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4 Commits
azureml-sd
...
azureml-sd
| Author | SHA1 | Date | |
|---|---|---|---|
|
33d6def8c3 | ||
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69d4344dff | ||
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34aeec1439 | ||
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a9b9ebbf7d |
@@ -103,7 +103,7 @@
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using version 1.0.72.1 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.0.74 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -334,8 +334,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
|
||||
"In this example, we specify `show_output = True` to print currently running iterations to the console."
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -230,8 +230,7 @@
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||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while.\n",
|
||||
"In this example, we specify `show_output = True` to print currently running iterations to the console."
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
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@@ -308,7 +308,7 @@
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"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
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"automl_run = experiment.submit(automl_config, show_output=False)"
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||||
"automl_run = experiment.submit(automl_config, show_output=True)"
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]
|
||||
},
|
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{
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@@ -320,15 +320,6 @@
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||||
"automl_run"
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||||
]
|
||||
},
|
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{
|
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"automl_run.wait_for_completion()"
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]
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},
|
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{
|
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"cell_type": "markdown",
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"metadata": {},
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@@ -357,7 +348,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
|
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"#best_run, fitted_model = local_run.get_output()"
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"#best_run, fitted_model = automl_run.get_output()"
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]
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},
|
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{
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|
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@@ -376,7 +376,7 @@
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"hidePrompt": false
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},
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"source": [
|
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"We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required. You will see the currently running iterations printing to the console."
|
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"We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required."
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]
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},
|
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{
|
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|
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@@ -345,7 +345,11 @@
|
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{
|
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
|
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"metadata": {
|
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"tags": [
|
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"sample-akscompute-provision"
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]
|
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},
|
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"outputs": [],
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"source": [
|
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"from azureml.core.compute import AksCompute, ComputeTarget\n",
|
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|
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@@ -682,7 +682,11 @@
|
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{
|
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"cell_type": "code",
|
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"execution_count": null,
|
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"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
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"sample-akswebservice-deploy-from-image"
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]
|
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},
|
||||
"outputs": [],
|
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"source": [
|
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"%%time\n",
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@@ -166,7 +166,11 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
|
||||
"metadata": {
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"tags": [
|
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"sample-localwebservice-deploy"
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]
|
||||
},
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"outputs": [],
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"source": [
|
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"from azureml.core.webservice import LocalWebservice\n",
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@@ -316,9 +316,6 @@
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"metadata": {},
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||||
"outputs": [],
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"source": [
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"from azureml.core.webservice import Webservice\n",
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"from random import randint\n",
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"\n",
|
||||
"aci_service_name = 'my-aci-service-15ad'\n",
|
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"print(\"Service\", aci_service_name)\n",
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"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
|
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@@ -386,6 +383,22 @@
|
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"name": "viswamy"
|
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}
|
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],
|
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"category": "deployment",
|
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"compute": [
|
||||
"local"
|
||||
],
|
||||
"datasets": [
|
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"PASCAL VOC"
|
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],
|
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"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
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"exclude_from_index": false,
