diff --git a/00.configuration.ipynb b/00.configuration.ipynb index 7827613c..01e666c6 100644 --- a/00.configuration.ipynb +++ b/00.configuration.ipynb @@ -18,7 +18,7 @@ "## Prerequisites:\n", "\n", "### 1. Install Azure ML SDK\n", - "Follow [SDK installation instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment).\n", + "Follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).\n", "\n", "### 2. Install some additional packages\n", "This Notebook requires some additional libraries. In the conda environment, run below commands: \n", @@ -185,35 +185,11 @@ }, "outputs": [], "source": [ - "# load workspace configuratio from ./aml_config/config.json file.ß\n", + "# load workspace configuratio from ./aml_config/config.json file.\n", "my_workspace = Workspace.from_config()\n", "my_workspace.get_details()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a folder to host all sample projects\n", - "Lastly, create a folder where all the sample projects will be hosted." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "sample_projects_folder = './sample_projects'\n", - "\n", - "if not os.path.isdir(sample_projects_folder):\n", - " os.mkdir(sample_projects_folder)\n", - " \n", - "print('Sample projects will be created in {}.'.format(sample_projects_folder))" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -225,9 +201,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { @@ -239,7 +215,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb b/01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb index 885a5e8a..41c381ec 100644 --- a/01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb +++ b/01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb @@ -277,6 +277,16 @@ " os.remove(path=model_name)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# now let's take a look at the experiment in Azure portal.\n", + "experiment" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -778,9 +788,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/01.getting-started/02.train-on-local/02.train-on-local.ipynb b/01.getting-started/02.train-on-local/02.train-on-local.ipynb index 4c2cea8e..8597c74f 100644 --- a/01.getting-started/02.train-on-local/02.train-on-local.ipynb +++ b/01.getting-started/02.train-on-local/02.train-on-local.ipynb @@ -88,7 +88,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Create a folder to store the training script." + "## View `train.py`\n", + "\n", + "`train.py` is already created for you." ] }, { @@ -97,18 +99,15 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "script_folder = './samples/train-on-local'\n", - "os.makedirs(script_folder, exist_ok=True)" + "with open('./train.py', 'r') as f:\n", + " print(f.read())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Create `train.py`\n", - "\n", - "Use `%%writefile` magic to write training code to `train.py` file under your script folder." + "Note `train.py` also references a `mylib.py` file." ] }, { @@ -117,73 +116,8 @@ "metadata": {}, "outputs": [], "source": [ - "%%writefile $script_folder/train.py\n", - "\n", - "import os\n", - "from sklearn.datasets import load_diabetes\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.model_selection import train_test_split\n", - "from azureml.core.run import Run\n", - "from sklearn.externals import joblib\n", - "\n", - "# example of referencing another script\n", - "import mylib\n", - "\n", - "X, y = load_diabetes(return_X_y=True)\n", - "\n", - "run = Run.get_submitted_run()\n", - "\n", - "X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2, random_state=0)\n", - "data = {\"train\": {\"X\": X_train, \"y\": y_train},\n", - " \"test\": {\"X\": X_test, \"y\": y_test}}\n", - "\n", - "# example of referencing another script\n", - "alphas = mylib.get_alphas()\n", - "\n", - "for alpha in alphas:\n", - " # Use Ridge algorithm to create a regression model\n", - " reg = Ridge(alpha=alpha)\n", - " reg.fit(data[\"train\"][\"X\"], data[\"train\"][\"y\"])\n", - "\n", - " preds = reg.predict(data[\"test\"][\"X\"])\n", - " mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n", - " run.log('alpha', alpha)\n", - " run.log('mse', mse)\n", - "\n", - " model_file_name='ridge_{0:.2f}.pkl'.format(alpha)\n", - " # save model in the outputs folder so it automatically get uploaded\n", - " with open(model_file_name, \"wb\") as file:\n", - " joblib.dump(value=reg, filename=model_file_name)\n", - " \n", - " # upload the model file explicitly into artifacts \n", - " run.upload_file(name=model_file_name, path_or_stream=model_file_name)\n", - " \n", - " # register the model\n", - " run.register_model(model_name='diabetes-model', model_path=model_file_name)\n", - "\n", - " print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`train.py` also references a `mylib.py` file. So let's create that too." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile $script_folder/mylib.py\n", - "import numpy as np\n", - "\n", - "def get_alphas():\n", - " # list of numbers from 0.0 to 1.0 with a 0.05 interval\n", - " return np.arange(0.0, 1.0, 0.05)" + "with open('./mylib.py', 'r') as f:\n", + " print(f.read())" ] }, { @@ -209,7 +143,7 @@ "run_config_user_managed.environment.python.user_managed_dependencies = True\n", "\n", "# You can choose a specific Python environment by pointing to a Python path \n", - "#run_config.environment.python.interpreter_path = '/home/ninghai/miniconda3/envs/sdk2/bin/python'" + "#run_config.environment.python.interpreter_path = '/home/johndoe/miniconda3/envs/sdk2/bin/python'" ] }, { @@ -228,9 +162,8 @@ "source": [ "from azureml.core import ScriptRunConfig\n", "\n", - "src = ScriptRunConfig(source_directory=script_folder, script='train.py', run_config=run_config_user_managed)\n", - "run = exp.submit(src)\n", - "run.wait_for_completion(show_output=True)" + "src = ScriptRunConfig(source_directory='./', script='train.py', run_config=run_config_user_managed)\n", + "run = exp.submit(src)" ] }, { @@ -249,6 +182,22 @@ "run" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Block to wait till run finishes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.wait_for_completion(show_output=True)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -290,9 +239,8 @@ "metadata": {}, "outputs": [], "source": [ - "src = ScriptRunConfig(source_directory=script_folder, script='train.py', run_config=run_config_system_managed)\n", - "run = exp.submit(src)\n", - "run.wait_for_completion(show_output = True)" + "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_system_managed)\n", + "run = exp.submit(src)" ] }, { @@ -311,12 +259,30 @@ "run" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Block and wait till run finishes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run.wait_for_completion(show_output = True)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Docker-based execution\n", - "**NOTE** You must have Docker engine installed locally in order to use this execution mode. You can also ask the system to pull down a Docker image and execute your scripts in it." + "**IMPORTANT**: You must have Docker engine installed locally in order to use this execution mode. If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n", + "\n", + "You can also ask the system to pull down a Docker image and execute your scripts in it." ] }, { @@ -356,7 +322,7 @@ "metadata": {}, "outputs": [], "source": [ - "src = ScriptRunConfig(source_directory=script_folder, script='train.py', run_config=run_config_docker)\n", + "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_docker)\n", "run = exp.submit(src)" ] }, @@ -376,7 +342,7 @@ "metadata": {}, "outputs": [], "source": [ - "run.wait_for_completion(show_output = True)" + "run.wait_for_completion(show_output=True)" ] }, { @@ -455,7 +421,7 @@ "outputs": [], "source": [ "# supply a model name, and the full path to the serialized model file.\n", - "model = run.register_model(model_name='best_ridge_model', model_path='ridge_0.40.pkl')" + "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')" ] }, { @@ -477,9 +443,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/01.getting-started/02.train-on-local/mylib.py b/01.getting-started/02.train-on-local/mylib.py new file mode 100644 index 00000000..08e4d1f4 --- /dev/null +++ b/01.getting-started/02.train-on-local/mylib.py @@ -0,0 +1,9 @@ +# Copyright (c) Microsoft. All rights reserved. +# Licensed under the MIT license. + +import numpy as np + + +def get_alphas(): + # list of numbers from 0.0 to 1.0 with a 0.05 interval + return np.arange(0.0, 1.0, 0.05) diff --git a/01.getting-started/02.train-on-local/train.py b/01.getting-started/02.train-on-local/train.py index 5eba27eb..58892411 100644 --- a/01.getting-started/02.train-on-local/train.py +++ b/01.getting-started/02.train-on-local/train.py @@ -1,24 +1,30 @@ +# Copyright (c) Microsoft. All rights reserved. +# Licensed under the MIT license. + from sklearn.datasets import load_diabetes from sklearn.linear_model import Ridge from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from azureml.core.run import Run from sklearn.externals import joblib - +import os import numpy as np +import mylib -# os.makedirs('./outputs', exist_ok = True) +os.makedirs('./outputs', exist_ok=True) X, y = load_diabetes(return_X_y=True) run = Run.get_submitted_run() -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) +X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size=0.2, + random_state=0) data = {"train": {"X": X_train, "y": y_train}, "test": {"X": X_test, "y": y_test}} # list of numbers from 0.0 to 1.0 with a 0.05 interval -alphas = np.arange(0.0, 1.0, 0.05) +alphas = mylib.get_alphas() for alpha in alphas: # Use Ridge algorithm to create a regression model @@ -33,13 +39,7 @@ for alpha in alphas: model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha) # save model in the outputs folder so it automatically get uploaded with open(model_file_name, "wb") as file: - joblib.dump(value=reg, filename=model_file_name) - - # upload the model file explicitly into artifacts - run.upload_file(name=model_file_name, path_or_stream=model_file_name) - - # register the model - # commented out for now until a bug is fixed - # run.register_model(file_name = model_file_name) + joblib.dump(value=reg, filename=os.path.join('./outputs/', + model_file_name)) print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse)) diff --git a/01.getting-started/03.train-on-aci/.ipynb_checkpoints/03.train-on-aci-checkpoint.ipynb b/01.getting-started/03.train-on-aci/.ipynb_checkpoints/03.train-on-aci-checkpoint.ipynb deleted file mode 100644 index 00667e74..00000000 --- a/01.getting-started/03.train-on-aci/.ipynb_checkpoints/03.train-on-aci-checkpoint.ipynb +++ /dev/null @@ -1,325 +0,0 @@ -{ - "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": [ - "# 03. Train on Azure Container Instance (EXPERIMENTAL)\n", - "\n", - "* Create Workspace\n", - "* Create Project\n", - "* Create `train.py` in the project folder.