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01.getting-started/04.train-on-remote-vm/.ipynb_checkpoints/04.train-on-remote-vm-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 04. Train in a remote VM (MLC managed DSVM)\n",
"* Create Workspace\n",
"* Create Project\n",
"* Create `train.py` file\n",
"* Create DSVM as Machine Learning Compute (MLC) resource\n",
"* Configure & execute a run in a conda environment in the default miniconda Docker container on DSVM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Experiment\n",
"\n",
"**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'train-on-remote-vm'\n",
"\n",
"from azureml.core import Experiment\n",
"\n",
"exp = Experiment(workspace = ws, name = experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## View `train.py`\n",
"\n",
"For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train.py` in a cell to show the file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('./train.py', 'r') as training_script:\n",
" print(training_script.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Linux DSVM as a compute target\n",
"\n",
"**Note**: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
" \n",
"**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you switch to a different port (such as 5022), you can append the port number to the address like the example below. [Read more](../../documentation/sdk/ssh-issue.md) on this."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import DsvmCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"compute_target_name = 'mydsvm'\n",
"\n",
"try:\n",
" dsvm_compute = DsvmCompute(workspace = ws, name = compute_target_name)\n",
" print('found existing:', dsvm_compute.name)\n",
"except ComputeTargetException:\n",
" print('creating new.')\n",
" dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
" dsvm_compute = DsvmCompute.create(ws, name = compute_target_name, provisioning_configuration = dsvm_config)\n",
" dsvm_compute.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Attach an existing Linux DSVM as a compute target\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
" from azureml.core.compute import RemoteCompute \n",
" # if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase \n",
" dsvm_compute = RemoteCompute.attach(ws,name=\"attach-from-sdk6\",username=<username>,address=<ipaddress>,ssh_port=22,password=<password>)\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure & Run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure a Docker run with new conda environment on the VM\n",
"You can execute in a Docker container in the VM. If you choose this route, you don't need to install anything on the VM yourself. Azure ML execution service will take care of it for you."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"\n",
"# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
"run_config = RunConfiguration(framework = \"python\")\n",
"\n",
"# Set compute target to the Linux DSVM\n",
"run_config.target = compute_target_name\n",
"\n",
"# Use Docker in the remote VM\n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# Use CPU base image from DockerHub\n",
"run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"print('Base Docker image is:', run_config.environment.docker.base_image)\n",
"\n",
"# Ask system to provision a new one based on the conda_dependencies.yml file\n",
"run_config.environment.python.user_managed_dependencies = False\n",
"\n",
"# Prepare the Docker and conda environment automatically when executingfor the first time.\n",
"run_config.prepare_environment = True\n",
"\n",
"# specify CondaDependencies obj\n",
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit the Experiment\n",
"Submit script to run in the Docker image in the remote VM. If you run this for the first time, the system will download the base image, layer in packages specified in the `conda_dependencies.yml` file on top of the base image, create a container and then execute the script in the container."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Run\n",
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory = '.', script = 'train.py', run_config = run_config)\n",
"run = exp.submit(src)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### View run history details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find the best run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get all metris logged in the run\n",
"run.get_metrics()\n",
"metrics = run.get_metrics()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
" min(metrics['mse']), \n",
" metrics['alpha'][np.argmin(metrics['mse'])]\n",
"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clean up compute resource"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dsvm_compute.delete()"
]
}
],
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"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"file_extension": ".py",
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