For running the random search.
This commit is contained in:
Mario Bourgoin
Родитель
53e9c9b20b
Коммит
17b6e3deb9
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Random Search\n",
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"\n",
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"## Introduction\n",
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"\n",
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"In This example of random search hyperparameter tuning we make use of the Batch AI extensions, including the JobFactory module to generate values for hyperparameters, and the ExperimentUtils module for bulk job submission.\n",
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"\n",
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"## 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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import print_function\n",
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"import sys\n",
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"import glob\n",
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"import azure.mgmt.batchai.models as models\n",
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"from azure.storage.blob import BlockBlobService\n",
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"from azure.storage.file import FileService\n",
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"sys.path.append('.')\n",
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"import utilities as utils\n",
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"from utilities.job_factory import ParameterSweep, NumericParameter, DiscreteParameter"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Read Configuration and Create Batch AI client"
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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": 2,
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"metadata": {
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"nbpresent": {
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"id": "bfa11f00-8866-4051-bbfe-a9646e004910"
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}
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},
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"outputs": [],
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"source": [
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"configuration_path = 'configuration.json'\n",
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"cfg = utils.config.Configuration(configuration_path)\n",
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"client = utils.config.create_batchai_client(cfg)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create the resource group and Batch AI workspace if they do not exist."
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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": 3,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"utils.config.create_resource_group(cfg)\n",
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"_ = client.workspaces.create(cfg.resource_group, cfg.workspace, cfg.location).result()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Prepare Training Dataset and Script in Azure Storage\n",
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"\n",
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"### Create Azure Blob Container\n",
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"\n",
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"We will create a new Blob Container with name `batchaisample` under your storage account. This will be used to store the training datasets.\n",
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"\n",
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"**Note** You don't need to create new blob Container for every cluster. We are doing this in this sample to simplify resource management for you."
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"azure_blob_container_name = 'batchaisample'\n",
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"blob_service = BlockBlobService(cfg.storage_account_name, cfg.storage_account_key)\n",
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"blob_service.create_container(azure_blob_container_name, fail_on_exist=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Upload the datasets to Azure Blob Container\n",
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"\n",
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"We will upload the TSVs created by the [data prep notebook](00_Data_Prep.ipynb) to an Azure blob container directory named `dataset` using the Azure SDK for Python."
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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": 5,
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"metadata": {},
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"outputs": [
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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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"questions.tsv\n",
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"balanced_pairs_train.tsv\n",
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"dupes_test.tsv\n",
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"dupes_train.tsv\n",
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"balanced_pairs_test.tsv\n"
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]
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}
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],
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"source": [
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"dataset_directory = 'dataset'\n",
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"dataset_files = glob.glob('*.tsv')\n",
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"for file in dataset_files:\n",
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" print(file)\n",
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" blob_service.create_blob_from_path(azure_blob_container_name, \n",
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" dataset_directory + '/' + file,\n",
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" file)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Create Azure File Share\n",
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"\n",
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"For this example we will create a new File Share with name `batchaisample` under your storage account. This will be used to share the training script file created in the [create model notebook](01_Create_Model.ipynb), and also the output files created by the script.\n",
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"\n",
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"**Note** You don't need to create new file share for every cluster. We are doing this in this sample to simplify resource management for you."
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"azure_file_share_name = 'batchaisample'\n",
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"file_service = FileService(cfg.storage_account_name, cfg.storage_account_key)\n",
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"file_service.create_share(azure_file_share_name, fail_on_exist=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Upload the training script to file share directory named `hyperparam_samples`."
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"script_path = 'hyperparam_samples'\n",
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"script_name = 'TrainTestClassifier.py'\n",
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"file_service.create_directory(\n",
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" azure_file_share_name, script_path, fail_on_exist=False)\n",
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"file_service.create_file_from_path(\n",
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" azure_file_share_name, script_path, script_name, script_name)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Create Azure Batch AI Compute Cluster\n",
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"\n",
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"### Configure Compute Cluster\n",
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"\n",
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"For this example we will use a cluster of `Standard_D4_v2` nodes. Number of nodes in the cluster is configured with `nodes_count` variable. We will call the cluster `d4`."
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"cluster_name = 'd4'\n",
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"nodes_count = 4\n",
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"vm_size = 'Standard_D4_v2'\n",
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"\n",
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"parameters = models.ClusterCreateParameters(\n",
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" vm_size=vm_size,\n",
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" scale_settings=models.ScaleSettings(\n",
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" manual=models.ManualScaleSettings(target_node_count=nodes_count)\n",
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" ),\n",
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" user_account_settings=models.UserAccountSettings(\n",
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" admin_user_name=cfg.admin,\n",
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" admin_user_password=cfg.admin_password or None,\n",
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" admin_user_ssh_public_key=cfg.admin_ssh_key or None,\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Create Compute Cluster"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"_ = client.clusters.create(cfg.resource_group, cfg.workspace, cluster_name, parameters).result()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Monitor Cluster Creation\n",
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"\n",
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"Monitor the just created cluster. The `utilities` module contains a helper function to print out detail status of the cluster."
