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Roope Astala
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training/01.train-hyperparameter-tune-deploy-with-pytorch/01.train-hyperparameter-tune-deploy-with-pytorch.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 01. Train, hyperparameter tune, and deploy with PyTorch\n",
"\n",
"In this tutorial, you will train, hyperparameter tune, and deploy a PyTorch model using the Azure Machine Learning (AML) Python SDK.\n",
"\n",
"This tutorial will train an image classification model using transfer learning, based on PyTorch's [Transfer Learning tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html). The model is trained to classify ants and bees by first using a pretrained ResNet18 model that has been trained on the [ImageNet](http://image-net.org/index) dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\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": "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": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"set_diagnostics_collection(send_diagnostics = True)"
]
},
{
"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": {},
"source": [
"## Create a remote compute target\n",
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
"\n",
"**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
]
},
{
"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",
"cluster_name = \"gpucluster\"\n",
"\n",
"try:\n",
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
" print('Found existing compute target.')\n",
"except ComputeTargetException:\n",
" print('Creating a new compute target...')\n",
" compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \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, cluster_name, compute_config)\n",
"\n",
" compute_target.wait_for_completion(show_output=True)\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": [
"The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload training data\n",
"The dataset we will use consists of about 120 training images each for ants and bees, with 75 validation images for each class.\n",
"\n",
"First, download the dataset (located [here](https://download.pytorch.org/tutorial/hymenoptera_data.zip) as a zip file) locally to your current directory and extract the files. This will create a folder called `hymenoptera_data` with two subfolders `train` and `val` that contain the training and validation images, respectively. [Hymenoptera](https://en.wikipedia.org/wiki/Hymenoptera) is the order of insects that includes ants and bees."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import urllib\n",
"from zipfile import ZipFile\n",
"\n",
"# download data\n",
"download_url = 'https://download.pytorch.org/tutorial/hymenoptera_data.zip'\n",
"data_file = './hymenoptera_data.zip'\n",
"urllib.request.urlretrieve(download_url, filename=data_file)\n",
"\n",
"# extract files\n",
"with ZipFile(data_file, 'r') as zip:\n",
" print('extracting files...')\n",
" zip.extractall()\n",
" print('done')\n",
" \n",
"# delete zip file\n",
"os.remove(data_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make the data accessible for remote training, you will need to upload the data from your local machine to the cloud. AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data, and interact with it from your remote compute targets. \n",
"\n",
"**Note: If your data is already stored in Azure, or you download the data as part of your training script, you will not need to do this step.**\n",
"\n",
"Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds = ws.get_default_datastore()\n",
"print(ds.datastore_type, ds.account_name, ds.container_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code will upload the training data to the path `./hymenoptera_data` on the default datastore."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds.upload(src_dir='./hymenoptera_data', target_path='hymenoptera_data')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's get a reference to the path on the datastore with the training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--data_dir` argument. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path_on_datastore = 'hymenoptera_data'\n",
"ds_data = ds.path(path_on_datastore)\n",
"print(ds_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train model on the remote compute\n",
"Now that you have your data and training script prepared, you are ready to train on your remote compute cluster. You can take advantage of Azure compute to leverage GPUs to cut down your training time. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a project directory\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"project_folder = './pytorch-hymenoptera'\n",
"os.makedirs(project_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare training script\n",
"Now you will need to create your training script. In this tutorial, the training script is already provided for you at `pytorch_train.py`. In practice, you should be able to take any custom training script as is and run it with AML without having to modify your code.\n",
"\n",
"However, if you would like to use AML's [tracking and metrics](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#metrics) capabilities, you will have to add a small amount of AML code inside your training script. \n",
"\n",
"In `pytorch_train.py`, we will log some metrics to our AML run. To do so, we will access the AML run object within the script:\n",
"```Python\n",
"from azureml.core.run import Run\n",
"run = Run.get_context()\n",
"```\n",
"Further within `pytorch_train.py`, we log the learning rate and momentum parameters, and the best validation accuracy the model achieves:\n",
"```Python\n",
"run.log('lr', np.float(learning_rate))\n",
"run.log('momentum', np.float(momentum))\n",
"\n",
"run.log('best_val_acc', np.float(best_acc))\n",
"```\n",
"These run metrics will become particularly important when we begin hyperparameter tuning our model in the \"Tune model hyperparameters\" section."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once your script is ready, copy the training script `pytorch_train.py` into your project directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"shutil.copy('pytorch_train.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an experiment\n",
"Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this transfer learning PyTorch tutorial. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = 'pytorch-hymenoptera'\n",
"experiment = Experiment(ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a PyTorch estimator\n",
"The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch). The following code will define a single-node PyTorch job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import PyTorch\n",
"\n",
"script_params = {\n",
" '--data_dir': ds_data,\n",
" '--num_epochs': 25,\n",
" '--output_dir': './outputs'\n",
"}\n",
"\n",
"estimator = PyTorch(source_directory=project_folder, \n",
" script_params=script_params,\n",
" compute_target=compute_target,\n",
" entry_script='pytorch_train.py',\n",
" use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`. Please note the following:\n",
"- We passed our training data reference `ds_data` to our script's `--data_dir` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the training data `hymenoptera_data` on our datastore.\n",
"- We specified the output directory as `./outputs`. The `outputs` directory is specially treated by AML in that all the content in this directory gets uploaded to your workspace as part of your run history. The files written to this directory are therefore accessible even once your remote run is over. In this tutorial, we will save our trained model to this output directory.\n",
"\n",
"To leverage the Azure VM's GPU for training, we set `use_gpu=True`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit job\n",
"Run your experiment by submitting your estimator object. Note that this call is asynchronous."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = experiment.submit(estimator)\n",
"print(run.get_details())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor your run\n",
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.widgets import RunDetails\n",
"RunDetails(run).show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tune model hyperparameters\n",
"Now that we've seen how to do a simple PyTorch training run using the SDK, let's see if we can further improve the accuracy of our model. We can optimize our model's hyperparameters using Azure Machine Learning's hyperparameter tuning capabilities."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Start a hyperparameter sweep\n",
"First, we will define the hyperparameter space to sweep over. Since our training script uses a learning rate schedule to decay the learning rate every several epochs, let's tune the initial learning rate and the momentum parameters. In this example we will use random sampling to try different configuration sets of hyperparameters to maximize our primary metric, the best validation accuracy (`best_val_acc`).\n",
"\n",
"Then, we specify the early termination policy to use to early terminate poorly performing runs. Here we use the `BanditPolicy`, which will terminate any run that doesn't fall within the slack factor of our primary evaluation metric. In this tutorial, we will apply this policy every epoch (since we report our `best_val_acc` metric every epoch and `evaluation_interval=1`). Notice we will delay the first policy evaluation until after the first `10` epochs (`delay_evaluation=10`).\n",
"Refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-tune-hyperparameters#specify-an-early-termination-policy) for more information on the BanditPolicy and other policies available."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.hyperdrive import *\n",
"\n",
"param_sampling = RandomParameterSampling( {\n",
" 'learning_rate': uniform(0.0005, 0.005),\n",
" 'momentum': uniform(0.9, 0.99)\n",
" }\n",
")\n",
"\n",
"early_termination_policy = BanditPolicy(slack_factor=0.15, evaluation_interval=1, delay_evaluation=10)\n",
"\n",
"hyperdrive_run_config = HyperDriveRunConfig(estimator=estimator,\n",
" hyperparameter_sampling=param_sampling, \n",
" policy=early_termination_policy,\n",
" primary_metric_name='best_val_acc',\n",
" primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,\n",
" max_total_runs=20,\n",
" max_concurrent_runs=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, lauch the hyperparameter tuning job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# start the HyperDrive run\n",
"hyperdrive_run = experiment.submit(hyperdrive_run_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor HyperDrive runs\n",
"You can monitor the progress of the runs with the following Jupyter widget. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.widgets import RunDetails\n",
"\n",
"RunDetails(hyperdrive_run).show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hyperdrive_run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find and register the best model\n",
"Once all the runs complete, we can find the run that produced the model with the highest accuracy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run = hyperdrive_run.get_best_run_by_primary_metric()\n",
"best_run_metrics = best_run.get_metrics()\n",
"print(best_run)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('Best Run is:\\n Validation accuracy: {0:.5f} \\n Learning rate: {1:.5f} \\n Momentum: {2:.5f}'.format(\n",
" best_run_metrics['best_val_acc'][-1],\n",
" best_run_metrics['lr'],\n",
" best_run_metrics['momentum'])\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, register the model from your best-performing run to your workspace. The `model_path` parameter takes in the relative path on the remote VM to the model file in your `outputs` directory. In the next section, we will deploy this registered model as a web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = best_run.register_model(model_name = 'pytorch-hymenoptera', model_path = 'outputs/model.pt')\n",
"print(model.name, model.id, model.version, sep = '\\t')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy model as web service\n",
"Once you have your trained model, you can deploy the model on Azure. In this tutorial, we will deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI). For more information on deploying models using Azure ML, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create scoring script\n",
"\n",
"First, we will create a scoring script that will be invoked by the web service call. Note that the scoring script must have two required functions:\n",
"* `init()`: In this function, you typically load the model into a `global` object. This function is executed only once when the Docker container is started. \n",
"* `run(input_data)`: In this function, the model is used to predict a value based on the input data. The input and output typically use JSON as serialization and deserialization format, but you are not limited to that.\n",
"\n",
"Refer to the scoring script `pytorch_score.py` for this tutorial. Our web service will use this file to predict whether an image is an ant or a bee. When writing your own scoring script, don't forget to test it locally first before you go and deploy the web service."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create environment file\n",
"Then, we will need to create an environment file (`myenv.yml`) that specifies all of the scoring script's package dependencies. This file is used to ensure that all of those dependencies are installed in the Docker image by AML. In this case, we need to specify `torch`, `torchvision`, `pillow`, and `azureml-sdk`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile myenv.yml\n",
"name: myenv\n",
"channels:\n",
" - defaults\n",
"dependencies:\n",
" - pip:\n",
" - torch\n",
" - torchvision\n",
" - pillow\n",
" - azureml-core"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure the container image\n",
"Now configure the Docker image that you will use to build your ACI container."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script='pytorch_score.py', \n",
" runtime='python', \n",
" conda_file='myenv.yml',\n",
" description='Image with hymenoptera model')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure the ACI container\n",
"We are almost ready to deploy. Create a deployment configuration file to specify the number of CPUs and gigabytes of RAM needed for your ACI container. While it depends on your model, the default of `1` core and `1` gigabyte of RAM is usually sufficient for many models."
]
},
{
"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={'data': 'hymenoptera', 'method':'transfer learning', 'framework':'pytorch'},\n",
" description='Classify ants/bees using transfer learning with PyTorch')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the registered model\n",
"Finally, let's deploy a web service from our registered model. First, retrieve the model from your workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import Model\n",
"\n",
"model = Model(ws, name='pytorch-hymenoptera')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, deploy the web service using the ACI config and image config files created in the previous steps. We pass the `model` object in a list to the `models` parameter. If you would like to deploy more than one registered model, append the additional models to this list."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"from azureml.core.webservice import Webservice\n",
"\n",
"service_name = 'aci-hymenoptera'\n",
"service = Webservice.deploy_from_model(workspace=ws,\n",
" name=service_name,\n",
" models=[model],\n",
" image_config=image_config,\n",
" deployment_config=aciconfig,)\n",
"\n",
"service.wait_for_deployment(show_output=True)\n",
"print(service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If your deployment fails for any reason and you need to redeploy, make sure to delete the service before you do so: `service.delete()`"
]
},
{
"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": [
"service.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the web service's HTTP endpoint, which accepts REST client calls. This endpoint can be shared with anyone who wants to test the web service or integrate it into an application."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(service.scoring_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test the web service\n",
"Finally, let's test our deployed web service. We will send the data as a JSON string to the web service hosted in ACI and use the SDK's `run` API to invoke the service. Here we will take an arbitrary image from our validation data to predict on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os, json, base64\n",
"from io import BytesIO\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def imgToBase64(img):\n",
" \"\"\"Convert pillow image to base64-encoded image\"\"\"\n",
" imgio = BytesIO()\n",
" img.save(imgio, 'JPEG')\n",
" img_str = base64.b64encode(imgio.getvalue())\n",
" return img_str.decode('utf-8')\n",
"\n",
"test_img = os.path.join('hymenoptera_data', 'val', 'bees', '10870992_eebeeb3a12.jpg') #arbitary image from val dataset\n",
"plt.imshow(Image.open(test_img))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"base64Img = imgToBase64(Image.open(test_img))\n",
"\n",
"result = service.run(input_data=json.dumps({'data': base64Img}))\n",
"print(json.loads(result))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete web service\n",
"Once you no longer need the web service, you should delete it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"service.delete()"
]
}
],
"metadata": {
"authors": [
{
"name": "minxia"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"msauthor": "minxia"
},
"nbformat": 4,
"nbformat_minor": 2
}

