mirror of
https://github.com/vale981/ray
synced 2025-03-07 02:51:39 -05:00
0ec198acc2
Removes unnecessary pandas usage from AIR examples. Helps ensure users do not follow bad practices.
442 lines
15 KiB
Text
442 lines
15 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c3192ac4",
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"metadata": {},
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"source": [
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"# Training a model with Sklearn\n",
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"In this example we will train a model in Ray AIR using a Sklearn classifier."
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]
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},
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{
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"cell_type": "markdown",
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"id": "5a4823bf",
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"metadata": {},
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"source": [
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"Let's start with installing our dependencies:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "88f4bb39",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"!pip install -qU \"ray[tune]\" sklearn"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c049c692",
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"metadata": {},
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"source": [
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"Then we need some imports:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "c02eb5cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Tuple\n",
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"\n",
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"\n",
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"import ray\n",
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"from ray.data.dataset import Dataset\n",
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"from ray.train.batch_predictor import BatchPredictor\n",
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"from ray.train.sklearn import SklearnPredictor\n",
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"from ray.data.preprocessors import Chain, OrdinalEncoder, StandardScaler\n",
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"from ray.air.result import Result\n",
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"from ray.air.util.datasets import train_test_split\n",
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"from ray.train.sklearn import SklearnTrainer\n",
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"\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"\n",
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"try:\n",
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" from cuml.ensemble import RandomForestClassifier as cuMLRandomForestClassifier\n",
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"except ImportError:\n",
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" cuMLRandomForestClassifier = None"
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]
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},
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{
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"cell_type": "markdown",
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"id": "52e017f1",
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"metadata": {},
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"source": [
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"Next we define a function to load our train, validation, and test datasets."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "3631ed1e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def prepare_data() -> Tuple[Dataset, Dataset, Dataset]:\n",
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" dataset = ray.data.read_csv(\"s3://air-example-data/breast_cancer_with_categorical.csv\")\n",
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" train_dataset, valid_dataset = train_test_split(dataset, test_size=0.3)\n",
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" test_dataset = valid_dataset.map_batches(lambda df: df.drop(\"target\", axis=1), batch_format=\"pandas\")\n",
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" return train_dataset, valid_dataset, test_dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8d6c6d17",
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"metadata": {},
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"source": [
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"The following function will create a Sklearn trainer, train it, and return the result."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "0fd39e42",
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"metadata": {},
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"outputs": [],
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"source": [
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"def train_sklearn(num_cpus: int, use_gpu: bool = False) -> Result:\n",
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" if use_gpu and not cuMLRandomForestClassifier:\n",
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" raise RuntimeError(\"cuML must be installed for GPU enabled sklearn estimators.\")\n",
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"\n",
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" train_dataset, valid_dataset, _ = prepare_data()\n",
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"\n",
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" # Scale some random columns\n",
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" columns_to_scale = [\"mean radius\", \"mean texture\"]\n",
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" preprocessor = Chain(\n",
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" OrdinalEncoder([\"categorical_column\"]), StandardScaler(columns=columns_to_scale)\n",
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" )\n",
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"\n",
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" if use_gpu:\n",
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" trainer_resources = {\"CPU\": 1, \"GPU\": 1}\n",
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" estimator = cuMLRandomForestClassifier()\n",
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" else:\n",
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" trainer_resources = {\"CPU\": num_cpus}\n",
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" estimator = RandomForestClassifier()\n",
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"\n",
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" trainer = SklearnTrainer(\n",
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" estimator=estimator,\n",
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" label_column=\"target\",\n",
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" datasets={\"train\": train_dataset, \"valid\": valid_dataset},\n",
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" preprocessor=preprocessor,\n",
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" cv=5,\n",
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" scaling_config={\n",
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" \"trainer_resources\": trainer_resources,\n",
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" },\n",
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" )\n",
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" result = trainer.fit()\n",
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" print(result.metrics)\n",
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"\n",
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" return result"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a2efb9d",
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"metadata": {},
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"source": [