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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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"metadata": {},
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"outputs": [
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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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"Cluster state: resizing Target: 4; Allocated: 0; Idle: 0; Unusable: 0; Running: 0; Preparing: 0; Leaving: 0\n"
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]
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}
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],
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"source": [
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"cluster = client.clusters.get(cfg.resource_group, cfg.workspace, cluster_name)\n",
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"utils.cluster.print_cluster_status(cluster)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Parameter Sweeping using Random Search\n",
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"Create a new experiment called ```random_search_experiment```."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"experiment_name = 'random_search_experiment'\n",
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"experiment = client.experiments.create(cfg.resource_group, cfg.workspace, experiment_name).result()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Define specifications for the hyperparameters"
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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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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"param_specs = [\n",
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" DiscreteParameter(\n",
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" parameter_name=\"ESTIMATORS\",\n",
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" values=[1, 2]\n",
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" ),\n",
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" DiscreteParameter(\n",
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" parameter_name=\"NGRAMS\",\n",
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" values=[1, 2]\n",
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" ),\n",
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create a parameter substitution object."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"parameters = ParameterSweep(param_specs)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We will use the parameter substitution object to specify where we would like to substitute the parameters. We substitute\n",
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"the values for those parameters into `models.JobCreateParameters.cntk_settings.command_line_args`. Note that the `parameters` variable is used like a dict, with the `parameter_name` being used as the key to specify which parameter to substitute. When `parameters.generate_jobs` is called, the `parameters[name]` variables will be replaced with actual values."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"azure_file_share_mount_path = 'afs'\n",
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"azure_blob_mount_path = 'bfs'\n",
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"jcp = models.JobCreateParameters(\n",
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" cluster=models.ResourceId(id=cluster.id),\n",
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" node_count=1,\n",
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" std_out_err_path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/{0}'.format(azure_file_share_mount_path),\n",
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" input_directories = [\n",
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" models.InputDirectory(\n",
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" id='SCRIPT',\n",
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" path='$AZ_BATCHAI_JOB_MOUNT_ROOT/{0}/{1}'.format(azure_blob_mount_path, dataset_directory))\n",
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" ],\n",
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" output_directories = [\n",
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" models.OutputDirectory(\n",
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" id='ALL',\n",
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" path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/{0}'.format(azure_file_share_mount_path))],\n",
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" mount_volumes = models.MountVolumes(\n",
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" azure_file_shares=[\n",
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" models.AzureFileShareReference(\n",
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||||
" account_name=cfg.storage_account_name,\n",
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" credentials=models.AzureStorageCredentialsInfo(\n",
|
||||
" account_key=cfg.storage_account_key),\n",
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||||
" azure_file_url='https://{0}.file.core.windows.net/{1}'.format(\n",
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" cfg.storage_account_name, azure_file_share_name),\n",
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" relative_mount_path=azure_file_share_mount_path)\n",
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" ],\n",
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" azure_blob_file_systems=[\n",
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" models.AzureBlobFileSystemReference(\n",
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||||
" account_name=cfg.storage_account_name,\n",
|
||||
" credentials=models.AzureStorageCredentialsInfo(\n",
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||||
" account_key=cfg.storage_account_key),\n",
|
||||
" container_name=azure_blob_container_name,\n",