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"framework": [
|
||||
"ONNX"
|
||||
],
|
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"friendly_name": "Convert and deploy TinyYolo with ONNX Runtime",
|
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"index_order": 5,
|
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"kernelspec": {
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"display_name": "Python 3.6",
|
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"language": "python",
|
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@@ -402,7 +415,14 @@
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
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"version": "3.6.5"
|
||||
}
|
||||
},
|
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"star_tag": [
|
||||
"featured"
|
||||
],
|
||||
"tags": [
|
||||
"ONNX Converter"
|
||||
],
|
||||
"task": "Object Detection"
|
||||
},
|
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"nbformat": 4,
|
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"nbformat_minor": 2
|
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|
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@@ -2,5 +2,6 @@ name: onnx-convert-aml-deploy-tinyyolo
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dependencies:
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||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- git+https://github.com/apple/coremltools@v2.1
|
||||
- onnxmltools==1.3.1
|
||||
|
||||
@@ -391,8 +391,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import Webservice\n",
|
||||
"\n",
|
||||
"aci_service_name = 'onnx-demo-emotion'\n",
|
||||
"print(\"Service\", aci_service_name)\n",
|
||||
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
|
||||
@@ -755,6 +753,22 @@
|
||||
"name": "viswamy"
|
||||
}
|
||||
],
|
||||
"category": "deployment",
|
||||
"compute": [
|
||||
"local"
|
||||
],
|
||||
"datasets": [
|
||||
"Emotion FER"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"ONNX"
|
||||
],
|
||||
"friendly_name": "Deploy Facial Expression Recognition (FER+) with ONNX Runtime",
|
||||
"index_order": 2,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
@@ -772,7 +786,12 @@
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"msauthor": "vinitra.swamy"
|
||||
"msauthor": "vinitra.swamy",
|
||||
"star_tag": [],
|
||||
"tags": [
|
||||
"ONNX Model Zoo"
|
||||
],
|
||||
"task": "Facial Expression Recognition"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
|
||||
@@ -378,8 +378,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import Webservice\n",
|
||||
"\n",
|
||||
"aci_service_name = 'onnx-demo-mnist'\n",
|
||||
"print(\"Service\", aci_service_name)\n",
|
||||
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
|
||||
@@ -763,6 +761,22 @@
|
||||
"name": "viswamy"
|
||||
}
|
||||
],
|
||||
"category": "deployment",
|
||||
"compute": [
|
||||
"local"
|
||||
],
|
||||
"datasets": [
|
||||
"MNIST"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"ONNX"
|
||||
],
|
||||
"friendly_name": "Deploy MNIST digit recognition with ONNX Runtime",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
@@ -780,7 +794,12 @@
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"msauthor": "vinitra.swamy"
|
||||
"msauthor": "vinitra.swamy",
|
||||
"star_tag": [],
|
||||
"tags": [
|
||||
"ONNX Model Zoo"
|
||||
],
|
||||
"task": "Image Classification"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
|
||||
@@ -302,7 +302,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import Webservice\n",
|
||||
"from random import randint\n",
|
||||
"\n",
|
||||
"aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n",
|
||||
@@ -372,6 +371,22 @@
|
||||
"name": "viswamy"
|
||||
}
|
||||
],
|
||||
"category": "deployment",
|
||||
"compute": [
|
||||
"local"
|
||||
],
|
||||
"datasets": [
|
||||
"ImageNet"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"ONNX"
|
||||
],
|
||||
"friendly_name": "Deploy ResNet50 with ONNX Runtime",
|
||||
"index_order": 4,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
@@ -388,7 +403,12 @@
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
}
|
||||
},
|
||||
"star_tag": [],
|
||||
"tags": [
|
||||
"ONNX Model Zoo"
|
||||
],
|
||||
"task": "Image Classification"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
|
||||
@@ -477,7 +477,6 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import Webservice\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"from random import randint\n",
|
||||
"\n",
|
||||
@@ -548,6 +547,22 @@
|
||||
"name": "viswamy"
|
||||
}
|
||||
],
|
||||
"category": "deployment",
|
||||
"compute": [
|
||||
"AML Compute"
|
||||
],
|
||||
"datasets": [
|
||||
"MNIST"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"ONNX"
|
||||
],
|
||||
"friendly_name": "Train MNIST in PyTorch, convert, and deploy with ONNX Runtime",
|
||||
"index_order": 3,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
@@ -565,6 +580,11 @@
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
},
|
||||
"star_tag": [],
|
||||
"tags": [
|
||||
"ONNX Converter"
|
||||
],
|
||||
"task": "Image Classification",
|
||||
"widgets": {
|
||||
"application/vnd.jupyter.widget-state+json": {
|
||||
"state": {
|
||||
|
||||
@@ -235,7 +235,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"create image"
|
||||
"create image",
|
||||
"sample-image-create"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -330,7 +331,8 @@
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
"aci",
|
||||
"sample-aciwebservice-deploy-config"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -349,7 +351,8 @@
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
"aci",
|
||||
"sample-aciwebservice-deploy-from-image"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
|
||||
@@ -110,7 +110,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-batchcompute-attach"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"batch_compute_name = 'mybatchcompute' # Name to associate with new compute in workspace\n",
|
||||
|
||||
@@ -88,7 +88,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-adlacompute-attach"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"adla_compute_name = 'testadl' # Name to associate with new compute in workspace\n",
|
||||
|
||||
@@ -142,7 +142,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-databrickscompute-attach"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Replace with your account info before running.\n",
|
||||
|
||||
@@ -475,7 +475,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "copeters"
|
||||
"name": "jamgan"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
|
||||
@@ -100,7 +100,7 @@
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using SDK version 1.0.72.1, you are currently running version\", azureml.core.VERSION)"
|
||||
"print(\"This notebook was created using SDK version 1.0.74, you are currently running version\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -3,4 +3,4 @@ dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-tensorboard
|
||||
- tensorflow<2.0.0
|
||||
- tensorflow<1.15
|
||||
|
||||
@@ -920,6 +920,8 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create()\n",
|
||||
"cd.add_tensorflow_conda_package()\n",
|
||||
"cd.add_conda_package('keras==2.2.5')\n",
|
||||
@@ -1041,7 +1043,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can retreive the API keys used for accessing the HTTP endpoint."