57
training/01.train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py
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@@ -1,57 +0,0 @@
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
import torch
import torch.nn as nn
from torchvision import transforms
import json
import base64
from io import BytesIO
from PIL import Image
from azureml.core.model import Model
def preprocess_image(image_file):
"""Preprocess the input image."""
data_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image = Image.open(image_file)
image = data_transforms(image).float()
image = torch.tensor(image)
image = image.unsqueeze(0)
return image
def base64ToImg(base64ImgString):
base64Img = base64ImgString.encode('utf-8')
decoded_img = base64.b64decode(base64Img)
return BytesIO(decoded_img)
def init():
global model
model_path = Model.get_model_path('pytorch-hymenoptera')
model = torch.load(model_path, map_location=lambda storage, loc: storage)
model.eval()
def run(input_data):
img = base64ToImg(json.loads(input_data)['data'])
img = preprocess_image(img)
# get prediction
output = model(img)
classes = ['ants', 'bees']
softmax = nn.Softmax(dim=1)
pred_probs = softmax(model(img)).detach().numpy()[0]
index = torch.argmax(output, 1)
result = {"label": classes[index], "probability": str(pred_probs[index])}
return result

176
training/01.train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py
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@@ -1,176 +0,0 @@
# Copyright (c) 2017, PyTorch contributors
# Modifications copyright (C) Microsoft Corporation
# Licensed under the BSD license
# Adapted from https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torchvision import datasets, models, transforms
import numpy as np
import time
import os
import copy
import argparse
from azureml.core.run import Run
# get the Azure ML run object
run = Run.get_context()
def load_data(data_dir):
"""Load the train/val data."""
# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
shuffle=True, num_workers=0)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
return dataloaders, dataset_sizes, class_names
def train_model(model, criterion, optimizer, scheduler, num_epochs, data_dir):
"""Train the model."""
# load training/validation data
dataloaders, dataset_sizes, class_names = load_data(data_dir)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
# log the best val accuracy to AML run
run.log('best_val_acc', np.float(best_acc))
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
def fine_tune_model(num_epochs, data_dir, learning_rate, momentum):
"""Load a pretrained model and reset the final fully connected layer."""
# log the hyperparameter metrics to the AML run
run.log('lr', np.float(learning_rate))
run.log('momentum', np.float(momentum))
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2) # only 2 classes to predict
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=learning_rate, momentum=momentum)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs, data_dir)
return model
def main():
# get command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, help='directory of training data')
parser.add_argument('--num_epochs', type=int, default=25, help='number of epochs to train')
parser.add_argument('--output_dir', type=str, help='output directory')
parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
args = parser.parse_args()
print("data directory is: " + args.data_dir)
model = fine_tune_model(args.num_epochs, args.data_dir, args.learning_rate, args.momentum)
os.makedirs(args.output_dir, exist_ok=True)
torch.save(model, os.path.join(args.output_dir, 'model.pt'))
if __name__ == "__main__":
main()

315
training/02.distributed-pytorch-with-horovod/02.distributed-pytorch-with-horovod.ipynb
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@@ -1,315 +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": [
"# 02. Distributed PyTorch with Horovod\n",
"In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\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`)\n",
"* Review the [tutorial](https://aka.ms/aml-notebook-pytorch) on single-node PyTorch training using the SDK"
]
},
{
"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": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"set_diagnostics_collection(send_diagnostics = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize workspace\n",
"\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": {},
"source": [
"## Create a remote compute target\n",
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
"\n",
"**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
]
},
{
"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",
"cluster_name = \"gpucluster\"\n",
"\n",
"try:\n",
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
" print('Found existing compute target.')\n",
"except ComputeTargetException:\n",
" print('Creating a new compute target...')\n",
" compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \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, cluster_name, compute_config)\n",
"\n",
" compute_target.wait_for_completion(show_output=True)\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": [
"The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train model on the remote compute\n",
"Now that we have the cluster ready to go, let's run our distributed training job."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a project directory\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"project_folder = './pytorch-distr-hvd'\n",
"os.makedirs(project_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copy the training script `pytorch_horovod_mnist.py` into this project directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"shutil.copy('pytorch_horovod_mnist.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an experiment\n",
"Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = 'pytorch-distr-hvd'\n",
"experiment = Experiment(ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a PyTorch estimator\n",
"The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import PyTorch\n",
"\n",
"estimator = PyTorch(source_directory=project_folder,\n",
" compute_target=compute_target,\n",
" entry_script='pytorch_horovod_mnist.py',\n",
" node_count=2,\n",
" process_count_per_node=1,\n",
" distributed_backend='mpi',\n",
" use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, PyTorch, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `PyTorch` constructor's `pip_packages` or `conda_packages` parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit job\n",
"Run your experiment by submitting your estimator object. Note that this call is asynchronous."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = experiment.submit(estimator)\n",
"print(run.get_details())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor your run\n",
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.widgets import RunDetails\n",
"RunDetails(run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can block until the script has completed training before running more code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True) # this provides a verbose log"
]
}
],
"metadata": {
"authors": [
{
"name": "minxia"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"msauthor": "minxia"
},
"nbformat": 4,
"nbformat_minor": 2
}