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||||
" relative_mount_path=azure_blob_mount_path)\n",
|
||||
" ]\n",
|
||||
" ),\n",
|
||||
" container_settings=models.ContainerSettings(\n",
|
||||
" image_source_registry=models.ImageSourceRegistry(image='microsoft/cntk:2.5.1-gpu-python2.7-cuda9.0-cudnn7.0')\n",
|
||||
" ),\n",
|
||||
" cntk_settings=models.CNTKsettings(\n",
|
||||
" python_script_file_path='$AZ_BATCHAI_JOB_MOUNT_ROOT/{0}/{1}/{2}'.format(\n",
|
||||
" azure_file_share_mount_path, script_path, script_name),\n",
|
||||
" command_line_args='--estimators {0} --ngrams {1} --inputs $AZ_BATCHAI_INPUT_SCRIPT --outputs $AZ_BATCHAI_OUTPUT_ALL'\n",
|
||||
" .format(parameters['ESTIMATORS'], parameters['NGRAMS']) # Substitute hyperparameters\n",
|
||||
" )\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Next, we generate a list of jobs to submit."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Parameters 1: {'PARAM_NGRAMS': 1, 'PARAM_ESTIMATORS': 2}\n",
|
||||
"Parameters 2: {'PARAM_NGRAMS': 2, 'PARAM_ESTIMATORS': 2}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Generate Jobs\n",
|
||||
"num_configs = 2\n",
|
||||
"jobs_to_submit, param_combinations = parameters.generate_jobs_random_search(jcp, num_configs)\n",
|
||||
"\n",
|
||||
"# Print the parameter combinations generated\n",
|
||||
"for idx, comb in enumerate(param_combinations):\n",
|
||||
" print(\"Parameters {0}: {1}\".format(idx + 1, comb))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"And we submit the jobs to the experiment."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Initialized JobSubmitter in resource group: maboumlb | workspace: maboumlb | experiment: random_search_experiment\n",
|
||||
"Created job \"hyperparam_job_1883dde79c53c99e\" with parameters {\"PARAM_ESTIMATORS\": \"2\", \"PARAM_NGRAMS\": \"2\"}\n",
|
||||
"Created job \"hyperparam_job_808f1cac5503f018\" with parameters {\"PARAM_ESTIMATORS\": \"2\", \"PARAM_NGRAMS\": \"1\"}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"experiment_utils = utils.experiment.ExperimentUtils(client, cfg.resource_group, cfg.workspace, experiment_name)\n",
|
||||
"jobs = experiment_utils.submit_jobs(jobs_to_submit, 'hyperparam_job').result()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Wait for all the jobs to complete."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0/2 jobs completed (0 succeeded, 0 failed)...............\n",
|
||||
"0/2 jobs completed (0 succeeded, 0 failed)...............\n",
|
||||
"0/2 jobs completed (0 succeeded, 0 failed)...............\n",
|
||||
"0/2 jobs completed (0 succeeded, 0 failed)...............\n",
|
||||
"0/2 jobs completed (0 succeeded, 0 failed)...............\n",
|
||||
"0/2 jobs completed (0 succeeded, 0 failed)...............\n",
|
||||
"0/2 jobs completed (0 succeeded, 0 failed)...............\n",
|
||||
"1/2 jobs completed (0 succeeded, 1 failed)...............\n",
|
||||
"1/2 jobs completed (0 succeeded, 1 failed)...............\n",
|
||||
"All jobs completed.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<azure.mgmt.batchai.models.job_py3.Job at 0x7f06eb996f60>,\n",
|
||||
" <azure.mgmt.batchai.models.job_py3.Job at 0x7f06eb996ef0>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"experiment_utils.wait_all_jobs()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We define the following metric extractor to extract desired metric from learning log file. \n",
|
||||
"- In this example, we extract the number between \"metric =\" and \"%\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metric_extractor = utils.job.MetricExtractor(\n",
|
||||
" output_dir_id='ALL',\n",
|
||||
" logfile='progress.log',\n",
|
||||
" regex='metric =(.*?)\\%')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We wait on the jobs the finish, then get the metric value from the log files of the finished jobs."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait for all jobs to complete\n",
|
||||
"experiment_utils.wait_all_jobs()\n",
|
||||
"\n",
|
||||
"# Get the metrics from the jobs\n",
|
||||
"results = experiment_utils.get_metrics_for_jobs(jobs, metric_extractor)\n",
|
||||
"results.sort(key=lambda r: r['metric_value'])\n",
|
||||
"\n",
|
||||
"# Print results\n",
|
||||
"for result in results:\n",
|
||||
" print(\"Job {0} completed with metric value {1}\".format(result['job_name'], result['metric_value']))\n",
|
||||
"print(\"Best job: {0} with parameters {1}\".format(\n",
|
||||
" results[0]['job_name'], \n",
|
||||
" {ev.name:ev.value for ev in results[0]['job'].environment_variables}\n",
|
||||
"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 4. Clean Up (Optional)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Delete the Experiment\n",
|
||||
"Delete the experiment and jobs inside it"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"_ = client.experiments.delete(cfg.resource_group, cfg.workspace, experiment_name).result()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Delete the Cluster\n",
|
||||
"When you are finished with the sample and don't want to submit any more jobs you can delete the cluster using the following code."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"_ = client.clusters.delete(cfg.resource_group, cfg.workspace, cluster_name).result()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"### Delete File Share\n",
|
||||
"When you are finished with the sample and don't want to submit any more jobs you can delete the file share completely with all files using the following code."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"service = FileService(cfg.storage_account_name, cfg.storage_account_key)\n",
|
||||
"service.delete_share(azure_file_share_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python [conda env:MLBatchAIHyperparameterTuning]",
|
||||
"language": "python",
|
||||
"name": "conda-env-MLBatchAIHyperparameterTuning-py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
Загрузка…
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