jprdonnelly/MachineLearningNotebooks
1
0
Fork 0
mirror of https://github.com/Azure/MachineLearningNotebooks.git synced 2025-12-23 20:00:06 -05:00
Code Issues Projects Releases Wiki Activity

update samples from Release-149 as a part of 1.0.65 SDK release

Browse Source
This commit is contained in:
vizhur
2019-09-30 17:08:52 +00:00
parent 314bad72a4
commit 45880114db
217 changed files with 3871 additions and 12156 deletions
Download Patch File Download Diff File Expand all files Collapse all files

59
how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb
View File

@@ -638,7 +638,7 @@
"source": [
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-defaults', 'azureml-sdk[automl]'])\n",
"\n",
"conda_env_file_name = 'mydeployenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -648,30 +648,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create ACI config"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#deploy to ACI\n",
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"myaci_config = AciWebservice.deploy_configuration(\n",
" cpu_cores = 2, \n",
" memory_gb = 2, \n",
" tags = {'name':'Databricks Azure ML ACI'}, \n",
" description = 'This is for ADB and AutoML example.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy the Image as a Web Service on Azure Container Instance\n",
"## Deploy the model as a Web Service on Azure Container Instance\n",
"Replace servicename with any meaningful name of service"
]
},
@@ -683,30 +660,26 @@
"source": [
"# this will take 10-15 minutes to finish\n",
"\n",
"from azureml.core.webservice import AciWebservice, Webservice\n",
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.model import Model\n",
"import uuid\n",
"from azureml.core.image import ContainerImage\n",
"\n",
"myaci_config = AciWebservice.deploy_configuration(\n",
" cpu_cores = 2, \n",
" memory_gb = 2, \n",
" tags = {'name':'Databricks Azure ML ACI'}, \n",
" description = 'This is for ADB and AutoML example.')\n",
"\n",
"inference_config = InferenceConfig(runtime= 'spark-py', \n",
" entry_script='score.py',\n",
" conda_file='mydeployenv.yml')\n",
"\n",
"guid = str(uuid.uuid4()).split(\"-\")[0]\n",
"service_name = \"myservice-{}\".format(guid)\n",
"print(\"Creating service with name: {}\".format(service_name))\n",
"runtime = \"spark-py\" \n",
"driver_file = \"score.py\"\n",
"my_conda_file = \"mydeployenv.yml\"\n",
"\n",
"# image creation\n",
"myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
" runtime = runtime, \n",
" conda_file = 'mydeployenv.yml')\n",
"\n",
"# Webservice creation\n",
"myservice = Webservice.deploy_from_model(\n",
" workspace=ws, \n",
" name=service_name,\n",
" deployment_config = myaci_config,\n",
" models = [model],\n",
" image_config = myimage_config\n",
" )\n",
"\n",
"myservice = Model.deploy(ws, service_name, [model], inference_config, myaci_config)\n",
"myservice.wait_for_deployment(show_output=True)"
]
},