From 8c0833f9134e77c7cac767bfe14ad946583ab4f1 Mon Sep 17 00:00:00 2001 From: "Rishi C." <77904151+rishic3@users.noreply.github.com> Date: Fri, 13 Dec 2024 20:33:27 -0500 Subject: [PATCH] Publish Optuna-Spark Examples (#462) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Support running optuna on Spark * play around with optuna + xgboost + joblibspark * update * update * update * Optuna: Add how optuna + xgboost + spark works * deploy optuna examples on databricks * Cleanup and prepare for open source * Update README.md * Update run-optuna-spark-xgboost.sh * Update README.md with chmod for run scripts * update/add copyright * Replace RDD example with Dataframe example, cleanups to README and repo structure * Move files to separate dir for merge to spark-rapids-examples * separate dir * Merge branch-24.10 * Remove gitignore Signed-off-by: Rishi Chandra * remove username * Update README.md * Update README.md * Update README.md * Update README.md * Fix run-sparkrapids-xgboost.sh implementation path * Fix corrupt mysql installation in init_optuna.sh * Fix corrupt mysql installation in init_optuna_xgboost.sh * Update run-joblibspark-xgboost.sh * Update run-sparkrapids-xgboost.sh * Update run-joblibspark-simple.sh * Update run-joblibspark-xgboost.sh * Update run-sparkrapids-xgboost.sh * Update to 24.10, include apt updates * use gpu_hist for older xgb versions * use gpu_hist for older xgb versions * Update sparkrapids-xgboost-read-per-worker.py * Update init script with mysql installation fixes * Address comments * Add cluster startup script * Move around files, add notebooks * Repo renovations * Update README, remove run scripts, fix PCA to use 24.10.1 * minor updates * Final cleanups, runs passed on databricks * README updates * Updates to comments, cleanup outputs * Address comments, minor reordering, update README * comment fix * remove unnecessary imports * tuning max bins and n_estimators bug fixes * 'max_bin' != 'max_bins' 🤦 * ensure QDM and XGB bins are the same * typos * cleanup * Address comments * Note about sampler serialization * Add link * Add link * Undo benchmark commit * typo --------- Signed-off-by: Rishi Chandra Co-authored-by: Bobby Wang (SW-TEGRA) Co-authored-by: Bobby Wang Co-authored-by: Erik Ordentlich --- .../ML+DL-Examples/Optuna-Spark/README.md | 243 ++++ .../Optuna-Spark/images/optuna.svg | 4 + .../Optuna-Spark/images/runtimes.png | Bin 0 -> 567196 bytes .../optuna-examples/databricks/init_optuna.sh | 71 ++ .../databricks/start_cluster.sh | 58 + .../optuna-examples/optuna-dataframe.ipynb | 1036 +++++++++++++++++ .../optuna-examples/optuna-joblibspark.ipynb | 868 ++++++++++++++ .../Spark-Rapids-ML/pca/README.md | 2 +- .../Spark-Rapids-ML/pca/notebooks/pca.ipynb | 4 +- 9 files changed, 2283 insertions(+), 3 deletions(-) create mode 100644 examples/ML+DL-Examples/Optuna-Spark/README.md create mode 100644 examples/ML+DL-Examples/Optuna-Spark/images/optuna.svg create mode 100644 examples/ML+DL-Examples/Optuna-Spark/images/runtimes.png create mode 100644 examples/ML+DL-Examples/Optuna-Spark/optuna-examples/databricks/init_optuna.sh create mode 100755 examples/ML+DL-Examples/Optuna-Spark/optuna-examples/databricks/start_cluster.sh create mode 100644 examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-dataframe.ipynb create mode 100644 examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-joblibspark.ipynb diff --git a/examples/ML+DL-Examples/Optuna-Spark/README.md b/examples/ML+DL-Examples/Optuna-Spark/README.md new file mode 100644 index 00000000..4c7e460b --- /dev/null +++ b/examples/ML+DL-Examples/Optuna-Spark/README.md @@ -0,0 +1,243 @@ + + +# Distributed Hyperparameter Tuning + +These examples demonstrate distributed hyperparameter tuning with [Optuna](https://optuna.readthedocs.io/en/stable/index.html) on Apache Spark, accelerated with [RAPIDS](https://rapids.ai/) on GPU. We showcase how to set up and tune XGBoost on GPU, with deployment on Spark Standalone or Databricks clusters. + +## Contents: +- [Overview](#overview) + - [Examples](#examples) +- [Running Optuna on Spark Standalone](#running-optuna-on-spark-standalone) + - [Setup Database for Optuna](#1-setup-database-for-optuna) + - [Setup Optuna Python Environment](#2-setup-optuna-python-environment) + - [Start Standalone Cluster and Run](#3-start-standalone-cluster-and-run) +- [Running Optuna on Databricks](#running-optuna-on-databricks) + - [Upload Init Script and Notebook](#1-upload-init-script-and-notebook) + - [Create Cluster](#2-create-cluster) + - [Run Notebook](#3-run-notebook) +- [Benchmarks](#benchmarks) +- [How Does it Work?](#how-does-it-work) + - [Implementation Notes](#implementation-notes) + +--- + +## Overview + +Optuna is a lightweight Python library for hyperparameter tuning, integrating state-of-the-art hyperparameter optimization algorithms. + +At a high level, we optimize hyperparameters in three steps: +1. Wrap model training with an `objective` function that returns a loss metric. +2. In each `trial`, suggest hyperparameters based on previous results. +3. Create a `study` object, which executes the optimization and stores the trial results. + +**Local example**: tuning XGBoost with Optuna (from [Optuna docs](https://optuna.org/#code_examples)): +```python +import xgboost as xgb +import optuna + +# 1. Define an objective function to be maximized. +def objective(trial): + ... + + # 2. Suggest values of the hyperparameters using a trial object. + param = { + "objective": "binary:logistic", + "booster": trial.suggest_categorical("booster", ["gbtree", "gblinear", "dart"]), + "lambda": trial.suggest_float("lambda", 1e-8, 1.0, log=True), + "alpha": trial.suggest_float("alpha", 1e-8, 1.0, log=True), + "subsample": trial.suggest_float("subsample", 0.2, 1.0), + "colsample_bytree": trial.suggest_float("colsample_bytree", 0.2, 1.0), + } + + booster = xgb.train(param, dtrain) + ... + return accuracy + +# 3. Create a study object and optimize the objective function. +study = optuna.create_study(direction='maximize') +study.optimize(objective, n_trials=100) +``` + +To run **distributed tuning** on Spark, we take the following steps: +1. Each worker receives a copy of the same dataset. +2. Each worker runs a subset of the trials in parallel. +3. Workers write trial results and receive new hyperparameters using a shared database. + +### Examples + +We provide **2 notebooks**, with differences in the backend/implementation. See [implementation notes](#implementation-notes) for more details. + +- `optuna-joblibspark.ipynb`: + - Uses the [Joblib Spark backend](https://github.com/joblib/joblib-spark) to distribute tasks on the Spark cluster. + - Implements *Worker-I/O*, where each worker reads the full dataset from a specified filepath (e.g., distributed file system). + - Builds on [this Databricks example](https://docs.databricks.com/en/machine-learning/automl-hyperparam-tuning/optuna.html). +- `optuna-dataframe.ipynb`: + - Uses Spark dataframes to distribute tasks on the cluster. + - Implements *Spark-I/O*, where Spark reads the dataset from a specified filepath, then duplicates and repartitions it so that each worker task is mapped onto a copy of the dataset. + - Dataframe operations are accelerated on GPU with the [Spark-RAPIDS Accelerator](https://nvidia.github.io/spark-rapids/). + +## Running Optuna on Spark Standalone + +### 1. Setup Database for Optuna + +Optuna offers an RDBStorage option which allows for the persistence of experiments across different machines and processes, thereby enabling Optuna tasks to be distributed. + +This section will walk you through setting up MySQL as the backend for RDBStorage in Optuna. + +We highly recommend installing MySQL on the driver node. This setup eliminates concerns regarding MySQL connectivity between worker nodes and the driver, simplifying the management of database connections. +(For Databricks, the installation is handled by the init script). + +1. Install MySql: + +``` shell +sudo apt install mysql-server +``` + +2. Configure MySQL bind address: + +in `/etc/mysql/mysql.conf.d/mysqld.cnf` + +``` shell +bind-address = YOUR_DRIVER_HOST_IP +mysqlx-bind-address = YOUR_DRIVER_HOST_IP +``` + +3. Restart MySQL: + +``` shell +sudo systemctl restart mysql.service +``` + +4. Setup user: + +```shell +sudo mysql +``` + +``` mysql +mysql> CREATE USER 'optuna_user'@'%' IDENTIFIED BY 'optuna_password'; +Query OK, 0 rows affected (0.01 sec) + +mysql> GRANT ALL PRIVILEGES ON *.