Compare commits
1 Commits
release_up
...
release_up
| Author | SHA1 | Date | |
|---|---|---|---|
|
5da664d65c |
@@ -103,7 +103,7 @@
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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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@@ -254,8 +254,6 @@
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"\n",
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"Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
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"\n",
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"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
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"\n",
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"To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors. Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
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"\n",
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"The cluster parameters are:\n",
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@@ -21,7 +21,7 @@ def fetch_openml_with_retries(data_id, max_retries=4, retry_delay=60):
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print("Download attempt {0} of {1}".format(i + 1, max_retries))
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data = fetch_openml(data_id=data_id, as_frame=True)
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break
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except Exception as e: # noqa: B902
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except Exception as e:
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print("Download attempt failed with exception:")
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print(e)
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if i + 1 != max_retries:
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@@ -47,7 +47,7 @@ _categorical_columns = [
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def fetch_census_dataset():
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"""Fetch the Adult Census Dataset.
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"""Fetch the Adult Census Dataset
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This uses a particular URL for the Adult Census dataset. The code
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is a simplified version of fetch_openml() in sklearn.
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@@ -63,35 +63,17 @@ def fetch_census_dataset():
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filename = "1595261.gz"
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data_url = "https://rainotebookscdn.blob.core.windows.net/datasets/"
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urlretrieve(data_url + filename, filename)
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remaining_attempts = 5
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sleep_duration = 10
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while remaining_attempts > 0:
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try:
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urlretrieve(data_url + filename, filename)
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http_stream = gzip.GzipFile(filename=filename, mode='rb')
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http_stream = gzip.GzipFile(filename=filename, mode='rb')
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with closing(http_stream):
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def _stream_generator(response):
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for line in response:
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yield line.decode('utf-8')
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with closing(http_stream):
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def _stream_generator(response):
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for line in response:
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yield line.decode('utf-8')
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stream = _stream_generator(http_stream)
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data = arff.load(stream)
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except Exception as exc: # noqa: B902
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remaining_attempts -= 1
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print("Error downloading dataset from {} ({} attempt(s) remaining)"
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.format(data_url, remaining_attempts))
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print(exc)
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time.sleep(sleep_duration)
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sleep_duration *= 2
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continue
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else:
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# dataset successfully downloaded
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break
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else:
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raise Exception("Could not retrieve dataset from {}.".format(data_url))
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stream = _stream_generator(http_stream)
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data = arff.load(stream)
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attributes = OrderedDict(data['attributes'])
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arff_columns = list(attributes)
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@@ -21,8 +21,8 @@ dependencies:
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.29.0
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- azureml-widgets~=1.27.0
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- pytorch-transformers==1.0.0
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- spacy==2.1.8
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlresources-prod.azureedge.net/validated-requirements/1.29.0/validated_win32_requirements.txt [--no-deps]
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- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.27.0/validated_win32_requirements.txt [--no-deps]
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@@ -21,8 +21,8 @@ dependencies:
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.29.0
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- azureml-widgets~=1.27.0
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- pytorch-transformers==1.0.0
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- spacy==2.1.8
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlresources-prod.azureedge.net/validated-requirements/1.29.0/validated_linux_requirements.txt [--no-deps]
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- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.27.0/validated_linux_requirements.txt [--no-deps]
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@@ -22,8 +22,8 @@ dependencies:
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.29.0
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- azureml-widgets~=1.27.0
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- pytorch-transformers==1.0.0
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- spacy==2.1.8
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlresources-prod.azureedge.net/validated-requirements/1.29.0/validated_darwin_requirements.txt [--no-deps]
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- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.27.0/validated_darwin_requirements.txt [--no-deps]
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@@ -105,7 +105,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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@@ -165,9 +165,6 @@
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"source": [
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"## Create or Attach existing AmlCompute\n",
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"You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
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"\n",
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"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
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"\n",
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"#### Creation of AmlCompute takes approximately 5 minutes. \n",
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"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
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"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
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@@ -93,7 +93,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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@@ -127,9 +127,6 @@
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"source": [
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"## Create or Attach existing AmlCompute\n",
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"A compute target is required to execute the Automated ML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
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"\n",
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"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
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"\n",
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"#### Creation of AmlCompute takes approximately 5 minutes. \n",
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"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
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"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
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||||
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@@ -96,7 +96,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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@@ -138,8 +138,6 @@
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"## Set up a compute cluster\n",
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"This section uses a user-provided compute cluster (named \"dnntext-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments.\n",
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"\n",
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"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
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"\n",
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"Whether you provide/select a CPU or GPU cluster, AutoML will choose the appropriate DNN for that setup - BiLSTM or BERT text featurizer will be included in the candidate featurizers on CPU and GPU respectively. If your goal is to obtain the most accurate model, we recommend you use GPU clusters since BERT featurizers usually outperform BiLSTM featurizers."
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]
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},
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@@ -487,7 +485,7 @@
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"outputs": [],
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"source": [
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"test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run,\n",
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" test_dataset, target_column_name, model_name)"
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" train_dataset, test_dataset, target_column_name, model_name)"
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]
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},
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{
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@@ -5,7 +5,7 @@ from azureml.core.run import Run
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def run_inference(test_experiment, compute_target, script_folder, train_run,
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test_dataset, target_column_name, model_name):
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train_dataset, test_dataset, target_column_name, model_name):
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inference_env = train_run.get_environment()
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@@ -16,6 +16,7 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
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'--model_name': model_name
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},
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inputs=[
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train_dataset.as_named_input('train_data'),
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test_dataset.as_named_input('test_data')
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],
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compute_target=compute_target,
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@@ -1,6 +1,5 @@
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import argparse
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import pandas as pd
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import numpy as np
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from sklearn.externals import joblib
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@@ -33,21 +32,22 @@ model = joblib.load(model_path)
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run = Run.get_context()
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# get input dataset by name
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test_dataset = run.input_datasets['test_data']
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train_dataset = run.input_datasets['train_data']
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X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
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.to_pandas_dataframe()
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y_test_df = test_dataset.with_timestamp_columns(None) \
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.keep_columns(columns=[target_column_name]) \
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.to_pandas_dataframe()
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y_train_df = test_dataset.with_timestamp_columns(None) \
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.keep_columns(columns=[target_column_name]) \
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.to_pandas_dataframe()
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predicted = model.predict_proba(X_test_df)
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if isinstance(predicted, pd.DataFrame):
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predicted = predicted.values
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# Use the AutoML scoring module
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class_labels = np.unique(np.concatenate((y_train_df.values, y_test_df.values)))
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train_labels = model.classes_
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class_labels = np.unique(np.concatenate((y_test_df.values, np.reshape(train_labels, (-1, 1)))))
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classification_metrics = list(constants.CLASSIFICATION_SCALAR_SET)
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scores = scoring.score_classification(y_test_df.values, predicted,
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classification_metrics,
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@@ -81,7 +81,7 @@
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||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -141,9 +141,6 @@
|
||||
"#### Create or Attach existing AmlCompute\n",
|
||||
"\n",
|
||||
"You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
|
||||
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -91,7 +91,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -113,7 +113,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -162,9 +162,7 @@
|
||||
},
|
||||
"source": [
|
||||
"### Using AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you use `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -87,7 +87,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -129,9 +129,6 @@
|
||||
"source": [
|
||||
"## Compute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
|
||||
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -94,7 +94,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -263,9 +263,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -82,7 +82,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -124,9 +124,6 @@
|
||||
"source": [
|
||||
"## Compute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
|
||||
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -96,7 +96,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -96,7 +96,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -130,8 +130,6 @@
|
||||
"### Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -92,7 +92,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -46,7 +46,7 @@
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace, Experiment\n",
|
||||
"from azureml.core import LinkedService, SynapseWorkspaceLinkedServiceConfiguration\n",
|
||||
"from azureml.core.compute import ComputeTarget, AmlCompute, SynapseCompute\n",
|
||||
"from azureml.core.compute import ComputeTarget, SynapseCompute\n",
|
||||
"from azureml.exceptions import ComputeTargetException\n",
|
||||
"from azureml.data import HDFSOutputDatasetConfig\n",
|
||||
"from azureml.core.datastore import Datastore\n",
|
||||
|
||||
@@ -157,9 +157,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Provision the AKS Cluster\n",
|
||||
"If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -267,9 +267,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create AKS compute if you haven't done so.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"### Create AKS compute if you haven't done so."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -211,8 +211,6 @@
|
||||
"# Provision the AKS Cluster with SSL\n",
|
||||
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -325,9 +325,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Provision the AKS Cluster\n",
|
||||
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -203,8 +203,6 @@
|
||||
"source": [
|
||||
"### Provision a compute target\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
|
||||
"\n",
|
||||
"* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
|
||||
|
||||
@@ -204,8 +204,6 @@
|
||||
"source": [
|
||||
"### Provision a compute target\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
|
||||
"\n",
|
||||
"* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
|
||||
|
||||
@@ -209,8 +209,6 @@
|
||||
"#### Retrieve or create a Azure Machine Learning compute\n",
|
||||
"Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"If we could not find the compute with the given name in the previous cell, then we will create a new compute here. We will create an Azure Machine Learning Compute containing **STANDARD_D2_V2 CPU VMs**. This process is broken down into the following steps:\n",
|
||||
"\n",
|
||||
"1. Create the configuration\n",
|
||||
|
||||
@@ -55,9 +55,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Compute Target\n",
|
||||
"Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"Retrieve an already attached Azure Machine Learning Compute to use in the Pipeline."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -210,8 +210,6 @@
|
||||
"## Retrieve or create a Azure Machine Learning compute\n",
|
||||
"Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"If we could not find the compute with the given name in the previous cell, then we will create a new compute here. This process is broken down into the following steps:\n",
|
||||
"\n",
|
||||
"1. Create the configuration\n",
|
||||
|
||||
@@ -68,9 +68,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Compute Targets\n",
|
||||
"#### Retrieve an already attached Azure Machine Learning Compute\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"#### Retrieve an already attached Azure Machine Learning Compute"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -54,9 +54,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Compute Targets\n",
|
||||
"#### Retrieve an already attached Azure Machine Learning Compute\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"#### Retrieve an already attached Azure Machine Learning Compute"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -78,9 +78,7 @@
|
||||
"source": [
|
||||
"#### Initialization, Steps to create a Pipeline\n",
|
||||
"\n",
|
||||
"The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -109,9 +109,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach an AmlCompute cluster\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -111,9 +111,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach an AmlCompute cluster\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -699,162 +699,12 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"### 5. Running demo notebook already added to the Databricks workspace using existing cluster\n",
|
||||
"First you need register DBFS datastore and make sure path_on_datastore does exist in databricks file system, you can browser the files by refering [this](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
|
||||
"\n",
|
||||
"Find existing_cluster_id by opeing Azure Databricks UI with Clusters page and in url you will find a string connected with '-' right after \"clusters/\"."
