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Merge pull request #143 from awslabs/arpin_free_hosting_instances
Arpin free hosting instances
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‎advanced_functionality/data_distribution_types/data_distribution_types.ipynb

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"sharded_endpoint_config_response = sm.create_endpoint_config(\n",
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" EndpointConfigName=sharded_endpoint_config,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType': 'ml.c4.2xlarge',\n",
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" 'InstanceType': 'ml.m4.xlarge',\n",
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" 'InitialInstanceCount': 1,\n",
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" 'ModelName': sharded_job,\n",
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" 'VariantName': 'AllTraffic'}])\n",
@@ -536,7 +536,7 @@
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"replicated_endpoint_config_response = sm.create_endpoint_config(\n",
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" EndpointConfigName=replicated_endpoint_config,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType': 'ml.c4.2xlarge',\n",
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" 'InstanceType': 'ml.m4.xlarge',\n",
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" 'InitialInstanceCount': 1,\n",
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" 'ModelName': replicated_job,\n",
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" 'VariantName': 'AllTraffic'}])\n",

‎advanced_functionality/kmeans_bring_your_own_model/kmeans_bring_your_own_model.ipynb

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"create_endpoint_config_response = sm.create_endpoint_config(\n",
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" EndpointConfigName=kmeans_endpoint_config,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType': 'ml.c4.xlarge',\n",
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" 'InstanceType': 'ml.m4.xlarge',\n",
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" 'InitialInstanceCount': 1,\n",
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" 'ModelName': kmeans_model,\n",
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" 'VariantName': 'AllTraffic'}])\n",

‎advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb

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"outputs": [],
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"source": [
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"from sagemaker.predictor import csv_serializer\n",
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"predictor = tree.deploy(1, 'ml.c4.xlarge', serializer=csv_serializer)"
436+
"predictor = tree.deploy(1, 'ml.m4.xlarge', serializer=csv_serializer)"
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]
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},
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{

‎advanced_functionality/tensorflow_iris_byom/tensorflow_BYOM_iris.ipynb

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"source": [
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"### Create endpoint\n",
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"\n",
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"Now the model is ready to be deployed at a SageMaker endpoint. We can use the ``sagemaker.mxnet.model.TensorFlowModel.deploy`` method to do this. Unless you have created or prefer other instances, we recommend using 1 ``'ml.c4.xlarge'`` instance for this example. These are supplied as arguments. "
246+
"Now the model is ready to be deployed at a SageMaker endpoint. We can use the ``sagemaker.mxnet.model.TensorFlowModel.deploy`` method to do this. Unless you have created or prefer other instances, we recommend using 1 ``'ml.m4.xlarge'`` instance for this example. These are supplied as arguments. "
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]
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},
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{
@@ -254,7 +254,7 @@
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"source": [
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"%%time\n",
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"predictor = sagemaker_model.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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},
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{

‎introduction_to_amazon_algorithms/factorization_machines_mnist/factorization_machines_mnist.ipynb

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@@ -277,7 +277,7 @@
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"outputs": [],
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"source": [
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"fm_predictor = fm.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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},
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{

‎introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining.ipynb

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@@ -393,7 +393,7 @@
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"endpoint_config_response = sage.create_endpoint_config(\n",
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" EndpointConfigName = endpoint_config_name,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType':'ml.p2.xlarge',\n",
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" 'InstanceType':'ml.m4.xlarge',\n",
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" 'InitialInstanceCount':1,\n",
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" 'ModelName':model_name,\n",
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" 'VariantName':'AllTraffic'}])\n",

‎introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning.ipynb

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"endpoint_config_response = sage.create_endpoint_config(\n",
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" EndpointConfigName = endpoint_config_name,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType':'ml.p2.xlarge',\n",
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" 'InstanceType':'ml.m4.xlarge',\n",
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" 'InitialInstanceCount':1,\n",
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" 'ModelName':model_name,\n",
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" 'VariantName':'AllTraffic'}])\n",