\n", - "* Configure an ACI (Azure Container Instance) run\n", - "* Execute in ACI" - ] - }, - { - "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": { - "tags": [ - "create workspace" - ] - }, - "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 An 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": [ - "from azureml.core import Experiment\n", - "experiment_name = 'train-on-aci'\n", - "experiment = Experiment(workspace = ws, name = experiment_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a folder to store the training script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "script_folder = './samples/train-on-aci'\n", - "os.makedirs(script_folder, exist_ok = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Remote execution on ACI\n", - "\n", - "Use `%%writefile` magic to write training code to `train.py` file under the project folder." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile $script_folder/train.py\n", - "\n", - "import os\n", - "from sklearn.datasets import load_diabetes\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.model_selection import train_test_split\n", - "from azureml.core.run import Run\n", - "from sklearn.externals import joblib\n", - "\n", - "import numpy as np\n", - "\n", - "os.makedirs('./outputs', exist_ok=True)\n", - "\n", - "X, y = load_diabetes(return_X_y = True)\n", - "\n", - "run = Run.get_submitted_run()\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n", - "data = {\"train\": {\"X\": X_train, \"y\": y_train},\n", - " \"test\": {\"X\": X_test, \"y\": y_test}}\n", - "\n", - "# list of numbers from 0.0 to 1.0 with a 0.05 interval\n", - "alphas = np.arange(0.0, 1.0, 0.05)\n", - "\n", - "for alpha in alphas:\n", - " # Use Ridge algorithm to create a regression model\n", - " reg = Ridge(alpha = alpha)\n", - " reg.fit(data[\"train\"][\"X\"], data[\"train\"][\"y\"])\n", - "\n", - " preds = reg.predict(data[\"test\"][\"X\"])\n", - " mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n", - " run.log('alpha', alpha)\n", - " run.log('mse', mse)\n", - " \n", - " model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)\n", - " with open(model_file_name, \"wb\") as file:\n", - " joblib.dump(value = reg, filename = 'outputs/' + model_file_name)\n", - "\n", - " print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure for using ACI\n", - "Linux-based ACI is available in `westus`, `eastus`, `westeurope`, `northeurope`, `westus2` and `southeastasia` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "configure run" - ] - }, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# create a new runconfig object\n", - "run_config = RunConfiguration()\n", - "\n", - "# signal that you want to use ACI to execute script.\n", - "run_config.target = \"containerinstance\"\n", - "\n", - "# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n", - "run_config.container_instance.region = 'eastus'\n", - "\n", - "# set the ACI CPU and Memory \n", - "run_config.container_instance.cpu_cores = 1\n", - "run_config.container_instance.memory_gb = 2\n", - "\n", - "# enable Docker \n", - "run_config.environment.docker.enabled = True\n", - "\n", - "# set Docker base image to the default CPU-based image\n", - "run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n", - "#run_config.environment.docker.base_image = 'microsoft/mmlspark:plus-0.9.9'\n", - "\n", - "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", - "run_config.environment.python.user_managed_dependencies = False\n", - "\n", - "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n", - "run_config.auto_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", - "Finally, run the training job on the ACI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remote run", - "aci" - ] - }, - "outputs": [], - "source": [ - "%%time \n", - "from azureml.core.script_run_config import ScriptRunConfig\n", - "\n", - "script_run_config = ScriptRunConfig(source_directory = script_folder,\n", - " script= 'train.py',\n", - " run_config = run_config)\n", - "\n", - "run = experiment.submit(script_run_config)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remote run", - "aci" - ] - }, - "outputs": [], - "source": [ - "%%time\n", - "# Shows output of the run on stdout.\n", - "run.wait_for_completion(show_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "query history" - ] - }, - "outputs": [], - "source": [ - "# Show run details\n", - "run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "get metrics" - ] - }, - "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", - "))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb b/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb index 4039b0b2..83cfe617 100644 --- a/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb +++ b/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb @@ -13,10 +13,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 03. Train on Azure Container Instance (EXPERIMENTAL)\n", + "# 03. Train on Azure Container Instance\n", "\n", "* Create Workspace\n", - "* Create Project\n", "* Create `train.py` in the project folder.\n", "* Configure an ACI (Azure Container Instance) run\n", "* Execute in ACI" @@ -87,31 +86,13 @@ "experiment = Experiment(workspace = ws, name = experiment_name)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a folder to store the training script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "script_folder = './samples/train-on-aci'\n", - "os.makedirs(script_folder, exist_ok = True)" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Remote execution on ACI\n", "\n", - "Use `%%writefile` magic to write training code to `train.py` file under the project folder." + "The training script `train.py` is already created for you. Let's have a look." ] }, { @@ -120,46 +101,8 @@ "metadata": {}, "outputs": [], "source": [ - "%%writefile $script_folder/train.py\n", - "\n", - "import os\n", - "from sklearn.datasets import load_diabetes\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.model_selection import train_test_split\n", - "from azureml.core.run import Run\n", - "from sklearn.externals import joblib\n", - "\n", - "import numpy as np\n", - "\n", - "os.makedirs('./outputs', exist_ok=True)\n", - "\n", - "X, y = load_diabetes(return_X_y = True)\n", - "\n", - "run = Run.get_submitted_run()\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n", - "data = {\"train\": {\"X\": X_train, \"y\": y_train},\n", - " \"test\": {\"X\": X_test, \"y\": y_test}}\n", - "\n", - "# list of numbers from 0.0 to 1.0 with a 0.05 interval\n", - "alphas = np.arange(0.0, 1.0, 0.05)\n", - "\n", - "for alpha in alphas:\n", - " # Use Ridge algorithm to create a regression model\n", - " reg = Ridge(alpha = alpha)\n", - " reg.fit(data[\"train\"][\"X\"], data[\"train\"][\"y\"])\n", - "\n", - " preds = reg.predict(data[\"test\"][\"X\"])\n", - " mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n", - " run.log('alpha', alpha)\n", - " run.log('mse', mse)\n", - " \n", - " model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)\n", - " with open(model_file_name, \"wb\") as file:\n", - " joblib.dump(value = reg, filename = 'outputs/' + model_file_name)\n", - "\n", - " print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))" + "with open('./train.py', 'r') as f:\n", + " print(f.read())" ] }, { @@ -167,7 +110,7 @@ "metadata": {}, "source": [ "## Configure for using ACI\n", - "Linux-based ACI is available in `westus`, `eastus`, `westeurope`, `northeurope`, `westus2` and `southeastasia` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)." + "Linux-based ACI is available in `West US`, `East US`, `West Europe`, `North Europe`, `West US 2`, `Southeast Asia`, `Australia East`, `East US 2`, and `Central US` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)." ] }, { @@ -190,7 +133,7 @@ "run_config.target = \"containerinstance\"\n", "\n", "# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n", - "run_config.container_instance.region = 'eastus'\n", + "run_config.container_instance.region = 'eastus2'\n", "\n", "# set the ACI CPU and Memory \n", "run_config.container_instance.cpu_cores = 1\n", @@ -201,7 +144,6 @@ "\n", "# set Docker base image to the default CPU-based image\n", "run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n", - "#run_config.environment.docker.base_image = 'microsoft/mmlspark:plus-0.9.9'\n", "\n", "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n", "run_config.environment.python.user_managed_dependencies = False\n", @@ -235,11 +177,25 @@ "%%time \n", "from azureml.core.script_run_config import ScriptRunConfig\n", "\n", - "script_run_config = ScriptRunConfig(source_directory = script_folder,\n", - " script= 'train.py',\n", - " run_config = run_config)\n", + "script_run_config = ScriptRunConfig(source_directory='./',\n", + " script='train.py',\n", + " run_config=run_config)\n", "\n", - "run = experiment.submit(script_run_config)\n" + "run = experiment.submit(script_run_config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "query history" + ] + }, + "outputs": [], + "source": [ + "# Show run details\n", + "run" ] }, { @@ -255,21 +211,7 @@ "source": [ "%%time\n", "# Shows output of the run on stdout.