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" dbfs_ds = Datastore.get(workspace=ws, datastore_name='dbfs_datastore')\n",
|
||||
" print('DBFS Datastore already exists')\n",
|
||||
"except Exception as ex:\n",
|
||||
" dbfs_ds = Datastore.register_dbfs(ws, datastore_name='dbfs_datastore')\n",
|
||||
"\n",
|
||||
"step_1_input = DataReference(datastore=dbfs_ds, path_on_datastore=\"FileStore\", data_reference_name=\"input\")\n",
|
||||
"step_1_output = PipelineData(\"output\", datastore=dbfs_ds)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dbNbWithExistingClusterStep = DatabricksStep(\n",
|
||||
" name=\"DBFSReferenceWithExisting\",\n",
|
||||
" inputs=[step_1_input],\n",
|
||||
" outputs=[step_1_output],\n",
|
||||
" notebook_path=notebook_path,\n",
|
||||
" notebook_params={'myparam': 'testparam', \n",
|
||||
" 'myparam2': pipeline_param},\n",
|
||||
" run_name='DBFS_Reference_With_Existing',\n",
|
||||
" compute_target=databricks_compute,\n",
|
||||
" existing_cluster_id=\"your existing cluster id\",\n",
|
||||
" allow_reuse=True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"#### Build and submit the Experiment"
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"steps = [dbNbWithExistingClusterStep]\n",
|
||||
"pipeline = Pipeline(workspace=ws, steps=steps)\n",
|
||||
"pipeline_run = Experiment(ws, 'DBFS_Reference_With_Existing').submit(pipeline)\n",
|
||||
"pipeline_run.wait_for_completion()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"#### View Run Details"
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(pipeline_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"### 6. Running a Python script in Databricks that currenlty is in local computer with existing cluster\n",
|
||||
"When you access azure blob or data lake storage from an existing (interactive) cluster, you need to ensure the Spark configuration is set up correctly to access this storage and this set up may require the cluster to be restarted.\n",
|
||||
"\n",
|
||||
"If you set permit_cluster_restart to True, AML will check if the spark configuration needs to be updated and restart the cluster for you if required. This will ensure that the storage can be correctly accessed from the Databricks cluster."
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n",
|
||||
" data_reference_name=\"input\")\n",
|
||||
"\n",
|
||||
"dbPythonInLocalWithExistingStep = DatabricksStep(\n",
|
||||
" name=\"DBPythonInLocalMachineWithExisting\",\n",
|
||||
" inputs=[step_1_input],\n",
|
||||
" python_script_name=python_script_name,\n",
|
||||
" source_directory=source_directory,\n",
|
||||
" run_name='DB_Python_Local_existing_demo',\n",
|
||||
" compute_target=databricks_compute,\n",
|
||||
" existing_cluster_id=\"your existing cluster id\",\n",
|
||||
" allow_reuse=False,\n",
|
||||
" permit_cluster_restart=True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"#### Build and submit the Experiment"
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"steps = [dbPythonInLocalWithExistingStep]\n",
|
||||
"pipeline = Pipeline(workspace=ws, steps=steps)\n",
|
||||
"pipeline_run = Experiment(ws, 'DB_Python_Local_existing_demo').submit(pipeline)\n",
|
||||
"pipeline_run.wait_for_completion()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"#### View Run Details"
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(pipeline_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"# Next: ADLA as a Compute Target\n",
|
||||
"To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
@@ -125,9 +125,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create or Attach an AmlCompute cluster\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -79,9 +79,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -77,9 +77,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -134,9 +134,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Retrieve or create an Aml compute\n",
|
||||
"Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's get the default Aml Compute in the current workspace. We will then run the training script on this compute target.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's get the default Aml Compute in the current workspace. We will then run the training script on this compute target."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -147,9 +147,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create or Attach an AmlCompute cluster\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.computetarget?view=azure-ml-py) for your remote run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.computetarget?view=azure-ml-py) for your remote run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -225,9 +225,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Setup Compute\n",
|
||||
"#### Create new or use an existing compute\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"#### Create new or use an existing compute"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -24,9 +24,9 @@
|
||||
"In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
|
||||
"\n",
|
||||
"> **Tip**\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"\n",
|
||||
"In this example will be take a digit identification model already-trained on MNIST dataset using the [AzureML training with deep learning example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb), and run that trained model on some of the MNIST test images in batch. \n",
|
||||
"In this example will be take a digit identification model already-trained on MNIST dataset using the [AzureML training with deep learning example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb), and run that trained model on some of the MNIST test images in batch. \n",
|
||||
"\n",
|
||||
"The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from [this repository](https://github.com/myleott/mnist_png). \n",
|
||||
"\n",
|
||||
@@ -86,8 +86,6 @@
|
||||
"### 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",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\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.**"
|
||||
]
|
||||
},
|
||||
@@ -182,7 +180,8 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create a FileDataset\n",
|
||||
"A [FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.\n",
|
||||
"A [FileDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.",
|
||||
"\n",
|
||||
"You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later."
|
||||
]
|
||||
},
|
||||
@@ -225,7 +224,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Intermediate/Output Data\n",
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -277,7 +276,7 @@
|
||||
"### Register the model with Workspace\n",
|
||||
"A registered model is a logical container for one or more files that make up your model. For example, if you have a model that's stored in multiple files, you can register them as a single model in the workspace. After you register the files, you can then download or deploy the registered model and receive all the files that you registered.\n",
|
||||
"\n",
|
||||
"Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where#registermodel) "
|
||||
"Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#registermodel) "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -363,6 +362,7 @@
|
||||
" \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
|
||||
"batch_env = Environment(name=\"batch_environment\")\n",
|
||||
"batch_env.python.conda_dependencies = batch_conda_deps\n",
|
||||
"batch_env.docker.enabled = True\n",
|
||||
"batch_env.docker.base_image = DEFAULT_CPU_IMAGE"
|
||||
]
|
||||
},
|
||||
@@ -379,6 +379,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core import PipelineParameter\n",
|
||||
"from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig\n",
|
||||
"\n",
|
||||
"parallel_run_config = ParallelRunConfig(\n",
|
||||
|
||||
@@ -24,7 +24,7 @@
|
||||
"In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
|
||||
"\n",
|
||||
"> **Tip**\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"\n",
|
||||
"In this example we will take use a machine learning model already trained to predict different types of iris flowers and run that trained model on some of the data in a CSV file which has characteristics of different iris flowers. However, the same example can be extended to manipulating data to any embarrassingly-parallel processing through a python script.\n",
|
||||
"\n",
|
||||
@@ -84,8 +84,6 @@
|
||||
"### 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",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\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.**"
|
||||
]
|
||||
},
|
||||
@@ -162,7 +160,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create a TabularDataset\n",
|
||||
"A [TabularDataSet](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) references single or multiple files which contain data in a tabular structure (ie like CSV files) in your datastores or public urls. TabularDatasets provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.\n",
|
||||
"A [TabularDataSet](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) references single or multiple files which contain data in a tabular structure (ie like CSV files) in your datastores or public urls. TabularDatasets provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred.\n",
|
||||
"You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later."