|
||||
"We can retrieve the API keys used for accessing the HTTP endpoint."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1050,7 +1052,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retreive the API keys. two keys were generated.\n",
|
||||
"# Retrieve the API keys. Two keys were generated.\n",
|
||||
"key1, Key2 = service.get_keys()\n",
|
||||
"print(key1)"
|
||||
]
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
name: train-hyperparameter-tune-deploy-with-keras
|
||||
dependencies:
|
||||
- matplotlib
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- tensorflow==1.13.1
|
||||
- keras==2.2.5
|
||||
- pandas
|
||||
- matplotlib==3.0.3
|
||||
- numpy==1.16.2
|
||||
- pandas
|
||||
|
||||
@@ -133,7 +133,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-hdinsightcompute-attach"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, HDInsightCompute\n",
|
||||
@@ -262,6 +266,22 @@
|
||||
"name": "aashishb"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
"compute": [
|
||||
"HDI cluster"
|
||||
],
|
||||
"datasets": [
|
||||
"None"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"PySpark"
|
||||
],
|
||||
"friendly_name": "Training in Spark",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
@@ -279,26 +299,10 @@
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
},
|
||||
"friendly_name": "Training in Spark",
|
||||
"exclude_from_index": false,
|
||||
"index_order": 1,
|
||||
"category": "training",
|
||||
"task": "Submiting a run on a spark cluster",
|
||||
"datasets": [
|
||||
"None"
|
||||
],
|
||||
"compute": [
|
||||
"HDI cluster"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"framework": [
|
||||
"PySpark"
|
||||
],
|
||||
"tags": [
|
||||
"None"
|
||||
]
|
||||
],
|
||||
"task": "Submiting a run on a spark cluster"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
|
||||
@@ -203,7 +203,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-amlcompute-provision"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
|
||||
@@ -293,7 +297,11 @@
|
||||
"* `idle_seconds_before_scaledown`: Idle time (default 120 seconds) to wait after run completion before auto-scaling to min_nodes\n",
|
||||
"* `vnet_resourcegroup_name`: Resource group of the **existing** VNet within which AmlCompute should be provisioned\n",
|
||||
"* `vnet_name`: Name of VNet\n",
|
||||
"* `subnet_name`: Name of SubNet within the VNet"
|
||||
"* `subnet_name`: Name of SubNet within the VNet\n",
|
||||
"* `admin_username`: Name of Admin user account which will be created on all the nodes of the cluster\n",
|
||||
"* `admin_user_password`: Password that you want to set for the user account above\n",
|
||||
"* `admin_user_ssh_key`: SSH Key for the user account above. You can specify either a password or an SSH key or both\n",
|
||||
"* `remote_login_port_public_access`: Flag to enable or disable the public SSH port. If you dont specify, AmlCompute will smartly close the port when deploying inside a VNet"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -320,7 +328,11 @@
|
||||
" idle_seconds_before_scaledown='300',\n",
|
||||
" vnet_resourcegroup_name='<my-resource-group>',\n",
|
||||
" vnet_name='<my-vnet-name>',\n",
|
||||
" subnet_name='<my-subnet-name>')\n",
|
||||
" subnet_name='<my-subnet-name>',\n",
|
||||
" admin_username='<my-username>',\n",
|
||||
" admin_user_password='<my-password>',\n",
|
||||
" admin_user_ssh_key='<my-sshkey>',\n",
|
||||
" remote_login_port_public_access='enabled')\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
"cpu_cluster.wait_for_completion(show_output=True)"
|
||||
@@ -381,10 +393,20 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Get_status () gets the latest status of the AmlCompute target\n",
|
||||
"#get_status () gets the latest status of the AmlCompute target\n",
|
||||
"cpu_cluster.get_status().serialize()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#list_nodes () gets the list of nodes on the cluster with status, IP and associated run\n",
|
||||
"cpu_cluster.list_nodes()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@@ -425,6 +447,22 @@
|
||||
"name": "nigup"
|
||||
}
|
||||
],
|
||||
"category": "training",
|
||||
"compute": [
|
||||
"AML Compute"
|
||||
],
|
||||
"datasets": [
|
||||
"Diabetes"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"None"
|
||||
],
|
||||
"friendly_name": "Train on Azure Machine Learning Compute",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
@@ -442,26 +480,10 @@
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
},
|
||||
"friendly_name": "Train on Azure Machine Learning Compute",
|
||||
"exclude_from_index": false,
|
||||
"index_order": 1,
|
||||
"category": "training",
|
||||
"task": "Submit an Azure Machine Leaarning Compute run",
|
||||
"datasets": [
|
||||
"Diabetes"
|
||||
],
|
||||
"compute": [
|
||||
"AML Compute"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"framework": [
|
||||
"None"
|
||||
],
|
||||
"tags": [
|
||||
"None"
|
||||
]
|
||||
],
|
||||
"task": "Submit a run on Azure Machine Learning Compute."