\n", - "run.wait_for_completion(show_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "query history" - ] - }, - "outputs": [], - "source": [ - "# Show run details\n", - "run" + "run.wait_for_completion(show_output=True)" ] }, { @@ -299,13 +241,30 @@ " metrics['alpha'][np.argmin(metrics['mse'])]\n", "))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# show all the files stored within the run record\n", + "run.get_file_names()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you can take a model produced here, register it and then deploy as a web service." + ] } ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/01.getting-started/03.train-on-aci/train.py b/01.getting-started/03.train-on-aci/train.py new file mode 100644 index 00000000..ad3a09cb --- /dev/null +++ b/01.getting-started/03.train-on-aci/train.py @@ -0,0 +1,44 @@ +# Copyright (c) Microsoft. All rights reserved. +# Licensed under the MIT license. + +from sklearn.datasets import load_diabetes +from sklearn.linear_model import Ridge +from sklearn.metrics import mean_squared_error +from sklearn.model_selection import train_test_split +from azureml.core.run import Run +from sklearn.externals import joblib +import os +import numpy as np + +os.makedirs('./outputs', exist_ok=True) + +X, y = load_diabetes(return_X_y=True) + +run = Run.get_submitted_run() + +X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size=0.2, + random_state=0) +data = {"train": {"X": X_train, "y": y_train}, + "test": {"X": X_test, "y": y_test}} + +# list of numbers from 0.0 to 1.0 with a 0.05 interval +alphas = np.arange(0.0, 1.0, 0.05) + +for alpha in alphas: + # Use Ridge algorithm to create a regression model + reg = Ridge(alpha=alpha) + reg.fit(data["train"]["X"], data["train"]["y"]) + + preds = reg.predict(data["test"]["X"]) + mse = mean_squared_error(preds, data["test"]["y"]) + run.log('alpha', alpha) + run.log('mse', mse) + + model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha) + # save model in the outputs folder so it automatically get uploaded + with open(model_file_name, "wb") as file: + joblib.dump(value=reg, filename=os.path.join('./outputs/', + model_file_name)) + + print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse)) diff --git a/01.getting-started/04.train-on-remote-vm/.ipynb_checkpoints/04.train-on-remote-vm-checkpoint.ipynb b/01.getting-started/04.train-on-remote-vm/.ipynb_checkpoints/04.train-on-remote-vm-checkpoint.ipynb deleted file mode 100644 index f5fe77c5..00000000 --- a/01.getting-started/04.train-on-remote-vm/.ipynb_checkpoints/04.train-on-remote-vm-checkpoint.ipynb +++ /dev/null @@ -1,321 +0,0 @@ -{ - "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=,address=,ssh_port=22,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()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/01.getting-started/04.train-on-remote-vm/04.train-on-remote-vm.ipynb b/01.getting-started/04.train-on-remote-vm/04.train-on-remote-vm.ipynb index 71f19676..75ce9591 100644 --- a/01.getting-started/04.train-on-remote-vm/04.train-on-remote-vm.ipynb +++ b/01.getting-started/04.train-on-remote-vm/04.train-on-remote-vm.ipynb @@ -299,9 +299,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/01.getting-started/05.train-in-spark/.ipynb_checkpoints/05.train-in-spark-checkpoint.ipynb b/01.getting-started/05.train-in-spark/.ipynb_checkpoints/05.train-in-spark-checkpoint.ipynb deleted file mode 100644 index 0eb8763f..00000000 --- a/01.getting-started/05.train-in-spark/.ipynb_checkpoints/05.train-in-spark-checkpoint.ipynb +++ /dev/null @@ -1,257 +0,0 @@ -{ - "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": [ - "# 05. Train in Spark\n", - "* Create Workspace\n", - "* Create Experiment\n", - "* Copy relevant files to the script folder\n", - "* Configure and Run" - ] - }, - { - "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" - ] - }, - { - "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-spark.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-spark.py` in a cell to show the file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open('train-spark.py', 'r') as training_script:\n", - " print(training_script.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure & Run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Attach an HDI cluster\n", - "To use HDI commpute target:\n", - " 1. Create an Spark for HDI cluster in Azure. Here is some [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor, NOT CentOS.\n", - " 2. Enter the IP address, username and password below" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import HDInsightCompute\n", - "\n", - "try:\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", - " hdi_compute_new = HDInsightCompute.attach(ws, \n", - " name=\"hdi-attach\", \n", - " address=\"hdi-ignite-demo-ssh.azurehdinsight.net\", \n", - " ssh_port=22, \n", - " username='', \n", - " password='')\n", - "\n", - "except UserErrorException as e:\n", - " print(\"Caught = {}\".format(e.message))\n", - " print(\"Compute config already attached.\")\n", - " \n", - " \n", - "hdi_compute_new.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Configure HDI run" - ] - }, - { - "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 = hdi_compute.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'])\n", - "# load the runconfig object from the \"myhdi.runconfig\" file generated by the attach operaton above." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Submit the script to HDI" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "script_run_config = ScriptRunConfig(source_directory = '.',\n", - " script= 'train-spark.py',\n", - " run_config = run_config)\n", - "run = experiment.submit(script_run_config)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# get the URL of the run history web page\n", - "run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.wait_for_completion(show_output = True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# get all metris logged in the run\n", - "metrics = run.get_metrics()\n", - "print(metrics)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb b/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb index 6acea698..0076a546 100644 --- a/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb +++ b/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb @@ -235,9 +235,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/01.getting-started/10.register-model-create-image-deploy-service/10.register-model-create-image-deploy-service.ipynb b/01.getting-started/10.register-model-create-image-deploy-service/10.register-model-create-image-deploy-service.ipynb index 7b86ca09..9468821d 100644 --- a/01.getting-started/10.register-model-create-image-deploy-service/10.register-model-create-image-deploy-service.ipynb +++ b/01.getting-started/10.register-model-create-image-deploy-service/10.register-model-create-image-deploy-service.ipynb @@ -398,9 +398,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/01.getting-started/11.production-deploy-to-aks/11.production-deploy-to-aks.ipynb b/01.getting-started/11.production-deploy-to-aks/11.production-deploy-to-aks.ipynb index bc300341..3b680880 100644 --- a/01.getting-started/11.production-deploy-to-aks/11.production-deploy-to-aks.ipynb +++ b/01.getting-started/11.production-deploy-to-aks/11.production-deploy-to-aks.ipynb @@ -313,9 +313,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb b/01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb index 8baa8db5..0db73f0f 100644 --- a/01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb +++ b/01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb @@ -105,8 +105,8 @@ "inputs_dc = ModelDataCollector(\"best_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\", \"feat3\". \"feat4\", \"feat5\", \"Feat6\"])\n", "prediction_dc = ModelDataCollector(\"best_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"])```\n", " \n", - "* Identifier: Identifier is later used for building the folder structure in your Blob, it can be used to divide “raw” data versus “processed”.\n", - "* CorrelationId: is an optional parameter, you do not need to set it up if your model doesn’t require it. Having a correlationId in place does help you for easier mapping with other data. (Examples include: LoanNumber, CustomerId, etc.)\n", + "* Identifier: Identifier is later used for building the folder structure in your Blob, it can be used to divide \"raw\" data versus \"processed\".\n", + "* CorrelationId: is an optional parameter, you do not need to set it up if your model doesn't require it. Having a correlationId in place does help you for easier mapping with other data. (Examples include: LoanNumber, CustomerId, etc.)\n", "* Feature Names: These need to be set up in the order of your features in order for them to have column names when the .csv is created.\n", "\n", "### c. In your run function add:\n", @@ -425,9 +425,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:myenv3]", + "display_name": "Python 3.6", "language": "python", - "name": "conda-env-myenv3-py" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/00.configuration.ipynb b/automl/00.configuration.ipynb index 9a847dae..bc12144d 100644 --- a/automl/00.configuration.ipynb +++ b/automl/00.configuration.ipynb @@ -203,7 +203,7 @@ "metadata": {}, "outputs": [], "source": [ - "# load workspace configuratio from ./aml_config/config.json file.ß\n", + "# load workspace configuratio from ./aml_config/config.json file.