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update samples from Release-139 as a part of 1.0.55 SDK release

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vizhur
2019-08-05 18:39:19 +00:00
parent e4d9a2b4c5
commit c0dae0c645
69 changed files with 6879 additions and 1147 deletions
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45
how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.ipynb
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@@ -77,7 +77,7 @@
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
]
},
{
@@ -108,11 +108,11 @@
"source": [
"from azureml.core.model import Model\n",
"\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
" model_name = \"sklearn_regression_model.pkl\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)"
"model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
" model_name=\"sklearn_regression_model.pkl\",\n",
" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
" description=\"Ridge regression model to predict diabetes\",\n",
" workspace=ws)"
]
},
{
@@ -177,7 +177,7 @@
"from azureml.core.webservice import AciWebservice, Webservice\n",
"from azureml.exceptions import WebserviceException\n",
"\n",
"deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)\n",
"deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
"aci_service_name = 'aciservice1'\n",
"\n",
"try:\n",
@@ -215,7 +215,7 @@
" [10,9,8,7,6,5,4,3,2,1]\n",
"]})\n",
"\n",
"test_sample_encoded = bytes(test_sample,encoding = 'utf8')\n",
"test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
"prediction = service.run(input_data=test_sample_encoded)\n",
"print(prediction)"
]
@@ -247,15 +247,38 @@
"source": [
"### Model Profiling\n",
"\n",
"you can also take advantage of profiling feature for model\n",
"You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
"\n",
"```python\n",
"\n",
"profile = model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
"profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
"profile.wait_for_profiling(True)\n",
"profiling_results = profile.get_results()\n",
"print(profiling_results)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Packaging\n",
"\n",
"If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n",
"\n",
"You must include an Environment object in your inference configuration to use `Model.package()`.\n",
"\n",
"```python\n",
"package = Model.package(ws, [model], inference_config)\n",
"package.wait_for_creation(show_output=True) # Or show_output=False to hide the Docker build logs.\n",
"package.pull()\n",
"```\n",
"\n",
"Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n",
"\n",
"```python\n",
"package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n",
"package.wait_for_creation(show_output=True)\n",
"package.save(\"./local_context_dir\")\n",
"```"
]
}