|
||||
]
|
||||
},
|
||||
@@ -186,7 +184,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Intermediate/Output Data\n",
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -313,6 +311,7 @@
|
||||
"\n",
|
||||
"predict_env = Environment(name=\"predict_environment\")\n",
|
||||
"predict_env.python.conda_dependencies = predict_conda_deps\n",
|
||||
"predict_env.docker.enabled = True\n",
|
||||
"predict_env.spark.precache_packages = False"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -178,9 +178,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create or use existing compute\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"# Create or use existing compute"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -98,8 +98,6 @@
|
||||
"## Create or attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -98,8 +98,6 @@
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -222,8 +222,6 @@
|
||||
"### Create or attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -272,9 +272,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -99,8 +99,6 @@
|
||||
"## Create or attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -99,8 +99,6 @@
|
||||
"## Create or attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -100,8 +100,6 @@
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -117,8 +117,6 @@
|
||||
"source": [
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
]
|
||||
},
|
||||
|
||||
@@ -101,8 +101,6 @@
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -101,8 +101,6 @@
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -270,9 +270,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -286,9 +286,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -101,8 +101,6 @@
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
|
||||
@@ -250,7 +250,7 @@
|
||||
"source": [
|
||||
"### Deploy model as web service\n",
|
||||
"\n",
|
||||
"The ```client.create_deployment``` function registers the logged Keras+Tensorflow model and deploys the model in a framework-aware manner. It automatically creates the Tensorflow-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n",
|
||||
"The ```mlflow.azureml.deploy``` function registers the logged Keras+Tensorflow model and deploys the model in a framework-aware manner. It automatically creates the Tensorflow-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n",
|
||||
"\n",
|
||||
"In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n",
|
||||
"\n",
|
||||
@@ -259,63 +259,131 @@
|
||||
"Note that the service deployment can take several minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"First define your deployment target and customize parameters in the deployment config. Refer to [this documentation](https://docs.microsoft.com/azure/machine-learning/reference-azure-machine-learning-cli#azure-container-instance-deployment-configuration-schema) for more information. "
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
" \n",
|
||||
"# Data to be written\n",
|
||||
"deploy_config ={\n",
|
||||
" \"computeType\": \"aci\"\n",
|
||||
"}\n",
|
||||
"# Serializing json \n",
|
||||
"json_object = json.dumps(deploy_config)\n",
|
||||
" \n",
|
||||
"# Writing to sample.json\n",
|
||||
"with open(\"deployment_config.json\", \"w\") as outfile:\n",
|
||||
" outfile.write(json_object)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from mlflow.deployments import get_deploy_client\n",
|
||||
"from azureml.core.webservice import AciWebservice, Webservice\n",
|
||||
"\n",
|
||||
"# set the tracking uri as the deployment client\n",
|
||||
"client = get_deploy_client(mlflow.get_tracking_uri())\n",
|
||||
"\n",
|
||||
"# set the model path \n",
|
||||
"model_path = \"model\"\n",
|
||||
"\n",
|
||||
"# set the deployment config\n",
|
||||
"deployment_config_path = \"deployment_config.json\"\n",
|
||||
"test_config = {'deploy-config-file': deployment_config_path}\n",
|
||||
"aci_config = AciWebservice.deploy_configuration(cpu_cores=2, \n",
|
||||
" memory_gb=5, \n",
|
||||
" tags={\"data\": \"MNIST\", \"method\" : \"keras\"}, \n",
|
||||
" description=\"Predict using webservice\")\n",
|
||||
"\n",
|
||||
"# define the model path and the name is the service name\n",
|
||||
"# the model gets registered automatically and a name is autogenerated using the \"name\" parameter below \n",
|
||||
"client.create_deployment(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
|
||||
" config=test_config,\n",
|
||||
" name=\"keras-aci-deployment\")"
|
||||
"webservice, azure_model = mlflow.azureml.deploy(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
|
||||
" workspace=ws,\n",
|
||||
" deployment_config=aci_config,\n",
|
||||
" service_name=\"keras-mnist-1\",\n",
|
||||
" model_name=\"keras_mnist\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Once the deployment has completed you can check the scoring URI of the web service in AzureML studio UI in the endpoints tab. Refer [mlflow predict](https://mlflow.org/docs/latest/python_api/mlflow.deployments.html#mlflow.deployments.BaseDeploymentClient.predict) on how to test your deployment. "
|
||||
"Once the deployment has completed you can check the scoring URI of the web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Scoring URI is: {}\".format(webservice.scoring_uri))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In case of a service creation issue, you can use ```webservice.get_logs()``` to get logs to debug."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Make predictions using a web service\n",
|
||||
"\n",
|
||||
"To make the web service, create a test data set as normalized NumPy array. \n",
|
||||
"\n",
|
||||
"Then, let's define a utility function that takes a random image and converts it into a format and shape suitable for input to the Keras inferencing end-point. The conversion is done by: \n",
|
||||
"\n",
|
||||
" 1. Select a random (image, label) tuple\n",
|
||||
" 2. Take the image and converting to to NumPy array \n",
|
||||
" 3. Reshape array into 1 x 1 x N array\n",
|
||||
" * 1 image in batch, 1 color channel, N = 784 pixels for MNIST images\n",
|
||||
" * Note also ```x = x.view(-1, 1, 28, 28)``` in net definition in ```train.py``` program to shape incoming scoring requests.\n",
|
||||
" 4. Convert the NumPy array to list to make it into a built-in type.\n",
|
||||
" 5. Create a dictionary {\"data\", <list>} that can be converted to JSON string for web service requests."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import keras\n",
|
||||
"import random\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"# the data, split between train and test sets\n",
|
||||
"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
|
||||
"\n",
|
||||
"# Scale images to the [0, 1] range\n",
|
||||
"x_test = x_test.astype(\"float32\") / 255\n",
|
||||
"x_test = x_test.reshape(len(x_test), -1)\n",
|
||||
"\n",
|
||||
"# convert class vectors to binary class matrices\n",
|
||||
"y_test = keras.utils.to_categorical(y_test, 10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"import json\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# send a random row from the test set to score\n",
|
||||
"random_index = np.random.randint(0, len(x_test)-1)\n",
|
||||
"input_data = \"{\\\"data\\\": [\" + str(list(x_test[random_index])) + \"]}\"\n",
|
||||
"\n",
|
||||
"response = webservice.run(input_data)\n",
|
||||
"\n",
|
||||
"response = sorted(response[0].items(), key = lambda x: x[1], reverse = True)\n",
|
||||
"\n",
|
||||
"print(\"Predicted label:\", response[0][0])\n",
|
||||
"plt.imshow(x_test[random_index].reshape(28,28), cmap = \"gray\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can also call the web service using a raw POST method against the web service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"response = requests.post(url=webservice.scoring_uri, data=input_data,headers={\"Content-type\": \"application/json\"})\n",
|
||||
"print(response.text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -332,7 +400,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"client.delete(\"keras-aci-deployment\")"
|
||||
"webservice.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -249,7 +249,7 @@
|
||||
"source": [
|
||||
"## Deploy model as web service\n",
|
||||
"\n",
|
||||
"The ```client.create_deployment``` function registers the logged PyTorch model and deploys the model in a framework-aware manner. It automatically creates the PyTorch-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n",
|
||||
"The ```mlflow.azureml.deploy``` function registers the logged PyTorch model and deploys the model in a framework-aware manner. It automatically creates the PyTorch-specific inferencing wrapper code and specifies package dependencies for you. See [this doc](https://mlflow.org/docs/latest/models.html#id34) for more information on deploying models on Azure ML using MLflow.\n",
|
||||
"\n",
|
||||
"In this example, we deploy the Docker image to Azure Container Instance: a serverless compute capable of running a single container. You can tag and add descriptions to help keep track of your web service. \n",
|
||||
"\n",
|
||||
@@ -258,63 +258,33 @@
|
||||
"Note that the service deployment can take several minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"First define your deployment target and customize parameters in the deployment config. Refer to [this documentation](https://docs.microsoft.com/azure/machine-learning/reference-azure-machine-learning-cli#azure-container-instance-deployment-configuration-schema) for more information. "
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
" \n",
|
||||
"# Data to be written\n",
|
||||
"deploy_config ={\n",
|
||||
" \"computeType\": \"aci\"\n",
|
||||
"}\n",
|
||||
"# Serializing json \n",
|
||||
"json_object = json.dumps(deploy_config)\n",
|
||||
" \n",
|
||||
"# Writing to sample.json\n",
|
||||
"with open(\"deployment_config.json\", \"w\") as outfile:\n",
|
||||
" outfile.write(json_object)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from mlflow.deployments import get_deploy_client\n",
|
||||
"from azureml.core.webservice import AciWebservice, Webservice\n",
|
||||
"\n",
|
||||
"# set the tracking uri as the deployment client\n",
|
||||
"client = get_deploy_client(mlflow.get_tracking_uri())\n",
|
||||
"\n",
|
||||
"# set the model path \n",
|
||||
"model_path = \"model\"\n",
|
||||
"\n",
|
||||
"# set the deployment config\n",
|
||||
"deployment_config_path = \"deployment_config.json\"\n",
|
||||
"test_config = {'deploy-config-file': deployment_config_path}\n",
|
||||
"aci_config = AciWebservice.deploy_configuration(cpu_cores=2, \n",
|
||||
" memory_gb=5, \n",
|
||||
" tags={\"data\": \"MNIST\", \"method\" : \"pytorch\"}, \n",
|
||||
" description=\"Predict using webservice\")\n",
|
||||
"\n",
|
||||
"# define the model path and the name is the service name\n",
|
||||
"# the model gets registered automatically and a name is autogenerated using the \"name\" parameter below \n",
|
||||
"client.create_deployment(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
|
||||
" config=test_config,\n",
|
||||
" name=\"keras-aci-deployment\")"
|
||||
"webservice, azure_model = mlflow.azureml.deploy(model_uri='runs:/{}/{}'.format(run.id, model_path),\n",
|
||||
" workspace=ws,\n",
|
||||
" deployment_config=aci_config,\n",
|
||||
" service_name=\"pytorch-mnist-1\",\n",
|
||||
" model_name=\"pytorch_mnist\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Once the deployment has completed you can check the scoring URI of the web service in AzureML studio UI in the endpoints tab. Refer [mlflow predict](https://mlflow.org/docs/latest/python_api/mlflow.deployments.html#mlflow.deployments.BaseDeploymentClient.predict) on how to test your deployment. "
|
||||
"Once the deployment has completed you can check the scoring URI of the web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -323,7 +293,133 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"client.delete(\"keras-aci-deployment\")"
|
||||
"print(\"Scoring URI is: {}\".format(webservice.scoring_uri))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In case of a service creation issue, you can use ```webservice.get_logs()``` to get logs to debug."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Make predictions using a web service\n",
|
||||
"\n",
|
||||
"To make the web service, create a test data set as normalized PyTorch tensors. \n",
|
||||
"\n",
|
||||
"Then, let's define a utility function that takes a random image and converts it into a format and shape suitable for input to the PyTorch inferencing end-point. The conversion is done by: \n",
|
||||
"\n",
|
||||
" 1. Select a random (image, label) tuple\n",
|
||||
" 2. Take the image and converting the tensor to NumPy array \n",
|
||||
" 3. Reshape array into 1 x 1 x N array\n",
|
||||
" * 1 image in batch, 1 color channel, N = 784 pixels for MNIST images\n",
|
||||
" * Note also ```x = x.view(-1, 1, 28, 28)``` in net definition in ```train.py``` program to shape incoming scoring requests.\n",
|
||||
" 4. Convert the NumPy array to list to make it into a built-in type.\n",
|
||||
" 5. Create a dictionary {\"data\", <list>} that can be converted to JSON string for web service requests."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from torchvision import datasets, transforms\n",
|
||||
"import random\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"# Use Azure Open Datasets for MNIST dataset\n",
|
||||
"datasets.MNIST.resources = [\n",
|
||||
" (\"https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz\",\n",
|
||||
" \"f68b3c2dcbeaaa9fbdd348bbdeb94873\"),\n",
|
||||
" (\"https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz\",\n",
|
||||
" \"d53e105ee54ea40749a09fcbcd1e9432\"),\n",
|
||||
" (\"https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz\",\n",
|
||||
" \"9fb629c4189551a2d022fa330f9573f3\"),\n",
|
||||
" (\"https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz\",\n",
|
||||
" \"ec29112dd5afa0611ce80d1b7f02629c\")\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"test_data = datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
|
||||
" transforms.ToTensor(),\n",
|
||||
" transforms.Normalize((0.1307,), (0.3081,))]))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_random_image():\n",
|
||||
" image_idx = random.randint(0,len(test_data))\n",
|
||||
" image_as_tensor = test_data[image_idx][0]\n",
|
||||
" return {\"data\": elem for elem in image_as_tensor.numpy().reshape(1,1,-1).tolist()}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then, invoke the web service using a random test image. Convert the dictionary containing the image to JSON string before passing it to web service.\n",
|
||||
"\n",
|
||||
"The response contains the raw scores for each label, with greater value indicating higher probability. Sort the labels and select the one with greatest score to get the prediction. Let's also plot the image sent to web service for comparison purposes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"import json\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"test_image = get_random_image()\n",
|
||||
"\n",
|
||||
"response = webservice.run(json.dumps(test_image))\n",
|
||||
"\n",
|
||||
"response = sorted(response[0].items(), key = lambda x: x[1], reverse = True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"print(\"Predicted label:\", response[0][0])\n",
|
||||
"plt.imshow(np.array(test_image[\"data\"]).reshape(28,28), cmap = \"gray\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can also call the web service using a raw POST method against the web service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"response = requests.post(url=webservice.scoring_uri, data=json.dumps(test_image),headers={\"Content-type\": \"application/json\"})\n",
|
||||
"print(response.text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Clean up\n",
|
||||
"You can delete the ACI deployment with a delete API call."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"webservice.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -141,20 +141,13 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create Virtual Network and Network Security Group\n",
|
||||
"### Create Virtual Network\n",
|
||||
"\n",
|
||||
"**If you are using separate compute targets for the Ray head and worker, as we do in this notebook**, a virtual network must be created in the resource group. If you have already created a virtual network in the resource group, you can skip this step.\n",
|
||||
"If you are using separate compute targets for the Ray head and worker, a virtual network must be created in the resource group. If you have alraeady created a virtual network in the resource group, you can skip this step.\n",
|
||||
"\n",
|
||||
"> Note that your user role must have permissions to create and manage virtual networks to run the cells below. Talk to your IT admin if you do not have these permissions.\n",
|
||||
"To do this, you first must install the Azure Networking API.\n",
|
||||
"\n",
|
||||
"#### Create Virtual Network\n",
|
||||
"To create the virtual network you first must install the [Azure Networking Python API](https://docs.microsoft.com/python/api/overview/azure/network?view=azure-python).\n",
|
||||
"\n",
|
||||
"`pip install --upgrade azure-mgmt-network`\n",
|
||||
"\n",
|
||||
"Note: In this section we are using [DefaultAzureCredential](https://docs.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python)\n",
|
||||
"class for authentication which, by default, examines several options in turn, and stops on the first option that provides\n",
|
||||
"a token. You will need to log in using Azure CLI, if none of the other options are available (please find more details [here](https://docs.microsoft.com/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python))."