\n", "my_workspace = Workspace.from_config()\n", "my_workspace.get_details()" ] @@ -243,9 +243,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/01.auto-ml-classification.ipynb b/automl/01.auto-ml-classification.ipynb index c42f2ca6..a5f6fe98 100644 --- a/automl/01.auto-ml-classification.ipynb +++ b/automl/01.auto-ml-classification.ipynb @@ -377,9 +377,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/02.auto-ml-regression.ipynb b/automl/02.auto-ml-regression.ipynb index c5e1ef05..d7b7aa16 100644 --- a/automl/02.auto-ml-regression.ipynb +++ b/automl/02.auto-ml-regression.ipynb @@ -387,9 +387,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/03.auto-ml-remote-execution.ipynb b/automl/03.auto-ml-remote-execution.ipynb index f153c0dd..f3c8b75f 100644 --- a/automl/03.auto-ml-remote-execution.ipynb +++ b/automl/03.auto-ml-remote-execution.ipynb @@ -449,9 +449,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/03b.auto-ml-remote-batchai.ipynb b/automl/03b.auto-ml-remote-batchai.ipynb index 567125cd..c63fbc37 100644 --- a/automl/03b.auto-ml-remote-batchai.ipynb +++ b/automl/03b.auto-ml-remote-batchai.ipynb @@ -500,9 +500,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb b/automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb index 10d90e89..6e4bb178 100644 --- a/automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb +++ b/automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb @@ -473,9 +473,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb b/automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb index 5345a67c..22bff7a0 100644 --- a/automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb +++ b/automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb @@ -374,9 +374,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/06.auto-ml-sparse-data-custom-cv-split.ipynb b/automl/06.auto-ml-sparse-data-custom-cv-split.ipynb index eb9c1b3c..ac683123 100644 --- a/automl/06.auto-ml-sparse-data-custom-cv-split.ipynb +++ b/automl/06.auto-ml-sparse-data-custom-cv-split.ipynb @@ -396,9 +396,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/07.auto-ml-exploring-previous-runs.ipynb b/automl/07.auto-ml-exploring-previous-runs.ipynb index 2717ae90..2258385d 100644 --- a/automl/07.auto-ml-exploring-previous-runs.ipynb +++ b/automl/07.auto-ml-exploring-previous-runs.ipynb @@ -154,7 +154,7 @@ "metadata": {}, "outputs": [], "source": [ - "run_id = 'AutoML_b7c4076b-181d-4ef4-ab9f-36bb44c1e36c'\n", + "run_id = '' # Filling your own run_id\n", "\n", "from azureml.train.widgets import RunDetails\n", "\n", @@ -304,9 +304,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb b/automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb index 65f5e475..9eead8a1 100644 --- a/automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb +++ b/automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb @@ -458,9 +458,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/09.auto-ml-classification-with-deployment.ipynb b/automl/09.auto-ml-classification-with-deployment.ipynb index 08eb64d7..220ad834 100644 --- a/automl/09.auto-ml-classification-with-deployment.ipynb +++ b/automl/09.auto-ml-classification-with-deployment.ipynb @@ -478,9 +478,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/10.auto-ml-multi-output-example.ipynb b/automl/10.auto-ml-multi-output-example.ipynb index e8dc247d..e699b7f5 100644 --- a/automl/10.auto-ml-multi-output-example.ipynb +++ b/automl/10.auto-ml-multi-output-example.ipynb @@ -270,9 +270,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/11.auto-ml-sample-weight.ipynb b/automl/11.auto-ml-sample-weight.ipynb index ac48493a..2afce723 100644 --- a/automl/11.auto-ml-sample-weight.ipynb +++ b/automl/11.auto-ml-sample-weight.ipynb @@ -229,9 +229,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb b/automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb index 2dee41be..4d6b5eb8 100644 --- a/automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb +++ b/automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb @@ -218,9 +218,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/automl/13.auto-ml-dataprep.ipynb b/automl/13.auto-ml-dataprep.ipynb index c280f40c..bd8c6175 100644 --- a/automl/13.auto-ml-dataprep.ipynb +++ b/automl/13.auto-ml-dataprep.ipynb @@ -545,9 +545,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/onnx/onnx-inference-emotion-recognition.ipynb b/onnx/onnx-inference-emotion-recognition.ipynb index e7b039b5..8f956b1a 100644 --- a/onnx/onnx-inference-emotion-recognition.ipynb +++ b/onnx/onnx-inference-emotion-recognition.ipynb @@ -706,9 +706,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:finaldemo]", + "display_name": "Python 3.6", "language": "python", - "name": "conda-env-finaldemo-py" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/onnx/onnx-inference-mnist.ipynb b/onnx/onnx-inference-mnist.ipynb index 8514984e..61c663c1 100644 --- a/onnx/onnx-inference-mnist.ipynb +++ b/onnx/onnx-inference-mnist.ipynb @@ -831,9 +831,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:finaldemo]", + "display_name": "Python 3.6", "language": "python", - "name": "conda-env-finaldemo-py" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/pipeline/00.pipeline-setup.ipynb b/pipeline/00.pipeline-setup.ipynb index 2f54e4e4..3dac5110 100644 --- a/pipeline/00.pipeline-setup.ipynb +++ b/pipeline/00.pipeline-setup.ipynb @@ -54,9 +54,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/pipeline/pipeline-batch-scoring.ipynb b/pipeline/pipeline-batch-scoring.ipynb index 9362d720..3a244ed2 100644 --- a/pipeline/pipeline-batch-scoring.ipynb +++ b/pipeline/pipeline-batch-scoring.ipynb @@ -9,6 +9,13 @@ "Licensed under the MIT License." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook demonstrates how to run batch scoring job. __[Inception-V3 model](https://arxiv.org/abs/1512.00567)__ and unlabeled images from __[ImageNet](http://image-net.org/)__ dataset will be used. It registers a pretrained inception model in model registry then uses the model to do batch scoring on images in a blob container." + ] + }, { "cell_type": "code", "execution_count": null, @@ -106,11 +113,13 @@ "import numpy as np\n", "import shutil\n", "from tensorflow.contrib.slim.python.slim.nets import inception_v3\n", + "from azureml.core.model import Model\n", "\n", "slim = tf.contrib.slim\n", "\n", "parser = argparse.ArgumentParser(description=\"Start a tensorflow model serving\")\n", - "parser.add_argument('--model_dir', dest=\"model_dir\", required=True)\n", + "parser.add_argument('--model_name', dest=\"model_name\", required=True)\n", + "parser.add_argument('--label_dir', dest=\"label_dir\", required=True)\n", "parser.add_argument('--dataset_path', dest=\"dataset_path\", required=True)\n", "parser.add_argument('--output_dir', dest=\"output_dir\", required=True)\n", "parser.add_argument('--batch_size', dest=\"batch_size\", type=int, required=True)\n", @@ -162,12 +171,14 @@ "\n", "def main(_):\n", " start_time = datetime.datetime.now()\n", - " label_file_name = os.path.join(args.model_dir, \"labels.txt\")\n", + " label_file_name = os.path.join(args.label_dir, \"labels.txt\")\n", " label_dict = get_class_label_dict(label_file_name)\n", " classes_num = len(label_dict)\n", " test_feeder = DataIterator(data_dir=args.dataset_path)\n", " total_size = len(test_feeder.labels)\n", " count = 0\n", + " # get model from model registry\n", + " model_path = Model.get_model_path(args.model_name)\n", " with tf.Session() as sess:\n", " test_images = test_feeder.input_pipeline(batch_size=args.batch_size)\n", " with slim.arg_scope(inception_v3.inception_v3_arg_scope()):\n", @@ -182,7 +193,6 @@ " coord = tf.train.Coordinator()\n", " threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n", " saver = tf.train.Saver()\n", - " model_path = os.path.join(args.model_dir, \"inception_v3.ckpt\")\n", " saver.restore(sess, model_path)\n", " out_filename = os.path.join(args.output_dir, \"result-labels.txt\")\n", " with open(out_filename, \"w\") as result_file:\n", @@ -208,13 +218,56 @@ " tf.app.run()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare Model and Input data" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "account_name = \"pipelinedata\"\n", + "# create directory for model\n", + "model_dir = 'models'\n", + "if not os.path.isdir(model_dir):\n", + " os.mkdir(model_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download Model\n", + "This manual step is required to register the model to the workspace\n", + "\n", + "Download and extract model from http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz to model_dir" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get samples images and upload to Datastore\n", + "This manual step is required to run batchai_score.py\n", + "\n", + "Download and extract sample images from ImageNet evaluation set and **upload** to a blob that will be registered as a Datastore in the next step\n", + "\n", + "A copy of sample images from ImageNet evaluation set can be found at __[BatchAI Samples Blob](https://batchaisamples.blob.core.windows.net/samples/imagenet_samples.zip?st=2017-09-29T18%3A29%3A00Z&se=2099-12-31T08%3A00%3A00Z&sp=rl&sv=2016-05-31&sr=c&sig=PmhL%2BYnYAyNTZr1DM2JySvrI12e%2F4wZNIwCtf7TRI%2BM%3D)__ \n", + "\n", + "There are multiple ways to create folders and upload files into Azure Blob Container - you can use __[Azure Portal](https://ms.portal.azure.com/)__, __[Storage Explorer](http://storageexplorer.com/)__, __[Azure CLI2](https://render.githubusercontent.com/azure-cli-extension)__ or Azure SDK for your preferable programming language. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "account_name = \"batchscoringdata\"\n", "sample_data = Datastore.register_azure_blob_container(ws, \"sampledata\", \"sampledata\", \n", " account_name=account_name, \n", " overwrite=True)" @@ -279,11 +332,42 @@ " path_on_datastore=\"batchscoring/models\",\n", " mode=\"download\" \n", " )\n", + "label_dir = DataReference(datastore=sample_data, \n", + " data_reference_name=\"input_labels\",\n", + " path_on_datastore=\"batchscoring/labels\",\n", + " mode=\"download\" \n", + " )\n", "output_dir = PipelineData(name=\"scores\", \n", " datastore_name=default_ds, \n", " output_path_on_compute=\"batchscoring/results\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Register the model with Workspace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "from azureml.core.model import Model\n", + "\n", + "# register downloaded model \n", + "model = Model.register(model_path = \"models/inception_v3.ckpt\",\n", + " model_name = \"inception\", # this is the name the model is registered as\n", + " tags = {'pretrained': \"inception\"},\n", + " description = \"Imagenet trained tensorflow inception\",\n", + " workspace = ws)\n", + "# remove the downloaded dir after registration if you wish\n", + "shutil.rmtree(\"models\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -337,19 +421,21 @@ "metadata": {}, "outputs": [], "source": [ - "step = PythonScriptStep(\n", + "inception_model_name = \"inception_v3.ckpt\"\n", + "\n", + "batch_score_step = PythonScriptStep(\n", " name=\"batch ai scoring\",\n", " script_name=\"batchai_score.py\",\n", " arguments=[\"--dataset_path\", input_images, \n", - " \"--model_dir\", model_dir, \n", + " \"--model_name\", \"inception\",\n", + " \"--label_dir\", label_dir, \n", " \"--output_dir\", output_dir, \n", " \"--batch_size\", batch_size_param],\n", " target=cluster,\n", - " inputs=[input_images, model_dir],\n", + " inputs=[input_images, label_dir],\n", " outputs=[output_dir],\n", " runconfig=batchai_run_config,\n", - " source_directory=project_folder,\n", - " allow_reuse=False\n", + " source_directory=project_folder\n", ")" ] }, @@ -359,7 +445,7 @@ "metadata": {}, "outputs": [], "source": [ - "pipeline = Pipeline(workspace=ws, steps=[step])\n", + "pipeline = Pipeline(workspace=ws, steps=[batch_score_step])\n", "pipeline_run = Experiment(ws, 'batch_scoring').submit(pipeline, pipeline_params={\"param_batch_size\": 20})" ] }, @@ -389,29 +475,21 @@ "pipeline_run.wait_for_completion(show_output=True)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "step_run = list(pipeline_run.get_children())[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "step_run.download_file(\"./outputs/result-labels.txt\")" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Display few results" + "# Download and review output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "step_run = list(pipeline_run.get_children())[0]\n", + "step_run.download_file(\"./outputs/result-labels.txt\")" ] }, { @@ -447,7 +525,7 @@ "outputs": [], "source": [ "published_pipeline = pipeline_run.publish_pipeline(\n", - " name=\"batch score\", description=\"scores images kept in container sampledata\", version=\"1.0\")\n", + " name=\"Inception v3 scoring\", description=\"Batch scoring using Inception v3 model\", version=\"1.0\")\n", "\n", "published_id = published_pipeline.id" ] @@ -483,7 +561,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Hit the REST endpoint" + "## Run published pipeline using its REST endpoint" ] }, { @@ -495,6 +573,7 @@ "from azureml.pipeline.core import PublishedPipeline\n", "\n", "rest_endpoint = PublishedPipeline.get_endpoint(published_id, ws)\n", + "# specify batch size when running the pipeline\n", "response = requests.post(rest_endpoint, headers=aad_token, json={\"param_batch_size\": 50})\n", "run_id = response.json()[\"Id\"]" ] @@ -517,20 +596,13 @@ "\n", "RunDetails(published_pipeline_run).show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { @@ -542,7 +614,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/project-brainwave/project-brainwave-custom-weights.ipynb b/project-brainwave/project-brainwave-custom-weights.ipynb index 99509235..0c352679 100644 --- a/project-brainwave/project-brainwave-custom-weights.ipynb +++ b/project-brainwave/project-brainwave-custom-weights.ipynb @@ -554,7 +554,7 @@ "\n", "New BSD License\n", "\n", - "Copyright (c) 2007–2018 The scikit-learn developers.\n", + "Copyright (c) 2007-2018 The scikit-learn developers.\n", "All rights reserved.\n", "\n", "\n", @@ -595,9 +595,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/project-brainwave/project-brainwave-quickstart.ipynb b/project-brainwave/project-brainwave-quickstart.ipynb index e0b72dce..2bcecd54 100644 --- a/project-brainwave/project-brainwave-quickstart.ipynb +++ b/project-brainwave/project-brainwave-quickstart.ipynb @@ -287,9 +287,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/project-brainwave/project-brainwave-transfer-learning.ipynb b/project-brainwave/project-brainwave-transfer-learning.ipynb index e5551f20..4f0ca4a2 100644 --- a/project-brainwave/project-brainwave-transfer-learning.ipynb +++ b/project-brainwave/project-brainwave-transfer-learning.ipynb @@ -545,9 +545,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/training/01.train-hyperparameter-tune-deploy-with-pytorch/01.train-hyperparameter-tune-deploy-with-pytorch.ipynb b/training/01.train-hyperparameter-tune-deploy-with-pytorch/01.train-hyperparameter-tune-deploy-with-pytorch.ipynb index 5bc54040..be802667 100644 --- a/training/01.train-hyperparameter-tune-deploy-with-pytorch/01.train-hyperparameter-tune-deploy-with-pytorch.ipynb +++ b/training/01.train-hyperparameter-tune-deploy-with-pytorch/01.train-hyperparameter-tune-deploy-with-pytorch.ipynb @@ -736,9 +736,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:amlsdk]", + "display_name": "Python 3.6", "language": "python", - "name": "conda-env-amlsdk-py" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/training/02.distributed-pytorch-with-horovod/02.distributed-pytorch-with-horovod.ipynb b/training/02.distributed-pytorch-with-horovod/02.distributed-pytorch-with-horovod.ipynb index da7c539f..3e31658c 100644 --- a/training/02.distributed-pytorch-with-horovod/02.distributed-pytorch-with-horovod.ipynb +++ b/training/02.distributed-pytorch-with-horovod/02.distributed-pytorch-with-horovod.ipynb @@ -266,9 +266,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/training/03.train-hyperparameter-tune-deploy-with-tensorflow/.ipynb_checkpoints/03.train-hyperparameter-tune-deploy-with-tensorflow-checkpoint.ipynb b/training/03.train-hyperparameter-tune-deploy-with-tensorflow/.ipynb_checkpoints/03.train-hyperparameter-tune-deploy-with-tensorflow-checkpoint.ipynb deleted file mode 100644 index 4d62b5f4..00000000 --- a/training/03.train-hyperparameter-tune-deploy-with-tensorflow/.ipynb_checkpoints/03.train-hyperparameter-tune-deploy-with-tensorflow-checkpoint.ipynb +++ /dev/null @@ -1,1624 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "bf74d2e9-2708-49b1-934b-e0ede342f475" - } - }, - "source": [ - "# 03. Training MNIST dataset with hyperparameter tuning & deploy to ACI\n", - "\n", - "## Introduction\n", - "This tutorial shows how to train a simple deep neural network using the MNIST dataset and TensorFlow on Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of `28x28` pixels, representing number from 0 to 9. The goal is to create a multi-class classifier to identify the digit each image represents, and deploy it as a web service in Azure.\n", - "\n", - "For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/).\n", - "\n", - "## Prerequisite:\n", - "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n", - "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n", - " * install the AML SDK\n", - " * create a workspace and its configuration file (`config.json`)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's get started. First let's import some Python libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "c377ea0c-0cd9-4345-9be2-e20fb29c94c3" - } - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import os\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "edaa7f2f-2439-4148-b57a-8c794c0945ec" - } - }, - "outputs": [], - "source": [ - "import azureml\n", - "from azureml.core import Workspace, Run\n", - "\n", - "# check core SDK version number\n", - "print(\"Azure ML SDK Version: \", azureml.core.VERSION)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize workspace\n", - "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.workspace import Workspace\n", - "\n", - "ws = Workspace.from_config()\n", - "print('Workspace name: ' + ws.name, \n", - " 'Azure region: ' + ws.location, \n", - " 'Subscription id: ' + ws.subscription_id, \n", - " 'Resource group: ' + ws.resource_group, sep = '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "59f52294-4a25-4c92-bab8-3b07f0f44d15" - } - }, - "source": [ - "## Create an Azure ML experiment\n", - "Let's create an experiment named \"tf-mnist\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "bc70f780-c240-4779-96f3-bc5ef9a37d59" - } - }, - "outputs": [], - "source": [ - "from azureml.core import Experiment\n", - "\n", - "script_folder = './tf-mnist'\n", - "os.makedirs(script_folder, exist_ok=True)\n", - "\n", - "exp = Experiment(workspace=ws, name='tf-mnist')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "defe921f-8097-44c3-8336-8af6700804a7" - } - }, - "source": [ - "## Download MNIST dataset\n", - "In order to train on the MNIST dataset we will first need to download it from Yan LeCun's web site directly and save them in a `data` folder locally." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import urllib\n", - "\n", - "os.makedirs('./data/mnist', exist_ok=True)\n", - "\n", - "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz', filename = './data/mnist/train-images.gz')\n", - "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', filename = './data/mnist/train-labels.gz')\n", - "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename = './data/mnist/test-images.gz')\n", - "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename = './data/mnist/test-labels.gz')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea" - } - }, - "source": [ - "## Show some sample images\n", - "Let's load the downloaded compressed file into numpy arrays using some utility functions included in the `utils.py` library file from the current folder. Then we use `matplotlib` to plot 30 random images from the dataset along with their labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "nbpresent": { - "id": "396d478b-34aa-4afa-9898-cdce8222a516" - } - }, - "outputs": [], - "source": [ - "from utils import load_data\n", - "\n", - "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster.\n", - "X_train = load_data('./data/mnist/train-images.gz', False) / 255.0\n", - "y_train = load_data('./data/mnist/train-labels.gz', True).reshape(-1)\n", - "\n", - "X_test = load_data('./data/mnist/test-images.gz', False) / 255.0\n", - "y_test = load_data('./data/mnist/test-labels.gz', True).reshape(-1)\n", - "\n", - "count = 0\n", - "sample_size = 30\n", - "plt.figure(figsize = (16, 6))\n", - "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n", - " count = count + 1\n", - " plt.subplot(1, sample_size, count)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " plt.text(x = 10, y = -10, s = y_train[i], fontsize = 18)\n", - " plt.imshow(X_train[i].reshape(28, 28), cmap = plt.cm.Greys)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Upload MNIST dataset to default datastore \n", - "A [datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data) is a place where data can be stored that is then made accessible to a Run either by means of mounting or copying the data to the compute target. A datastore can either be backed by an Azure Blob Storage or and Azure File Share (ADLS will be supported in the future). For simple data handling, each workspace provides a default datastore that can be used, in case the data is not already in Blob Storage or File Share.\n", - "\n", - "In this next step, we will upload the training and test set into the workspace's default datastore, which we will then later be mount on a Batch AI cluster for training.