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"Once we have the result, we can do batch inference on the obtained model. Let's define a utility function for this."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "59eeadd8",
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict_sklearn(result: Result, use_gpu: bool = False):\n",
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" _, _, test_dataset = prepare_data()\n",
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"\n",
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" batch_predictor = BatchPredictor.from_checkpoint(\n",
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" result.checkpoint, SklearnPredictor\n",
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" )\n",
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"\n",
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" predicted_labels = (\n",
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" batch_predictor.predict(\n",
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" test_dataset,\n",
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" num_gpus_per_worker=int(use_gpu),\n",
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" )\n",
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" .map_batches(lambda df: (df > 0.5).astype(int), batch_format=\"pandas\")\n",
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" )\n",
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" print(f\"PREDICTED LABELS\")\n",
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" predicted_labels.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7d073994",
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"metadata": {},
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"source": [
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"Now we can run the training:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "43f9170a",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-06-22 17:27:37,741\tINFO services.py:1477 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8269\u001b[39m\u001b[22m\n",
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"2022-06-22 17:27:39,822\tWARNING read_api.py:260 -- The number of blocks in this dataset (1) limits its parallelism to 1 concurrent tasks. This is much less than the number of available CPU slots in the cluster. Use `.repartition(n)` to increase the number of dataset blocks.\n",
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"Map_Batches: 100%|██████████| 1/1 [00:00<00:00, 44.05it/s]\n"
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]
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},
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{
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"data": {
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"text/html": [
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"== Status ==<br>Current time: 2022-06-22 17:27:59 (running for 00:00:18.31)<br>Memory usage on this node: 10.7/31.0 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/8 CPUs, 0/0 GPUs, 0.0/12.9 GiB heap, 0.0/6.45 GiB objects<br>Result logdir: /home/ubuntu/ray_results/SklearnTrainer_2022-06-22_17-27-40<br>Number of trials: 1/1 (1 TERMINATED)<br><table>\n",
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"<thead>\n",
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"<tr><th>Trial name </th><th>status </th><th>loc </th><th style=\"text-align: right;\"> iter</th><th style=\"text-align: right;\"> total time (s)</th><th style=\"text-align: right;\"> fit_time</th></tr>\n",
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"</thead>\n",
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"<tbody>\n",
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"<tr><td>SklearnTrainer_9dec8_00000</td><td>TERMINATED</td><td>172.31.43.110:1492629</td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 15.6842</td><td style=\"text-align: right;\"> 2.31571</td></tr>\n",
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"</tbody>\n",
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"</table><br><br>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[2m\u001b[36m(SklearnTrainer pid=1492629)\u001b[0m 2022-06-22 17:27:45,647\tWARNING pool.py:591 -- The 'context' argument is not supported using ray. Please refer to the documentation for how to control ray initialization.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Result for SklearnTrainer_9dec8_00000:\n",
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" cv:\n",
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" fit_time:\n",
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" - 2.221003770828247\n",
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" - 2.215489387512207\n",
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" - 2.2075674533843994\n",
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" - 2.222351312637329\n",
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" - 2.312389612197876\n",
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" fit_time_mean: 2.235760307312012\n",
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" fit_time_std: 0.03866614559685742\n",
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" score_time:\n",
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" - 0.022464990615844727\n",
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" - 0.0230865478515625\n",
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" - 0.02564835548400879\n",
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" - 0.029137849807739258\n",
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" - 0.021221637725830078\n",
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" score_time_mean: 0.02431187629699707\n",
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" score_time_std: 0.0028120522003997595\n",
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" test_score:\n",
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" - 0.9625\n",
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" - 0.9125\n",
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" - 0.9875\n",
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" - 1.0\n",
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" - 0.9367088607594937\n",
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" test_score_mean: 0.9598417721518986\n",
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" test_score_std: 0.032128186960552516\n",
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" date: 2022-06-22_17-27-59\n",
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" done: false\n",
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" experiment_id: f8215019c10e4a81ba2187c38e875365\n",
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" fit_time: 2.3157050609588623\n",
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" hostname: ip-172-31-43-110\n",
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" iterations_since_restore: 1\n",
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" node_ip: 172.31.43.110\n",
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" pid: 1492629\n",
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" should_checkpoint: true\n",
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" time_since_restore: 15.684244871139526\n",
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" time_this_iter_s: 15.684244871139526\n",
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" time_total_s: 15.684244871139526\n",
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" timestamp: 1655918879\n",
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" timesteps_since_restore: 0\n",
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" training_iteration: 1\n",
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" trial_id: 9dec8_00000\n",
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" valid:\n",
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" score_time: 0.03549623489379883\n",
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" test_score: 0.9532163742690059\n",
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" warmup_time: 0.0057866573333740234\n",
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" \n",
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"Result for SklearnTrainer_9dec8_00000:\n",