|
||||
"`pip install --upgrade azure-mgmt-network==12.0.0`"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -164,7 +157,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# If you need to install the Azure Networking SDK, uncomment the following line.\n",
|
||||
"#!pip install --upgrade azure-mgmt-network"
|
||||
"#!pip install --upgrade azure-mgmt-network==12.0.0"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -174,7 +167,6 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azure.mgmt.network import NetworkManagementClient\n",
|
||||
"from azure.identity import DefaultAzureCredential\n",
|
||||
"\n",
|
||||
"# Virtual network name\n",
|
||||
"vnet_name =\"rl_pong_vnet\"\n",
|
||||
@@ -191,9 +183,9 @@
|
||||
"# Azure region of the resource group\n",
|
||||
"location=ws.location\n",
|
||||
"\n",
|
||||
"network_client = NetworkManagementClient(credential=DefaultAzureCredential(), subscription_id=subscription_id)\n",
|
||||
"network_client = NetworkManagementClient(ws._auth_object, subscription_id)\n",
|
||||
"\n",
|
||||
"async_vnet_creation = network_client.virtual_networks.begin_create_or_update(\n",
|
||||
"async_vnet_creation = network_client.virtual_networks.create_or_update(\n",
|
||||
" resource_group,\n",
|
||||
" vnet_name,\n",
|
||||
" {\n",
|
||||
@@ -212,9 +204,9 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Set up Network Security Group on Virtual Network\n",
|
||||
"### Set up Network Security Group on Virtual Network\n",
|
||||
"\n",
|
||||
"Depending on your Azure setup, you may need to open certain ports to make it possible for Azure to manage the compute targets that you create. The ports that need to be opened are described [here](https://docs.microsoft.com/azure/machine-learning/how-to-enable-virtual-network).\n",
|
||||
"Depending on your Azure setup, you may need to open certain ports to make it possible for Azure to manage the compute targets that you create. The ports that need to be opened are described [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-virtual-network).\n",
|
||||
"\n",
|
||||
"A common situation is that ports `29876-29877` are closed. The following code will add a security rule to open these ports. Or you can do this manually in the [Azure portal](https://portal.azure.com).\n",
|
||||
"\n",
|
||||
@@ -251,7 +243,7 @@
|
||||
" ],\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"async_nsg_creation = network_client.network_security_groups.begin_create_or_update(\n",
|
||||
"async_nsg_creation = network_client.network_security_groups.create_or_update(\n",
|
||||
" resource_group,\n",
|
||||
" security_group_name,\n",
|
||||
" nsg_params,\n",
|
||||
@@ -273,7 +265,7 @@
|
||||
" )\n",
|
||||
" \n",
|
||||
"# Create subnet on virtual network\n",
|
||||
"async_subnet_creation = network_client.subnets.begin_create_or_update(\n",
|
||||
"async_subnet_creation = network_client.subnets.create_or_update(\n",
|
||||
" resource_group_name=resource_group,\n",
|
||||
" virtual_network_name=vnet_name,\n",
|
||||
" subnet_name=subnet_name,\n",
|
||||
@@ -288,7 +280,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Review the virtual network security rules\n",
|
||||
"### Review the virtual network security rules\n",
|
||||
"Ensure that the virtual network is configured correctly with required ports open. It is possible that you have configured rules with broader range of ports that allows ports 29876-29877 to be opened. Kindly review your network security group rules. "
|
||||
]
|
||||
},
|
||||
@@ -299,24 +291,17 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from files.networkutils import *\n",
|
||||
"from azure.identity import DefaultAzureCredential\n",
|
||||
"\n",
|
||||
"check_vnet_security_rules(DefaultAzureCredential(), ws.subscription_id, ws.resource_group, vnet_name, True)"
|
||||
"check_vnet_security_rules(ws._auth_object, ws.subscription_id, ws.resource_group, vnet_name, True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create compute targets\n",
|
||||
"### Create head compute target\n",
|
||||
"\n",
|
||||
"In this example, we show how to set up separate compute targets for the Ray head and Ray worker nodes.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"#### Create head compute target\n",
|
||||
"\n",
|
||||
"First we define the head cluster with GPU for the Ray head node. One CPU of the head node will be used for the Ray head process and the rest of the CPUs will be used by the Ray worker processes."
|
||||
"In this example, we show how to set up separate compute targets for the Ray head and Ray worker nodes. First we define the head cluster with GPU for the Ray head node. One CPU of the head node will be used for the Ray head process and the rest of the CPUs will be used by the Ray worker processes."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -368,7 +353,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create worker compute target\n",
|
||||
"### Create worker compute target\n",
|
||||
"\n",
|
||||
"Now we create a compute target with CPUs for the additional Ray worker nodes. CPUs in these worker nodes are used by Ray worker processes. Each Ray worker node, depending on the CPUs on the node, may have multiple Ray worker processes. There can be multiple worker tasks on each worker process (core)."
|
||||
]
|
||||
|
||||
@@ -5,5 +5,4 @@ dependencies:
|
||||
- azureml-contrib-reinforcementlearning
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- azure-mgmt-network
|
||||
- azure-cli
|
||||
- azure-mgmt-network==12.0.0
|
||||
|
||||
@@ -118,8 +118,6 @@
|
||||
"\n",
|
||||
"A compute target is a designated compute resource where you run your training and simulation scripts. This location may be your local machine or a cloud-based compute resource. The code below shows how to create a cloud-based compute target. For more information see [What are compute targets in Azure Machine Learning?](https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target)\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"**Note: Creation of a compute resource can take several minutes**. Please make sure to change `STANDARD_D2_V2` to a [size available in your region](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=virtual-machines)."
|
||||
]
|
||||
},
|
||||
|
||||
@@ -138,8 +138,6 @@
|
||||
"\n",
|
||||
"A compute target is a designated compute resource where you run your training script. For more information, see [What are compute targets in Azure Machine Learning service?](https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target).\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"#### CPU target for Ray head\n",
|
||||
"\n",
|
||||
"In the experiment setup for this tutorial, the Ray head node will\n",
|
||||
|
||||
@@ -10,7 +10,6 @@ from contextlib import closing
|
||||
import gzip
|
||||
import pandas as pd
|
||||
from sklearn.utils import Bunch
|
||||
from time import sleep
|
||||
|
||||
|
||||
def _is_gzip_encoded(_fsrc):
|
||||
@@ -30,7 +29,7 @@ _categorical_columns = [
|
||||
|
||||
|
||||
def fetch_census_dataset():
|
||||
"""Fetch the Adult Census Dataset.
|
||||
"""Fetch the Adult Census Dataset
|
||||
|
||||
This uses a particular URL for the Adult Census dataset. The code
|
||||
is a simplified version of fetch_openml() in sklearn.