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds = ws.get_default_datastore()\n", - "ds.upload(src_dir='./data/mnist', target_path='mnist', overwrite=True, show_progress=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Batch AI cluster as compute target\n", - "[Batch AI](https://docs.microsoft.com/en-us/azure/batch-ai/overview) is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Batch AI cluster in the current workspace, if it doesn't already exist. We will then run the training script on this compute target." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we could not find the cluster with the given name in the previous cell, then we will create a new cluster here. We will create a Batch AI Cluster of `STANDARD_D2_V2` CPU VMs. This process is broken down into 3 steps:\n", - "1. create the configuration (this step is local and only takes a second)\n", - "2. create the Batch AI cluster (this step will take about **20 seconds**)\n", - "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, BatchAiCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# choose a name for your cluster\n", - "batchai_cluster_name = \"gpucluster\"\n", - "\n", - "try:\n", - " # look for the existing cluster by name\n", - " compute_target = ComputeTarget(workspace=ws, name=batchai_cluster_name)\n", - " if compute_target is BatchAiCompute:\n", - " print('found compute target {}, just use it.'.format(batchai_cluster_name))\n", - " else:\n", - " print('{} exists but it is not a Batch AI cluster. Please choose a different name.'.format(batchai_cluster_name))\n", - "except ComputeTargetException:\n", - " print('creating a new compute target...')\n", - " compute_config = BatchAiCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\", # GPU-based VM\n", - " #vm_priority='lowpriority', # optional\n", - " autoscale_enabled=True,\n", - " cluster_min_nodes=0, \n", - " cluster_max_nodes=4)\n", - "\n", - " # create the cluster\n", - " compute_target = ComputeTarget.create(ws, batchai_cluster_name, compute_config)\n", - " \n", - " # can poll for a minimum number of nodes and for a specific timeout. \n", - " # if no min node count is provided it uses the scale settings for the cluster\n", - " compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", - " \n", - " # Use the 'status' property to get a detailed status for the current cluster. \n", - " print(compute_target.status.serialize())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that you have created the compute target, let's see what the workspace's `compute_targets()` function returns. You should now see one entry named 'cpucluster' of type BatchAI." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for ct in ws.compute_targets():\n", - " print(ct.name, ct.type, ct.provisioning_state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Copy the training files into the script folder\n", - "The TensorFlow training script is already created for you. You can simply copy it into the script folder, together with the utility library used to load compressed data file into numpy array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "# the training logic is in the tf_mnist.py file.\n", - "shutil.copy('./tf_mnist.py', script_folder)\n", - "\n", - "# the utils.py just helps loading data from the downloaded MNIST dataset into numpy arrays.\n", - "shutil.copy('./utils.py', script_folder)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "2039d2d5-aca6-4f25-a12f-df9ae6529cae" - } - }, - "source": [ - "## Construct neural network in TensorFlow\n", - "In the training script `tf_mnist.py`, it creates a very simple DNN (deep neural network), with just 2 hidden layers. The input layer has 28 * 28 = 784 neurons, each representing a pixel in an image. The first hidden layer has 300 neurons, and the second hidden layer has 100 neurons. The output layer has 10 neurons, each representing a targeted label from 0 to 9.\n", - "\n", - "![DNN](nn.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Azure ML concepts \n", - "Please note the following three things in the code below:\n", - "1. The script accepts arguments using the argparse package. In this case there is one argument `--data_folder` which specifies the file system folder in which the script can find the MNIST data\n", - "```\n", - " parser = argparse.ArgumentParser()\n", - " parser.add_argument('--data_folder')\n", - "```\n", - "2. The script is accessing the Azure ML `Run` object by executing `run = Run.get_submitted_run()`. Further down the script is using the `run` to report the training accuracy and the validation accuracy as training progresses.\n", - "```\n", - " run.log('training_acc', np.float(acc_train))\n", - " run.log('validation_acc', np.float(acc_val))\n", - "```\n", - "3. When running the script on Azure ML, you can write files out to a folder `./outputs` that is relative to the root directory. This folder is specially tracked by Azure ML in the sense that any files written to that folder during script execution on the remote target will be picked up by Run History; these files (known as artifacts) will be available as part of the run history record." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next cell will print out the training code for you to inspect it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open(os.path.join(script_folder, './tf_mnist.py'), 'r') as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create TensorFlow estimator\n", - "Next, we construct an `azureml.train.dnn.TensorFlow` estimator object, use the Batch AI cluster as compute target, and pass the mount-point of the datastore to the training code as a parameter.\n", - "The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed -- if additional pip or conda packages are required, their names can be passed in via the `pip_packages` and `conda_packages` arguments and they will be included in the resulting docker." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.dnn import TensorFlow\n", - "\n", - "script_params = {\n", - " '--data-folder': ws.get_default_datastore().as_mount(),\n", - " '--batch-size': 50,\n", - " '--first-layer-neurons': 300,\n", - " '--second-layer-neurons': 100,\n", - " '--learning-rate': 0.01\n", - "}\n", - "\n", - "est = TensorFlow(source_directory=script_folder,\n", - " script_params=script_params,\n", - " compute_target=compute_target,\n", - " entry_script='tf_mnist.py', \n", - " use_gpu=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submit job to run\n", - "Calling the `fit` function on the estimator submits the job to Azure ML for execution. Submitting the job should only take a few seconds." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run = exp.submit(config=est)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Monitor the Run\n", - "As the Run is executed, it will go through the following stages:\n", - "1. Preparing: A docker image is created matching the Python environment specified by the TensorFlow estimator and it will be uploaded to the workspace's Azure Container Registry. This step will only happen once for each Python environment -- the container will then be cached for subsequent runs. Creating and uploading the image takes about **5 minutes**. While the job is preparing, logs are streamed to the run history and can be viewed to monitor the progress of the image creation.\n", - "\n", - "2. Scaling: If the compute needs to be scaled up (i.e. the Batch AI cluster requires more nodes to execute the run than currently available), the Batch AI cluster will attempt to scale up in order to make the required amount of nodes available. Scaling typically takes about **5 minutes**.\n", - "\n", - "3. Running: All scripts in the script folder are uploaded to the compute target, data stores are mounted/copied and the `entry_script` is executed. While the job is running, stdout and the `./logs` folder are streamed to the run history and can be viewed to monitor the progress of the run.\n", - "\n", - "4. Post-Processing: The `./outputs` folder of the run is copied over to the run history\n", - "\n", - "There are multiple ways to check the progress of a running job. We can use a Jupyter notebook widget. \n", - "\n", - "**Note: The widget will automatically update ever 10-15 seconds, always showing you the most up-to-date information about the run**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.widgets import RunDetails\n", - "RunDetails(run).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also periodically check the status of the run object, and navigate to Azure portal to monitor the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The Run object\n", - "The Run object provides the interface to the run history -- both to the job and to the control plane (this notebook), and both while the job is running and after it has completed. It provides a number of interesting features for instance:\n", - "* `run.get_details()`: Provides a rich set of properties of the run\n", - "* `run.get_metrics()`: Provides a dictionary with all the metrics that were reported for the Run\n", - "* `run.get_file_names()`: List all the files that were uploaded to the run history for this Run. This will include the `outputs` and `logs` folder, azureml-logs and other logs, as well as files that were explicitly uploaded to the run using `run.upload_file()`\n", - "\n", - "Below are some examples -- please run through them and inspect their output. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_details()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_metrics()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run.get_file_names()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot accuracy over epochs\n", - "Since we can retrieve the metrics from the run, we can easily make plots using `matplotlib` in the notebook. Then we can add the plotted image to the run using `run.log_image()`, so all information about the run is kept together." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "os.makedirs('./imgs', exist_ok = True)\n", - "metrics = run.get_metrics()\n", - "\n", - "plt.figure(figsize = (13,5))\n", - "plt.plot(metrics['validation_acc'], 'r-', lw = 4, alpha = .6)\n", - "plt.plot(metrics['training_acc'], 'b--', alpha = 0.5)\n", - "plt.legend(['Full evaluation set', 'Training set mini-batch'])\n", - "plt.xlabel('epochs', fontsize = 14)\n", - "plt.ylabel('accuracy', fontsize = 14)\n", - "plt.title('Accuracy over Epochs', fontsize = 16)\n", - "run.log_image(name = 'acc_over_epochs.png', plot = plt)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download the saved model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the training script, a TensorFlow `saver` object is used to persist the model in a local folder (local to the compute target). The model was saved to the `./outputs` folder on the disk of the Batch AI cluster node where the job is run. Azure ML automatically uploaded anything written in the `./outputs` folder into run history file store. Subsequently, we can use the `Run` object to download the model files the `saver` object saved. They are under the the `outputs/model` folder in the run history file store, and are downloaded into a local folder named `model`. Note the TensorFlow model consists of four files in binary format and they are not human-readable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create a model folder in the current directory\n", - "os.makedirs('./model', exist_ok = True)\n", - "\n", - "for f in run.get_file_names():\n", - " if f.startswith('outputs/model'):\n", - " output_file_path = os.path.join('./model', f.split('/')[-1])\n", - " print('Downloading from {} to {} ...'.format(f, output_file_path))\n", - " run.download_file(name = f, output_file_path = output_file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predict on the test set\n", - "Now load the saved TensorFlow graph, and list all operations under the `network` scope. This way we can discover the input tensor `network/X:0` and the output tensor `network/output/MatMul:0`, and use them in the scoring script in the next step.\n", - "\n", - "Note: if your local TensorFlow version is different than the version running in the cluster where the model is trained, you might see a \"compiletime version mismatch\" warning. You can ignore it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "tf.reset_default_graph()\n", - "\n", - "saver = tf.train.import_meta_graph(\"./model/mnist-tf.model.meta\")\n", - "graph = tf.get_default_graph()\n", - "\n", - "for op in graph.get_operations():\n", - " if op.name.startswith('network'):\n", - " print(op.name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Feed test dataset to the persisted model to get predictions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# input tensor. this is an array of 784 elements, each representing the intensity of a pixel in the digit image.\n", - "X = tf.get_default_graph().get_tensor_by_name(\"network/X:0\")\n", - "# output tensor. this is an array of 10 elements, each representing the probability of predicted value of the digit.\n", - "output = tf.get_default_graph().get_tensor_by_name(\"network/output/MatMul:0\")\n", - "\n", - "with tf.Session() as sess:\n", - " saver.restore(sess, './model/mnist-tf.model')\n", - " k = output.eval(feed_dict = {X : X_test})\n", - "# get the prediction, which is the index of the element that has the largest probability value.\n", - "y_hat = np.argmax(k, axis = 1)\n", - "\n", - "# print the first 30 labels and predictions\n", - "print('labels: \\t', y_test[:30])\n", - "print('predictions:\\t', y_hat[:30])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate the overall accuracy by comparing the predicted value against the test set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Accuracy on the test set:\", np.average(y_hat == y_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Intelligent hyperparameter tuning\n", - "We have trained the model with one set of hyperparameters, now let's how we can do hyperparameter tuning by launching multiple runs on the cluster. First let's define the parameter space using random sampling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.train.hyperdrive import *\n", - "\n", - "ps = RandomParameterSampling(\n", - " {\n", - " '--batch-size': choice(25, 50, 100),\n", - " '--first-layer-neurons': choice(10, 50, 200, 300, 500),\n", - " '--second-layer-neurons': choice(10, 50, 200, 500),\n", - " '--learning-rate': loguniform(-6, -1)\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will create a new estimator without the above parameters since they will be passed in later. Note we still need to keep the `data-folder` parameter since that's not a hyperparamter we will sweep." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "est = TensorFlow(source_directory=script_folder,\n", - " script_params={'--data-folder': ws.get_default_datastore().as_mount()},\n", - " compute_target=compute_target,\n", - " entry_script='tf_mnist.py', \n", - " use_gpu=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will define an early termnination policy. The `BanditPolicy` basically states to check the job every 2 iterations. If the primary metric (defined later) falls outside of the top 10% range, Azure ML terminate the job. This saves us from continuing to explore hyperparameters that don't show promise of helping reach our target metric." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "policy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are ready to configure a run configuration object, and specify the primary metric `validation_acc` that's recorded in your training runs. If you go back to visit the training script, you will notice that this value is being logged after every epoch (a full batch set). We also want to tell the service that we are looking to maximizing this value. We also set the number of samples to 20, and maximal concurrent job to 4, which is the same as the number of nodes in our computer cluster." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "htc = HyperDriveRunConfig(estimator=est, \n", - " hyperparameter_sampling=ps, \n", - " primary_metric_name='validation_acc', \n", - " primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, \n", - " max_total_runs=20,\n", - " max_concurrent_runs=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's launch the hyperparameter tuning job." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "htr = exp.submit(config=htc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use a run history widget to show the progress. Be patient as this might take a while to complete." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RunDetails(htr).show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Find and register best model\n", - "When all the jobs finish, we can find out the one that has the highest accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "best_run = htr.get_best_run_by_primary_metric()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's list the model files uploaded during the run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(best_run.get_file_names()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then register the folder (and all files in it) as a model named `tf-dnn-mnist` under the workspace for deployment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = best_run.register_model(model_name='tf-dnn-mnist', model_path='outputs/model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deploy the model in ACI\n", - "Now we are ready to deploy the model as a web service running in Azure Container Instance [ACI](https://azure.microsoft.com/en-us/services/container-instances/). Azure Machine Learning accomplishes this by constructing a Docker image with the scoring logic and model baked in.\n", - "### Create score.py\n", - "First, we will create a scoring script that will be invoked by the web service call. \n", - "\n", - "* Note that the scoring script must have two required functions, `init()` and `run(input_data)`. \n", - " * In `init()` function, you typically load the model into a global object. This function is executed only once when the Docker container is started. \n", - " * In `run(input_data)` function, the model is used to predict a value based on the input data. The input and output to `run` typically use JSON as serialization and de-serialization format but you are not limited to that." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile score.py\n", - "import json\n", - "import numpy as np\n", - "import os\n", - "import tensorflow as tf\n", - "\n", - "from azureml.core.model import Model\n", - "\n", - "def init():\n", - " global X, output, sess\n", - " tf.reset_default_graph()\n", - " model_root = Model.get_model_path('tf-dnn-mnist')\n", - " saver = tf.train.import_meta_graph(os.path.join(model_root, 'mnist-tf.model.meta'))\n", - " X = tf.get_default_graph().get_tensor_by_name(\"network/X:0\")\n", - " output = tf.get_default_graph().get_tensor_by_name(\"network/output/MatMul:0\")\n", - " \n", - " sess = tf.Session()\n", - " saver.restore(sess, os.path.join(model_root, 'mnist-tf.model'))\n", - "\n", - "def run(raw_data):\n", - " data = np.array(json.loads(raw_data)['data'])\n", - " # make prediction\n", - " out = output.eval(session = sess, feed_dict = {X: data})\n", - " y_hat = np.argmax(out, axis = 1)\n", - " return json.dumps(y_hat.tolist())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create myenv.yml\n", - "We also need to create an environment file so that Azure Machine Learning can install the necessary packages in the Docker image which are required by your scoring script. In this case, we need to specify packages `numpy`, `tensorflow`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import CondaDependencies\n", - "cd = CondaDependencies.create()\n", - "cd.add_conda_package('numpy')\n", - "cd.add_tensorflow_conda_package()\n", - "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n", - "\n", - "print(cd.serialize_to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deploy to ACI\n", - "We are almost ready to deploy. Create a deployment configuration and specify the number of CPUs and gigbyte of RAM needed for your ACI container. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.webservice import AciWebservice\n", - "\n", - "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n", - " memory_gb=1, \n", - " tags={'name':'mnist', 'framework': 'TensorFlow DNN'},\n", - " description='Tensorflow DNN on MNIST')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Deployment Process\n", - "Now we can deploy. **This cell will run for about 7-8 minutes**. Behind the scene, it will do the following:\n", - "1. **Register model** \n", - "Take the local `model` folder (which contains our previously downloaded trained model files) and register it (and the files inside that folder) as a model named `model` under the workspace. Azure ML will register the model directory or model file(s) we specify to the `model_paths` parameter of the `Webservice.deploy` call.