From e443fd13429699862a7b033bb094391f833d768a Mon Sep 17 00:00:00 2001 From: vizhur Date: Wed, 11 Mar 2020 19:51:02 +0000 Subject: [PATCH] update samples from Release-6 as a part of 1.1.5rc0 SDK stable release --- configuration.ipynb | 2 +- .../automated-machine-learning/README.md | 2 +- .../automated-machine-learning/automl_env.yml | 1 + .../automl_env_mac.yml | 1 + ...ynb => auto-ml-forecasting-function.ipynb} | 0 ...n.yml => auto-ml-forecasting-function.yml} | 2 +- ...auto-ml-forecasting-orange-juice-sales.yml | 1 + ...rmance-explanation-and-featurization.ipynb | 9 +- .../regression/auto-ml-regression.yml | 1 + .../model-register-and-deploy.ipynb | 104 ++++++++++++++++-- .../register-model-deploy-local.ipynb | 104 ++++++++++++++++++ .../production-deploy-to-aks.ipynb | 100 +++++++++++++++++ .../logging-api/logging-api.ipynb | 2 +- .../train-on-local/train-on-local.ipynb | 15 ++- .../train-within-notebook.ipynb | 13 ++- index.md | 2 +- setup-environment/configuration.ipynb | 2 +- 17 files changed, 331 insertions(+), 30 deletions(-) rename how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/{automl-forecasting-function.ipynb => auto-ml-forecasting-function.ipynb} (100%) rename how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/{automl-forecasting-function.yml => auto-ml-forecasting-function.yml} (79%) diff --git a/configuration.ipynb b/configuration.ipynb index 5d12562d4..7ce013ca5 100644 --- a/configuration.ipynb +++ b/configuration.ipynb @@ -103,7 +103,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.1.2rc0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.1.5rc0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/how-to-use-azureml/automated-machine-learning/README.md b/how-to-use-azureml/automated-machine-learning/README.md index c93069a66..94af87ae6 100644 --- a/how-to-use-azureml/automated-machine-learning/README.md +++ b/how-to-use-azureml/automated-machine-learning/README.md @@ -144,7 +144,7 @@ jupyter notebook - Dataset: forecasting for a bike-sharing - Example of training an automated ML forecasting model on multiple time-series -- [automl-forecasting-function.ipynb](forecasting-high-frequency/automl-forecasting-function.ipynb) +- [auto-ml-forecasting-function.ipynb](forecasting-high-frequency/auto-ml-forecasting-function.ipynb) - Example of training an automated ML forecasting model on multiple time-series - [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb) diff --git a/how-to-use-azureml/automated-machine-learning/automl_env.yml b/how-to-use-azureml/automated-machine-learning/automl_env.yml index c907ce70e..3a77d177d 100644 --- a/how-to-use-azureml/automated-machine-learning/automl_env.yml +++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml @@ -21,6 +21,7 @@ dependencies: - pip: # Required packages for AzureML execution, history, and data preparation. - azureml-defaults + - azureml-dataprep[pandas] - azureml-train-automl - azureml-train - azureml-widgets diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml index 11841b72f..bfd8b358d 100644 --- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml +++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml @@ -22,6 +22,7 @@ dependencies: - pip: # Required packages for AzureML execution, history, and data preparation. - azureml-defaults + - azureml-dataprep[pandas] - azureml-train-automl - azureml-train - azureml-widgets diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.ipynb similarity index 100% rename from how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb rename to how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.ipynb diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml b/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.yml similarity index 79% rename from how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml rename to how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.yml index efeec1734..58af92aa5 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml +++ b/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.yml @@ -1,4 +1,4 @@ -name: automl-forecasting-function +name: auto-ml-forecasting-function dependencies: - fbprophet==0.5 - py-xgboost<=0.80 diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml index 773669645..1be86b5e5 100644 --- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml +++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml @@ -4,6 +4,7 @@ dependencies: - py-xgboost<=0.80 - pip: - azureml-sdk + - pandas==0.23.4 - azureml-train-automl - azureml-widgets - matplotlib diff --git a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb index 3935e0b3a..71e53b515 100644 --- a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb +++ b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb @@ -532,8 +532,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Create conda configuration for model explanations