|
||||
@@ -46,38 +45,21 @@ def fetch_census_dataset():
|
||||
|
||||
filename = "1595261.gz"
|
||||
data_url = "https://rainotebookscdn.blob.core.windows.net/datasets/"
|
||||
urlretrieve(data_url + filename, filename)
|
||||
|
||||
remaining_attempts = 5
|
||||
sleep_duration = 10
|
||||
while remaining_attempts > 0:
|
||||
try:
|
||||
urlretrieve(data_url + filename, filename)
|
||||
http_stream = gzip.GzipFile(filename=filename, mode='rb')
|
||||
|
||||
http_stream = gzip.GzipFile(filename=filename, mode='rb')
|
||||
with closing(http_stream):
|
||||
def _stream_generator(response):
|
||||
for line in response:
|
||||
yield line.decode('utf-8')
|
||||
|
||||
with closing(http_stream):
|
||||
def _stream_generator(response):
|
||||
for line in response:
|
||||
yield line.decode('utf-8')
|
||||
|
||||
stream = _stream_generator(http_stream)
|
||||
data = arff.load(stream)
|
||||
except Exception as exc: # noqa: B902
|
||||
remaining_attempts -= 1
|
||||
print("Error downloading dataset from {} ({} attempt(s) remaining)"
|
||||
.format(data_url, remaining_attempts))
|
||||
print(exc)
|
||||
sleep(sleep_duration)
|
||||
sleep_duration *= 2
|
||||
continue
|
||||
else:
|
||||
# dataset successfully downloaded
|
||||
break
|
||||
else:
|
||||
raise Exception("Could not retrieve dataset from {}.".format(data_url))
|
||||
stream = _stream_generator(http_stream)
|
||||
data = arff.load(stream)
|
||||
|
||||
attributes = OrderedDict(data['attributes'])
|
||||
arff_columns = list(attributes)
|
||||
|
||||
raw_df = pd.DataFrame(data=data['data'], columns=arff_columns)
|
||||
|
||||
target_column_name = 'class'
|
||||
|
||||
@@ -100,7 +100,7 @@
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using SDK version 1.29.0, you are currently running version\", azureml.core.VERSION)"
|
||||
"print(\"This notebook was created using SDK version 1.27.0, you are currently running version\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -390,9 +390,7 @@
|
||||
"source": [
|
||||
"## Once more, with an AmlCompute cluster\n",
|
||||
"\n",
|
||||
"Just to prove we can, let's create an AmlCompute CPU cluster, and run our demo there, as well.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"Just to prove we can, let's create an AmlCompute CPU cluster, and run our demo there, as well."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -67,9 +67,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's also create a Machine Learning Compute cluster for submitting the remote run. \n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"Let's also create a Machine Learning Compute cluster for submitting the remote run. "
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -195,8 +195,6 @@
|
||||
"source": [
|
||||
"### Provision as a persistent compute target (Basic)\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
|
||||
"\n",
|
||||
"* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
|
||||
@@ -289,8 +287,6 @@
|
||||
"source": [
|
||||
"### Provision as a persistent compute target (Advanced)\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
|
||||
"\n",
|
||||
"In addition to `vm_size` and `max_nodes`, you can specify:\n",
|
||||
|
||||
@@ -162,8 +162,6 @@
|
||||
"source": [
|
||||
"## Create compute target\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"Create an Azure Machine Learning compute cluster to run the data drift monitor and associated runs. The below cell will create a compute cluster named `'cpu-cluster'`. "
|
||||
]
|
||||
},
|
||||
@@ -433,7 +431,7 @@
|
||||
"Azure ML"
|
||||
],
|
||||
"friendly_name": "Data drift quickdemo",
|
||||
"index_order": 1,
|
||||
"index_order": 1.0,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
|
||||
@@ -125,8 +125,6 @@
|
||||
"### 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",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\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."
|
||||
]
|
||||
},
|
||||
|
||||
@@ -59,9 +59,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -101,8 +101,6 @@
|
||||
"## 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",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\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."
|
||||
]
|
||||
},
|
||||
|
||||
11
index.md
11
index.md
@@ -132,12 +132,17 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
|
||||
| [rai-loan-decision](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.ipynb) | | | | | | |
|
||||
| [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results | None | None | None | None | None |
|
||||
| [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master//setup-environment/configuration.ipynb) | | | | | | |
|
||||
| [quickstart-azureml-automl](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb) | | | | | | |
|
||||
| [quickstart-azureml-in-10mins](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb) | | | | | | |
|
||||
| [quickstart-azureml-python-sdk](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb) | | | | | | |
|
||||
| [tutorial-1st-experiment-sdk-train](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb) | | | | | | |
|
||||
| [day1-part1-setup](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/get-started-day1/day1-part1-setup.ipynb) | | | | | | |
|
||||
| [day1-part2-hello-world](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/get-started-day1/day1-part2-hello-world.ipynb) | | | | | | |
|
||||
| [day1-part3-train-model](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/get-started-day1/day1-part3-train-model.ipynb) | | | | | | |
|
||||
| [day1-part4-data](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/get-started-day1/day1-part4-data.ipynb) | | | | | | |
|
||||
| [img-classification-part1-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb) | | | | | | |
|
||||
| [img-classification-part2-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb) | | | | | | |
|
||||
| [img-classification-part3-deploy-encrypted](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb) | | | | | | |
|
||||
| [tutorial-pipeline-batch-scoring-classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb) | | | | | | |
|
||||
| [azureml-quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/quickstart/azureml-quickstart.ipynb) | | | | | | |
|
||||
| [AzureMLIn10mins](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/quickstart-ci/AzureMLIn10mins.ipynb) | | | | | | |
|
||||
| [ClassificationWithAutomatedML](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/quickstart-ci/ClassificationWithAutomatedML.ipynb) | | | | | | |
|
||||
| [GettingStartedWithPythonSDK](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/quickstart-ci/GettingStartedWithPythonSDK.ipynb) | | | | | | |
|
||||
| [regression-automated-ml](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb) | | | | | | |
|
||||
|
||||
@@ -102,7 +102,7 @@
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using version 1.29.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.27.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -16,14 +16,16 @@ The following tutorials are intended to provide an introductory overview of Azur
|
||||
|
||||
| Tutorial | Description | Notebook | Task | Framework |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| Azure Machine Learning in 10 minutes | Learn how to create and attach compute instances to notebooks, run an image classification model, track model metrics, and deploy a model| [quickstart](quickstart/azureml-quickstart.ipynb) | Learn Azure Machine Learning Concepts | PyTorch
|
||||
| [Get Started (day1)](https://docs.microsoft.com/azure/machine-learning/tutorial-1st-experiment-sdk-setup-local) | Learn the fundamental concepts of Azure Machine Learning to help onboard your existing code to Azure Machine Learning. This tutorial focuses heavily on submitting machine learning jobs to scalable cloud-based compute clusters. | [get-started-day1](get-started-day1/day1-part1-setup.ipynb) | Learn Azure Machine Learning Concepts | PyTorch
|
||||
| [Train your first ML Model](https://docs.microsoft.com/azure/machine-learning/tutorial-1st-experiment-sdk-train) | Learn the foundational design patterns in Azure Machine Learning and train a scikit-learn model based on a diabetes data set. | [tutorial-quickstart-train-model.ipynb](create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb) | Regression | Scikit-Learn
|
||||
| [Train an image classification model](https://docs.microsoft.com/azure/machine-learning/tutorial-train-models-with-aml) | Train a scikit-learn image classification model. | [img-classification-part1-training.ipynb](image-classification-mnist-data/img-classification-part1-training.ipynb) | Image Classification | Scikit-Learn
|
||||
| [Deploy an image classification model](https://docs.microsoft.com/azure/machine-learning/tutorial-deploy-models-with-aml) | Deploy a scikit-learn image classification model to Azure Container Instances. | [img-classification-part2-deploy.ipynb](image-classification-mnist-data/img-classification-part2-deploy.ipynb) | Image Classification | Scikit-Learn
|
||||
| [Deploy an encrypted inferencing service](https://docs.microsoft.com/azure/machine-learning/tutorial-deploy-models-with-aml) |Deploy an image classification model for encrypted inferencing in Azure Container Instances | [img-classification-part3-deploy-encrypted.ipynb](image-classification-mnist-data/img-classification-part3-deploy-encrypted.ipynb) | Image Classification | Scikit-Learn
|
||||
| [Use automated machine learning to predict taxi fares](https://docs.microsoft.com/azure/machine-learning/tutorial-auto-train-models) | Train a regression model to predict taxi fares using Automated Machine Learning. | [regression-part2-automated-ml.ipynb](regression-automl-nyc-taxi-data/regression-automated-ml.ipynb) | Regression | Automated ML
|
||||
| Azure ML in 10 minutes (Compute instance required) |Learn how to run an image classification model, track model metrics, and deploy a model in 10 minutes. | [quickstart-azureml-in-10mins.ipynb](compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb) | Image Classification | Scikit-Learn |
|
||||
| Get started with Azure ML Job Submission (Compute instance required) |Learn how to use the Azure Machine Learning Python SDK to submit batch jobs. | [quickstart-azureml-python-sdk.ipynb](compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb) | Image Classification | Scikit-Learn |
|
||||
| Get started with Automated ML (Compute instance required) | Learn how to use Automated ML for Fraud classification. | [quickstart-azureml-automl.ipynb](compute-instance-quickstarts/quickstart-azureml-automl/quickstart-azureml-automl.ipynb) | Classification | Automated ML |
|
||||
| Azure ML in 10 minutes, to be run on a Compute Instance |Learn how to run an image classification model, track model metrics, and deploy a model in 10 minutes. | [AzureMLIn10mins.ipynb](quickstart-ci/AzureMLIn10mins.ipynb) | Image Classification | Scikit-Learn |
|
||||
| Get started with Azure ML Job Submission, to be run on a Compute Instance |Learn how to use the Azure Machine Learning Python SDK to submit batch jobs. | [GettingStartedWithPythonSDK.ipynb](quickstart-ci/GettingStartedWithPythonSDK.ipynb) | Image Classification | Scikit-Learn |
|
||||
| Get started with Automated ML, to be run on a Compute Instance | Learn how to use Automated ML for Fraud classification. | [ClassificationWithAutomatedML.ipynb](quickstart-ci/ClassificationWithAutomatedML.ipynb) | Classification | Automated ML |
|
||||
|
||||
|
||||
## Advanced Samples
|
||||
|
||||
12
tutorials/get-started-day1/IDE-users/01-create-workspace.py
Normal file
12
tutorials/get-started-day1/IDE-users/01-create-workspace.py
Normal file
@@ -0,0 +1,12 @@
|
||||
# 01-create-workspace.py
|
||||
from azureml.core import Workspace
|
||||
|
||||
# Example locations: 'westeurope' or 'eastus2' or 'westus2' or 'southeastasia'.
|
||||
ws = Workspace.create(name='<my_workspace_name>',
|
||||
subscription_id='<azure-subscription-id>',
|
||||
resource_group='<myresourcegroup>',
|
||||
create_resource_group=True,
|
||||
location='<NAME_OF_REGION>')
|
||||
|
||||
# write out the workspace details to a configuration file: .azureml/config.json
|
||||
ws.write_config(path='.azureml')
|
||||
23
tutorials/get-started-day1/IDE-users/02-create-compute.py
Normal file
23
tutorials/get-started-day1/IDE-users/02-create-compute.py
Normal file
@@ -0,0 +1,23 @@
|
||||
# 02-create-compute.py
|
||||
from azureml.core import Workspace
|
||||
from azureml.core.compute import ComputeTarget, AmlCompute
|
||||
from azureml.core.compute_target import ComputeTargetException
|
||||
|
||||
ws = Workspace.from_config()
|
||||
|
||||
# Choose a name for your CPU cluster
|
||||
cpu_cluster_name = "cpu-cluster"
|
||||
|
||||
# Verify that cluster does not exist already
|
||||
try:
|
||||
cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
|
||||
print('Found existing cluster, use it.')