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training/03.train-hyperparameter-tune-deploy-with-tensorflow/tf_mnist.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import numpy as np
import argparse
import os
import tensorflow as tf
from azureml.core import Run
from utils import load_data
print("TensorFlow version:", tf.VERSION)
parser = argparse.ArgumentParser()
parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')
parser.add_argument('--batch-size', type=int, dest='batch_size', default=50, help='mini batch size for training')
parser.add_argument('--first-layer-neurons', type=int, dest='n_hidden_1', default=100,
help='# of neurons in the first layer')
parser.add_argument('--second-layer-neurons', type=int, dest='n_hidden_2', default=100,
help='# of neurons in the second layer')
parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.01, help='learning rate')
args = parser.parse_args()
data_folder = os.path.join(args.data_folder, 'mnist')
print('training dataset is stored here:', data_folder)
X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0
X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)
y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n')
training_set_size = X_train.shape[0]
n_inputs = 28 * 28
n_h1 = args.n_hidden_1
n_h2 = args.n_hidden_2
n_outputs = 10
learning_rate = args.learning_rate
n_epochs = 50
batch_size = args.batch_size
with tf.name_scope('network'):
# construct the DNN
X = tf.placeholder(tf.float32, shape=(None, n_inputs), name='X')
y = tf.placeholder(tf.int64, shape=(None), name='y')
h1 = tf.layers.dense(X, n_h1, activation=tf.nn.relu, name='h1')
h2 = tf.layers.dense(h1, n_h2, activation=tf.nn.relu, name='h2')
output = tf.layers.dense(h2, n_outputs, name='output')
with tf.name_scope('train'):
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=output)
loss = tf.reduce_mean(cross_entropy, name='loss')
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss)
with tf.name_scope('eval'):
correct = tf.nn.in_top_k(output, y, 1)
acc_op = tf.reduce_mean(tf.cast(correct, tf.float32))
init = tf.global_variables_initializer()
saver = tf.train.Saver()
# start an Azure ML run
run = Run.get_context()
with tf.Session() as sess:
init.run()
for epoch in range(n_epochs):
# randomly shuffle training set
indices = np.random.permutation(training_set_size)
X_train = X_train[indices]
y_train = y_train[indices]
# batch index
b_start = 0
b_end = b_start + batch_size
for _ in range(training_set_size // batch_size):
# get a batch
X_batch, y_batch = X_train[b_start: b_end], y_train[b_start: b_end]
# update batch index for the next batch
b_start = b_start + batch_size
b_end = min(b_start + batch_size, training_set_size)
# train
sess.run(train_op, feed_dict={X: X_batch, y: y_batch})
# evaluate training set
acc_train = acc_op.eval(feed_dict={X: X_batch, y: y_batch})
# evaluate validation set
acc_val = acc_op.eval(feed_dict={X: X_test, y: y_test})
# log accuracies
run.log('training_acc', np.float(acc_train))
run.log('validation_acc', np.float(acc_val))
print(epoch, '-- Training accuracy:', acc_train, '\b Validation accuracy:', acc_val)
y_hat = np.argmax(output.eval(feed_dict={X: X_test}), axis=1)
run.log('final_acc', np.float(acc_val))
os.makedirs('./outputs/model', exist_ok=True)
# files saved in the "./outputs" folder are automatically uploaded into run history
saver.save(sess, './outputs/model/mnist-tf.model')

27
training/03.train-hyperparameter-tune-deploy-with-tensorflow/utils.py
View File

@@ -1,27 +0,0 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import gzip
import numpy as np
import struct
# load compressed MNIST gz files and return numpy arrays
def load_data(filename, label=False):
with gzip.open(filename) as gz:
struct.unpack('I', gz.read(4))
n_items = struct.unpack('>I', gz.read(4))
if not label:
n_rows = struct.unpack('>I', gz.read(4))[0]
n_cols = struct.unpack('>I', gz.read(4))[0]
res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
res = res.reshape(n_items[0], n_rows * n_cols)
else:
res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
res = res.reshape(n_items[0], 1)
return res
# one-hot encode a 1-D array
def one_hot_encode(array, num_of_classes):
return np.eye(num_of_classes)[array.reshape(-1)]

2
training/04.distributed-tensorflow-with-horovod/.gitignore vendored
View File

@@ -1,2 +0,0 @@
/data/
/tf-distr-hvd/

386
training/04.distributed-tensorflow-with-horovod/04.distributed-tensorflow-with-horovod.ipynb
View File

@@ -1,386 +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. Distributed Tensorflow with Horovod\n",
"In this tutorial, you will train a word2vec model in TensorFlow using distributed training via [Horovod](https://github.com/uber/horovod)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\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`)\n",
"* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
]
},
{
"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": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"set_diagnostics_collection(send_diagnostics = True)"
]
},
{
"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": {},
"source": [
"## Create a remote compute target\n",
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
"\n",
"**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
]
},
{
"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",
"cluster_name = \"gpucluster\"\n",
"\n",
"try:\n",
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
" print('Found existing compute target')\n",
"except ComputeTargetException:\n",
" print('Creating a new compute target...')\n",
" compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \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, cluster_name, compute_config)\n",
"\n",
" compute_target.wait_for_completion(show_output=True)\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": [
"The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload data to datastore\n",
"To make data accessible for remote training, AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data to Azure Storage, and interact with it from your remote compute targets. \n",
"\n",
"If your data is already stored in Azure, or you download the data as part of your training script, you will not need to do this step. For this tutorial, although you can download the data in your training script, we will demonstrate how to upload the training data to a datastore and access it during training to illustrate the datastore functionality."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, download the training data from [here](http://mattmahoney.net/dc/text8.zip) to your local machine:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import urllib\n",
"\n",
"os.makedirs('./data', exist_ok=True)\n",
"download_url = 'http://mattmahoney.net/dc/text8.zip'\n",
"urllib.request.urlretrieve(download_url, filename='./data/text8.zip')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore. The below code will upload the contents of the data directory to the path `./data` on the default datastore."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds = ws.get_default_datastore()\n",
"print(ds.datastore_type, ds.account_name, ds.container_name)\n",
"\n",
"ds.upload(src_dir='data', target_path='data', overwrite=True, show_progress=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For convenience, let's get a reference to the path on the datastore with the zip file of training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--input_data` argument. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path_on_datastore = 'data/text8.zip'\n",
"ds_data = ds.path(path_on_datastore)\n",
"print(ds_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train model on the remote compute"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a project directory\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"project_folder = './tf-distr-hvd'\n",
"os.makedirs(project_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copy the training script `tf_horovod_word2vec.py` into this project directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"shutil.copy('tf_horovod_word2vec.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an experiment\n",
"Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = 'tf-distr-hvd'\n",
"experiment = Experiment(ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a TensorFlow estimator\n",
"The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import TensorFlow\n",
"\n",
"script_params={\n",
" '--input_data': ds_data\n",
"}\n",
"\n",
"estimator= TensorFlow(source_directory=project_folder,\n",
" compute_target=compute_target,\n",
" script_params=script_params,\n",
" entry_script='tf_horovod_word2vec.py',\n",
" node_count=2,\n",
" process_count_per_node=1,\n",
" distributed_backend='mpi',\n",
" use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, TensorFlow, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters.\n",
"\n",
"Note that we passed our training data reference `ds_data` to our script's `--input_data` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the data zip file on our datastore."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit job\n",
"Run your experiment by submitting your estimator object. Note that this call is asynchronous."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = experiment.submit(estimator)\n",
"print(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor your run\n",
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.widgets import RunDetails\n",
"RunDetails(run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can block until the script has completed training before running more code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True)"
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"msauthor": "minxia"
},
"nbformat": 4,
"nbformat_minor": 2
}

1
training/05.distributed-tensorflow-with-parameter-server/.gitignore vendored
View File

@@ -1 +0,0 @@
/tf-distr-ps/

313
training/05.distributed-tensorflow-with-parameter-server/05.distributed-tensorflow-with-parameter-server.ipynb
View File