experiment\n", - "We need `azureml-explain-model`, `azureml-train-automl` and `azureml-core` packages for computing model explanations for your AutoML model on remote compute." + "#### Create conda configuration for model explanations experiment from automl_run object" ] }, { @@ -552,13 +551,9 @@ "# Set compute target to AmlCompute\n", "conda_run_config.target = compute_target\n", "conda_run_config.environment.docker.enabled = True\n", - "azureml_pip_packages = [\n", - " 'azureml-train-automl', 'azureml-core', 'azureml-explain-model'\n", - "]\n", "\n", "# specify CondaDependencies obj\n", - "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n", - " pip_packages=azureml_pip_packages)" + "conda_run_config.environment.python.conda_dependencies = automl_run.get_environment().python.conda_dependencies" ] }, { diff --git a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml index 5fc794b70..01892fb3f 100644 --- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml +++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml @@ -2,6 +2,7 @@ name: auto-ml-regression dependencies: - pip: - azureml-sdk + - pandas==0.23.4 - azureml-train-automl - azureml-widgets - matplotlib diff --git a/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb index 38960fcf7..44bfde497 100644 --- a/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb +++ b/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb @@ -341,9 +341,6 @@ "metadata": {}, "outputs": [], "source": [ - "import json\n", - "\n", - "\n", "input_payload = json.dumps({\n", " 'data': [\n", " [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n", @@ -376,16 +373,101 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Model profiling\n", + "### Model Profiling\n", "\n", - "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n", + "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n", "\n", - "```python\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", - "```" + "In order to profile your model you will need:\n", + "- a registered model\n", + "- an entry script\n", + "- an inference configuration\n", + "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n", + "\n", + "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n", + "\n", + "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Datastore\n", + "from azureml.core.dataset import Dataset\n", + "from azureml.data import dataset_type_definitions\n", + "\n", + "\n", + "# create a string that can be utf-8 encoded and\n", + "# put in the body of the request\n", + "serialized_input_json = json.dumps({\n", + " 'data': [\n", + " [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n", + " -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n", + " ]\n", + "})\n", + "dataset_content = []\n", + "for i in range(100):\n", + " dataset_content.append(serialized_input_json)\n", + "dataset_content = '\\n'.join(dataset_content)\n", + "file_name = 'sample_request_data.txt'\n", + "f = open(file_name, 'w')\n", + "f.write(dataset_content)\n", + "f.close()\n", + "\n", + "# upload the txt file created above to the Datastore and create a dataset from it\n", + "data_store = Datastore.get_default(ws)\n", + "data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n", + "datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n", + "sample_request_data = Dataset.Tabular.from_delimited_files(\n", + " datastore_path,\n", + " separator='\\n',\n", + " infer_column_types=True,\n", + " header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n", + "sample_request_data = sample_request_data.register(workspace=ws,\n", + " name='diabetes_sample_request_data',\n", + " create_new_version=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "\n", + "environment = Environment('my-sklearn-environment')\n", + "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", + " 'azureml-defaults',\n", + " 'inference-schema[numpy-support]',\n", + " 'joblib',\n", + " 'numpy',\n", + " 'scikit-learn'\n", + "])\n", + "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", + "# if cpu and memory_in_gb parameters are not provided\n", + "# the model will be profiled on default configuration of\n", + "# 3.5CPU and 15GB memory\n", + "profile = Model.profile(ws,\n", + " 'rgrsn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n", + " [model],\n", + " inference_config,\n", + " input_dataset=sample_request_data,\n", + " cpu=1.0,\n", + " memory_in_gb=0.5)\n", + "\n", + "profile.wait_for_completion(True)\n", + "details = profile.get_details()" ] }, { diff --git a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb index 6b9d519c4..0b7660a21 100644 --- a/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb +++ b/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb @@ -145,6 +145,110 @@ " environment=environment)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Profiling\n", + "\n", + "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n", + "\n", + "In