* TO 'optuna_user'@'%' WITH GRANT OPTION; +Query OK, 0 rows affected (0.01 sec) + +mysql> FLUSH PRIVILEGES; +Query OK, 0 rows affected (0.01 sec) + +mysql> EXIT; +Bye +``` + +Create a database for Optuna: + +``` shell +mysql -u optuna_user -p -e "CREATE DATABASE IF NOT EXISTS optuna" +``` + +Troubleshooting: +> If you encounter +`"ERROR 2002 (HY000): Can't connect to local MySQL server through socket '/tmp/mysql.sock' (2)"`, +try the command: +`ln -s /var/run/mysqld/mysqld.sock /tmp/mysql.sock` + +### 2. Setup Optuna Python Environment + +Install the MySQL client and create a conda environment with the required libraries. +We use [RAPIDS](https://docs.rapids.ai/install/#get-rapids) for GPU-accelerated ETL. See the [docs](https://docs.rapids.ai/install/#get-rapids) for version selection. +``` shell +sudo apt install libmysqlclient-dev + +conda create -n rapids-24.10 -c rapidsai -c conda-forge -c nvidia \ + cudf=24.10 cuml=24.10 python=3.10 'cuda-version>=12.0,<=12.5' +conda activate optuna-spark +pip install mysqlclient +pip install optuna joblib joblibspark ipywidgets +``` + +### 3. Start Standalone Cluster and Run + +Configure your standalone cluster settings. +This example just creates local cluster with a single GPU worker: +```shell +export SPARK_HOME=/path/to/spark +export SPARK_WORKER_OPTS="-Dspark.worker.resource.gpu.amount=1 \ + -Dspark.worker.resource.gpu.discoveryScript=$SPARK_HOME/examples/src/main/scripts/getGpusResources.sh" +export MASTER=spark://$(hostname):7077; export SPARK_WORKER_INSTANCES=1; export CORES_PER_WORKER=8 + +${SPARK_HOME}/sbin/start-master.sh; ${SPARK_HOME}/sbin/start-worker.sh -c ${CORES_PER_WORKER} -m 16G ${MASTER} +``` + +You can now run the notebook using the `optuna-spark` Python kernel! +The notebook contains instructions to attach to the standalone cluster. + + +## Running Optuna on Databricks + +### 1. Upload Init Script and Notebook + +- Make sure your [Databricks CLI]((https://docs.databricks.com/en/dev-tools/cli/tutorial.html)) is configured for your Databricks workspace. +- Copy the desired notebook into your Databricks workspace. For example: + ```shell + databricks workspace import /Users/someone@example.com/optuna/optuna-joblibspark.ipynb --format JUPYTER --file optuna-joblibspark.ipynb + ``` +- Copy the init script ```databricks/init_optuna.sh```: + ```shell + databricks workspace import /Users/someone@example.com/optuna/init_optuna.sh --format AUTO --file databricks/init_optuna.sh + ``` + +### 2. Create Cluster + +*For Databricks Azure*: Use the cluster startup script, which is configured to create a 4 node GPU cluster: +```shell +export INIT_PATH=/Users/someone@example.com/optuna/init_optuna.sh +cd databricks +chmod +x start_cluster.sh +./start_cluster.sh +``` + +Or, create a cluster via the web UI: +- Go to `Compute > Create compute` and set the desired cluster settings. +- Under `Advanced Options > Init Scripts`, upload the init script from your workspace. +- Under `Advanced Options > Spark > Environment variables`, set `LIBCUDF_CUFILE_POLICY=OFF`. +- Make sure to use a GPU cluster and include task GPU resources. + +The init script will install the required libraries on all nodes, including RAPIDS and the Spark-RAPIDS plugin for GPU-accelerated ETL. On the driver, it will setup the MySQL server backend. + +### 3. Run Notebook + +Locate the notebook in your workspace and click on `Connect` to attach it to the cluster. The notebook is ready to run! + +## Benchmarks + +The graph below shows running times comparing distributed (8 GPUs) vs. single GPU hyperparameter tuning with 100 trials on synthetic regression datasets. + +![Databricks benchmarking results](images/runtimes.png) + +## How does it work? + +The Optuna tasks will be serialized into bytes and distributed to Spark workers to run. The Optuna task on the executor side that loads the Optuna study from RDBStorage, and then runs its set of trials. + +During tuning, the Optuna tasks send intermediate results back to RDBStorage to persist, and ask for the parameters from RDBStorage sampled by Optuna on the driver to run next. + +**Using JoblibSpark**: each Optuna task is a Spark application that has only 1 job, 1 stage, 1 task, and the Spark application will be submitted on the local threads. Here the parameter `n_jobs` configures the Spark backend to limit how many Spark applications are submitted at the same time. + +Thus Optuna with JoblibSpark uses Spark application level parallelism, rather than task-level parallelism. For larger datasets, ensure that a single XGBoost task can run on a single node without any CPU/GPU OOM. + +Application parallelism with JoblibSpark: + +![Optuna on JoblibSpark](images/optuna.svg) + +### Implementation Notes + +###### Data I/O: +Since each worker requires the full dataset to perform hyperparameter tuning, there are two strategies to get the data into worker memory: + - **Worker I/O**: *each worker reads the dataset* from the filepath once the task has begun. In practice, this requires the dataset to be written to a distributed file system accessible to all workers prior to tuning. The `optuna-joblibspark` notebook demonstrates this. + - **Spark I/O**: Spark reads the dataset and *creates a copy of the dataset for each worker*, then maps the tuning task onto each copy. In practice, this enables the code to be chained to other Dataframe operations (e.g. ETL stages) without the intermediate step of writing to DBFS, at the cost of some overhead during duplication. The `optuna-dataframe` notebook demonstrates this. + - To achieve this, we coalesce the input Dataframe to a single partition, and recursively self-union until we have the desired number of copies (number of workers). Thus each partition will contain a duplicate of the entire dataset, and the Optuna task can be mapped directly onto the partitions. + + +###### Misc: +- Please be aware that Optuna studies will continue where they left off from previous trials; delete and recreate the study if you would like to start anew. +- Optuna in distributed mode is **non-deterministic** (see [this link](https://optuna.readthedocs.io/en/stable/faq.html#how-can-i-obtain-reproducible-optimization-results)), as trials are executed asynchronously by executors. Deterministic behavior can be achieved using Spark barriers to coordinate reads/writes to the database. +- Reading data with GPU using cuDF requires disabling [GPUDirect Storage](https://docs.rapids.ai/api/cudf/nightly/user_guide/io/io/#magnum-io-gpudirect-storage-integration), i.e., setting the environment variable `LIBCUDF_CUFILE_POLICY=OFF`, to be compatible with the Databricks file system. Without GDS, cuDF will use a CPU bounce buffer when reading files, but all parsing and decoding will still be accelerated by the GPU. +- Note that the storage doesn’t store the state of the instance of samplers and pruners. To resume a study with a sampler whose seed argument is specified, [the sampler can be pickled](https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/001_rdb.html#resume-study) and returned to the driver alongside the results. diff --git a/examples/ML+DL-Examples/Optuna-Spark/images/optuna.svg b/examples/ML+DL-Examples/Optuna-Spark/images/optuna.svg new file mode 100644 index 00000000..17103908 --- /dev/null +++ b/examples/ML+DL-Examples/Optuna-Spark/images/optuna.svg @@ -0,0 +1,4 @@ + + + +
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/var/lib/apt/lists/* +sudo apt clean && sudo apt update --fix-missing -y + +if [[ $DB_IS_DRIVER = "TRUE" ]]; then + # setup database for optuna on driver + + # install mysql server + sudo apt install -y mysql-server + + if [[ ! -f "/etc/mysql/mysql.conf.d/mysqld.cnf" ]]; then + sudo apt remove --purge mysql\* + sudo apt clean && sudo apt update --fix-missing -y + sudo apt install -y mysql-server + fi + + if [[ ! -f "/etc/mysql/mysql.conf.d/mysqld.cnf" ]]; then + echo "ERROR: MYSQL installation failed" + exit 1 + fi + + # configure mysql + BIND_ADDRESS=$DB_DRIVER_IP + MYSQL_CONFIG_FILE="/etc/mysql/mysql.conf.d/mysqld.cnf" + sudo sed -i "s/^bind-address\s*=.*/bind-address = $BIND_ADDRESS/" "$MYSQL_CONFIG_FILE" + sudo sed -i "s/^mysqlx-bind-address\s*=.*/mysqlx-bind-address = $BIND_ADDRESS/" "$MYSQL_CONFIG_FILE" + sudo systemctl restart mysql.service + + # setup user + OPTUNA_USER="optuna_user" + OPTUNA_PASSWORD="optuna_password" + sudo mysql -u root -e " + CREATE USER IF NOT EXISTS '$OPTUNA_USER'@'%' IDENTIFIED BY '$OPTUNA_PASSWORD'; + GRANT ALL PRIVILEGES ON *.* TO '$OPTUNA_USER'@'%' WITH GRANT OPTION; + FLUSH PRIVILEGES;" +fi + + +# rapids import +SPARK_RAPIDS_VERSION=24.10.1 +curl -L https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/${SPARK_RAPIDS_VERSION}/rapids-4-spark_2.12-${SPARK_RAPIDS_VERSION}.jar -o \ + /databricks/jars/rapids-4-spark_2.12-${SPARK_RAPIDS_VERSION}.jar + +# setup cuda: install cudatoolkit 11.8 via runfile approach +wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run +sh cuda_11.8.0_520.61.05_linux.run --silent --toolkit +# reset symlink and update library loading paths +rm /usr/local/cuda +ln -s /usr/local/cuda-11.8 /usr/local/cuda + +sudo /databricks/python3/bin/pip3 install \ + --extra-index-url=https://pypi.nvidia.com \ + "cudf-cu11==24.10.