\n", - "2. **Build Docker image** \n", - "Build a Docker image using the scoring file (`score.py`), the environment file (`myenv.yml`), and the `model` folder containing the TensorFlow model files. \n", - "3. **Register image** \n", - "Register that image under the workspace. \n", - "4. **Ship to ACI** \n", - "And finally ship the image to the ACI infrastructure, start up a container in ACI using that image, and expose an HTTP endpoint to accept REST client calls." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.image import ContainerImage\n", - "imgconfig = ContainerImage.image_configuration(execution_script=\"score.py\", \n", - " runtime=\"python\", \n", - " conda_file=\"myenv.yml\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "from azureml.core.webservice import Webservice\n", - "\n", - "service = Webservice.deploy_from_model(workspace=ws,\n", - " name='tf-mnist-svc',\n", - " deployment_config=aciconfig,\n", - " models=[model],\n", - " image_config=imgconfig)\n", - "\n", - "service.wait_for_deployment(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Tip: If something goes wrong with the deployment, the first thing to look at is the logs from the service by running the following command:**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(service.get_logs())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the scoring web service endpoint:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(service.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test the deployed model\n", - "Let's test the deployed model. Pick 30 random samples from the test set, and send it to the web service hosted in ACI. Note here we are using the `run` API in the SDK to invoke the service. You can also make raw HTTP calls using any HTTP tool such as curl.\n", - "\n", - "After the invocation, we print the returned predictions and plot them along with the input images. Use red font color and inversed image (white on black) to highlight the misclassified samples. Note since the model accuracy is pretty high, you might have to run the below cell a few times before you can see a misclassified sample." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "# find 30 random samples from test set\n", - "n = 30\n", - "sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n", - "\n", - "test_samples = json.dumps({\"data\": X_test[sample_indices].tolist()})\n", - "test_samples = bytes(test_samples, encoding = 'utf8')\n", - "\n", - "# predict using the deployed model\n", - "result = json.loads(service.run(input_data = test_samples))\n", - "\n", - "# compare actual value vs. the predicted values:\n", - "i = 0\n", - "plt.figure(figsize = (20, 1))\n", - "\n", - "for s in sample_indices:\n", - " plt.subplot(1, n, i + 1)\n", - " plt.axhline('')\n", - " plt.axvline('')\n", - " \n", - " # use different color for misclassified sample\n", - " font_color = 'red' if y_test[s] != result[i] else 'black'\n", - " clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n", - " \n", - " plt.text(x = 10, y = -10, s = y_hat[s], fontsize = 18, color = font_color)\n", - " plt.imshow(X_test[s].reshape(28, 28), cmap = clr_map)\n", - " \n", - " i = i + 1\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also send raw HTTP request to the service." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "import json\n", - "\n", - "# send a random row from the test set to score\n", - "random_index = np.random.randint(0, len(X_test)-1)\n", - "input_data = \"{\\\"data\\\": [\" + str(list(X_test[random_index])) + \"]}\"\n", - "\n", - "headers = {'Content-Type':'application/json'}\n", - "\n", - "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n", - "\n", - "print(\"POST to url\", service.scoring_uri)\n", - "#print(\"input data:\", input_data)\n", - "print(\"label:\", y_test[random_index])\n", - "print(\"prediction:\", resp.text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the workspace after the web service was deployed. You should see \n", - "* a registered model named 'model' and with the id 'model:1'\n", - "* an image called 'tf-mnist' and with a docker image location pointing to your workspace's Azure Container Registry (ACR) \n", - "* a webservice called 'tf-mnist' with some scoring URL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for model in ws.models():\n", - " print(\"Model:\", model.name, model.id)\n", - "\n", - "for image in ws.images():\n", - " print(\"Image:\", image.name, image.image_location)\n", - "\n", - "for webservice in ws.webservices():\n", - " print(\"Webservice:\", webservice.name, webservice.scoring_uri)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Clean up\n", - "You can delete the ACI deployment with a simple delete API call." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "service.delete()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also delete the computer cluster. But remember if you set the `cluster_min_nodes` value to 0 when you created the cluster, once the jobs are finished, all nodes are deleted automatically. So you don't have to delete the cluster itself since it won't incur any cost. Next time you submit jobs to it, the cluster will then automatically \"grow\" up to the `cluster_min_nodes` which is set to 4." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# delete the cluster if you need to.\n", - "compute_target.delete()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "nbpresent": { - "slides": { - "05bb34ad-74b0-42b3-9654-8357d1ba9c99": { - "id": "05bb34ad-74b0-42b3-9654-8357d1ba9c99", - "prev": "851089af-9725-40c9-8f0b-9bf892b2b1fe", - "regions": { - "23fb396d-50f9-4770-adb3-0d6abcb40767": { - "attrs": { - "height": 0.8, - "width": 0.8, - "x": 0.1, - "y": 0.1 - }, - 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"display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/training/04.distributed-tensorflow-with-horovod/04.distributed-tensorflow-with-horovod.ipynb b/training/04.distributed-tensorflow-with-horovod/04.distributed-tensorflow-with-horovod.ipynb index 221444e0..a360ba52 100644 --- a/training/04.distributed-tensorflow-with-horovod/04.distributed-tensorflow-with-horovod.ipynb +++ b/training/04.distributed-tensorflow-with-horovod/04.distributed-tensorflow-with-horovod.ipynb @@ -337,9 +337,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/training/05.distributed-tensorflow-with-parameter-server/05.distributed-tensorflow-with-parameter-server.ipynb b/training/05.distributed-tensorflow-with-parameter-server/05.distributed-tensorflow-with-parameter-server.ipynb index 92daf093..a77eaf78 100644 --- a/training/05.distributed-tensorflow-with-parameter-server/05.distributed-tensorflow-with-parameter-server.ipynb +++ b/training/05.distributed-tensorflow-with-parameter-server/05.distributed-tensorflow-with-parameter-server.ipynb @@ -263,9 +263,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/training/06.distributed-cntk-with-custom-docker/06.distributed-cntk-with-custom-docker.ipynb b/training/06.distributed-cntk-with-custom-docker/06.distributed-cntk-with-custom-docker.ipynb index c24c1c91..4769985a 100644 --- a/training/06.distributed-cntk-with-custom-docker/06.distributed-cntk-with-custom-docker.ipynb +++ b/training/06.distributed-cntk-with-custom-docker/06.distributed-cntk-with-custom-docker.ipynb @@ -342,9 +342,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/training/07.tensorboard/07.tensorboard.ipynb b/training/07.tensorboard/07.tensorboard.ipynb index ec0f9b65..55759993 100644 --- a/training/07.tensorboard/07.tensorboard.ipynb +++ b/training/07.tensorboard/07.tensorboard.ipynb @@ -482,9 +482,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/training/08.export-run-history-to-tensorboard/08.export-run-history-to-tensorboard.ipynb b/training/08.export-run-history-to-tensorboard/08.export-run-history-to-tensorboard.ipynb index bac37e58..410105a2 100644 --- a/training/08.export-run-history-to-tensorboard/08.export-run-history-to-tensorboard.ipynb +++ b/training/08.export-run-history-to-tensorboard/08.export-run-history-to-tensorboard.ipynb @@ -221,9 +221,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/01.train-models.ipynb b/tutorials/01.train-models.ipynb index d12b04e7..41041dff 100644 --- a/tutorials/01.train-models.ipynb +++ b/tutorials/01.train-models.ipynb @@ -676,9 +676,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/02.deploy-models.ipynb b/tutorials/02.deploy-models.ipynb index 3a36c1fc..2b15288b 100644 --- a/tutorials/02.deploy-models.ipynb +++ b/tutorials/02.deploy-models.ipynb @@ -541,9 +541,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": { diff --git a/tutorials/03.auto-train-models.ipynb b/tutorials/03.auto-train-models.ipynb index 76e7d3a9..87401fe8 100644 --- a/tutorials/03.auto-train-models.ipynb +++ b/tutorials/03.auto-train-models.ipynb @@ -191,7 +191,7 @@ "|**max_time_sec**|12,000|Time limit in seconds for each iteration|\n", "|**iterations**|20|Number of iterations. In each iteration, the model trains with the data with a specific pipeline|\n", "|**n_cross_validations**|3|Number of cross validation splits|\n", - "|**preprocess**|True| *True/False* Enables experiment to perform preprocessing on the input. Preprocessing handles *missing data*, and performs some common *feature extraction*|\n", + "|**preprocess**|False| *True/False* Enables experiment to perform preprocessing on the input. Preprocessing handles *missing data*, and performs some common *feature extraction*|\n", "|**exit_score**|0.995|*double* value indicating the target for *primary_metric*. Once the target is surpassed the run terminates|\n", "|**blacklist_algos**|['kNN','LinearSVM']|*Array* of *strings* indicating algorithms to ignore.\n" ] @@ -210,7 +210,7 @@ " max_time_sec = 12000,\n", " iterations = 20,\n", " n_cross_validations = 3,\n", - " preprocess = True,\n", + " preprocess = False,\n", " exit_score = 0.995,\n", " blacklist_algos = ['kNN','LinearSVM'],\n", " X = X_digits,\n", @@ -380,9 +380,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "python36" }, "language_info": { "codemirror_mode": {