time\n",
"grid = dcv.GridSearchCV(clf, params, cv=5)\n",
"grid.fit(dup_data, dup_train)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:34288\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8789/status' target='_blank'>http://127.0.0.1:8789/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>540.95 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:34288' processes=7 cores=7>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del grid\n",
"client.restart()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 18.2 s, sys: 6.84 s, total: 25 s\n",
"Wall time: 2min\n"
]
}
],
"source": [
"%%time\n",
"grid = dcv.GridSearchCV(clf, params, cv=5)\n",
"grid.fit(dup_data, dup_train)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"params = {'alpha': np.logspace(-3, -1, 10)}\n",
"clf2 = linear_model.Ridge(alpha=alpha, fit_intercept=fit_intercept, normalize=normalize, solver='cholesky')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:34288\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8789/status' target='_blank'>http://127.0.0.1:8789/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>540.95 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:34288' processes=6 cores=6>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del grid\n",
"client.restart()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 18.9 s, sys: 7.4 s, total: 26.3 s\n",
"Wall time: 2min 9s\n"
]
}
],
"source": [
"%%time\n",
"grid = dcv.GridSearchCV(clf2, params, cv=5)\n",
"grid.fit(dup_data, dup_train)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:34288\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8789/status' target='_blank'>http://127.0.0.1:8789/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>540.95 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:34288' processes=0 cores=0>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del grid\n",
"del cu_grid\n",
"client.restart()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 9.92 s, sys: 9.2 s, total: 19.1 s\n",
"Wall time: 1min 33s\n"
]
}
],
"source": [
"%%time\n",
"cu_grid = dcv.GridSearchCV(cu_clf, params)\n",
"cu_grid.fit(gdf_data, gdf_train.train)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"from dask_ml.model_selection._search import build_graph"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[0;31mSignature:\u001b[0m\n",
"\u001b[0mbuild_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcandidate_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mfit_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0miid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mrefit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0merror_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'warn'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcache_cv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mmultimetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mDocstring:\u001b[0m <no docstring>\n",
"\u001b[0;31mFile:\u001b[0m ~/miniconda3/envs/rapids/lib/python3.6/site-packages/dask_ml/model_selection/_search.py\n",
"\u001b[0;31mType:\u001b[0m function\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"build_graph?"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cu_grid.scorer_"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'alpha': array([0.001 , 0.0016681 , 0.00278256, 0.00464159, 0.00774264,\n",
" 0.0129155 , 0.02154435, 0.03593814, 0.05994843, 0.1 ])}"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cu_grid.param_grid"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"387 ms ± 2.96 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%%timeit\n",
"cu_clf.fit(gdf_data, gdf_train.train)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"({'array-80f681a86008c2025046b30b34736840': <cudf.DataFrame ncols=10 nrows=35300000 >,\n",
" 'array-60147a35aa41972ee18147641d08e6f4': <cudf.Series nrows=35300000 >,\n",
" 'cv-split-7dab565824ab47c3c298cbd49edc145d': (<function dask_ml.model_selection.methods.cv_split(cv, X, y, groups, is_pairwise, cache)>,\n",
" KFold(n_splits=5, random_state=None, shuffle=False),\n",
" 'array-80f681a86008c2025046b30b34736840',\n",
" 'array-60147a35aa41972ee18147641d08e6f4',\n",
" None,\n",
" False,\n",
" True),\n",
" 'cv-n-samples-7dab565824ab47c3c298cbd49edc145d': (<function dask_ml.model_selection.methods.cv_n_samples(cvs)>,\n",
" 'cv-split-7dab565824ab47c3c298cbd49edc145d'),\n",
" 'ridge-7dab565824ab47c3c298cbd49edc145d': <cuml.linear_model.ridge.Ridge at 0x7f53342a7a20>,\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
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" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
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" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 1,\n",
" 1): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 1, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.0016681005372000592,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 1,\n",
" 2): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 2, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.0016681005372000592,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 1,\n",
" 3): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 3, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.0016681005372000592,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 1,\n",
" 4): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 4, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.0016681005372000592,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 2,\n",
" 0): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 0, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.0027825594022071257,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 2,\n",