*" "cuml-cu11==24.10.*" + +# setup python environment +sudo apt clean && sudo apt update --fix-missing -y +sudo apt install pkg-config +sudo apt install -y libmysqlclient-dev +sudo /databricks/python3/bin/pip3 install --upgrade pip +sudo /databricks/python3/bin/pip3 install mysqlclient xgboost +sudo /databricks/python3/bin/pip3 install optuna joblib joblibspark + +if [[ $DB_IS_DRIVER = "TRUE" ]]; then + # create optuna database and study + sudo mysql -u $OPTUNA_USER -p$OPTUNA_PASSWORD -e "CREATE DATABASE IF NOT EXISTS optuna;" +fi +set +x diff --git a/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/databricks/start_cluster.sh b/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/databricks/start_cluster.sh new file mode 100755 index 00000000..998ad7e3 --- /dev/null +++ b/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/databricks/start_cluster.sh @@ -0,0 +1,58 @@ +#!/bin/bash +# Copyright (c) 2024, NVIDIA CORPORATION. + +if [[ -z ${INIT_PATH} ]]; then + echo "Please export INIT_PATH per README.md" + exit 1 +fi + +json_config=$(cat <\n", + "\n", + "# Distributed Hyperparameter Tuning: Optuna + Spark Dataframes\n", + "\n", + "\n", + "This demo demonstrates distributed hyperparameter tuning for XGBoost using Spark Dataframes. \n", + "We implement best practices to precompute data and maximize computations on the GPU. \n", + "\n", + "Reference: https://forecastegy.com/posts/xgboost-hyperparameter-tuning-with-optuna/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Note:\n", + "Before running, please make sure you've followed the relevant [setup instructions](../README.md) for your environment (standalone or databricks).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Iterable, List, Dict, Optional, Union, Sequence, Any\n", + "import math\n", + "import os\n", + "import requests\n", + "import pandas as pd\n", + "import optuna\n", + "from optuna.samplers import TPESampler\n", + "import xgboost as xgb\n", + "from pyspark.sql import SparkSession, DataFrame\n", + "from pyspark import TaskContext, SparkConf\n", + "from pyspark.sql.types import StructType, StructField, DoubleType, IntegerType, StringType, BooleanType" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download the dataset\n", + "\n", + "We'll use the [red wine quality dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv) to regress wine quality based on features such as acidity, sugar content, etc. \n", + "\n", + "**Note**: This example uses a small dataset for demonstration purposes. The performance advantages of distributed training are best realized with large datasets and computational workloads." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "cwd = os.getcwd()\n", + "os.mkdir(os.path.join(cwd, \"data\")) if not os.path.exists(os.path.join(cwd, \"data\")) else None\n", + "filepath = os.path.join(cwd, \"data\", \"winequality-red.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File downloaded and saved to /home/rishic/Code/myforks/spark-rapids-examples/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/data/winequality-red.csv\n" + ] + } + ], + "source": [ + "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n", + "\n", + "response = requests.get(url)\n", + "if response.status_code == 200:\n", + " with open(filepath, \"wb\") as f:\n", + " f.write(response.content)\n", + " print(f\"File downloaded and saved to {filepath}\")\n", + "else:\n", + " print(f\"Failed to download the file. Status code: {response.status_code}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1. Running Optuna locally" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import cudf\n", + "from cuml.metrics.regression import mean_squared_error\n", + "from cuml.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
07.40.700.001.90.07611.034.00.99783.510.569.45
17.80.880.002.60.09825.067.00.99683.200.689.85
27.80.760.042.30.09215.054.00.99703.260.659.85
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\n", + "
" + ], + "text/plain": [ + " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", + "0 7.4 0.70 0.00 1.9 0.076 \n", + "1 7.8 0.88 0.00 2.6 0.098 \n", + "2 7.8 0.76 0.04 2.3 0.092 \n", + "3 11.2 0.28 0.56 1.9 0.075 \n", + "4 7.4 0.70 0.00 1.9 0.076 \n", + "\n", + " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", + "0 11.0 34.0 0.9978 3.51 0.56 \n", + "1 25.0 67.0 0.9968 3.20 0.68 \n", + "2 15.0 54.0 0.9970 3.26 0.65 \n", + "3 17.0 60.0 0.9980 3.16 0.58 \n", + "4 11.0 34.0 0.9978 3.51 0.56 \n", + "\n", + " alcohol quality \n", + "0 9.4 5 \n", + "1 9.8 5 \n", + "2 9.8 5 \n", + "3 9.8 6 \n", + "4 9.4 5 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = cudf.read_csv(filepath, delimiter=\";\")\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prepare the train/validation sets. Precompute the Quantile DMatrix, which is used by histogram-based tree methods to save memory." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X = data.iloc[:, :-1].values\n", + "y = data[\"quality\"].values\n", + "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "Xy_train_qdm = xgb.QuantileDMatrix(X_train, y_train) # Precompute Quantile DMatrix to avoid repeated quantization every trial." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Objective function\n", + "\n", + "We define the objective and a hyperparameter search space to optimize via the `trial.suggest_` methods. \n", + "\n", + "In each trial, new hyperparameters will be suggested based on previous results. See [optuna.trial.Trial](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html) API for a full list of functions." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def objective(trial):\n", + " params = {\n", + " \"objective\": \"reg:squarederror\",\n", + " \"verbosity\": 0,\n", + " \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-3, 0.1, log=True),\n", + " \"max_depth\": trial.suggest_int(\"max_depth\", 1, 10),\n", + " \"subsample\": trial.suggest_float(\"subsample\", 0.05, 1.0),\n", + " \"colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.05, 1.0),\n", + " \"min_child_weight\": trial.suggest_int(\"min_child_weight\", 1, 20),\n", + " \"tree_method\": \"gpu_hist\",\n", + " \"device\": \"cuda\",\n", + " }\n", + "\n", + " booster = xgb.train(params=params, dtrain=Xy_train_qdm, num_boost_round=trial.suggest_int(\"num_boost_round\", 100, 500))\n", + " predictions = booster.inplace_predict(X_val)\n", + " rmse = mean_squared_error(y_val, predictions, squared=False).get()\n", + " \n", + " return rmse " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the study and optimize. By default, the study results will be stored in memory." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-12-11 23:47:48,356] A new study created in memory with name: optuna-xgboost-local\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-12-11 23:47:48,724] Trial 0 finished with value: 0.6377619522504244 and parameters: {'learning_rate': 0.005611516415334507, 'max_depth': 10, 'subsample': 0.7453942447208348, 'colsample_bytree': 0.6187255599871848, 'min_child_weight': 4, 'num_boost_round': 162}. Best is trial 0 with value: 0.6377619522504244.\n", + "[I 2024-12-11 23:47:49,676] Trial 1 finished with value: 0.6703788974319568 and parameters: {'learning_rate': 0.0013066739238053278, 'max_depth': 9, 'subsample': 0.6210592611560484, 'colsample_bytree': 0.7226689489062432, 'min_child_weight': 1, 'num_boost_round': 488}. Best is trial 0 with value: 0.6377619522504244.\n", + "[I 2024-12-11 23:47:49,819] Trial 2 finished with value: 0.6181751362616256 and parameters: {'learning_rate': 0.04622589001020832, 'max_depth': 3, 'subsample': 0.2227337188467456, 'colsample_bytree': 0.22423428436076215, 'min_child_weight': 7, 'num_boost_round': 310}. Best is trial 2 with value: 0.6181751362616256.\n", + "[I 2024-12-11 23:47:49,942] Trial 3 finished with value: 0.6698576232920956 and parameters: {'learning_rate': 0.007309539835912915, 'max_depth': 3, 'subsample': 0.6312602499862605, 'colsample_bytree': 0.18251916761943976, 'min_child_weight': 6, 'num_boost_round': 246}. Best is trial 2 with value: 0.6181751362616256.\n", + "[I 2024-12-11 23:47:50,060] Trial 4 finished with value: 0.6704590546150145 and parameters: {'learning_rate': 0.008168455894760165, 'max_depth': 8, 'subsample': 0.23969009305044175, 'colsample_bytree': 0.538522716492931, 'min_child_weight': 12, 'num_boost_round': 118}. Best is trial 2 with value: 0.6181751362616256.\n", + "[I 2024-12-11 23:47:50,214] Trial 5 finished with value: 0.6088806682631155 and parameters: {'learning_rate': 0.016409286730647923, 'max_depth': 2, 'subsample': 0.11179901333601554, 'colsample_bytree': 0.9514412603906666, 'min_child_weight': 20, 'num_boost_round': 424}. Best is trial 5 with value: 0.6088806682631155.