‎introduction_to_amazon_algorithms/lda_topic_modeling/LDA-Introduction.ipynb

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"source": [
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"lda_inference = lda.deploy(\n",
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" initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge', # LDA inference works best on ml.c4 instances\n",
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" instance_type='ml.m4.xlarge', # LDA inference may work better at scale on ml.c4 instances\n",
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")"
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]
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},

‎introduction_to_amazon_algorithms/linear_learner_mnist/linear_learner_mnist.ipynb

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"outputs": [],
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"source": [
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"linear_predictor = linear.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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},
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{

‎introduction_to_amazon_algorithms/ntm_synthetic/ntm_synthetic.ipynb

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"outputs": [],
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"source": [
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"ntm_predictor = ntm.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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},
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{

‎introduction_to_amazon_algorithms/pca_mnist/pca_mnist.ipynb

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"outputs": [],
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"source": [
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"pca_predictor = pca.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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},
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{

‎introduction_to_amazon_algorithms/seq2seq_translation_en-de/SageMaker-Seq2Seq-Translation-English-German.ipynb

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"### Create endpoint configuration\n",
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"Use the model to create an endpoint configuration. The endpoint configuration also contains information about the type and number of EC2 instances to use when hosting the model.\n",
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"\n",
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"Since SageMaker Seq2Seq is based on Neural Nets, we will use an ml.p2.xlarge (GPU) instance in this example."
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"Since SageMaker Seq2Seq is based on Neural Nets, we could use an ml.p2.xlarge (GPU) instance, but for this example we will use a free tier eligible ml.m4.xlarge."
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]
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},
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{
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"create_endpoint_config_response = sage.create_endpoint_config(\n",
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" EndpointConfigName = endpoint_config_name,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType':'ml.p2.xlarge',\n",
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" 'InstanceType':'ml.m4.xlarge',\n",
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" 'InitialInstanceCount':1,\n",
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" 'ModelName':model_name,\n",
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" 'VariantName':'AllTraffic'}])\n",

‎introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist.ipynb

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"create_endpoint_config_response = sm.create_endpoint_config(\n",
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" EndpointConfigName = endpoint_config_name,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType':'ml.c4.xlarge',\n",
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" 'InstanceType':'ml.m4.xlarge',\n",
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" 'InitialVariantWeight':1,\n",
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" 'InitialInstanceCount':1,\n",
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" 'ModelName':model_name,\n",

‎introduction_to_applying_machine_learning/breast_cancer_prediction/Breast Cancer Prediction.ipynb

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"create_endpoint_config_response = sm.create_endpoint_config(\n",
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" EndpointConfigName=linear_endpoint_config,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType': 'ml.c4.2xlarge',\n",
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" 'InstanceType': 'ml.m4.xlarge',\n",
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" 'InitialInstanceCount': 1,\n",
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" 'ModelName': linear_job,\n",
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" 'VariantName': 'AllTraffic'}])\n",

‎introduction_to_applying_machine_learning/ensemble_modeling/EnsembleLearnerCensusIncome.ipynb

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"create_endpoint_config_response = sm.create_endpoint_config(\n",
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" EndpointConfigName=linear_endpoint_config,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType': 'ml.c4.2xlarge',\n",
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" 'InstanceType': 'ml.m4.xlarge',\n",
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" 'InitialInstanceCount': 1,\n",
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" 'ModelName': linear_job,\n",
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" 'VariantName': 'AllTraffic'}])\n",

‎introduction_to_applying_machine_learning/linear_time_series_forecast/linear_time_series_forecast.ipynb

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"outputs": [],
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"source": [
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"linear_predictor = linear.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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},
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{

‎introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb

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"outputs": [],
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"xgb_predictor = xgb.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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{

‎introduction_to_applying_machine_learning/xgboost_direct_marketing/xgboost_direct_marketing_sagemaker.ipynb

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"outputs": [],
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"source": [
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"xgb_predictor = xgb.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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{