|
||||
except ComputeTargetException:
|
||||
cfg = AmlCompute.provisioning_configuration(
|
||||
vm_size='STANDARD_D2_V2',
|
||||
max_nodes=4,
|
||||
idle_seconds_before_scaledown=2400
|
||||
)
|
||||
cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, cfg)
|
||||
|
||||
cpu_cluster.wait_for_completion(show_output=True)
|
||||
13
tutorials/get-started-day1/IDE-users/03-run-hello.py
Normal file
13
tutorials/get-started-day1/IDE-users/03-run-hello.py
Normal file
@@ -0,0 +1,13 @@
|
||||
# 03-run-hello.py
|
||||
from azureml.core import Workspace, Experiment, ScriptRunConfig
|
||||
|
||||
ws = Workspace.from_config()
|
||||
experiment = Experiment(workspace=ws, name='day1-experiment-hello')
|
||||
|
||||
config = ScriptRunConfig(source_directory='./src',
|
||||
script='hello.py',
|
||||
compute_target='cpu-cluster')
|
||||
|
||||
run = experiment.submit(config)
|
||||
aml_url = run.get_portal_url()
|
||||
print(aml_url)
|
||||
24
tutorials/get-started-day1/IDE-users/04-run-pytorch.py
Normal file
24
tutorials/get-started-day1/IDE-users/04-run-pytorch.py
Normal file
@@ -0,0 +1,24 @@
|
||||
# 04-run-pytorch.py
|
||||
from azureml.core import Workspace
|
||||
from azureml.core import Experiment
|
||||
from azureml.core import Environment
|
||||
from azureml.core import ScriptRunConfig
|
||||
|
||||
if __name__ == "__main__":
|
||||
ws = Workspace.from_config()
|
||||
experiment = Experiment(workspace=ws, name='day1-experiment-train')
|
||||
config = ScriptRunConfig(source_directory='./src',
|
||||
script='train.py',
|
||||
compute_target='cpu-cluster')
|
||||
|
||||
# set up pytorch environment
|
||||
env = Environment.from_conda_specification(
|
||||
name='pytorch-env',
|
||||
file_path='./environments/pytorch-env.yml'
|
||||
)
|
||||
config.run_config.environment = env
|
||||
|
||||
run = experiment.submit(config)
|
||||
|
||||
aml_url = run.get_portal_url()
|
||||
print(aml_url)
|
||||
7
tutorials/get-started-day1/IDE-users/05-upload-data.py
Normal file
7
tutorials/get-started-day1/IDE-users/05-upload-data.py
Normal file
@@ -0,0 +1,7 @@
|
||||
# 05-upload-data.py
|
||||
from azureml.core import Workspace
|
||||
ws = Workspace.from_config()
|
||||
datastore = ws.get_default_datastore()
|
||||
datastore.upload(src_dir='./data',
|
||||
target_path='datasets/cifar10',
|
||||
overwrite=True)
|
||||
35
tutorials/get-started-day1/IDE-users/06-run-pytorch-data.py
Normal file
35
tutorials/get-started-day1/IDE-users/06-run-pytorch-data.py
Normal file
@@ -0,0 +1,35 @@
|
||||
# 06-run-pytorch-data.py
|
||||
from azureml.core import Workspace
|
||||
from azureml.core import Experiment
|
||||
from azureml.core import Environment
|
||||
from azureml.core import ScriptRunConfig
|
||||
from azureml.core import Dataset
|
||||
|
||||
if __name__ == "__main__":
|
||||
ws = Workspace.from_config()
|
||||
datastore = ws.get_default_datastore()
|
||||
dataset = Dataset.File.from_files(path=(datastore, 'datasets/cifar10'))
|
||||
|
||||
experiment = Experiment(workspace=ws, name='day1-experiment-data')
|
||||
|
||||
config = ScriptRunConfig(
|
||||
source_directory='./src',
|
||||
script='train.py',
|
||||
compute_target='cpu-cluster',
|
||||
arguments=[
|
||||
'--data_path', dataset.as_named_input('input').as_mount(),
|
||||
'--learning_rate', 0.003,
|
||||
'--momentum', 0.92],
|
||||
)
|
||||
# set up pytorch environment
|
||||
env = Environment.from_conda_specification(
|
||||
name='pytorch-env',
|
||||
file_path='./environments/pytorch-env.yml'
|
||||
)
|
||||
config.run_config.environment = env
|
||||
|
||||
run = experiment.submit(config)
|
||||
aml_url = run.get_portal_url()
|
||||
print("Submitted to compute cluster. Click link below")
|
||||
print("")
|
||||
print(aml_url)
|
||||
25
tutorials/get-started-day1/IDE-users/README.md
Normal file
25
tutorials/get-started-day1/IDE-users/README.md
Normal file
@@ -0,0 +1,25 @@
|
||||
# Get Started (day 1) with Azure Machine Learning: IDE Users
|
||||
|
||||
This folder has been setup for IDE user (for example, VS Code or Pycharm) following the [Get started (day 1) with Azure Machine Learning tutorial series](https://aka.ms/day1aml).
|
||||
|
||||
The directory is structured as follows:
|
||||
|
||||
```Text
|
||||
IDE-users
|
||||
└──environments
|
||||
| └──pytorch-env.yml
|
||||
└──src
|
||||
| └──hello.py
|
||||
| └──model.py
|
||||
| └──train.py
|
||||
└──01-create-workspace.py
|
||||
└──02-create-compute.py
|
||||
└──03-run-hello.py
|
||||
└──04-run-pytorch.py
|
||||
└──05-upload-data.py
|
||||
└──06-run-pytorch-data.py
|
||||
```
|
||||
|
||||
Please refer to [the documentation](https://aka.ms/day1aml) for more details on these files.
|
||||
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
|
||||
name: pytorch-env
|
||||
channels:
|
||||
- defaults
|
||||
- pytorch
|
||||
dependencies:
|
||||
- python=3.6.2
|
||||
- pytorch
|
||||
- torchvision
|
||||
2
tutorials/get-started-day1/IDE-users/src/hello.py
Normal file
2
tutorials/get-started-day1/IDE-users/src/hello.py
Normal file
@@ -0,0 +1,2 @@
|
||||
|
||||
print("hello world!")
|
||||
22
tutorials/get-started-day1/IDE-users/src/model.py
Normal file
22
tutorials/get-started-day1/IDE-users/src/model.py
Normal file
@@ -0,0 +1,22 @@
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.conv1 = nn.Conv2d(3, 6, 5)
|
||||
self.pool = nn.MaxPool2d(2, 2)
|
||||
self.conv2 = nn.Conv2d(6, 16, 5)
|
||||
self.fc1 = nn.Linear(16 * 5 * 5, 120)
|
||||
self.fc2 = nn.Linear(120, 84)
|
||||
self.fc3 = nn.Linear(84, 10)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.pool(F.relu(self.conv1(x)))
|
||||
x = self.pool(F.relu(self.conv2(x)))
|
||||
x = x.view(-1, 16 * 5 * 5)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = F.relu(self.fc2(x))
|
||||
x = self.fc3(x)
|
||||
return x
|
||||
52
tutorials/get-started-day1/IDE-users/src/train.py
Normal file
52
tutorials/get-started-day1/IDE-users/src/train.py
Normal file
@@ -0,0 +1,52 @@
|
||||
import torch
|
||||
import torch.optim as optim
|
||||
import torchvision
|
||||
import torchvision.transforms as transforms
|
||||
|
||||
from model import Net
|
||||
|
||||
# download CIFAR 10 data
|
||||
trainset = torchvision.datasets.CIFAR10(
|
||||
root="./data",
|
||||
train=True,
|
||||
download=True,
|
||||
transform=torchvision.transforms.ToTensor(),
|
||||
)
|
||||
trainloader = torch.utils.data.DataLoader(
|
||||
trainset, batch_size=4, shuffle=True, num_workers=2
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# define convolutional network
|
||||
net = Net()
|
||||
|
||||
# set up pytorch loss / optimizer
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
|
||||
|
||||
# train the network
|
||||
for epoch in range(2):
|
||||
|
||||
running_loss = 0.0
|
||||
for i, data in enumerate(trainloader, 0):
|
||||
# unpack the data
|
||||
inputs, labels = data
|
||||
|
||||
# zero the parameter gradients
|
||||
optimizer.zero_grad()
|
||||
|
||||
# forward + backward + optimize
|
||||
outputs = net(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# print statistics
|
||||
running_loss += loss.item()
|
||||
if i % 2000 == 1999:
|
||||
loss = running_loss / 2000
|
||||
print(f"epoch={epoch + 1}, batch={i + 1:5}: loss {loss:.2f}")
|
||||
running_loss = 0.0
|
||||
|
||||
print("Finished Training")
|
||||
2
tutorials/get-started-day1/code/hello/hello.py
Normal file
2
tutorials/get-started-day1/code/hello/hello.py
Normal file
@@ -0,0 +1,2 @@
|
||||
|
||||
print("hello world!")