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" cv:\n",
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" fit_time:\n",
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" - 2.221003770828247\n",
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" - 2.215489387512207\n",
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" - 2.2075674533843994\n",
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" - 2.222351312637329\n",
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" - 2.312389612197876\n",
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" fit_time_mean: 2.235760307312012\n",
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" fit_time_std: 0.03866614559685742\n",
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" score_time:\n",
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" - 0.022464990615844727\n",
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" - 0.0230865478515625\n",
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" - 0.02564835548400879\n",
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" - 0.029137849807739258\n",
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" - 0.021221637725830078\n",
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" score_time_mean: 0.02431187629699707\n",
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" score_time_std: 0.0028120522003997595\n",
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" test_score:\n",
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" - 0.9625\n",
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" - 0.9125\n",
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" - 0.9875\n",
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" - 1.0\n",
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" - 0.9367088607594937\n",
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" test_score_mean: 0.9598417721518986\n",
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" test_score_std: 0.032128186960552516\n",
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" date: 2022-06-22_17-27-59\n",
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" done: true\n",
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" experiment_id: f8215019c10e4a81ba2187c38e875365\n",
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" experiment_tag: '0'\n",
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" fit_time: 2.3157050609588623\n",
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" hostname: ip-172-31-43-110\n",
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" iterations_since_restore: 1\n",
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" node_ip: 172.31.43.110\n",
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" pid: 1492629\n",
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" should_checkpoint: true\n",
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" time_since_restore: 15.684244871139526\n",
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" time_this_iter_s: 15.684244871139526\n",
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" time_total_s: 15.684244871139526\n",
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" timestamp: 1655918879\n",
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" timesteps_since_restore: 0\n",
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" training_iteration: 1\n",
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" trial_id: 9dec8_00000\n",
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" valid:\n",
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" score_time: 0.03549623489379883\n",
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" test_score: 0.9532163742690059\n",
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" warmup_time: 0.0057866573333740234\n",
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" \n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-06-22 17:27:59,333\tINFO tune.py:734 -- Total run time: 19.09 seconds (18.31 seconds for the tuning loop).\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'valid': {'score_time': 0.03549623489379883, 'test_score': 0.9532163742690059}, 'cv': {'fit_time': array([2.22100377, 2.21548939, 2.20756745, 2.22235131, 2.31238961]), 'score_time': array([0.02246499, 0.02308655, 0.02564836, 0.02913785, 0.02122164]), 'test_score': array([0.9625 , 0.9125 , 0.9875 , 1. , 0.93670886]), 'fit_time_mean': 2.235760307312012, 'fit_time_std': 0.03866614559685742, 'score_time_mean': 0.02431187629699707, 'score_time_std': 0.0028120522003997595, 'test_score_mean': 0.9598417721518986, 'test_score_std': 0.032128186960552516}, 'fit_time': 2.3157050609588623, 'time_this_iter_s': 15.684244871139526, 'should_checkpoint': True, 'done': True, 'timesteps_total': None, 'episodes_total': None, 'training_iteration': 1, 'trial_id': '9dec8_00000', 'experiment_id': 'f8215019c10e4a81ba2187c38e875365', 'date': '2022-06-22_17-27-59', 'timestamp': 1655918879, 'time_total_s': 15.684244871139526, 'pid': 1492629, 'hostname': 'ip-172-31-43-110', 'node_ip': '172.31.43.110', 'config': {}, 'time_since_restore': 15.684244871139526, 'timesteps_since_restore': 0, 'iterations_since_restore': 1, 'warmup_time': 0.0057866573333740234, 'experiment_tag': '0'}\n"
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]
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}
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],
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"source": [
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"result = train_sklearn(num_cpus=2, use_gpu=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0daba603",
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"metadata": {},
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"source": [
|
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"And perform inference on the obtained model:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "24b16ede",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-06-22 17:27:59,658\tWARNING read_api.py:260 -- The number of blocks in this dataset (1) limits its parallelism to 1 concurrent tasks. This is much less than the number of available CPU slots in the cluster. Use `.repartition(n)` to increase the number of dataset blocks.\n",
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"Map_Batches: 100%|██████████| 1/1 [00:00<00:00, 64.73it/s]\n",
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"Map Progress (1 actors 1 pending): 100%|██████████| 1/1 [00:01<00:00, 1.60s/it]\n",
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"Map_Batches: 100%|██████████| 1/1 [00:00<00:00, 71.41it/s]"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"PREDICTED LABELS\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 0}\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 0}\n",
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"{'predictions': 1}\n",
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"{'predictions': 0}\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 0}\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 1}\n",
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"{'predictions': 0}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"predict_sklearn(result, use_gpu=False)"
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]
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}
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],
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"metadata": {
|
|
"jupytext": {
|
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"cell_metadata_filter": "-all",
|
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"main_language": "python",
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"notebook_metadata_filter": "-all"
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},
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"kernelspec": {
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"display_name": "Python 3.8.10 ('venv': venv)",
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"language": "python",
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"name": "python3"
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