@@ -1,313 +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. Distributed TensorFlow with parameter server\n",
"In this tutorial, you will train a TensorFlow model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using native [distributed TensorFlow](https://www.tensorflow.org/deploy/distributed)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\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`)\n",
"* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
]
},
{
"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": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"set_diagnostics_collection(send_diagnostics = True)"
]
},
{
"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": {},
"source": [
"## Create a remote compute target\n",
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
"\n",
"**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
]
},
{
"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",
"cluster_name = \"gpucluster\"\n",
"\n",
"try:\n",
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
" print('Found existing compute target.')\n",
"except ComputeTargetException:\n",
" print('Creating a new compute target...')\n",
" compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \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, cluster_name, compute_config)\n",
"\n",
" compute_target.wait_for_completion(show_output=True)\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": [
"## Train model on the remote compute\n",
"Now that we have the cluster ready to go, let's run our distributed training job."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a project directory\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"project_folder = './tf-distr-ps'\n",
"os.makedirs(project_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copy the training script `tf_mnist_replica.py` into this project directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"shutil.copy('tf_mnist_replica.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an experiment\n",
"Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = 'tf-distr-ps'\n",
"experiment = Experiment(ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a TensorFlow estimator\n",
"The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import TensorFlow\n",
"\n",
"script_params={\n",
" '--num_gpus': 1\n",
"}\n",
"\n",
"estimator = TensorFlow(source_directory=project_folder,\n",
" compute_target=compute_target,\n",
" script_params=script_params,\n",
" entry_script='tf_mnist_replica.py',\n",
" node_count=2,\n",
" worker_count=2,\n",
" parameter_server_count=1, \n",
" distributed_backend='ps',\n",
" use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code specifies that we will run our training script on `2` nodes, with two workers and one parameter server. In order to execute a native distributed TensorFlow run, you must provide the argument `distributed_backend='ps'`. Using this estimator with these settings, TensorFlow and its dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit job\n",
"Run your experiment by submitting your estimator object. Note that this call is asynchronous."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = experiment.submit(estimator)\n",
"print(run.get_details())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor your run\n",
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.widgets import RunDetails\n",
"RunDetails(run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can block until the script has completed training before running more code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True) # this provides a verbose log"
]
}
],
"metadata": {
"authors": [
{
"name": "minxia"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"msauthor": "minxia"
},
"nbformat": 4,
"nbformat_minor": 2
}

391
training/06.distributed-cntk-with-custom-docker/06.distributed-cntk-with-custom-docker.ipynb
View File

@@ -1,391 +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": [
"# 06. Distributed CNTK using custom docker images\n",
"In this tutorial, you will train a CNTK model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using a custom docker image and distributed training."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\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]() notebook to:\n",
" * install the AML SDK\n",
" * create a workspace and its configuration file (`config.json`)"
]
},
{
"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": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"set_diagnostics_collection(send_diagnostics = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize workspace\n",
"\n",
"Initialize a [Workspace](https://review.docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture?branch=release-ignite-aml#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": {},
"source": [
"## Create a remote compute target\n",
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
"\n",
"**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
]
},
{
"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",
"cluster_name = \"gpucluster\"\n",
"\n",
"try:\n",
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
" print('Found existing compute target.')\n",
"except ComputeTargetException:\n",
" print('Creating a new compute target...')\n",
" compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \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, cluster_name, compute_config)\n",
"\n",
" compute_target.wait_for_completion(show_output=True)\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": [
"## Upload training data\n",
"For this tutorial, we will be using the MNIST dataset.\n",
"\n",
"First, let's download the dataset. We've included the `install_mnist.py` script to download the data and convert it to a CNTK-supported format. Our data files will get written to a directory named `'mnist'`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import install_mnist\n",
"\n",
"install_mnist.main('mnist')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make the data accessible for remote training, you will need to upload the data from your local machine to the cloud. AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data, and interact with it from your remote compute targets. \n",
"\n",
"Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore, which we will then mount on the remote compute for training in the next section."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds = ws.get_default_datastore()\n",
"print(ds.datastore_type, ds.account_name, ds.container_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code will upload the training data to the path `./mnist` on the default datastore."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds.upload(src_dir='./mnist', target_path='./mnist')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's get a reference to the path on the datastore with the training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--data_dir` argument. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path_on_datastore = 'mnist'\n",
"ds_data = ds.path(path_on_datastore)\n",
"print(ds_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train model on the remote compute\n",
"Now that we have the cluster ready to go, let's run our distributed training job."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a project directory\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"project_folder = './cntk-distr'\n",
"os.makedirs(project_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copy the training script `cntk_distr_mnist.py` into this project directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"shutil.copy('cntk_distr_mnist.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an experiment\n",
"Create an [experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed CNTK tutorial. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = 'cntk-distr'\n",
"experiment = Experiment(ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an Estimator\n",
"The AML SDK's base Estimator enables you to easily submit custom scripts for both single-node and distributed runs. You should this generic estimator for training code using frameworks such as sklearn or CNTK that don't have corresponding custom estimators. For more information on using the generic estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.estimator import *\n",
"\n",
"script_params = {\n",
" '--num_epochs': 50,\n",
" '--data_dir': ds_data.as_mount(),\n",
" '--output_dir': './outputs'\n",
"}\n",
"\n",
"estimator = Estimator(source_directory=project_folder,\n",
" compute_target=compute_target,\n",
" entry_script='cntk_distr_mnist.py',\n",
" script_params=script_params,\n",
" node_count=2,\n",
" process_count_per_node=1,\n",
" distributed_backend='mpi', \n",
" pip_packages=['cntk-gpu==2.6'],\n",
" custom_docker_base_image='microsoft/mmlspark:gpu-0.12',\n",
" use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We would like to train our model using a [pre-built Docker container](https://hub.docker.com/r/microsoft/mmlspark/). To do so, specify the name of the docker image to the argument `custom_docker_base_image`. You can only provide images available in public docker repositories such as Docker Hub using this argument. To use an image from a private docker repository, use the constructor's `environment_definition` parameter instead. Finally, we provide the `cntk` package to `pip_packages` to install CNTK 2.6 on our custom image.\n",
"\n",
"The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to run distributed CNTK, which uses MPI, you must provide the argument `distributed_backend='mpi'`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit job\n",
"Run your experiment by submitting your estimator object. Note that this call is asynchronous."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = experiment.submit(estimator)\n",
"print(run.get_details())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor your run\n",
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.widgets import RunDetails\n",
"RunDetails(run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can block until the script has completed training before running more code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True)"
]
}
],
"metadata": {
"authors": [
{
"name": "minxia"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

117
training/06.distributed-cntk-with-custom-docker/cntk_distr_mnist.py
View File

@@ -1,117 +0,0 @@
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
# Adapted from:
# https://github.com/Microsoft/CNTK/blob/master/Examples/Image/Classification/ConvNet/Python/ConvNet_MNIST.py
# ====================================================================
"""Train a CNN model on the MNIST dataset via distributed training."""
from __future__ import print_function
import numpy as np
import os
import cntk as C
import argparse
from cntk.train.training_session import CheckpointConfig, TestConfig
def create_reader(path, is_training, input_dim, label_dim, total_number_of_samples):
"""Define the reader for both training and evaluation action."""
return C.io.MinibatchSource(C.io.CTFDeserializer(path, C.io.StreamDefs(
features=C.io.StreamDef(field='features', shape=input_dim),
labels=C.io.StreamDef(field='labels', shape=label_dim)
)), randomize=is_training, max_samples=total_number_of_samples)
def convnet_mnist(max_epochs, output_dir, data_dir, debug_output=False, epoch_size=60000, minibatch_size=64):
"""Creates and trains a feedforward classification model for MNIST images."""
image_height = 28
image_width = 28
num_channels = 1
input_dim = image_height * image_width * num_channels
num_output_classes = 10
# Input variables denoting the features and label data
input_var = C.ops.input_variable((num_channels, image_height, image_width), np.float32)
label_var = C.ops.input_variable(num_output_classes, np.float32)
# Instantiate the feedforward classification model
scaled_input = C.ops.element_times(C.ops.constant(0.00390625), input_var)
with C.layers.default_options(activation=C.ops.relu, pad=False):
conv1 = C.layers.Convolution2D((5, 5), 32, pad=True)(scaled_input)
pool1 = C.layers.MaxPooling((3, 3), (2, 2))(conv1)
conv2 = C.layers.Convolution2D((3, 3), 48)(pool1)
pool2 = C.layers.MaxPooling((3, 3), (2, 2))(conv2)
conv3 = C.layers.Convolution2D((3, 3), 64)(pool2)
f4 = C.layers.Dense(96)(conv3)
drop4 = C.layers.Dropout(0.5)(f4)
z = C.layers.Dense(num_output_classes, activation=None)(drop4)
ce = C.losses.cross_entropy_with_softmax(z, label_var)
pe = C.metrics.classification_error(z, label_var)
# Load train data
reader_train = create_reader(os.path.join(data_dir, 'Train-28x28_cntk_text.txt'), True,
input_dim, num_output_classes, max_epochs * epoch_size)
# Load test data
reader_test = create_reader(os.path.join(data_dir, 'Test-28x28_cntk_text.txt'), False,
input_dim, num_output_classes, C.io.FULL_DATA_SWEEP)
# Set learning parameters
lr_per_sample = [0.001] * 10 + [0.0005] * 10 + [0.0001]
lr_schedule = C.learning_parameter_schedule_per_sample(lr_per_sample, epoch_size=epoch_size)
mms = [0] * 5 + [0.9990239141819757]
mm_schedule = C.learners.momentum_schedule_per_sample(mms, epoch_size=epoch_size)
# Instantiate the trainer object to drive the model training
local_learner = C.learners.momentum_sgd(z.parameters, lr_schedule, mm_schedule)
progress_printer = C.logging.ProgressPrinter(
tag='Training',
rank=C.train.distributed.Communicator.rank(),
num_epochs=max_epochs,
)
learner = C.train.distributed.data_parallel_distributed_learner(local_learner)
trainer = C.Trainer(z, (ce, pe), learner, progress_printer)
# define mapping from reader streams to network inputs
input_map_train = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
input_map_test = {
input_var: reader_test.streams.features,
label_var: reader_test.streams.labels
}
C.logging.log_number_of_parameters(z)
print()
C.train.training_session(
trainer=trainer,
mb_source=reader_train,
model_inputs_to_streams=input_map_train,
mb_size=minibatch_size,
progress_frequency=epoch_size,
checkpoint_config=CheckpointConfig(frequency=epoch_size,
filename=os.path.join(output_dir, "ConvNet_MNIST")),
test_config=TestConfig(reader_test, minibatch_size=minibatch_size,
model_inputs_to_streams=input_map_test)
).train()
return
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--num_epochs', help='Total number of epochs to train', type=int, default='40')
parser.add_argument('--output_dir', help='Output directory', required=False, default='outputs')
parser.add_argument('--data_dir', help='Directory with training data')
args = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
convnet_mnist(args.num_epochs, args.output_dir, args.data_dir)
# Must call MPI finalize when process exit without exceptions
C.train.distributed.Communicator.finalize()