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
|
||||
@@ -243,7 +243,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-remotecompute-attach"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, RemoteCompute\n",
|
||||
|
||||
@@ -409,7 +409,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "sihhu"
|
||||
"name": "jamgan"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
|
||||
22
index.md
22
index.md
@@ -107,7 +107,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
|
||||
|
||||
| [Training in Spark](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb) | Submiting a run on a spark cluster | None | HDI cluster | None | PySpark | None |
|
||||
|
||||
| [Train on Azure Machine Learning Compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) | Submit an Azure Machine Leaarning Compute run | Diabetes | AML Compute | None | None | None |
|
||||
| [Train on Azure Machine Learning Compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb) | Submit a run on Azure Machine Learning Compute. | Diabetes | AML Compute | None | None | None |
|
||||
|
||||
| [Train on local compute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-on-local/train-on-local.ipynb) | Train a model locally | Diabetes | Local | None | None | None |
|
||||
|
||||
@@ -132,10 +132,20 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
|
||||
|
||||
|Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
|
||||
|:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
|
||||
| [Deploy MNIST digit recognition with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | Image Classification | MNIST | local | Azure Container Instance | ONNX | ONNX Model Zoo |
|
||||
|
||||
| [Deploy Facial Expression Recognition (FER+) with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | Facial Expression Recognition | Emotion FER | local | Azure Container Instance | ONNX | ONNX Model Zoo |
|
||||
|
||||
| :star:[Register model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Container Instance | Scikit-learn | None |
|
||||
|
||||
| [Train MNIST in PyTorch, convert, and deploy with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | Image Classification | MNIST | AML Compute | Azure Container Instance | ONNX | ONNX Converter |
|
||||
|
||||
| [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | local | Azure Container Instance | ONNX | ONNX Model Zoo |
|
||||
|
||||
| [Deploy a model as a web service using MLflow](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.ipynb) | Use MLflow with AML | Diabetes | None | Azure Container Instance | Scikit-learn | None |
|
||||
|
||||
| :star:[Convert and deploy TinyYolo with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) | Object Detection | PASCAL VOC | local | Azure Container Instance | ONNX | ONNX Converter |
|
||||
|
||||
|
||||
|
||||
## Other Notebooks
|
||||
@@ -191,18 +201,8 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
|
||||
|
||||
| [enable-app-insights-in-production-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) | | | | | | |
|
||||
|
||||
| [onnx-convert-aml-deploy-tinyyolo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) | | | | | | |
|
||||
|
||||
| [onnx-inference-facial-expression-recognition-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | | | | | | |
|
||||
|
||||
| [onnx-inference-mnist-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | | | | | | |
|
||||
|
||||
| [onnx-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb) | | | | | | |
|
||||
|
||||
| [onnx-modelzoo-aml-deploy-resnet50](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | | | | | | |
|
||||
|
||||
| [onnx-train-pytorch-aml-deploy-mnist](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | | | | | | |
|
||||
|
||||
| [production-deploy-to-aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb) | | | | | | |
|
||||
|
||||
| [register-model-create-image-deploy-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb) | | | | | | |
|
||||
|
||||
@@ -102,7 +102,7 @@
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using version 1.0.72.1 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.0.74 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user