" 1): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 1, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.0027825594022071257,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 2,\n",
" 2): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 2, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.0027825594022071257,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 2,\n",
" 3): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 3, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.0027825594022071257,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 2,\n",
" 4): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 4, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.0027825594022071257,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 3,\n",
" 0): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 0, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.004641588833612777,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 3,\n",
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" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 3,\n",
" 2): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 2, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.004641588833612777,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 3,\n",
" 3): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 3, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.004641588833612777,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 3,\n",
" 4): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 4, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.004641588833612777,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 4,\n",
" 0): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 0, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.007742636826811269,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 4,\n",
" 1): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 1, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.007742636826811269,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 4,\n",
" 2): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 2, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.007742636826811269,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 4,\n",
" 3): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 3, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.007742636826811269,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
" 4,\n",
" 4): (<function dask_ml.model_selection.methods.fit_and_score(est, cv, X, y, n, scorer, error_score='raise', fields=None, params=None, fit_params=None, return_train_score=True)>, 'ridge-7dab565824ab47c3c298cbd49edc145d', 'cv-split-7dab565824ab47c3c298cbd49edc145d', 'array-80f681a86008c2025046b30b34736840', 'array-60147a35aa41972ee18147641d08e6f4', 4, <function sklearn.metrics.scorer._passthrough_scorer(estimator, *args, **kwargs)>, 'raise', ['alpha'], (0.007742636826811269,), None, 'warn'),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d',\n",
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" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d', 4, 4),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d', 5, 4),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d', 6, 4),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d', 7, 4),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d', 8, 4),\n",
" ('ridge-fit-score-7dab565824ab47c3c298cbd49edc145d', 9, 4)],\n",
" 'cv-parameters-7dab565824ab47c3c298cbd49edc145d',\n",
" 5,\n",
" 'raise',\n",
" 'cv-n-samples-7dab565824ab47c3c298cbd49edc145d',\n",
" None),\n",
" 'best-params-7dab565824ab47c3c298cbd49edc145d': (<function dask_ml.model_selection.methods.get_best_params(candidate_params, cv_results, scorer)>,\n",
" 'cv-parameters-7dab565824ab47c3c298cbd49edc145d',\n",
" 'cv-results-7dab565824ab47c3c298cbd49edc145d',\n",
" 'score'),\n",
" 'best-estimator-7dab565824ab47c3c298cbd49edc145d': (<function dask_ml.model_selection.methods.fit_best(estimator, params, X, y, fit_params)>,\n",
" <cuml.linear_model.ridge.Ridge at 0x7f52d16cccf8>,\n",
" 'best-params-7dab565824ab47c3c298cbd49edc145d',\n",
" 'array-80f681a86008c2025046b30b34736840',\n",
" 'array-60147a35aa41972ee18147641d08e6f4',\n",
" {})},\n",
" ['cv-results-7dab565824ab47c3c298cbd49edc145d',\n",
" 'best-estimator-7dab565824ab47c3c298cbd49edc145d'],\n",
" 5)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"build_graph(cu_clf, cv=5, scorer=cu_grid.scorer_, \n",
" candidate_params=list(cu_grid._get_param_iterator()), X=gdf_data, y=gdf_train.train)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:34288\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8789/status' target='_blank'>http://127.0.0.1:8789/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>8</li>\n",
" <li><b>Cores: </b>8</li>\n",
" <li><b>Memory: </b>540.95 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:34288' processes=7 cores=7>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"client.restart()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 8.01 s, sys: 8.88 s, total: 16.9 s\n",
"Wall time: 1min 16s\n"
]
},
{
"data": {
"text/html": [
"<b>Future: DataFrame</b> <font color=\"gray\">status: </font><font color=\"black\">finished</font>, <font color=\"gray\">type: </font>DataFrame, <font color=\"gray\">key: </font>DataFrame-dd7b649f5cc211b72119c7ac0e32f375"
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"text/plain": [
"<Future: status: finished, type: DataFrame, key: DataFrame-dd7b649f5cc211b72119c7ac0e32f375>"