96
training/06.distributed-cntk-with-custom-docker/install_mnist.py
View File

@@ -1,96 +0,0 @@
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
# Script from:
# https://github.com/Microsoft/CNTK/blob/master/Examples/Image/DataSets/MNIST/install_mnist.py
from __future__ import print_function
try:
from urllib.request import urlretrieve
except ImportError:
from urllib import urlretrieve
import gzip
import os
import struct
import numpy as np
def loadData(src, cimg):
print('Downloading ' + src)
gzfname, h = urlretrieve(src, './delete.me')
print('Done.')
try:
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x3080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))[0]
if n != cimg:
raise Exception('Invalid file: expected {0} entries.'.format(cimg))
crow = struct.unpack('>I', gz.read(4))[0]
ccol = struct.unpack('>I', gz.read(4))[0]
if crow != 28 or ccol != 28:
raise Exception('Invalid file: expected 28 rows/cols per image.')
# Read data.
res = np.fromstring(gz.read(cimg * crow * ccol), dtype=np.uint8)
finally:
os.remove(gzfname)
return res.reshape((cimg, crow * ccol))
def loadLabels(src, cimg):
print('Downloading ' + src)
gzfname, h = urlretrieve(src, './delete.me')
print('Done.')
try:
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x1080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))
if n[0] != cimg:
raise Exception('Invalid file: expected {0} rows.'.format(cimg))
# Read labels.
res = np.fromstring(gz.read(cimg), dtype=np.uint8)
finally:
os.remove(gzfname)
return res.reshape((cimg, 1))
def load(dataSrc, labelsSrc, cimg):
data = loadData(dataSrc, cimg)
labels = loadLabels(labelsSrc, cimg)
return np.hstack((data, labels))
def savetxt(filename, ndarray):
with open(filename, 'w') as f:
labels = list(map(' '.join, np.eye(10, dtype=np.uint).astype(str)))
for row in ndarray:
row_str = row.astype(str)
label_str = labels[row[-1]]
feature_str = ' '.join(row_str[:-1])
f.write('|labels {} |features {}\n'.format(label_str, feature_str))
def main(data_dir):
os.makedirs(data_dir, exist_ok=True)
train = load('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', 60000)
print('Writing train text file...')
train_txt = os.path.join(data_dir, 'Train-28x28_cntk_text.txt')
savetxt(train_txt, train)
print('Done.')
test = load('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', 10000)
print('Writing test text file...')
test_txt = os.path.join(data_dir, 'Test-28x28_cntk_text.txt')
savetxt(test_txt, test)
print('Done.')
if __name__ == "__main__":
main('mnist')

531
training/07.tensorboard/07.tensorboard.ipynb
View File

@@ -1,531 +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": [
"# 40. Tensorboard Integration with Run History\n",
"\n",
"1. Run a Tensorflow job locally and view its TB output live.\n",
"2. The same, for a DSVM.\n",
"3. And once more, with Batch AI.\n",
"4. Finally, we'll collect all of these historical runs together into a single Tensorboard graph."
]
},
{
"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": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"set_diagnostics_collection(send_diagnostics = True)"
]
},
{
"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('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": {},
"source": [
"## Set experiment name and create project\n",
"Choose a name for your run history container in the workspace, and create a folder for the project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from os import path, makedirs\n",
"experiment_name = 'tensorboard-demo'\n",
"\n",
"# experiment folder\n",
"exp_dir = './sample_projects/' + experiment_name\n",
"\n",
"if not path.exists(exp_dir):\n",
" makedirs(exp_dir)\n",
"\n",
"# runs we started in this session, for the finale\n",
"runs = []"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Tensorflow Tensorboard demo code\n",
"\n",
"Tensorflow's repository has an MNIST demo with extensive Tensorboard instrumentation. We'll use it here for our purposes.\n",
"\n",
"Note that we don't need to make any code changes at all - the code works without modification from the Tensorflow repository."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import os\n",
"import tempfile\n",
"tf_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r1.8/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\")\n",
"with open(os.path.join(exp_dir, \"mnist_with_summaries.py\"), \"w\") as file:\n",
" file.write(tf_code.text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure and run locally\n",
"\n",
"We'll start by running this locally. While it might not initially seem that useful to use this for a local run - why not just run TB against the files generated locally? - even in this case there is some value to using this feature. Your local run will be registered in the run history, and your Tensorboard logs will be uploaded to the artifact store associated with this run. Later, you'll be able to restore the logs from any run, regardless of where it happened.\n",
"\n",
"Note that for this run, you will need to install Tensorflow on your local machine by yourself. Further, the Tensorboard module (that is, the one included with Tensorflow) must be accessible to this notebook's kernel, as the local machine is what runs Tensorboard."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"\n",
"# Create a run configuration.\n",
"run_config = RunConfiguration()\n",
"run_config.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'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment, Run\n",
"from azureml.core.script_run_config import ScriptRunConfig\n",
"import tensorflow as tf\n",
"\n",
"logs_dir = os.path.join(os.curdir, \"logs\")\n",
"data_dir = os.path.abspath(os.path.join(os.curdir, \"mnist_data\"))\n",
"\n",
"if not path.exists(data_dir):\n",
" makedirs(data_dir)\n",
"\n",
"os.environ[\"TEST_TMPDIR\"] = data_dir\n",
"\n",
"# Writing logs to ./logs results in their being uploaded to Artifact Service,\n",
"# and thus, made accessible to our Tensorboard instance.\n",
"arguments_list = [\"--log_dir\", logs_dir]\n",
"\n",
"# Create an experiment\n",
"exp = Experiment(ws, experiment_name)\n",
"\n",
"# If you would like the run to go for longer, add --max_steps 5000 to the arguments list:\n",
"# arguments_list += [\"--max_steps\", \"5000\"]\n",
"\n",
"script = ScriptRunConfig(exp_dir,\n",
" script=\"mnist_with_summaries.py\",\n",
" run_config=run_config,\n",
" arguments=arguments_list)\n",
"\n",
"run = exp.submit(script)\n",
"# You can also wait for the run to complete\n",
"# run.wait_for_completion(show_output=True)\n",
"runs.append(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start Tensorboard\n",
"\n",
"Now, while the run is in progress, we just need to start Tensorboard with the run as its target, and it will begin streaming logs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.contrib.tensorboard import Tensorboard\n",
"\n",
"# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
"tb = Tensorboard([run])\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Now, with a DSVM\n",
"\n",
"Tensorboard uploading works with all compute targets. Here we demonstrate it from a DSVM.\n",
"Note that the Tensorboard instance itself will be run by the notebook kernel. Again, this means this notebook's kernel must have access to the Tensorboard module.\n",
"\n",
"If you are unfamiliar with DSVM configuration, check [04. Train in a remote VM (Ubuntu DSVM)](04.train-on-remote-vm.ipynb) for a more detailed breakdown."
]
},
{
"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 = 'cpu-dsvm'\n",
"\n",
"try:\n",
" compute_target = DsvmCompute(workspace = ws, name = compute_target_name)\n",
" print('found existing:', compute_target.name)\n",
"except ComputeTargetException:\n",
" print('creating new.')\n",
" dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
" compute_target = DsvmCompute.create(ws, name = compute_target_name, provisioning_configuration = dsvm_config)\n",
" compute_target.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submit run using TensorFlow estimator\n",
"\n",
"Instead of manually configuring the DSVM environment, we can use the TensorFlow estimator and everything is set up automatically."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import TensorFlow\n",
"\n",
"script_params = {\"--log_dir\": \"./logs\"}\n",
"\n",
"# If you want the run to go longer, set --max-steps to a higher number.\n",
"# script_params[\"--max_steps\"] = \"5000\"\n",
"\n",
"tf_estimator = TensorFlow(source_directory=exp_dir,\n",
" compute_target=compute_target,\n",
" entry_script='mnist_with_summaries.py',\n",
" script_params=script_params)\n",
"\n",
"run = exp.submit(tf_estimator)\n",
"\n",
"runs.append(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start Tensorboard with this run\n",
"\n",
"Just like before."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
"tb = Tensorboard([run])\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Once more, with a Batch AI cluster\n",
"\n",
"Just to prove we can, let's create a Batch AI cluster using MLC, and run our demo there, as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import BatchAiCompute\n",
"\n",
"clust_name = ws.name + \"cpu\"\n",
"\n",
"try:\n",
" # If you already have a cluster named this, we don't need to make a new one.\n",
" cts = ws.compute_targets \n",
" compute_target = cts[clust_name]\n",
" assert compute_target.type == 'BatchAI'\n",
"except:\n",
" # Let's make a new one here.\n",
" provisioning_config = BatchAiCompute.provisioning_configuration(cluster_max_nodes=2, \n",
" autoscale_enabled=True, \n",
" cluster_min_nodes=1,\n",
" vm_size='Standard_D11_V2')\n",
" \n",
" compute_target = BatchAiCompute.create(ws, clust_name, provisioning_config)\n",
" compute_target.wait_for_completion(show_output=True, min_node_count=1, timeout_in_minutes=20)\n",
"print(compute_target.name)\n",
" # For a more detailed view of current BatchAI cluster status, use the 'status' property \n",
" # print(compute_target.status.serialize())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submit run using TensorFlow estimator\n",
"\n",
"Again, we can use the TensorFlow estimator and everything is set up automatically."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"script_params = {\"--log_dir\": \"./logs\"}\n",
"\n",
"# If you want the run to go longer, set --max-steps to a higher number.\n",
"# script_params[\"--max_steps\"] = \"5000\"\n",
"\n",
"tf_estimator = TensorFlow(source_directory=exp_dir,\n",
" compute_target=compute_target,\n",
" entry_script='mnist_with_summaries.py',\n",
" script_params=script_params)\n",
"\n",
"run = exp.submit(tf_estimator)\n",
"\n",
"runs.append(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start Tensorboard with this run\n",
"\n",
"Once more..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
"tb = Tensorboard([run])\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finale\n",
"\n",
"If you've paid close attention, you'll have noticed that we've been saving the run objects in an array as we went along. We can start a Tensorboard instance that combines all of these run objects into a single process. This way, you can compare historical runs. You can even do this with live runs; if you made some of those previous runs longer via the `--max_steps` parameter, they might still be running, and you'll see them live in this instance as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The Tensorboard constructor takes an array of runs...\n",
"# and it turns out that we have been building one of those all along.\n",
"tb = Tensorboard(runs)\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"As you might already know, make sure to call the `stop()` method of the Tensorboard object, or it will stay running (until you kill the kernel associated with this notebook, at least)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