order to profile your model you will need:\n", + "- a registered model\n", + "- an entry script\n", + "- an inference configuration\n", + "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n", + "\n", + "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n", + "\n", + "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from azureml.core import Datastore\n", + "from azureml.core.dataset import Dataset\n", + "from azureml.data import dataset_type_definitions\n", + "\n", + "\n", + "# create a string that can be put in the body of the request\n", + "serialized_input_json = json.dumps({\n", + " 'data': [\n", + " [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n", + " [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n", + " ]\n", + "})\n", + "dataset_content = []\n", + "for i in range(100):\n", + " dataset_content.append(serialized_input_json)\n", + "dataset_content = '\\n'.join(dataset_content)\n", + "file_name = 'sample_request_data_diabetes.txt'\n", + "f = open(file_name, 'w')\n", + "f.write(dataset_content)\n", + "f.close()\n", + "\n", + "# upload the txt file created above to the Datastore and create a dataset from it\n", + "data_store = Datastore.get_default(ws)\n", + "data_store.upload_files(['./' + file_name], target_path='sample_request_data_diabetes')\n", + "datastore_path = [(data_store, 'sample_request_data_diabetes' +'/' + file_name)]\n", + "sample_request_data_diabetes = Dataset.Tabular.from_delimited_files(\n", + " datastore_path,\n", + " separator='\\n',\n", + " infer_column_types=True,\n", + " header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n", + "sample_request_data_diabetes = sample_request_data_diabetes.register(workspace=ws,\n", + " name='sample_request_data_diabetes',\n", + " create_new_version=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "from azureml.core import Environment\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "from azureml.core.model import Model, InferenceConfig\n", + "\n", + "\n", + "environment = Environment('my-sklearn-environment')\n", + "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", + " 'azureml-defaults',\n", + " 'inference-schema[numpy-support]',\n", + " 'joblib',\n", + " 'numpy',\n", + " 'scikit-learn'\n", + "])\n", + "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", + "# if cpu and memory_in_gb parameters are not provided\n", + "# the model will be profiled on default configuration of\n", + "# 3.5CPU and 15GB memory\n", + "profile = Model.profile(ws,\n", + " 'profile-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n", + " [model],\n", + " inference_config,\n", + " input_dataset=sample_request_data_diabetes,\n", + " cpu=1.0,\n", + " memory_in_gb=0.5)\n", + "\n", + "profile.wait_for_completion(True)\n", + "details = profile.get_details()" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb index 3a9990932..8878db806 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb @@ -198,6 +198,106 @@ "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Profiling\n", + "\n", + "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n", + "\n", + "In order to profile your model you will need:\n", + "- a registered model\n", + "- an entry script\n", + "- an inference configuration\n", + "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n", + "\n", + "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n", + "\n", + "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from azureml.core import Datastore\n", + "from azureml.core.dataset import Dataset\n", + "from azureml.data import dataset_type_definitions\n", + "\n", + "input_json = {'data': [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n", + " [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\n", + "# create a string that can be put in the body of the request\n", + "serialized_input_json = json.dumps(input_json)\n", + "dataset_content = []\n", + "for i in range(100):\n", + " dataset_content.append(serialized_input_json)\n", + "sample_request_data = '\\n'.join(dataset_content)\n", + "file_name = 'sample_request_data.txt'\n", + "f = open(file_name, 'w')\n", + "f.write(sample_request_data)\n", + "f.close()\n", + "\n", + "# upload the txt file created above to the Datastore and create a dataset from it\n", + "data_store = Datastore.get_default(ws)\n", + "data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n", + "datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n", + "sample_request_data = Dataset.Tabular.from_delimited_files(\n", + " datastore_path,\n", + " separator='\\n',\n", + " infer_column_types=True,\n", + " header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n", + "sample_request_data = sample_request_data.register(workspace=ws,\n", + " name='sample_request_data',\n", + " create_new_version=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "from azureml.core import