‎sagemaker-python-sdk/1P_kmeans_highlevel/kmeans_mnist.ipynb

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"%%time\n",
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"\n",
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"kmeans_predictor = kmeans.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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‎sagemaker-python-sdk/1P_kmeans_lowlevel/kmeans_mnist_lowlevel.ipynb

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"create_endpoint_config_response = sagemaker.create_endpoint_config(\n",
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" EndpointConfigName = endpoint_config_name,\n",
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" ProductionVariants=[{\n",
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" 'InstanceType':'ml.c4.xlarge',\n",
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" 'InitialInstanceCount':3,\n",
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" 'InstanceType':'ml.m4.xlarge',\n",
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" 'InitialInstanceCount':1,\n",
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" 'ModelName':model_name,\n",
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" 'VariantName':'AllTraffic'}])\n",
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"\n",

‎sagemaker-python-sdk/mxnet_gluon_cifar10/cifar10.ipynb

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"## Prediction\n",
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"\n",
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"After training, we use the MXNet estimator object to create and deploy a hosted prediction endpoint. We can use a CPU-based instance for inference (in this case an `ml.c4.xlarge`), even though we trained on GPU instances.\n",
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"After training, we use the MXNet estimator object to create and deploy a hosted prediction endpoint. We can use a CPU-based instance for inference (in this case an `ml.m4.xlarge`), even though we trained on GPU instances.\n",
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"\n",
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"The predictor object returned by `deploy` lets us call the new endpoint and perform inference on our sample images. "
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"predictor = m.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
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"predictor = m.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
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]
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{

‎sagemaker-python-sdk/mxnet_gluon_mnist/mnist_with_gluon.ipynb

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},
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"outputs": [],
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"source": [
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"predictor = m.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
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"predictor = m.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
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{

‎sagemaker-python-sdk/mxnet_mnist/mxnet_mnist.ipynb

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"\n",
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"After training, we use the ``MXNet estimator`` object to build and deploy an ``MXNetPredictor``. This creates a Sagemaker **Endpoint** -- a hosted prediction service that we can use to perform inference. \n",
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"\n",
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"The arguments to the ``deploy`` function allow us to set the number and type of instances that will be used for the Endpoint. These do not need to be the same as the values we used for the training job. For example, you can train a model on a set of GPU-based instances, and then deploy the Endpoint to a fleet of CPU-based instances. Here we will deploy the model to a single ``ml.c4.xlarge`` instance."
135+
"The arguments to the ``deploy`` function allow us to set the number and type of instances that will be used for the Endpoint. These do not need to be the same as the values we used for the training job. For example, you can train a model on a set of GPU-based instances, and then deploy the Endpoint to a fleet of CPU-based instances. Here we will deploy the model to a single ``ml.m4.xlarge`` instance."
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{
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"%%time\n",
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"\n",
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"predictor = mnist_estimator.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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‎sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras/tensorflow_abalone_age_predictor_using_keras.ipynb

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},
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"outputs": [],
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"abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
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"abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
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{

‎sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_layers/tensorflow_abalone_age_predictor_using_layers.ipynb

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"outputs": [],
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"abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
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"abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
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]
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{

‎sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_distributed_mnist.ipynb

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"outputs": [],
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"source": [
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"mnist_predictor = mnist_estimator.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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‎sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators/tensorflow_iris_dnn_classifier_using_estimators.ipynb

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"%%time\n",
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"iris_predictor = iris_estimator.deploy(initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge')"
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" instance_type='ml.m4.xlarge')"
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]
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‎sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb

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},
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"source": [
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"predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
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"predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
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{

‎scientific_details_of_algorithms/lda_topic_modeling/LDA-Science.ipynb

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"lda_inference = lda.deploy(\n",
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" initial_instance_count=1,\n",
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" instance_type='ml.c4.xlarge', # LDA inference works best on ml.c4 instances\n",
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" instance_type='ml.m4.xlarge', # LDA inference may work better at scale on ml.c4 instances\n",
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")"
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]
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},

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