\n", + "[I 2024-12-11 23:47:50,289] Trial 6 finished with value: 0.7103495949713845 and parameters: {'learning_rate': 0.0040665633135147945, 'max_depth': 1, 'subsample': 0.700021375186549, 'colsample_bytree': 0.4681448690526212, 'min_child_weight': 3, 'num_boost_round': 298}. Best is trial 5 with value: 0.6088806682631155.\n", + "[I 2024-12-11 23:47:50,693] Trial 7 finished with value: 0.7255199474722185 and parameters: {'learning_rate': 0.001171593739230706, 'max_depth': 10, 'subsample': 0.29584098252001606, 'colsample_bytree': 0.6793961701362828, 'min_child_weight': 7, 'num_boost_round': 308}. Best is trial 5 with value: 0.6088806682631155.\n", + "[I 2024-12-11 23:47:50,858] Trial 8 finished with value: 0.6060010014477214 and parameters: {'learning_rate': 0.0123999678368461, 'max_depth': 2, 'subsample': 0.9711053963763306, 'colsample_bytree': 0.7863761821930588, 'min_child_weight': 19, 'num_boost_round': 458}. Best is trial 8 with value: 0.6060010014477214.\n", + "[I 2024-12-11 23:47:51,199] Trial 9 finished with value: 0.6292433375858283 and parameters: {'learning_rate': 0.015696396388661146, 'max_depth': 10, 'subsample': 0.13406787694932354, 'colsample_bytree': 0.23618371929818793, 'min_child_weight': 1, 'num_boost_round': 230}. Best is trial 8 with value: 0.6060010014477214.\n" + ] + } + ], + "source": [ + "study = optuna.create_study(study_name=\"optuna-xgboost-local\", sampler=TPESampler(seed=42))\n", + "study.optimize(objective, n_trials=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best RMSE: 0.6060010014477214\n", + "Best hyperparameters: {'learning_rate': 0.0123999678368461, 'max_depth': 2, 'subsample': 0.9711053963763306, 'colsample_bytree': 0.7863761821930588, 'min_child_weight': 19, 'num_boost_round': 458}\n" + ] + } + ], + "source": [ + "trial = study.best_trial\n", + "print(\"Best RMSE: \", trial.value)\n", + "print(\"Best hyperparameters: \", trial.params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2. Distributed Optuna on Spark " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PySpark\n", + "\n", + "For standalone users, we need to create the Spark session with the Spark-Rapids plugin. For Databricks users, the Spark session will be preconfigured and this cell can be skipped." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plugin file already exists. Skipping download.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "24/12/11 23:47:51 WARN Utils: Your hostname, cb4ae00-lcedt resolves to a loopback address: 127.0.1.1; using 10.110.47.100 instead (on interface eno1)\n", + "24/12/11 23:47:51 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address\n", + "24/12/11 23:47:52 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", + "24/12/11 23:47:52 WARN RapidsPluginUtils: RAPIDS Accelerator 24.10.1 using cudf 24.10.0, private revision bd4e99e18e20234ee0c54f95f4b0bfce18a6255e\n", + "24/12/11 23:47:52 WARN RapidsPluginUtils: RAPIDS Accelerator is enabled, to disable GPU support set `spark.rapids.sql.enabled` to false.\n" + ] + } + ], + "source": [ + "def get_rapids_jar():\n", + " SPARK_RAPIDS_VERSION = \"24.10.1\"\n", + " rapids_jar = f\"rapids-4-spark_2.12-{SPARK_RAPIDS_VERSION}.jar\"\n", + " if not os.path.exists(rapids_jar):\n", + " print(\"Downloading Spark Rapids jar\")\n", + " url = f\"https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/{SPARK_RAPIDS_VERSION}/{rapids_jar}\"\n", + " response = requests.get(url)\n", + " if response.status_code == 200:\n", + " with open(rapids_jar, \"wb\") as f:\n", + " f.write(response.content)\n", + " print(f\"File '{rapids_jar}' downloaded and saved successfully.\")\n", + " else:\n", + " print(f\"Failed to download the plugin. Status code: {response.status_code}\")\n", + " else:\n", + " print(\"Plugin file already exists. Skipping download.\")\n", + " return rapids_jar\n", + "\n", + "def initialize_spark(rapids_jar: str):\n", + " import socket\n", + " hostname = socket.gethostname()\n", + " conda_env = os.environ.get(\"CONDA_PREFIX\")\n", + "\n", + " conf = SparkConf()\n", + " conf.setMaster(f\"spark://{hostname}:7077\") # Assuming master is on host and default port. \n", + " conf.set(\"spark.task.maxFailures\", \"1\")\n", + " conf.set(\"spark.task.resource.gpu.amount\", f\"{1/4}\") # Setting to 1/4 for single-node demo. In practice, set to 1. \n", + " conf.set(\"spark.executor.resource.gpu.amount\", \"1\")\n", + " conf.set(\"spark.sql.execution.arrow.pyspark.enabled\", \"true\")\n", + " conf.set(\"spark.pyspark.python\", f\"{conda_env}/bin/python\")\n", + " conf.set(\"spark.pyspark.driver.python\", f\"{conda_env}/bin/python\")\n", + " conf.set(\"spark.jars\", rapids_jar)\n", + " conf.set(\"spark.executorEnv.PYTHONPATH\", rapids_jar)\n", + " conf.set(\"spark.rapids.memory.gpu.minAllocFraction\", \"0.0001\")\n", + " conf.set(\"spark.plugins\", \"com.nvidia.spark.SQLPlugin\")\n", + " conf.set(\"spark.locality.wait\", \"0s\")\n", + " conf.set(\"spark.sql.cache.serializer\", \"com.nvidia.spark.ParquetCachedBatchSerializer\")\n", + " conf.set(\"spark.rapids.memory.gpu.pooling.enabled\", \"false\")\n", + " conf.set(\"spark.sql.execution.sortBeforeRepartition\", \"false\")\n", + " conf.set(\"spark.rapids.sql.format.parquet.reader.type\", \"MULTITHREADED\")\n", + " conf.set(\"spark.rapids.sql.format.parquet.multiThreadedRead.maxNumFilesParallel\", \"20\")\n", + " conf.set(\"spark.rapids.sql.multiThreadedRead.numThreads\", \"20\")\n", + " conf.set(\"spark.rapids.sql.python.gpu.enabled\", \"true\")\n", + " conf.set(\"spark.rapids.memory.pinnedPool.size\", \"2G\")\n", + " conf.set(\"spark.python.daemon.module\", \"rapids.daemon\")\n", + " conf.set(\"spark.rapids.sql.batchSizeBytes\", \"512m\")\n", + " conf.set(\"spark.sql.adaptive.enabled\", \"false\")\n", + " conf.set(\"spark.sql.files.maxPartitionBytes\", \"512m\")\n", + " conf.set(\"spark.rapids.sql.concurrentGpuTasks\", \"2\")\n", + " conf.set(\"spark.rapids.sql.explain\", \"NONE\")\n", + " \n", + " spark = SparkSession.builder.appName(\"optuna-spark-xgboost\").config(conf=conf).getOrCreate()\n", + " return spark\n", + "\n", + "if 'spark' not in globals():\n", + " rapids_jar = get_rapids_jar()\n", + " spark = initialize_spark(rapids_jar)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Helper Class\n", + "\n", + "First we'll define a helper class. This will store the hyperparameters we want optimized in each trial, and easily convert that into a schema for the output dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "class OptunaParams:\n", + " def __init__(self):\n", + " self.hyperparameters = {}\n", + "\n", + " def add_categorical_param(self, name: str, choices: Sequence[Union[None, bool, int, float, str]]):\n", + " \"\"\"\n", + " Adds a categorical hyperparameter to be tuned via Optuna's trial.suggest_categorical().\n", + " \"\"\"\n", + " self.hyperparameters[name] = { \"type\": \"categorical\", \"choices\": choices }\n", + " \n", + " def add_int_param(self, name: str, low: int, high: int, step: int = 1, log: bool = False):\n", + " \"\"\"\n", + " Adds an integer hyperparameter to be tuned via Optuna's trial.suggest_int().\n", + " \"\"\"\n", + " self.hyperparameters[name] = { \"type\": \"int\", \"low\": low, \"high\": high, \"step\": step, \"log\": log }\n", + " \n", + " def add_float_param(self, name: str, low: float, high: float, step: Optional[float] = None, log: bool = False):\n", + " \"\"\"\n", + " Adds a float hyperparameter to be tuned via Optuna's trial.suggest_float().\n", + " \"\"\"\n", + " self.hyperparameters[name] = { \"type\": \"float\", \"low\": low, \"high\": high, \"step\": step,\"log\": log }\n", + "\n", + " def suggest_params(self, trial) -> Dict[str, Union[int, float, str, bool]]:\n", + " \"\"\"\n", + " Converts the hyperparameter space into a dictionary of suggested values in Optuna format,\n", + " to be called within the objective function.\n", + " \"\"\"\n", + " suggested_params = {}\n", + " for name, config in self.hyperparameters.items():\n", + " if config[\"type\"] == \"categorical\":\n", + " suggested_params[name] = trial.suggest_categorical(name, config[\"choices\"])\n", + " elif config[\"type\"] == \"int\":\n", + " suggested_params[name] = trial.suggest_int(\n", + " name, config[\"low\"], config[\"high\"], step=config[\"step\"], log=config[\"log\"]\n", + " )\n", + " elif config[\"type\"] == \"float\":\n", + " suggested_params[name] = trial.suggest_float(\n", + " name, config[\"low\"], config[\"high\"], step=config.get(\"step\", None), log=config[\"log\"]\n", + " )\n", + " return suggested_params\n", + "\n", + " def to_schema(self) -> StructType:\n", + " \"\"\"\n", + " Converts the hyperparameter space into a Spark StructType output schema.