Environment\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "from azureml.core.model import Model, InferenceConfig\n", + "\n", + "\n", + "environment = Environment('my-sklearn-environment')\n", + "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", + " 'azureml-defaults',\n", + " 'inference-schema[numpy-support]',\n", + " 'joblib',\n", + " 'numpy',\n", + " 'scikit-learn'\n", + "])\n", + "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", + "# if cpu and memory_in_gb parameters are not provided\n", + "# the model will be profiled on default configuration of\n", + "# 3.5CPU and 15GB memory\n", + "profile = Model.profile(ws,\n", + " 'sklearn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n", + " [model],\n", + " inference_config,\n", + " input_dataset=sample_request_data,\n", + " cpu=1.0,\n", + " memory_in_gb=0.5)\n", + "\n", + "profile.wait_for_completion(True)\n", + "details = profile.get_details()" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb index 80956a09a..c2dc47596 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb @@ -100,7 +100,7 @@ "\n", "# Check core SDK version number\n", "\n", - "print(\"This notebook was created using SDK version 1.1.2rc0, you are currently running version\", azureml.core.VERSION)" + "print(\"This notebook was created using SDK version 1.1.5rc0, you are currently running version\", azureml.core.VERSION)" ] }, { diff --git a/how-to-use-azureml/training/train-on-local/train-on-local.ipynb b/how-to-use-azureml/training/train-on-local/train-on-local.ipynb index 5fe09adf1..11ef5a218 100644 --- a/how-to-use-azureml/training/train-on-local/train-on-local.ipynb +++ b/how-to-use-azureml/training/train-on-local/train-on-local.ipynb @@ -167,7 +167,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "name": "user_managed_env", + "msdoc": "how-to-track-experiments.md" + }, "outputs": [], "source": [ "from azureml.core import Environment\n", @@ -192,7 +195,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "name": "src", + "msdoc": "how-to-track-experiments.md" + }, "outputs": [], "source": [ "from azureml.core import ScriptRunConfig\n", @@ -204,7 +210,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "name": "run", + "msdoc": "how-to-track-experiments.md" + }, "outputs": [], "source": [ "run = exp.submit(src)" diff --git a/how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb b/how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb index 95b5df68e..7e3cbdde5 100644 --- a/how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb +++ b/how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb @@ -80,7 +80,9 @@ "metadata": { "tags": [ "install" - ] + ], + "name": "load_ws", + "msdoc": "how-to-track-experiments.md" }, "outputs": [], "source": [ @@ -113,7 +115,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "name": "load_data", + "msdoc": "how-to-track-experiments.md" + }, "outputs": [], "source": [ "from sklearn.datasets import load_diabetes\n", @@ -155,7 +160,9 @@ "tags": [ "local run", "outputs upload" - ] + ], + "name": "create_experiment", + "msdoc": "how-to-track-experiments.md" }, "outputs": [], "source": [ diff --git a/index.md b/index.md index 797684ddc..9a180c610 100644 --- a/index.md +++ b/index.md @@ -28,7 +28,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an | :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun | | :star:[Filtering data using Tabular Timeseiries Dataset related API](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | Local | None | Azure ML | Dataset, Tabular Timeseries | | :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) | Train | Iris, Diabetes | Remote | None | Azure ML | Dataset, Estimator, ScriptRun | -| [Forecasting away from training data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb) | Forecasting | None | Remote | None | Azure ML AutoML | Forecasting, Confidence Intervals | +| [Forecasting away from training data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.ipynb) | Forecasting | None | Remote | None | Azure ML AutoML | Forecasting, Confidence Intervals | | [Automated ML run with basic edition features.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb) | Classification | Bankmarketing | AML | ACI | None | featurization, explainability, remote_run, AutomatedML | | [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb) | Classification | Creditcard | AML Compute | None | None | remote_run, AutomatedML | | [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML | diff --git a/setup-environment/configuration.ipynb b/setup-environment/configuration.ipynb index a849870bb..c65aef57a 100644 --- a/setup-environment/configuration.ipynb +++ b/setup-environment/configuration.ipynb @@ -102,7 +102,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.1.2rc0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.1.5rc0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] },