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},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"client.scatter(gdf_data, broadcast=True)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 744 ms, sys: 756 ms, total: 1.5 s\n",
"Wall time: 6.74 s\n"
]
},
{
"data": {
"text/html": [
"<b>Future: DataFrame</b> <font color=\"gray\">status: </font><font color=\"black\">finished</font>, <font color=\"gray\">type: </font>DataFrame, <font color=\"gray\">key: </font>DataFrame-8de0c8b2183be61b889bc4fed65e6d20"
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"<Future: status: finished, type: DataFrame, key: DataFrame-8de0c8b2183be61b889bc4fed65e6d20>"
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"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"client.scatter(gdf_train, broadcast=True)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 760 ms, sys: 748 ms, total: 1.51 s\n",
"Wall time: 6.36 s\n"
]
},
{
"data": {
"text/html": [
"<b>Future: Series</b> <font color=\"gray\">status: </font><font color=\"black\">finished</font>, <font color=\"gray\">type: </font>Series, <font color=\"gray\">key: </font>Series-904efb0dadd8ae962b3065e9c6245729"
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"<Future: status: finished, type: Series, key: Series-904efb0dadd8ae962b3065e9c6245729>"
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"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"client.scatter(gdf_train.train, broadcast=True)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"import sys, pickle"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"857"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pickle.dumps(cu_clf))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "__deepcopy__() takes 1 positional argument but 2 were given",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/dask_ml/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m 1194\u001b[0m \u001b[0mscheduler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_sync\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1196\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1198\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandle_deprecated_train_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_train_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[1;32m 2566\u001b[0m \u001b[0mshould_rejoin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2567\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2568\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpacked\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masynchronous\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masynchronous\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdirect\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2569\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2570\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mgather\u001b[0;34m(self, futures, errors, maxsize, direct, asynchronous)\u001b[0m\n\u001b[1;32m 1820\u001b[0m \u001b[0mdirect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdirect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1821\u001b[0m \u001b[0mlocal_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1822\u001b[0;31m \u001b[0masynchronous\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masynchronous\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1823\u001b[0m )\n\u001b[1;32m 1824\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 751\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 752\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 753\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 754\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(loop, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 331\u001b[0;31m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 332\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m 691\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 692\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 694\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 695\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/dask_ml/model_selection/methods.py\u001b[0m in \u001b[0;36mfit_and_score\u001b[0;34m()\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 321\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m \u001b[0mest_and_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 323\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/dask_ml/model_selection/methods.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m()\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault_timer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m \u001b[0mest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 242\u001b[0m \u001b[0mest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/dask_ml/model_selection/methods.py\u001b[0m in \u001b[0;36mset_params\u001b[0;34m()\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mset_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0mest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy_estimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfields\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/site-packages/dask_ml/model_selection/utils.py\u001b[0m in \u001b[0;36mcopy_estimator\u001b[0;34m()\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;31m# tasks. Since `est` is guaranteed to not be a fit estimator, we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;31m# use `copy.deepcopy` here without fear of copying large data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m()\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m()\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 281\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m()\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m()\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 241\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/rapids/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m()\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__deepcopy__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 162\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0mreductor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdispatch_table\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: __deepcopy__() takes 1 positional argument but 2 were given"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"distributed.utils - ERROR - '<' not supported between instances of 'NoneType' and 'NoneType'\n",