248
training/08.export-run-history-to-tensorboard/08.export-run-history-to-tensorboard.ipynb
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@@ -1,248 +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": [
"# 41. Export Run History as Tensorboard logs\n",
"\n",
"1. Run some training and log some metrics into Run History\n",
"2. Export the run history to some directory as Tensorboard logs\n",
"3. Launch a local Tensorboard to view the run history"
]
},
{
"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, Run, Experiment\n",
"\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": {},
"source": [
"## Set experiment name and start the run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'export-to-tensorboard'\n",
"exp = Experiment(ws, experiment_name)\n",
"root_run = exp.start_logging()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load diabetes dataset, a well-known built-in small dataset that comes with scikit-learn\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",
"\n",
"X, y = load_diabetes(return_X_y=True)\n",
"\n",
"columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
"\n",
"x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"data = {\n",
" \"train\":{\"x\":x_train, \"y\":y_train}, \n",
" \"test\":{\"x\":x_test, \"y\":y_test}\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example experiment\n",
"from tqdm import tqdm\n",
"\n",
"alphas = [.1, .2, .3, .4, .5, .6 , .7]\n",
"\n",
"# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
"for alpha in tqdm(alphas):\n",
" # create a bunch of child runs\n",
" with root_run.child_run(\"alpha\" + str(alpha)) as run:\n",
" # More data science stuff\n",
" reg = Ridge(alpha=alpha)\n",
" reg.fit(data[\"train\"][\"x\"], data[\"train\"][\"y\"])\n",
" # TODO save model\n",
" preds = reg.predict(data[\"test\"][\"x\"])\n",
" mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
" # End train and eval\n",
"\n",
" # log alpha, mean_squared_error and feature names in run history\n",
" root_run.log(\"alpha\", alpha)\n",
" root_run.log(\"mse\", mse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export Run History to Tensorboard logs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Export Run History to Tensorboard logs\n",
"from azureml.contrib.tensorboard.export import export_to_tensorboard\n",
"import os\n",
"import tensorflow as tf\n",
"\n",
"logdir = 'exportedTBlogs'\n",
"log_path = os.path.join(os.getcwd(), logdir)\n",
"try:\n",
" os.stat(log_path)\n",
"except os.error:\n",
" os.mkdir(log_path)\n",
"print(logdir)\n",
"\n",
"# export run history for the project\n",
"export_to_tensorboard(root_run, logdir)\n",
"\n",
"# or export a particular run\n",
"# export_to_tensorboard(run, logdir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"root_run.complete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start Tensorboard\n",
"\n",
"Or you can start the Tensorboard outside this notebook to view the result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.contrib.tensorboard import Tensorboard\n",
"\n",
"# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
"tb = Tensorboard([], local_root=logdir, port=6006)\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"When you're done, make sure to call the `stop()` method of the Tensorboard object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"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
}

6
training/README.md Normal file
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@@ -0,0 +1,6 @@
## Azure Machine Learning service training examples
These examples show you:
* Distributed training of models on Machine Learning Compute cluster
* Hyperparameter tuning at scale
* Using Tensorboard with Azure ML Python SDK.

316
training/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Distributed PyTorch with Horovod\n",
"In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\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`)\n",
"* Review the [tutorial](https://aka.ms/aml-notebook-pytorch) on single-node PyTorch training using the SDK"
]
},
{
"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": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"\n",
"set_diagnostics_collection(send_diagnostics=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize workspace\n",
"\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": {},
"source": [
"## Create a remote compute target\n",
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an `AmlCompute` cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
"\n",
"**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# choose a name for your cluster\n",
"cluster_name = \"gpucluster\"\n",
"\n",
"try:\n",
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
" print('Found existing compute target.')\n",
"except ComputeTargetException:\n",
" print('Creating a new compute target...')\n",
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
" max_nodes=4)\n",
"\n",
" # create the cluster\n",
" compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
"\n",
" compute_target.wait_for_completion(show_output=True)\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": [
"The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train model on the remote compute\n",
"Now that we have the cluster ready to go, let's run our distributed training job."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a project directory\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"project_folder = './pytorch-distr-hvd'\n",
"os.makedirs(project_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copy the training script `pytorch_horovod_mnist.py` into this project directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"\n",
"shutil.copy('pytorch_horovod_mnist.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an experiment\n",
"Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = 'pytorch-distr-hvd'\n",
"experiment = Experiment(ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a PyTorch estimator\n",
"The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import PyTorch\n",
"\n",
"estimator = PyTorch(source_directory=project_folder,\n",
" compute_target=compute_target,\n",
" entry_script='pytorch_horovod_mnist.py',\n",
" node_count=2,\n",
" process_count_per_node=1,\n",
" distributed_backend='mpi',\n",
" use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, PyTorch, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `PyTorch` constructor's `pip_packages` or `conda_packages` parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit job\n",
"Run your experiment by submitting your estimator object. Note that this call is asynchronous."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = experiment.submit(estimator)\n",
"print(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor your run\n",
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"\n",
"RunDetails(run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can block until the script has completed training before running more code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True) # this provides a verbose log"
]
}
],
"metadata": {
"authors": [
{
"name": "minxia"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"msauthor": "minxia"
},
"nbformat": 4,
"nbformat_minor": 2
}