\n", + " \"\"\"\n", + " fields = []\n", + " for name, config in self.hyperparameters.items():\n", + " if config[\"type\"] == \"float\":\n", + " fields.append(StructField(name, DoubleType(), False))\n", + " elif config[\"type\"] == \"int\":\n", + " fields.append(StructField(name, IntegerType(), False))\n", + " elif config[\"type\"] == \"categorical\":\n", + " if isinstance(config[\"choices\"][0], str):\n", + " fields.append(StructField(name, StringType(), False))\n", + " elif isinstance(config[\"choices\"][0], bool):\n", + " fields.append(StructField(name, BooleanType(), False))\n", + " elif isinstance(config[\"choices\"][0], (int, float)):\n", + " fields.append(StructField(name, DoubleType(), False))\n", + " else:\n", + " raise ValueError(f\"Unsupported categorical type for field {name}\")\n", + " \n", + " # Study will also return the best achieved loss:\n", + " fields.append(StructField(\"best_value\", DoubleType(), False)) \n", + " return StructType(fields)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optuna Task\n", + "\n", + "This implementation demonstrates **Spark I/O**.\n", + "\n", + "This means that Spark will read the dataset and create a duplicate of the dataset for each worker (1 partition = 1 duplicate), then map the tuning task onto each partition. \n", + "In practice, this enables the code to be chained to other Dataframe operations (e.g. ETL stages) without the intermediate step of writing to DBFS, at the cost of some overhead during duplication.\n", + "\n", + "For the alternative implementation using **Worker I/O**, see the [JoblibSpark notebook](optuna-joblibspark.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the task, each worker will:\n", + "1. Concatenate the pandas partition batches to form the dataset\n", + "2. Load the study from the MySQL storage backend\n", + "3. Optimize over the objective for the assigned number of trials, sending results back to the database after each iteration" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def task_udf(pdf_iter: Iterable[pd.DataFrame],\n", + " xgb_params: Dict[str, Any],\n", + " optuna_params: OptunaParams,\n", + " trials_per_task: List[int], \n", + " driver_ip: str,\n", + " study_name: str,\n", + " seed: int) -> Iterable[pd.DataFrame]:\n", + "\n", + " import cudf\n", + " from cuml.metrics.regression import mean_squared_error\n", + " from cuml.model_selection import train_test_split\n", + " \n", + " tc = TaskContext.get()\n", + " assert \"gpu\" in tc.resources(), \"GPU resource not found.\"\n", + " num_trials = trials_per_task[tc.partitionId()]\n", + "\n", + " df_list = []\n", + " for pdf in pdf_iter:\n", + " df_list.append(cudf.DataFrame.from_pandas(pdf))\n", + " \n", + " data = cudf.concat(df_list)\n", + " X = data.iloc[:, :-1].values\n", + " y = data[\"quality\"].values\n", + " X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + " tuning_max_bin = \"max_bin\" in optuna_params.hyperparameters\n", + " if not tuning_max_bin:\n", + " max_bin = xgb_params.get(\"max_bin\", 256)\n", + " # Precompute Quantile DMatrix to avoid repeated quantization every trial.\n", + " Xy_train_qdm = xgb.QuantileDMatrix(X_train, y_train, max_bin=max_bin)\n", + "\n", + " def objective(trial):\n", + " tuning_params = optuna_params.suggest_params(trial)\n", + " xgb_params.update(tuning_params)\n", + "\n", + " if tuning_max_bin:\n", + " # If tuning the max_bin param, we must recompute the QDM every trial, since the quantiles change.\n", + " if \"n_estimators\" not in xgb_params:\n", + " xgb_params[\"n_estimators\"] = 100 # Default value if not tuning.\n", + "\n", + " model = xgb.XGBRegressor(**xgb_params)\n", + " model.fit(X_train, y_train)\n", + " booster = model.get_booster()\n", + " else:\n", + " # Train the model with xgb.train() API using the precomputed QDM.\n", + " num_boost_round = xgb_params.get(\"n_estimators\", 100)\n", + " booster = xgb.train(params=xgb_params, dtrain=Xy_train_qdm, num_boost_round=num_boost_round)\n", + " \n", + " predictions = booster.inplace_predict(X_val)\n", + " rmse = mean_squared_error(y_val, predictions, squared=False).get()\n", + " \n", + " return rmse\n", + "\n", + " study = optuna.load_study(\n", + " study_name=study_name,\n", + " storage=f\"mysql://optuna_user:optuna_password@{driver_ip}/optuna\",\n", + " sampler=TPESampler(seed=seed),\n", + " )\n", + "\n", + " print(f\"Running {num_trials} trials on partition {tc.partitionId()}.\")\n", + " study.optimize(objective, n_trials=num_trials)\n", + "\n", + " result_dict = {f\"{key}\": [value] for key, value in study.best_params.items()}\n", + " result_dict['best_value'] = [study.best_value]\n", + " \n", + " yield pd.DataFrame(result_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup and run the Optuna study" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the driver IP for the MySQL database. \n", + "- For standalone users, make sure you've followed the [database setup instructions](../README.md#setup-database-for-optuna). The database should be on 'localhost'. \n", + "- For databricks users, the database should already be setup on the driver node by the init script." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# check if we're running on databricks\n", + "on_databricks = os.environ.get(\"DATABRICKS_RUNTIME_VERSION\", False)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MySQL database is hosted on localhost\n" + ] + } + ], + "source": [ + "if on_databricks:\n", + " driver_ip = spark.conf.get(\"spark.driver.host\")\n", + "else:\n", + " driver_ip = \"localhost\"\n", + "\n", + "print(f\"MySQL database is hosted on {driver_ip}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a new study, referencing the MySQL database as the storage backend." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-12-11 23:47:53,347] A new study created in RDB with name: optuna-xgboost-dataframe\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "study_name = \"optuna-xgboost-dataframe\"\n", + "seed = 42\n", + "\n", + "try:\n", + " # Delete the study if it already exists\n", + " optuna.delete_study(\n", + " study_name=study_name, \n", + " storage=f\"mysql://optuna_user:optuna_password@{driver_ip}/optuna\"\n", + " )\n", + "except:\n", + " pass\n", + "\n", + "optuna.create_study(\n", + " study_name=study_name,\n", + " storage=f\"mysql://optuna_user:optuna_password@{driver_ip}/optuna\",\n", + " sampler=TPESampler(seed=seed)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the number of tasks, number of trials, and trials per task. \n", + "\n", + "**NOTE**: for standalone users running on a single worker, the 4 tasks will all be assigned to the same worker and will time-share the GPU for demonstration. In practice, you should set `spark.task.resource.gpu.amount=1` and set num_tasks to the number of workers in the cluster so that each task gets full access to the GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def partition_trials(total_trials: int, total_tasks: int) -> List[int]:\n", + " base_size = total_trials // total_tasks\n", + " extra = total_trials % total_tasks\n", + " partitions = [base_size] * total_tasks\n", + " for i in range(extra):\n", + " partitions[i] += 1\n", + " \n", + " return partitions" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trials per task: [25, 25, 25, 25]\n" + ] + } + ], + "source": [ + "num_tasks = 4\n", + "num_trials = 100\n", + "trials_per_task = partition_trials(num_trials, num_tasks)\n", + "print(f\"Trials per task: {trials_per_task}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define params\n", + "Define the XGBoost model params and the hyperparams for Optuna to tune. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Keep these params consistent:\n", + "xgb_params = {\n", + " \"objective\": \"reg:squarederror\",\n", + " \"verbosity\": 0,\n", + " \"tree_method\": \"gpu_hist\",\n", + " \"device\": \"cuda\",\n", + " \"seed\": seed,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Tune these params:\n", + "hyperparams = OptunaParams()\n", + "hyperparams.add_int_param(\"n_estimators\", low=100, high=500)\n", + "hyperparams.add_float_param(\"learning_rate\", low=1e-3, high=0.1, log=True)\n", + "hyperparams.add_int_param(\"max_depth\", low=1, high=10)\n", + "hyperparams.add_float_param(\"subsample\", low=0.05, high=1.0)\n", + "hyperparams.add_float_param(\"colsample_bytree\", low=0.05, high=1.0)\n", + "hyperparams.add_int_param(\"min_child_weight\", low=1, high=20)\n", + "\n", + "out_schema = hyperparams.to_schema()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll also define the following helper function, which will create duplicates of the dataframe held in separate partitions." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def coalesce_tree_union(df: DataFrame, num_duplicates: int):\n", + " \"\"\"\n", + " Coalesce the DataFrame to a single partition and recursively self-union to create duplicates.