|
||||
@@ -0,0 +1,22 @@
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.conv1 = nn.Conv2d(3, 6, 5)
|
||||
self.pool = nn.MaxPool2d(2, 2)
|
||||
self.conv2 = nn.Conv2d(6, 16, 5)
|
||||
self.fc1 = nn.Linear(16 * 5 * 5, 120)
|
||||
self.fc2 = nn.Linear(120, 84)
|
||||
self.fc3 = nn.Linear(84, 10)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.pool(F.relu(self.conv1(x)))
|
||||
x = self.pool(F.relu(self.conv2(x)))
|
||||
x = x.view(-1, 16 * 5 * 5)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = F.relu(self.fc2(x))
|
||||
x = self.fc3(x)
|
||||
return x
|
||||
@@ -0,0 +1,62 @@
|
||||
import torch
|
||||
import torch.optim as optim
|
||||
import torchvision
|
||||
import torchvision.transforms as transforms
|
||||
|
||||
from model import Net
|
||||
from azureml.core import Run
|
||||
|
||||
|
||||
# ADDITIONAL CODE: get AML run from the current context
|
||||
run = Run.get_context()
|
||||
|
||||
# download CIFAR 10 data
|
||||
trainset = torchvision.datasets.CIFAR10(
|
||||
root='./data',
|
||||
train=True,
|
||||
download=True,
|
||||
transform=torchvision.transforms.ToTensor()
|
||||
)
|
||||
trainloader = torch.utils.data.DataLoader(
|
||||
trainset,
|
||||
batch_size=4,
|
||||
shuffle=True,
|
||||
num_workers=2
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# define convolutional network
|
||||
net = Net()
|
||||
|
||||
# set up pytorch loss / optimizer
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
|
||||
|
||||
# train the network
|
||||
for epoch in range(2):
|
||||
|
||||
running_loss = 0.0
|
||||
for i, data in enumerate(trainloader, 0):
|
||||
# unpack the data
|
||||
inputs, labels = data
|
||||
|
||||
# zero the parameter gradients
|
||||
optimizer.zero_grad()
|
||||
|
||||
# forward + backward + optimize
|
||||
outputs = net(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# print statistics
|
||||
running_loss += loss.item()
|
||||
if i % 2000 == 1999:
|
||||
loss = running_loss / 2000
|
||||
# ADDITIONAL CODE: log loss metric to AML
|
||||
run.log('loss', loss)
|
||||
print(f'epoch={epoch + 1}, batch={i + 1:5}: loss {loss:.2f}')
|
||||
running_loss = 0.0
|
||||
|
||||
print('Finished Training')
|
||||
@@ -0,0 +1,22 @@
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.conv1 = nn.Conv2d(3, 6, 5)
|
||||
self.pool = nn.MaxPool2d(2, 2)
|
||||
self.conv2 = nn.Conv2d(6, 16, 5)
|
||||
self.fc1 = nn.Linear(16 * 5 * 5, 120)
|
||||
self.fc2 = nn.Linear(120, 84)
|
||||
self.fc3 = nn.Linear(84, 10)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.pool(F.relu(self.conv1(x)))
|
||||
x = self.pool(F.relu(self.conv2(x)))
|
||||
x = x.view(-1, 16 * 5 * 5)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = F.relu(self.fc2(x))
|
||||
x = self.fc3(x)
|
||||
return x
|
||||
@@ -0,0 +1,52 @@
|
||||
import torch
|
||||
import torch.optim as optim
|
||||
import torchvision
|
||||
import torchvision.transforms as transforms
|
||||
|
||||
from model import Net
|
||||
|
||||
# download CIFAR 10 data
|
||||
trainset = torchvision.datasets.CIFAR10(
|
||||
root="./data",
|
||||
train=True,
|
||||
download=True,
|
||||
transform=torchvision.transforms.ToTensor(),
|
||||
)
|
||||
trainloader = torch.utils.data.DataLoader(
|
||||
trainset, batch_size=4, shuffle=True, num_workers=2
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
# define convolutional network
|
||||
net = Net()
|
||||
|
||||
# set up pytorch loss / optimizer
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
|
||||
|
||||
# train the network
|
||||
for epoch in range(2):
|
||||
|
||||
running_loss = 0.0
|
||||
for i, data in enumerate(trainloader, 0):
|
||||
# unpack the data
|
||||
inputs, labels = data
|
||||
|
||||
# zero the parameter gradients
|
||||
optimizer.zero_grad()
|
||||
|
||||
# forward + backward + optimize
|
||||
outputs = net(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# print statistics
|
||||
running_loss += loss.item()
|
||||
if i % 2000 == 1999:
|
||||
loss = running_loss / 2000
|
||||
print(f"epoch={epoch + 1}, batch={i + 1:5}: loss {loss:.2f}")
|
||||
running_loss = 0.0
|
||||
|
||||
print("Finished Training")
|
||||
@@ -0,0 +1,22 @@
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.conv1 = nn.Conv2d(3, 6, 5)
|
||||
self.pool = nn.MaxPool2d(2, 2)
|
||||
self.conv2 = nn.Conv2d(6, 16, 5)
|
||||
self.fc1 = nn.Linear(16 * 5 * 5, 120)
|
||||
self.fc2 = nn.Linear(120, 84)
|
||||
self.fc3 = nn.Linear(84, 10)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.pool(F.relu(self.conv1(x)))
|
||||
x = self.pool(F.relu(self.conv2(x)))
|
||||
x = x.view(-1, 16 * 5 * 5)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = F.relu(self.fc2(x))
|
||||
x = self.fc3(x)
|
||||
return x
|
||||
@@ -0,0 +1,96 @@
|
||||
|
||||
import os
|
||||
import argparse
|
||||
import torch
|
||||
import torch.optim as optim
|
||||
import torchvision
|
||||
import torchvision.transforms as transforms
|
||||
|
||||
from model import Net
|
||||
from azureml.core import Run
|
||||
|
||||
run = Run.get_context()
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
'--data_path',
|
||||
type=str,
|
||||
help='Path to the training data'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--learning_rate',
|
||||
type=float,
|
||||
default=0.001,
|
||||
help='Learning rate for SGD'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--momentum',
|
||||
type=float,
|
||||
default=0.9,
|
||||
help='Momentum for SGD'
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
print("===== DATA =====")
|
||||
print("DATA PATH: " + args.data_path)
|
||||
print("LIST FILES IN DATA PATH...")
|
||||
print(os.listdir(args.data_path))
|
||||
print("================")
|
||||
|
||||
# prepare DataLoader for CIFAR10 data
|
||||
transform = transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
|
||||
])
|
||||
trainset = torchvision.datasets.CIFAR10(
|
||||
root=args.data_path,
|
||||
train=True,
|
||||
download=False,
|
||||
transform=transform,
|
||||
)
|
||||
trainloader = torch.utils.data.DataLoader(
|
||||
trainset,
|
||||
batch_size=4,
|
||||
shuffle=True,
|
||||
num_workers=2
|
||||
)
|
||||
|
||||
# define convolutional network
|
||||
net = Net()
|
||||
|
||||
# set up pytorch loss / optimizer
|
||||
criterion = torch.nn.CrossEntropyLoss()
|
||||
optimizer = optim.SGD(
|
||||
net.parameters(),
|
||||
lr=args.learning_rate,
|
||||
momentum=args.momentum,
|
||||
)
|
||||
|
||||
# train the network
|
||||
for epoch in range(2):
|
||||
|
||||
running_loss = 0.0
|
||||
for i, data in enumerate(trainloader, 0):
|
||||
# unpack the data
|
||||
inputs, labels = data
|
||||
|
||||
# zero the parameter gradients
|
||||
optimizer.zero_grad()
|
||||
|
||||
# forward + backward + optimize
|
||||
outputs = net(inputs)
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
# print statistics
|
||||
running_loss += loss.item()
|
||||
if i % 2000 == 1999:
|
||||
loss = running_loss / 2000
|
||||
run.log('loss', loss) # log loss metric to AML
|
||||
print(f'epoch={epoch + 1}, batch={i + 1:5}: loss {loss:.2f}')
|
||||
running_loss = 0.0
|
||||
|
||||
print('Finished Training')
|
||||
11
tutorials/get-started-day1/configuration/pytorch-aml-env.yml
Normal file
11
tutorials/get-started-day1/configuration/pytorch-aml-env.yml
Normal file
@@ -0,0 +1,11 @@
|
||||
name: pytorch-aml-env
|
||||
channels:
|
||||
- defaults
|
||||
- pytorch
|
||||
dependencies:
|
||||
- python=3.6.2
|
||||
- pytorch
|
||||
- torchvision
|
||||
- pip
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
9
tutorials/get-started-day1/configuration/pytorch-env.yml
Normal file
9
tutorials/get-started-day1/configuration/pytorch-env.yml
Normal file
@@ -0,0 +1,9 @@
|
||||
|
||||
name: pytorch-env
|
||||
channels:
|
||||
- defaults
|
||||
- pytorch
|
||||
dependencies:
|
||||
- python=3.6.2
|
||||
- pytorch
|
||||
- torchvision
|
||||
166
tutorials/get-started-day1/day1-part1-setup.ipynb
Normal file
166
tutorials/get-started-day1/day1-part1-setup.ipynb
Normal file
@@ -0,0 +1,166 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Tutorial: Get started (day 1) with Azure Machine Learning (Part 1 of 4)\n",
|
||||
"\n",
|
||||
"---\n",
|
||||
"## Introduction <a id='intro'></a>\n",
|
||||
"\n",
|
||||
"In this **four-part tutorial series**, you will learn the fundamentals of Azure Machine Learning and complete jobs-based Python machine learning tasks in the Azure cloud, including:\n",
|
||||
"\n",
|
||||
"1. Set up a compute cluster\n",
|
||||
"2. Run code in the cloud using Azure Machine Learning's Python SDK.\n",
|
||||
"3. Manage the Python environment you use for model training.\n",
|
||||
"4. Upload data to Azure and consume that data in training.\n",
|
||||
"\n",
|
||||
"In this first part of the tutorial series you learn how to create an Azure Machine Learning Compute Cluster that will be used in subsequent parts of the series to submit jobs to. This notebook follows the steps provided on the [Python (day 1) - set up local computer documentation page](https://aka.ms/day1aml).\n",
|
||||
"\n",
|
||||
"## Pre-requisites <a id='pre-reqs'></a>\n",
|
||||
"\n",
|
||||
"- An Azure Subscription. If you don't have an Azure subscription, create a free account before you begin. Try [Azure Machine Learning](https://aka.ms/AMLFree) today.\n",
|
||||
"- Familiarity with Python and Machine Learning concepts. For example, environments, training, scoring, and so on.\n",
|
||||
"- If you are using a compute instance in Azure Machine Learning to run this notebook series, you are all set. Otherwise, please follow the [Configure a development environment for Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/how-to-configure-environment)\n",
|
||||
"\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Ensure you have the latest Azure Machine Learning Python SDK\n",
|
||||
"\n",
|
||||
"This tutorial series depends on having the Azure Machine Learning SDK version 1.14.0 onwards installed. You can check your version using the code cell below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import VERSION\n",
|
||||
"\n",
|
||||
"print ('Version: ' + VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If your version is below 1.14.0, then upgrade the SDK using `pip` (**Note: You may need to restart your kernel for the changes to take effect. Re-run the cell above to ensure you have the right version**).\n",
|
||||
"\n",
|
||||
"```bash\n",
|
||||
"!pip install -U azureml-sdk\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create an Azure Machine Learning compute cluster <a id='createcc'></a>\n",
|
||||
"\n",
|
||||
"As this tutorial focuses on jobs-based machine learning tasks, you will be submitting python code to run on an Azure Machine Learning **Compute cluster**, which is well suited for large jobs and production. Therefore, you create an Azure Machine Learning compute cluster that will auto-scale between zero and four nodes:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"create mlc",