0
training/02.distributed-pytorch-with-horovod/pytorch_horovod_mnist.py → training/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py
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402
training/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb Normal file
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@@ -0,0 +1,402 @@
{
"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": [
"# Distributed Tensorflow with Horovod\n",
"In this tutorial, you will train a word2vec model in TensorFlow using distributed training via [Horovod](https://github.com/uber/horovod)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\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`)\n",
"* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
]
},
{
"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": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"\n",
"set_diagnostics_collection(send_diagnostics=True)"
]
},
{
"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": {},
"source": [
"## Create a remote compute target\n",
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an `AmlCompute` cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
"\n",
"**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# choose a name for your cluster\n",
"cluster_name = \"gpucluster\"\n",
"\n",
"try:\n",
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
" print('Found existing compute target')\n",
"except ComputeTargetException:\n",
" print('Creating a new compute target...')\n",
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
" max_nodes=4)\n",
"\n",
" # create the cluster\n",
" compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
"\n",
" compute_target.wait_for_completion(show_output=True)\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": [
"The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload data to datastore\n",
"To make data accessible for remote training, AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data to Azure Storage, and interact with it from your remote compute targets. \n",
"\n",
"If your data is already stored in Azure, or you download the data as part of your training script, you will not need to do this step. For this tutorial, although you can download the data in your training script, we will demonstrate how to upload the training data to a datastore and access it during training to illustrate the datastore functionality."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, download the training data from [here](http://mattmahoney.net/dc/text8.zip) to your local machine:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import urllib\n",
"\n",
"os.makedirs('./data', exist_ok=True)\n",
"download_url = 'http://mattmahoney.net/dc/text8.zip'\n",
"urllib.request.urlretrieve(download_url, filename='./data/text8.zip')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds = ws.get_default_datastore()\n",
"print(ds.datastore_type, ds.account_name, ds.container_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Upload the contents of the data directory to the path `./data` on the default datastore."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds.upload(src_dir='data', target_path='data', overwrite=True, show_progress=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For convenience, let's get a reference to the path on the datastore with the zip file of training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--input_data` argument. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path_on_datastore = 'data/text8.zip'\n",
"ds_data = ds.path(path_on_datastore)\n",
"print(ds_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train model on the remote compute"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a project directory\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"project_folder = './tf-distr-hvd'\n",
"os.makedirs(project_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copy the training script `tf_horovod_word2vec.py` into this project directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"\n",
"shutil.copy('tf_horovod_word2vec.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an experiment\n",
"Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = 'tf-distr-hvd'\n",
"experiment = Experiment(ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a TensorFlow estimator\n",
"The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import TensorFlow\n",
"\n",
"script_params={\n",
" '--input_data': ds_data\n",
"}\n",
"\n",
"estimator= TensorFlow(source_directory=project_folder,\n",
" compute_target=compute_target,\n",
" script_params=script_params,\n",
" entry_script='tf_horovod_word2vec.py',\n",
" node_count=2,\n",
" process_count_per_node=1,\n",
" distributed_backend='mpi',\n",
" use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, TensorFlow, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters.\n",
"\n",
"Note that we passed our training data reference `ds_data` to our script's `--input_data` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the data zip file on our datastore."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit job\n",
"Run your experiment by submitting your estimator object. Note that this call is asynchronous."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = experiment.submit(estimator)\n",
"print(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor your run\n",
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"RunDetails(run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can block until the script has completed training before running more code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True)"
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"msauthor": "minxia"
},
"nbformat": 4,
"nbformat_minor": 2
}

0
training/04.distributed-tensorflow-with-horovod/tf_horovod_word2vec.py → training/distributed-tensorflow-with-horovod/tf_horovod_word2vec.py
View File

315
training/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb Normal file
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@@ -0,0 +1,315 @@
{
"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": [
"# Distributed TensorFlow with parameter server\n",
"In this tutorial, you will train a TensorFlow model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using native [distributed TensorFlow](https://www.tensorflow.org/deploy/distributed)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\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`)\n",
"* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
]
},
{
"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": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"\n",
"set_diagnostics_collection(send_diagnostics=True)"
]
},
{
"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": {},
"source": [
"## Create a remote compute target\n",
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an `AmlCompute` cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
"\n",
"**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# choose a name for your cluster\n",
"cluster_name = \"gpucluster\"\n",
"\n",
"try:\n",
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
" print('Found existing compute target.')\n",
"except ComputeTargetException:\n",
" print('Creating a new compute target...')\n",
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
" max_nodes=4)\n",
"\n",
" # create the cluster\n",
" compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
"\n",
" compute_target.wait_for_completion(show_output=True)\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": [
"## Train model on the remote compute\n",
"Now that we have the cluster ready to go, let's run our distributed training job."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a project directory\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"project_folder = './tf-distr-ps'\n",
"os.makedirs(project_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copy the training script `tf_mnist_replica.py` into this project directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"\n",
"shutil.copy('tf_mnist_replica.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create an experiment\n",
"Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"\n",
"experiment_name = 'tf-distr-ps'\n",
"experiment = Experiment(ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a TensorFlow estimator\n",
"The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import TensorFlow\n",
"\n",
"script_params={\n",
" '--num_gpus': 1,\n",
" '--train_steps': 500\n",
"}\n",
"\n",
"estimator = TensorFlow(source_directory=project_folder,\n",
" compute_target=compute_target,\n",
" script_params=script_params,\n",
" entry_script='tf_mnist_replica.py',\n",
" node_count=2,\n",
" worker_count=2,\n",
" parameter_server_count=1, \n",
" distributed_backend='ps',\n",
" use_gpu=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code specifies that we will run our training script on `2` nodes, with two workers and one parameter server. In order to execute a native distributed TensorFlow run, you must provide the argument `distributed_backend='ps'`. Using this estimator with these settings, TensorFlow and its dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit job\n",
"Run your experiment by submitting your estimator object. Note that this call is asynchronous."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = experiment.submit(estimator)\n",
"print(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitor your run\n",
"You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"\n",
"RunDetails(run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can block until the script has completed training before running more code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True) # this provides a verbose log"
]
}
],
"metadata": {
"authors": [
{
"name": "minxia"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
},
"msauthor": "minxia"
},
"nbformat": 4,
"nbformat_minor": 2
}

0
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Export Run History as Tensorboard logs\n",
"\n",
"1. Run some training and log some metrics into Run History\n",
"2. Export the run history to some directory as Tensorboard logs\n",
"3. Launch a local Tensorboard to view the run history"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\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": "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": [
"Install the Azure ML TensorBoard integration package if you haven't already."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install azureml-contrib-tensorboard"
]
},
{
"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, Run, Experiment\n",
"\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": {},
"source": [
"## Set experiment name and start the run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'export-to-tensorboard'\n",
"exp = Experiment(ws, experiment_name)\n",
"root_run = exp.start_logging()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load diabetes dataset, a well-known built-in small dataset that comes with scikit-learn\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",
"\n",
"X, y = load_diabetes(return_X_y=True)\n",
"\n",
"columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
"\n",
"x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"data = {\n",
" \"train\":{\"x\":x_train, \"y\":y_train}, \n",
" \"test\":{\"x\":x_test, \"y\":y_test}\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example experiment\n",
"from tqdm import tqdm\n",
"\n",
"alphas = [.1, .2, .3, .4, .5, .6 , .7]\n",
"\n",
"# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
"for alpha in tqdm(alphas):\n",
" # create a bunch of child runs\n",
" with root_run.child_run(\"alpha\" + str(alpha)) as run:\n",
" # More data science stuff\n",
" reg = Ridge(alpha=alpha)\n",
" reg.fit(data[\"train\"][\"x\"], data[\"train\"][\"y\"])\n",
" # TODO save model\n",
" preds = reg.predict(data[\"test\"][\"x\"])\n",
" mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
" # End train and eval\n",
"\n",
" # log alpha, mean_squared_error and feature names in run history\n",
" root_run.log(\"alpha\", alpha)\n",
" root_run.log(\"mse\", mse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export Run History to Tensorboard logs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Export Run History to Tensorboard logs\n",
"from azureml.contrib.tensorboard.export import export_to_tensorboard\n",
"import os\n",
"import tensorflow as tf\n",
"\n",
"logdir = 'exportedTBlogs'\n",
"log_path = os.path.join(os.getcwd(), logdir)\n",
"try:\n",
" os.stat(log_path)\n",
"except os.error:\n",
" os.mkdir(log_path)\n",
"print(logdir)\n",
"\n",
"# export run history for the project\n",
"export_to_tensorboard(root_run, logdir)\n",
"\n",
"# or export a particular run\n",
"# export_to_tensorboard(run, logdir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"root_run.complete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start Tensorboard\n",
"\n",
"Or you can start the Tensorboard outside this notebook to view the result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.contrib.tensorboard import Tensorboard\n",
"\n",
"# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
"tb = Tensorboard([], local_root=logdir, port=6006)\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"When you're done, make sure to call the `stop()` method of the Tensorboard object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"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
}

