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Roope Astala
2019-05-14 16:01:14 -04:00
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2
tutorials/README.md
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@@ -18,5 +18,3 @@ If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwi
* [Part 2](regression-part2-automated-ml.ipynb): Train a model using Automated Machine Learning.
Also find quickstarts and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/README.png)

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tutorials/img-classification-part1-training.ipynb
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@@ -9,13 +9,6 @@
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/img-classification-part1-training.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -133,9 +126,7 @@
"metadata": {},
"source": [
"### Create or Attach existing compute resource\n",
"By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
"\n",
"**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
"By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you use default Azure Machine Learning Compute as your training environment."
]
},
{
@@ -149,38 +140,10 @@
},
"outputs": [],
"source": [
"from azureml.core.compute import AmlCompute\n",
"from azureml.core.compute import ComputeTarget\n",
"import os\n",
"\n",
"# choose a name for your cluster\n",
"compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpucluster\")\n",
"compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n",
"compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 4)\n",
"\n",
"# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n",
"vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n",
"\n",
"\n",
"if compute_name in ws.compute_targets:\n",
" compute_target = ws.compute_targets[compute_name]\n",
" if compute_target and type(compute_target) is AmlCompute:\n",
" print('found compute target. just use it. ' + compute_name)\n",
"else:\n",
" print('creating a new compute target...')\n",
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n",
" min_nodes = compute_min_nodes, \n",
" max_nodes = compute_max_nodes)\n",
"\n",
" # create the cluster\n",
" compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
" \n",
" # can poll for a minimum number of nodes and for a specific timeout. \n",
" # if no min node count is provided it will use the scale settings for the cluster\n",
" compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
" \n",
" # For a more detailed view of current AmlCompute status, use get_status()\n",
" print(compute_target.get_status().serialize())"
"cluster_type = os.environ.get(\"AML_COMPUTE_CLUSTER_TYPE\", \"CPU\")\n",
"compute_target = ws.get_default_compute_target(cluster_type)"
]
},
{
@@ -692,4 +655,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}