|
||||
"batchai"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config() # this automatically looks for a directory .azureml\n",
|
||||
"\n",
|
||||
"# Choose a name for your CPU cluster\n",
|
||||
"cpu_cluster_name = \"cpu-cluster\"\n",
|
||||
"\n",
|
||||
"# Verify that cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
|
||||
" print('Found existing cluster, use it.')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
|
||||
" max_nodes=4, \n",
|
||||
" idle_seconds_before_scaledown=2400)\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
"cpu_cluster.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"> <span style=\"color:darkblue;font-weight:bold\"> ! INFORMATION \n",
|
||||
"> When the cluster has been created it will have 0 nodes provisioned. Therefore, the cluster does not incur costs until you submit a job. This cluster will scale down when it has been idle for 2400 seconds (40 minutes).</span>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Next Steps\n",
|
||||
"\n",
|
||||
"In the next tutorial, you walk through submitting a script to the Azure Machine Learning compute cluster.\n",
|
||||
"\n",
|
||||
"[Tutorial: Run \"Hello World\" Python Script on Azure](day1-part2-hello-world.ipynb)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "samkemp"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -1,4 +1,4 @@
|
||||
name: quickstart-azureml-automl
|
||||
name: day1-part1-setup
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
204
tutorials/get-started-day1/day1-part2-hello-world.ipynb
Normal file
204
tutorials/get-started-day1/day1-part2-hello-world.ipynb
Normal file
@@ -0,0 +1,204 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Tutorial: \"Hello World\" (Part 2 of 4)\n",
|
||||
"\n",
|
||||
"---\n",
|
||||
"## Introduction\n",
|
||||
"In **part 2 of this get started series**, you will submit a trivial \"hello world\" python script to the cloud by:\n",
|
||||
"\n",
|
||||
"- Running Python code in the cloud with Azure Machine Learning SDK\n",
|
||||
"- Switching between debugging locally on a compute instance.\n",
|
||||
"- Submitting remote runs in the cloud\n",
|
||||
"- Monitoring and recording runs in the Azure Machine Learning studio\n",
|
||||
"\n",
|
||||
"This notebook follows the steps provided on the [Python (day 1) - \"hello world\" documentation page](https://aka.ms/day1aml). This tutorial is part of a **four-part tutorial series** in which you learn the fundamentals of Azure Machine Learning and complete simple jobs-based machine learning tasks in the Azure cloud. It builds off the work you completed in [Tutorial part 1: set up an Azure Machine Learning compute cluster](day1-part1-setup.ipynb).\n",
|
||||
"\n",
|
||||
"## Pre-requisites\n",
|
||||
"\n",
|
||||
"- Complete [Tutorial part 1: set up an Azure Machine Learning compute cluster](day1-part1-setup.ipynb) if you don't already have an Azure Machine Learning compute cluster.\n",
|
||||
"- Familiarity with Python and Machine Learning concepts.\n",
|
||||
"- If you are using a compute instance in Azure Machine Learning to run this notebook series, you are all set. Otherwise, please follow the [Configure a development environment for Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/how-to-configure-environment)\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Your code\n",
|
||||
"\n",
|
||||
"In the `code/hello` subdirectory you will find a trivial python script [hello.py](code/hello/hello.py) that has the following code:\n",
|
||||
"\n",
|
||||
"```Python\n",
|
||||
"# code/hello/hello.py\n",
|
||||
"print(\"hello world!\")\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"In this tutorial you are going to submit this trivial python script to an Azure Machine Learning Compute Cluster."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Test in your development environment\n",
|
||||
"\n",
|
||||
"You can test your code works on a compute instance or locally (for example, a laptop), which has the benefit of interactive debugging of code:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!python code/hello/hello.py"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Submit your code to Azure Machine Learning\n",
|
||||
"\n",
|
||||
"Below you create a __*control script*__ this is where you specify _how_ your code is submitted to Azure Machine Learning. The code you submit to Azure Machine Learning (in this case `hello.py`) does not need anything specific to Azure Machine Learning - it can be any valid Python code. It is only the control script that is Azure Machine Learning specific.\n",
|
||||
"\n",
|
||||
"The code below will show a Jupyter widget that tracks the progress of your run, and displays logs.\n",
|
||||
"\n",
|
||||
"> <span style=\"color:purple; font-weight:bold\">! NOTE <br>\n",
|
||||
"> The very first run will take 5-10minutes to complete. This is because in the background a docker image is built in the cloud, the compute cluster is resized from 0 to 1 node, and the docker image is downloaded to the compute. Subsequent runs are much quicker (~15 seconds) as the docker image is cached on the compute - you can test this by resubmitting the code below after the first run has completed.</span>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"remote run",
|
||||
"batchai",
|
||||
"configure run",
|
||||
"use notebook widget"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace, Experiment, ScriptRunConfig\n",
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"experiment = Experiment(workspace=ws, name='day1-experiment-hello')\n",
|
||||
"\n",
|
||||
"config = ScriptRunConfig(source_directory='./code/hello', script='hello.py', compute_target='cpu-cluster')\n",
|
||||
"\n",
|
||||
"run = experiment.submit(config)\n",
|
||||
"RunDetails(run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Understanding the control code\n",
|
||||
"\n",
|
||||
"| Code |Description | \n",
|
||||
"|---|---|\n",
|
||||
"| `ws = Workspace.from_config()` | [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py&preserve-view=true) connects to your Azure Machine Learning workspace, so that you can communicate with your Azure Machine Learning resources. |\n",
|
||||
"| `experiment = Experiment( ... )` | [Experiment](https://docs.microsoft.com/python/api/azureml-core/azureml.core.experiment.experiment?view=azure-ml-py&preserve-view=true) provides a simple way to organize multiple runs under a single name. <br>Later you can see how experiments make it easy to compare metrics between dozens of runs. |\n",
|
||||
"| `config = ScriptRunConfig( ... )` | [ScriptRunConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.scriptrunconfig?view=azure-ml-py&preserve-view=true) wraps your `hello.py` code and passes it to your workspace.<br> As the name suggests, you can use this class to _configure_ how you want your _script_ to _run_ in Azure Machine Learning. <br>Also specifies what compute target the script will run on. <br>In this code, the target is the compute cluster you created in the [setup tutorial](tutorial-1st-experiment-sdk-setup-local.md). |\n",
|
||||
"| `run = experiment.submit(config)` | Submits your script. This submission is called a [Run](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py&preserve-view=true). <br>A run encapsulates a single execution of your code. Use a run to monitor the script progress, capture the output,<br> analyze the results, visualize metrics and more. |\n",
|
||||
"| `aml_url = run.get_portal_url()` | The `run` object provides a handle on the execution of your code. Monitor its progress from <br> the Azure Machine Learning Studio with the URL that is printed from the python script. |\n",
|
||||
"|`RunDetails(run).show()` | There is an Azure Machine Learning widget that shows the progress of your job along with streaming the log files.\n",
|
||||
"\n",
|
||||
"## View the logs\n",
|
||||
"\n",
|
||||
"The widget has a dropdown box titled **Output logs** select `70_driver_log.txt`, which shows the following standard output: \n",
|
||||
"\n",
|
||||
"```\n",
|
||||
" 1: [2020-08-04T22:15:44.407305] Entering context manager injector.\n",
|
||||
" 2: [context_manager_injector.py] Command line Options: Namespace(inject=['ProjectPythonPath:context_managers.ProjectPythonPath', 'RunHistory:context_managers.RunHistory', 'TrackUserError:context_managers.TrackUserError', 'UserExceptions:context_managers.UserExceptions'], invocation=['hello.py'])\n",
|
||||
" 3: Starting the daemon thread to refresh tokens in background for process with pid = 31263\n",
|
||||
" 4: Entering Run History Context Manager.\n",
|
||||
" 5: Preparing to call script [ hello.py ] with arguments: []\n",
|
||||
" 6: After variable expansion, calling script [ hello.py ] with arguments: []\n",
|
||||
" 7:\n",
|
||||
" 8: Hello world!\n",
|
||||
" 9: Starting the daemon thread to refresh tokens in background for process with pid = 31263\n",
|
||||
"10:\n",
|
||||
"11:\n",
|
||||
"12: The experiment completed successfully. Finalizing run...\n",
|
||||
"13: Logging experiment finalizing status in history service.\n",
|
||||
"14: [2020-08-04T22:15:46.541334] TimeoutHandler __init__\n",
|
||||
"15: [2020-08-04T22:15:46.541396] TimeoutHandler __enter__\n",
|
||||
"16: Cleaning up all outstanding Run operations, waiting 300.0 seconds\n",
|
||||
"17: 1 items cleaning up...\n",
|
||||
"18: Cleanup took 0.1812913417816162 seconds\n",
|
||||
"19: [2020-08-04T22:15:47.040203] TimeoutHandler __exit__\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"On line 8 above, you see the \"Hello world!\" output. The 70_driver_log.txt file contains the standard output from run and can be useful when debugging remote runs in the cloud. You can also view the run by clicking on the **Click here to see the run in Azure Machine Learning studio** link in the wdiget.\n",
|
||||
"\n",
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"In this tutorial, you took a simple \"hello world\" script and ran it on Azure. You saw how to connect to your Azure Machine Learning workspace, create an Experiment, and submit your `hello.py` code to the cloud.\n",
|
||||
"\n",
|
||||
"In the [next tutorial](day1-part3-train-model.ipynb), you build on these learnings by running something more interesting than `print(\"Hello world!\")`.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "samkemp"
|
||||
}
|
||||
],
|
||||
"celltoolbar": "Edit Metadata",
|
||||
"kernel_info": {
|
||||
"name": "python3-azureml"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
|
||||
"nteract": {
|
||||
"version": "nteract-front-end@1.0.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
5
tutorials/get-started-day1/day1-part2-hello-world.yml
Normal file
5
tutorials/get-started-day1/day1-part2-hello-world.yml
Normal file
@@ -0,0 +1,5 @@
|
||||
name: day1-part2-hello-world
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user