560
training/tensorboard/tensorboard.ipynb Normal file
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@@ -0,0 +1,560 @@
{
"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": [
"# Tensorboard Integration with Run History\n",
"\n",
"1. Run a Tensorflow job locally and view its TB output live.\n",
"2. The same, for a DSVM.\n",
"3. And once more, with an AmlCompute cluster.\n",
"4. Finally, we'll collect all of these historical runs together into a single Tensorboard graph."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\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": "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": [
"Install the Azure ML TensorBoard package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install azureml-contrib-tensorboard"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diagnostics\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"Diagnostics"
]
},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"\n",
"set_diagnostics_collection(send_diagnostics=True)"
]
},
{
"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('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": {},
"source": [
"## Set experiment name and create project\n",
"Choose a name for your run history container in the workspace, and create a folder for the project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from os import path, makedirs\n",
"experiment_name = 'tensorboard-demo'\n",
"\n",
"# experiment folder\n",
"exp_dir = './sample_projects/' + experiment_name\n",
"\n",
"if not path.exists(exp_dir):\n",
" makedirs(exp_dir)\n",
"\n",
"# runs we started in this session, for the finale\n",
"runs = []"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Tensorflow Tensorboard demo code\n",
"\n",
"Tensorflow's repository has an MNIST demo with extensive Tensorboard instrumentation. We'll use it here for our purposes.\n",
"\n",
"Note that we don't need to make any code changes at all - the code works without modification from the Tensorflow repository."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import os\n",
"import tempfile\n",
"tf_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r1.8/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\")\n",
"with open(os.path.join(exp_dir, \"mnist_with_summaries.py\"), \"w\") as file:\n",
" file.write(tf_code.text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure and run locally\n",
"\n",
"We'll start by running this locally. While it might not initially seem that useful to use this for a local run - why not just run TB against the files generated locally? - even in this case there is some value to using this feature. Your local run will be registered in the run history, and your Tensorboard logs will be uploaded to the artifact store associated with this run. Later, you'll be able to restore the logs from any run, regardless of where it happened.\n",
"\n",
"Note that for this run, you will need to install Tensorflow on your local machine by yourself. Further, the Tensorboard module (that is, the one included with Tensorflow) must be accessible to this notebook's kernel, as the local machine is what runs Tensorboard."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"\n",
"# Create a run configuration.\n",
"run_config = RunConfiguration()\n",
"run_config.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'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment, Run\n",
"from azureml.core.script_run_config import ScriptRunConfig\n",
"import tensorflow as tf\n",
"\n",
"logs_dir = os.path.join(os.curdir, \"logs\")\n",
"data_dir = os.path.abspath(os.path.join(os.curdir, \"mnist_data\"))\n",
"\n",
"if not path.exists(data_dir):\n",
" makedirs(data_dir)\n",
"\n",
"os.environ[\"TEST_TMPDIR\"] = data_dir\n",
"\n",
"# Writing logs to ./logs results in their being uploaded to Artifact Service,\n",
"# and thus, made accessible to our Tensorboard instance.\n",
"arguments_list = [\"--log_dir\", logs_dir]\n",
"\n",
"# Create an experiment\n",
"exp = Experiment(ws, experiment_name)\n",
"\n",
"# If you would like the run to go for longer, add --max_steps 5000 to the arguments list:\n",
"# arguments_list += [\"--max_steps\", \"5000\"]\n",
"\n",
"script = ScriptRunConfig(exp_dir,\n",
" script=\"mnist_with_summaries.py\",\n",
" run_config=run_config,\n",
" arguments=arguments_list)\n",
"\n",
"run = exp.submit(script)\n",
"# You can also wait for the run to complete\n",
"# run.wait_for_completion(show_output=True)\n",
"runs.append(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start Tensorboard\n",
"\n",
"Now, while the run is in progress, we just need to start Tensorboard with the run as its target, and it will begin streaming logs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.contrib.tensorboard import Tensorboard\n",
"\n",
"# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
"tb = Tensorboard([run])\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Now, with a DSVM\n",
"\n",
"Tensorboard uploading works with all compute targets. Here we demonstrate it from a DSVM.\n",
"Note that the Tensorboard instance itself will be run by the notebook kernel. Again, this means this notebook's kernel must have access to the Tensorboard module.\n",
"\n",
"If you are unfamiliar with DSVM configuration, check [04. Train in a remote VM](04.train-on-remote-vm.ipynb) for a more detailed breakdown.\n",
"\n",
"**Note**: To streamline the compute that Azure Machine Learning creates, we are making updates to support creating only single to multi-node AmlCompute. The `DSVMCompute` class will be deprecated in a later release, but the DSVM can be created using the below single line command and then attached(like any VM) using the sample code below. Also note that we only support Linux VMs and the commands below will spin a Linux VM only.\n",
"\n",
"```shell\n",
"# create a DSVM in your resource group\n",
"# note you need to be at least a contributor to the resource group in order to execute this command successfully.\n",
"(myenv) $ az vm create --resource-group <resource_group_name> --name <some_vm_name> --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --admin-username <username> --admin-password <password> --generate-ssh-keys --authentication-type password\n",
"```\n",
"You can also use [this url](https://portal.azure.com/#create/microsoft-dsvm.linux-data-science-vm-ubuntulinuxdsvmubuntu) to create the VM using the Azure Portal."
]
},
{
"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 = 'cpudsvm'\n",
"\n",
"try:\n",
" compute_target = DsvmCompute(workspace=ws, name=compute_target_name)\n",
" print('found existing:', compute_target.name)\n",
"except ComputeTargetException:\n",
" print('creating new.')\n",
" dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
" compute_target = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
"compute_target.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submit run using TensorFlow estimator\n",
"\n",
"Instead of manually configuring the DSVM environment, we can use the TensorFlow estimator and everything is set up automatically."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.dnn import TensorFlow\n",
"\n",
"script_params = {\"--log_dir\": \"./logs\"}\n",
"\n",
"# If you want the run to go longer, set --max-steps to a higher number.\n",
"# script_params[\"--max_steps\"] = \"5000\"\n",
"\n",
"tf_estimator = TensorFlow(source_directory=exp_dir,\n",
" compute_target=compute_target,\n",
" entry_script='mnist_with_summaries.py',\n",
" script_params=script_params)\n",
"\n",
"run = exp.submit(tf_estimator)\n",
"\n",
"runs.append(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start Tensorboard with this run\n",
"\n",
"Just like before."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
"tb = Tensorboard([run])\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Once more, with an AmlCompute cluster\n",
"\n",
"Just to prove we can, let's create an AmlCompute CPU cluster, and run our demo there, as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"# choose a name for your cluster\n",
"cluster_name = \"cpucluster\"\n",
"\n",
"try:\n",
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
" print('Found existing compute target.')\n",
"except ComputeTargetException:\n",
" print('Creating a new compute target...')\n",
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', \n",
" max_nodes=4)\n",
"\n",
" # create the cluster\n",
" compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
"\n",
"compute_target.wait_for_completion(show_output=True, min_node_count=1, 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": [
"## Submit run using TensorFlow estimator\n",
"\n",
"Again, we can use the TensorFlow estimator and everything is set up automatically."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"script_params = {\"--log_dir\": \"./logs\"}\n",
"\n",
"# If you want the run to go longer, set --max-steps to a higher number.\n",
"# script_params[\"--max_steps\"] = \"5000\"\n",
"\n",
"tf_estimator = TensorFlow(source_directory=exp_dir,\n",
" compute_target=compute_target,\n",
" entry_script='mnist_with_summaries.py',\n",
" script_params=script_params)\n",
"\n",
"run = exp.submit(tf_estimator)\n",
"\n",
"runs.append(run)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start Tensorboard with this run\n",
"\n",
"Once more..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
"tb = Tensorboard([run])\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"When you're done, make sure to call the `stop()` method of the Tensorboard object, or it will stay running even after your job completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finale\n",
"\n",
"If you've paid close attention, you'll have noticed that we've been saving the run objects in an array as we went along. We can start a Tensorboard instance that combines all of these run objects into a single process. This way, you can compare historical runs. You can even do this with live runs; if you made some of those previous runs longer via the `--max_steps` parameter, they might still be running, and you'll see them live in this instance as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The Tensorboard constructor takes an array of runs...\n",
"# and it turns out that we have been building one of those all along.\n",
"tb = Tensorboard(runs)\n",
"\n",
"# If successful, start() returns a string with the URI of the instance.\n",
"tb.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stop Tensorboard\n",
"\n",
"As you might already know, make sure to call the `stop()` method of the Tensorboard object, or it will stay running (until you kill the kernel associated with this notebook, at least)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tb.stop()"
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
],
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