"Traceback (most recent call last):\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/utils.py\", line 713, in log_errors\n",
" yield\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/bokeh/scheduler.py\", line 1416, in graph_doc\n",
" graph = GraphPlot(scheduler, sizing_mode=\"stretch_both\")\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/bokeh/scheduler.py\", line 758, in __init__\n",
" self.layout = GraphLayout(scheduler)\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/diagnostics/graph_layout.py\", line 41, in __init__\n",
" self.scheduler, dependencies=dependencies, priority=priority\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/diagnostics/graph_layout.py\", line 45, in update_graph\n",
" stack = sorted(dependencies, key=lambda k: priority.get(k, 0), reverse=True)\n",
"TypeError: '<' not supported between instances of 'NoneType' and 'NoneType'\n",
"tornado.application - ERROR - Uncaught exception GET /graph (10.2.171.71)\n",
"HTTPServerRequest(protocol='http', host='10.31.241.45:8789', method='GET', uri='/graph', version='HTTP/1.1', remote_ip='10.2.171.71')\n",
"Traceback (most recent call last):\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/tornado/web.py\", line 1699, in _execute\n",
" result = await result\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/tornado/gen.py\", line 736, in run\n",
" yielded = self.gen.throw(*exc_info) # type: ignore\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/bokeh/server/views/doc_handler.py\", line 55, in get\n",
" session = yield self.get_session()\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/tornado/gen.py\", line 729, in run\n",
" value = future.result()\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/tornado/gen.py\", line 736, in run\n",
" yielded = self.gen.throw(*exc_info) # type: ignore\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/bokeh/server/views/session_handler.py\", line 77, in get_session\n",
" session = yield self.application_context.create_session_if_needed(session_id, self.request)\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/tornado/gen.py\", line 729, in run\n",
" value = future.result()\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/tornado/gen.py\", line 742, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/bokeh/server/contexts.py\", line 215, in create_session_if_needed\n",
" self._application.initialize_document(doc)\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/bokeh/application/application.py\", line 178, in initialize_document\n",
" h.modify_document(doc)\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/bokeh/application/handlers/function.py\", line 133, in modify_document\n",
" self._func(doc)\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/bokeh/scheduler.py\", line 1416, in graph_doc\n",
" graph = GraphPlot(scheduler, sizing_mode=\"stretch_both\")\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/bokeh/scheduler.py\", line 758, in __init__\n",
" self.layout = GraphLayout(scheduler)\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/diagnostics/graph_layout.py\", line 41, in __init__\n",
" self.scheduler, dependencies=dependencies, priority=priority\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/diagnostics/graph_layout.py\", line 45, in update_graph\n",
" stack = sorted(dependencies, key=lambda k: priority.get(k, 0), reverse=True)\n",
"TypeError: '<' not supported between instances of 'NoneType' and 'NoneType'\n",
"Exception ignored in: <bound method GraphPlot.__del__ of <distributed.bokeh.scheduler.GraphPlot object at 0x7f52cb4b19b0>>\n",
"Traceback (most recent call last):\n",
" File \"/home/nfs/bzaitlen/miniconda3/envs/rapids/lib/python3.6/site-packages/distributed/bokeh/scheduler.py\", line 912, in __del__\n",
" self.scheduler.remove_plugin(self.layout)\n",
"AttributeError: 'GraphPlot' object has no attribute 'layout'\n"
]
}
],
"source": [
"%%time\n",
"cu_grid = dcv.GridSearchCV(cu_clf, params)\n",
"cu_grid.fit(gdf_data, gdf_train.train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"start = time.time()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = client.get_task_stream(start=start, filename='dask-cuml-gridsearchcv-profile-rapids-task.html')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cu_grid = dcv.GridSearchCV(cu_clf, params)\n",
"cu_grid.fit(gdf_data, gdf_train.train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = client.profile(start=start, filename='dask-cuml-gridsearchcv-profile-rapids.html')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = client.get_task_stream(start=start, plot='save', filename='dask-cuml-gridsearchcv-profile-rapids-task.html')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:rapids]",
"language": "python",
"name": "conda-env-rapids-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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