\n", + " \"\"\"\n", + " input_df = df.coalesce(1).cache()\n", + " current_df = input_df\n", + " \n", + " if num_duplicates <= 1:\n", + " return current_df\n", + "\n", + " recursions = int(math.log(num_duplicates, 2))\n", + " remainder = num_duplicates - 2 ** recursions\n", + "\n", + " for _ in range(recursions):\n", + " current_df = current_df.union(current_df)\n", + "\n", + " for _ in range(remainder):\n", + " current_df = current_df.union(input_df)\n", + " \n", + " return current_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load dataset\n", + "\n", + "Read the data from the local directory with Spark and then duplicate it to prepare to run the task." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "if on_databricks:\n", + " # once the dataset is in dbfs, databricks appends \"dbfs:\" to the filepath automatically\n", + " filepath = '/FileStore/optuna-data/winequality-red.csv'\n", + "else:\n", + " cwd = os.getcwd()\n", + " filepath = os.path.join(cwd, \"data\", \"winequality-red.csv\")\n", + "\n", + "in_schema = StructType([\n", + " StructField(\"fixed acidity\", DoubleType(), True),\n", + " StructField(\"volatile acidity\", DoubleType(), True),\n", + " StructField(\"citric acid\", DoubleType(), True),\n", + " StructField(\"residual sugar\", DoubleType(), True),\n", + " StructField(\"chlorides\", DoubleType(), True),\n", + " StructField(\"free sulfur dioxide\", DoubleType(), True),\n", + " StructField(\"total sulfur dioxide\", DoubleType(), True),\n", + " StructField(\"density\", DoubleType(), True),\n", + " StructField(\"pH\", DoubleType(), True),\n", + " StructField(\"sulphates\", DoubleType(), True),\n", + " StructField(\"alcohol\", DoubleType(), True),\n", + " StructField(\"quality\", IntegerType(), True)\n", + "])\n", + "\n", + "data_df = spark.read.csv(filepath, header=True, schema=in_schema, sep=\";\")\n", + "data_df = coalesce_tree_union(data_df, num_duplicates=num_tasks) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the study\n", + "\n", + "Map the Optuna task onto the dataframe and collect the results (it might take a few minutes)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "result_df = data_df.mapInPandas(lambda pdf_iter: \n", + " task_udf(pdf_iter,\n", + " xgb_params=xgb_params,\n", + " optuna_params=hyperparams,\n", + " trials_per_task=trials_per_task,\n", + " driver_ip=driver_ip,\n", + " study_name=study_name,\n", + " seed=seed),\n", + " schema=out_schema).toPandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'n_estimators': 419.0, 'learning_rate': 0.015039610889407229, 'max_depth': 10.0, 'subsample': 0.6630214978050138, 'colsample_bytree': 0.8524338650689898, 'min_child_weight': 2.0}\n", + "Best value: 0.533100375625104\n" + ] + } + ], + "source": [ + "results = result_df.iloc[0].to_dict()\n", + "best_value = results.pop(\"best_value\")\n", + "\n", + "print(f\"Best parameters: {results}\")\n", + "print(f\"Best value: {best_value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "optuna-spark", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-joblibspark.ipynb b/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-joblibspark.ipynb new file mode 100644 index 00000000..ffb468f5 --- /dev/null +++ b/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-joblibspark.ipynb @@ -0,0 +1,868 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# Copyright (c) 2024, NVIDIA CORPORATION.\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Distributed Hyperparameter Tuning: Optuna + JoblibSpark\n", + "\n", + "\n", + "This demo demonstrates distributed hyperparameter tuning for XGBoost using the [JoblibSpark backend](https://github.com/joblib/joblib-spark), building on this [example from Databricks](https://docs.databricks.com/en/machine-learning/automl-hyperparam-tuning/optuna.html). \n", + "We implement best practices to precompute data and maximize computations on the GPU. \n", + "\n", + "\n", + "\n", + "Reference: https://forecastegy.com/posts/xgboost-hyperparameter-tuning-with-optuna/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Note:\n", + "Before running, please make sure you've followed the relevant [setup instructions](../README.md) for your environment (standalone or databricks).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import List\n", + "import os\n", + "import requests\n", + "import joblib\n", + "from joblibspark import register_spark\n", + "import optuna\n", + "from optuna.samplers import TPESampler\n", + "import xgboost as xgb\n", + "from pyspark.sql import SparkSession\n", + "from pyspark import TaskContext, SparkConf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download the dataset\n", + "\n", + "We'll use the [red wine quality dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv) to regress wine quality based on features such as acidity, sugar content, etc. \n", + "\n", + "**Note**: This example uses a small dataset for demonstration purposes. The performance advantages of distributed training are best realized with large datasets and computational workloads." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "cwd = os.getcwd()\n", + "os.mkdir(os.path.join(cwd, \"data\")) if not os.path.exists(os.path.join(cwd, \"data\")) else None\n", + "filepath = os.path.join(cwd, \"data\", \"winequality-red.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File downloaded and saved to /home/rishic/Code/myforks/spark-rapids-examples/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/data/winequality-red.csv\n" + ] + } + ], + "source": [ + "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n", + "\n", + "response = requests.get(url)\n", + "if response.status_code == 200:\n", + " with open(filepath, \"wb\") as f:\n", + " f.write(response.content)\n", + " print(f\"File downloaded and saved to {filepath}\")\n", + "else:\n", + " print(f\"Failed to download the file. Status code: {response.status_code}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1. Running Optuna locally" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import cudf\n", + "from cuml.metrics.regression import mean_squared_error\n", + "from cuml.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
07.40.700.001.90.07611.034.00.99783.510.569.45
17.80.880.002.60.09825.067.00.99683.200.689.85
27.80.760.042.30.09215.054.00.99703.260.659.85
311.20.280.561.90.07517.060.00.99803.160.589.86
47.40.700.001.90.07611.034.00.99783.510.569.45
\n", + "
" + ], + "text/plain": [ + " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", + "0 7.4 0.70 0.00 1.9 0.076 \n", + "1 7.8 0.88 0.00 2.6 0.098 \n", + "2 7.8 0.76 0.04 2.3 0.092 \n", + "3 11.2 0.28 0.56 1.9 0.075 \n", + "4 7.4 0.70 0.00 1.9 0.076 \n", + "\n", + " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", + "0 11.0 34.0 0.9978 3.51 0.56 \n", + "1 25.0 67.0 0.9968 3.20 0.68 \n", + "2 15.0 54.0 0.9970 3.26 0.65 \n", + "3 17.0 60.0 0.9980 3.16 0.58 \n", + "4 11.0 34.0 0.9978 3.51 0.56 \n", + "\n", + " alcohol quality \n", + "0 9.4 5 \n", + "1 9.8 5 \n", + "2 9.8 5 \n", + "3 9.8 6 \n", + "4 9.4 5 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = cudf.read_csv(filepath, delimiter=\";\")\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prepare the train/validation sets. Precompute the Quantile DMatrix, which is used by histogram-based tree methods to save memory." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X = data.iloc[:, :-1].values\n", + "y = data[\"quality\"].values\n", + "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "Xy_train_qdm = xgb.QuantileDMatrix(X_train, y_train) # Precompute Quantile DMatrix to avoid repeated quantization every trial." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Objective function\n", + "\n", + "We define the objective and a hyperparameter search space to optimize via the `trial.suggest_` methods. \n", + "\n", + "In each trial, new hyperparameters will be suggested based on previous results. See [optuna.trial.Trial](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html) API for a full list of functions." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def objective(trial):\n", + " params = {\n", + " \"objective\": \"reg:squarederror\",\n", + " \"verbosity\": 0,\n", + " \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-3, 0.1, log=True),\n", + " \"max_depth\": trial.suggest_int(\"max_depth\", 1, 10),\n", + " \"subsample\": trial.suggest_float(\"subsample\", 0.05, 1.0),\n", + " \"colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.05, 1.0),\n", + " \"min_child_weight\": trial.suggest_int(\"min_child_weight\", 1, 20),\n", + " \"tree_method\": \"gpu_hist\",\n", + " \"device\": \"cuda\",\n", + " }\n", + "\n", + " booster = xgb.train(params=params, dtrain=Xy_train_qdm, num_boost_round=trial.suggest_int(\"num_boost_round\", 100, 500))\n", + " predictions = booster.inplace_predict(X_val)\n", + " rmse = mean_squared_error(y_val, predictions, squared=False).get()\n", + " \n", + " return rmse " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the study and optimize. By default, the study results will be stored in memory." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-12-11 23:42:09,341] A new study created in memory with name: optuna-xgboost-local\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-12-11 23:42:09,715] Trial 0 finished with value: 0.6377619522504244 and parameters: {'learning_rate': 0.005611516415334507, 'max_depth': 10, 'subsample': 0.7453942447208348, 'colsample_bytree': 0.6187255599871848, 'min_child_weight': 4, 'num_boost_round': 162}. Best is trial 0 with value: 0.6377619522504244.\n", + "[I 2024-12-11 23:42:10,666] Trial 1 finished with value: 0.6703788974319568 and parameters: {'learning_rate': 0.0013066739238053278, 'max_depth': 9, 'subsample': 0.6210592611560484, 'colsample_bytree': 0.7226689489062432, 'min_child_weight': 1, 'num_boost_round': 488}. Best is trial 0 with value: 0.6377619522504244.\n", + "[I 2024-12-11 23:42:10,806] Trial 2 finished with value: 0.6181751362616256 and parameters: {'learning_rate': 0.04622589001020832, 'max_depth': 3, 'subsample': 0.2227337188467456, 'colsample_bytree': 0.22423428436076215, 'min_child_weight': 7, 'num_boost_round': 310}. Best is trial 2 with value: 0.6181751362616256.\n", + "[I 2024-12-11 23:42:10,922] Trial 3 finished with value: 0.6698576232920956 and parameters: {'learning_rate': 0.007309539835912915, 'max_depth': 3, 'subsample': 0.6312602499862605, 'colsample_bytree': 0.18251916761943976, 'min_child_weight': 6, 'num_boost_round': 246}. Best is trial 2 with value: 0.6181751362616256.\n", + "[I 2024-12-11 23:42:11,039] Trial 4 finished with value: 0.6704590546150145 and parameters: {'learning_rate': 0.008168455894760165, 'max_depth': 8, 'subsample': 0.23969009305044175, 'colsample_bytree': 0.538522716492931, 'min_child_weight': 12, 'num_boost_round': 118}. Best is trial 2 with value: 0.6181751362616256.\n", + "[I 2024-12-11 23:42:11,191] Trial 5 finished with value: 0.6088806682631155 and parameters: {'learning_rate': 0.016409286730647923, 'max_depth': 2, 'subsample': 0.11179901333601554, 'colsample_bytree': 0.9514412603906666, 'min_child_weight': 20, 'num_boost_round': 424}. Best is trial 5 with value: 0.6088806682631155.\n", + "[I 2024-12-11 23:42:11,266] Trial 6 finished with value: 0.7103495949713845 and parameters: {'learning_rate': 0.0040665633135147945, 'max_depth': 1, 'subsample': 0.700021375186549, 'colsample_bytree': 0.4681448690526212, 'min_child_weight': 3, 'num_boost_round': 298}. Best is trial 5 with value: 0.6088806682631155.\n", + "[I 2024-12-11 23:42:11,666] Trial 7 finished with value: 0.7255199474722185 and parameters: {'learning_rate': 0.001171593739230706, 'max_depth': 10, 'subsample': 0.29584098252001606, 'colsample_bytree': 0.6793961701362828, 'min_child_weight': 7, 'num_boost_round': 308}. Best is trial 5 with value: 0.6088806682631155.\n", + "[I 2024-12-11 23:42:11,829] Trial 8 finished with value: 0.6060010014477214 and parameters: {'learning_rate': 0.0123999678368461, 'max_depth': 2, 'subsample': 0.9711053963763306, 'colsample_bytree': 0.7863761821930588, 'min_child_weight': 19, 'num_boost_round': 458}. Best is trial 8 with value: 0.6060010014477214.\n", + "[I 2024-12-11 23:42:12,168] Trial 9 finished with value: 0.6292433375858283 and parameters: {'learning_rate': 0.015696396388661146, 'max_depth': 10, 'subsample': 0.13406787694932354, 'colsample_bytree': 0.23618371929818793, 'min_child_weight': 1, 'num_boost_round': 230}. Best is trial 8 with value: 0.6060010014477214.\n" + ] + } + ], + "source": [ + "study = optuna.create_study(study_name=\"optuna-xgboost-local\", sampler=TPESampler(seed=42))\n", + "study.optimize(objective, n_trials=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best RMSE: 0.6060010014477214\n", + "Best hyperparameters: {'learning_rate': 0.0123999678368461, 'max_depth': 2, 'subsample': 0.9711053963763306, 'colsample_bytree': 0.7863761821930588, 'min_child_weight': 19, 'num_boost_round': 458}\n" + ] + } + ], + "source": [ + "trial = study.best_trial\n", + "print(\"Best RMSE: \", trial.value)\n", + "print(\"Best hyperparameters: \", trial.params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2. Distributed Optuna on Spark " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PySpark\n", + "\n", + "For standalone users, we need to create the Spark session. For Databricks users, the Spark session will be preconfigured." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "24/12/11 23:42:12 WARN Utils: Your hostname, cb4ae00-lcedt resolves to a loopback address: 127.0.1.1; using 10.110.47.100 instead (on interface eno1)\n", + "24/12/11 23:42:12 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", + "24/12/11 23:42:13 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n" + ] + } + ], + "source": [ + "def initialize_spark():\n", + " import socket\n", + " hostname = socket.gethostname()\n", + " conda_env = os.environ.get(\"CONDA_PREFIX\")\n", + "\n", + " conf = SparkConf()\n", + " conf.setMaster(f\"spark://{hostname}:7077\") # Assuming master is on host and default port. \n", + " conf.set(\"spark.task.maxFailures\", \"1\")\n", + " conf.set(\"spark.task.resource.gpu.amount\", \"1\")\n", + " conf.set(\"spark.executor.resource.gpu.amount\", \"1\")\n", + " conf.set(\"spark.pyspark.python\", f\"{conda_env}/bin/python\")\n", + " conf.set(\"spark.pyspark.driver.python\", f\"{conda_env}/bin/python\")\n", + " \n", + " spark = SparkSession.builder.appName(\"optuna-joblibspark-xgboost\").config(conf=conf).getOrCreate()\n", + " return spark\n", + "\n", + "if 'spark' not in globals():\n", + " spark = initialize_spark()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optuna Task\n", + "\n", + "This implementation demonstrates **Worker I/O**. \n", + "\n", + "This means that each worker will read the full dataset from the filepath rather than passing the data in a dataframe. \n", + "In practice, this requires the dataset to be written to a distributed file system accessible to all workers prior to tuning. \n", + "\n", + "For the alternative implementation using **Spark I/O**, see the [Spark Dataframe notebook](optuna-dataframe.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the task, each worker will:\n", + "1. Read the dataset from the filepath\n", + "2. Load the study from the MySQL storage backend\n", + "3. Optimize over the objective for the assigned number of trials, sending results back to the database after each iteration\n", + "\n", + "Here we use Optuna's [Define-and-Run](https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/009_ask_and_tell.html#define-and-run) API, which allows us to predefine the hyperparameter space and pass it to the task." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def task(num_trials: int, xgb_params: dict, optuna_params: dict, driver_ip: str, study_name: str, seed: int, filepath: str):\n", + " import cudf\n", + " from cuml.metrics.regression import mean_squared_error\n", + " from cuml.model_selection import train_test_split\n", + "\n", + " tc = TaskContext.get()\n", + " assert \"gpu\" in tc.resources(), \"GPU resource not found.\"\n", + "\n", + " if filepath.startswith(\"/dbfs/\"):\n", + " # Check to ensure GPU direct storage is disabled for cuDF on databricks.\n", + " libcudf_policy = os.environ.get('LIBCUDF_CUFILE_POLICY')\n", + " if libcudf_policy != 'OFF':\n", + " raise RuntimeError(\"Set LIBCUDF_CUFILE_POLICY=OFF to read from DBFS with cuDF.\")\n", + " \n", + " data = cudf.read_csv(filepath, delimiter=\";\")\n", + " X = data.iloc[:, :-1].values\n", + " y = data[\"quality\"].values\n", + " X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=seed)\n", + "\n", + " tuning_max_bin = \"max_bin\" in optuna_params\n", + " if not tuning_max_bin:\n", + " max_bin = xgb_params.get(\"max_bin\", 256)\n", + " # Precompute Quantile DMatrix to avoid repeated quantization every trial.\n", + " Xy_train_qdm = xgb.QuantileDMatrix(X_train, y_train, max_bin=max_bin)\n", + "\n", + " study = optuna.load_study(\n", + " study_name=study_name,\n", + " storage=f\"mysql://optuna_user:optuna_password@{driver_ip}/optuna\",\n", + " sampler=TPESampler(seed=seed),\n", + " )\n", + "\n", + " print(f\"Running {num_trials} trials on partition {tc.partitionId()}.\")\n", + "\n", + " ### Objective ###\n", + " for _ in range(num_trials):\n", + " trial = study.ask(optuna_params)\n", + " xgb_params.update(trial.params)\n", + "\n", + " if tuning_max_bin:\n", + " # If tuning the max_bin param, we must recompute the QDM every trial.\n", + " if \"n_estimators\" not in xgb_params:\n", + " xgb_params[\"n_estimators\"] = 100 # Default value if not tuning.\n", + "\n", + " model = xgb.XGBRegressor(**xgb_params)\n", + " model.fit(X_train, y_train)\n", + " booster = model.get_booster()\n", + " else:\n", + " # Train the model with xgb.train() API using the precomputed QDM.\n", + " num_boost_round = xgb_params.get(\"n_estimators\", 100)\n", + " booster = xgb.train(params=xgb_params, dtrain=Xy_train_qdm, num_boost_round=num_boost_round)\n", + " \n", + " # Perform in-place predictions on GPU using the booster.\n", + " predictions = booster.inplace_predict(X_val)\n", + " rmse = mean_squared_error(y_val, predictions, squared=False).get()\n", + " \n", + " study.tell(trial, rmse)\n", + "\n", + " return study.best_params, study.best_value" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# This will register the Spark Session with the Joblib Spark backend.\n", + "register_spark()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup and run the Optuna study" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the driver IP for the MySQL database. \n", + "- For standalone users, make sure you've followed the [database setup instructions](../README.md#setup-database-for-optuna). The database should be on 'localhost'. \n", + "- For databricks users, the database should already be setup on the driver node by the init script." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# check if we're running on databricks\n", + "on_databricks = os.environ.get(\"DATABRICKS_RUNTIME_VERSION\", False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MySQL database is hosted on localhost\n" + ] + } + ], + "source": [ + "if on_databricks:\n", + " driver_ip = spark.conf.get(\"spark.driver.host\")\n", + "else:\n", + " driver_ip = \"localhost\"\n", + "\n", + "print(f\"MySQL database is hosted on {driver_ip}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a new study, referencing the MySQL database as the storage backend." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2024-12-11 23:42:13,928] A new study created in RDB with name: optuna-xgboost-joblibspark\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "study_name = \"optuna-xgboost-joblibspark\"\n", + "seed = 42\n", + "\n", + "try:\n", + " # Delete the study if it already exists\n", + " optuna.delete_study(\n", + " study_name=study_name, \n", + " storage=f\"mysql://optuna_user:optuna_password@{driver_ip}/optuna\"\n", + " )\n", + "except:\n", + " pass\n", + "\n", + "optuna.create_study(\n", + " study_name=study_name,\n", + " storage=f\"mysql://optuna_user:optuna_password@{driver_ip}/optuna\",\n", + " sampler=TPESampler(seed=seed)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the number of tasks, number of trials, and trials per task. \n", + "\n", + "**NOTE**: for standalone users running on a single worker, the 4 tasks will all be assigned to the same worker and executed sequentially in this demonstration. This can easily be scaled up to run concurrently by adding more workers." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def partition_trials(total_trials: int, total_tasks: int) -> List[int]:\n", + " base_size = total_trials // total_tasks\n", + " extra = total_trials % total_tasks\n", + " partitions = [base_size] * total_tasks\n", + " for i in range(extra):\n", + " partitions[i] += 1\n", + " \n", + " return partitions" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trials per task: [25, 25, 25, 25]\n" + ] + } + ], + "source": [ + "num_tasks = 4\n", + "num_trials = 100\n", + "trials_per_task = partition_trials(num_trials, num_tasks)\n", + "print(f\"Trials per task: {trials_per_task}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define params\n", + "Define the XGBoost model params and the hyperparams for Optuna to tune. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Keep these params consistent:\n", + "xgb_params = {\n", + " \"objective\": \"reg:squarederror\",\n", + " \"verbosity\": 0,\n", + " \"tree_method\": \"gpu_hist\",\n", + " \"device\": f\"cuda\",\n", + " \"seed\": seed,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Tune these params:\n", + "optuna_params = {\n", + " \"n_estimators\": optuna.distributions.IntDistribution(100, 500),\n", + " \"learning_rate\": optuna.distributions.FloatDistribution(1e-3, 0.1, log=True),\n", + " \"max_depth\": optuna.distributions.IntDistribution(1, 10),\n", + " \"subsample\": optuna.distributions.FloatDistribution(0.05, 1.0),\n", + " \"colsample_bytree\": optuna.distributions.FloatDistribution(0.05, 1.0),\n", + " \"min_child_weight\": optuna.distributions.IntDistribution(1, 20),\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**For Databricks**: we must download the dataset to DBFS so that all workers can access it." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "if on_databricks:\n", + " dbutils.fs.mkdirs(\"/FileStore/optuna-data\")\n", + " filepath = \"/dbfs/FileStore/optuna-data/winequality-red.csv\"\n", + " url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n", + "\n", + " response = requests.get(url)\n", + " if response.status_code == 200:\n", + " with open(filepath, \"wb\") as f:\n", + " f.write(response.content)\n", + " print(f\"File downloaded and saved to {filepath}\")\n", + " else:\n", + " print(f\"Failed to download the file. Status code: {response.status_code}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the study\n", + "\n", + "Run parallel threads to execute the Optuna task and collect the reuslts (it might take a few minutes)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rishic/anaconda3/envs/optuna-spark/lib/python3.10/site-packages/joblibspark/backend.py:115: UserWarning: Spark version does not support stage-level scheduling.\n", + " warnings.warn(\"Spark version does not support stage-level scheduling.\")\n", + "/home/rishic/anaconda3/envs/optuna-spark/lib/python3.10/site-packages/joblibspark/backend.py:154: UserWarning: User-specified n_jobs (4) is greater than the max number of concurrent tasks (1) this cluster can run now.If dynamic allocation is enabled for the cluster, you might see more executors allocated.\n", + " warnings.warn(f\"User-specified n_jobs ({n_jobs}) is greater than the max number of \"\n", + " \r" + ] + } + ], + "source": [ + "with joblib.parallel_backend(\"spark\", n_jobs=num_tasks):\n", + " results = joblib.Parallel()(\n", + " joblib.delayed(task)(num_trials,\n", + " xgb_params,\n", + " optuna_params,\n", + " driver_ip,\n", + " study_name,\n", + " seed,\n", + " filepath) for num_trials in trials_per_task\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'n_estimators': 463, 'learning_rate': 0.05206124631137337, 'max_depth': 9, 'subsample': 0.7434942725744815, 'colsample_bytree': 0.877391644494205, 'min_child_weight': 4}\n", + "Best value: 0.5324732150787205\n" + ] + } + ], + "source": [ + "best_params = min(results, key=lambda x: x[1])[0]\n", + "best_value = min(results, key=lambda x: x[1])[1]\n", + "\n", + "print(f\"Best parameters: {best_params}\")\n", + "print(f\"Best value: {best_value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "optuna-spark", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ML+DL-Examples/Spark-Rapids-ML/pca/README.md b/examples/ML+DL-Examples/Spark-Rapids-ML/pca/README.md index 9de395c0..f66615b7 100644 --- a/examples/ML+DL-Examples/Spark-Rapids-ML/pca/README.md +++ b/examples/ML+DL-Examples/Spark-Rapids-ML/pca/README.md @@ -10,7 +10,7 @@ Please refer to the Spark-Rapids-ML [README](https://github.com/NVIDIA/spark-rap ## Download RAPIDS Jar from Maven Central Download the [Spark-Rapids plugin](https://nvidia.github.io/spark-rapids/docs/download.html#download-rapids-accelerator-for-apache-spark-v24081). -For Spark-RAPIDS-ML version 24.10, download the RAPIDS jar from Maven Central: [rapids-4-spark_2.12-24.10.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/24.10.0/rapids-4-spark_2.12-24.10.0.jar). +For Spark-RAPIDS-ML version 24.10.1, download the RAPIDS jar from Maven Central: [rapids-4-spark_2.12-24.10.1.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/24.10.1/rapids-4-spark_2.12-24.10.1.jar). ## Running the Notebooks diff --git a/examples/ML+DL-Examples/Spark-Rapids-ML/pca/notebooks/pca.ipynb b/examples/ML+DL-Examples/Spark-Rapids-ML/pca/notebooks/pca.ipynb index 9af1f9c3..55d34e7e 100644 --- a/examples/ML+DL-Examples/Spark-Rapids-ML/pca/notebooks/pca.ipynb +++ b/examples/ML+DL-Examples/Spark-Rapids-ML/pca/notebooks/pca.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -57,7 +57,7 @@ " import os\n", " import requests\n", "\n", - " SPARK_RAPIDS_VERSION = \"24.10.0\"\n", + " SPARK_RAPIDS_VERSION = \"24.10.1\"\n", " rapids_jar = f\"rapids-4-spark_2.12-{SPARK_RAPIDS_VERSION}.jar\"\n", " if not os.path.exists(rapids_jar):\n", " print(\"Downloading spark rapids jar\")\n",