Merge pull request aws#143 from awslabs/arpin_free_hosting_instances

djarpin · web-flow · commit f2559d8d0e76 · 2017-12-12T13:21:34.000-08:00
Arpin free hosting instances
diff --git a/advanced_functionality/data_distribution_types/data_distribution_types.ipynb b/advanced_functionality/data_distribution_types/data_distribution_types.ipynb
@@ -517,7 +517,7 @@
     "sharded_endpoint_config_response = sm.create_endpoint_config(\n",
     "    EndpointConfigName=sharded_endpoint_config,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType': 'ml.c4.2xlarge',\n",
+    "        'InstanceType': 'ml.m4.xlarge',\n",
     "        'InitialInstanceCount': 1,\n",
     "        'ModelName': sharded_job,\n",
     "        'VariantName': 'AllTraffic'}])\n",
@@ -536,7 +536,7 @@
     "replicated_endpoint_config_response = sm.create_endpoint_config(\n",
     "    EndpointConfigName=replicated_endpoint_config,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType': 'ml.c4.2xlarge',\n",
+    "        'InstanceType': 'ml.m4.xlarge',\n",
     "        'InitialInstanceCount': 1,\n",
     "        'ModelName': replicated_job,\n",
     "        'VariantName': 'AllTraffic'}])\n",
diff --git a/advanced_functionality/kmeans_bring_your_own_model/kmeans_bring_your_own_model.ipynb b/advanced_functionality/kmeans_bring_your_own_model/kmeans_bring_your_own_model.ipynb
@@ -236,7 +236,7 @@
     "create_endpoint_config_response = sm.create_endpoint_config(\n",
     "    EndpointConfigName=kmeans_endpoint_config,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType': 'ml.c4.xlarge',\n",
+    "        'InstanceType': 'ml.m4.xlarge',\n",
     "        'InitialInstanceCount': 1,\n",
     "        'ModelName': kmeans_model,\n",
     "        'VariantName': 'AllTraffic'}])\n",
diff --git a/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb b/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb
@@ -433,7 +433,7 @@
    "outputs": [],
    "source": [
     "from sagemaker.predictor import csv_serializer\n",
-    "predictor = tree.deploy(1, 'ml.c4.xlarge', serializer=csv_serializer)"
+    "predictor = tree.deploy(1, 'ml.m4.xlarge', serializer=csv_serializer)"
    ]
   },
   {
diff --git a/advanced_functionality/tensorflow_iris_byom/tensorflow_BYOM_iris.ipynb b/advanced_functionality/tensorflow_iris_byom/tensorflow_BYOM_iris.ipynb
@@ -243,7 +243,7 @@
    "source": [
     "### Create endpoint\n",
     "\n",
-    "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. "
+    "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. "
    ]
   },
   {
@@ -254,7 +254,7 @@
    "source": [
     "%%time\n",
     "predictor = sagemaker_model.deploy(initial_instance_count=1,\n",
-    "                                          instance_type='ml.c4.xlarge')"
+    "                                          instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/introduction_to_amazon_algorithms/factorization_machines_mnist/factorization_machines_mnist.ipynb b/introduction_to_amazon_algorithms/factorization_machines_mnist/factorization_machines_mnist.ipynb
@@ -277,7 +277,7 @@
    "outputs": [],
    "source": [
     "fm_predictor = fm.deploy(initial_instance_count=1,\n",
-    "                         instance_type='ml.c4.xlarge')"
+    "                         instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining.ipynb b/introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining.ipynb
@@ -393,7 +393,7 @@
     "endpoint_config_response = sage.create_endpoint_config(\n",
     "    EndpointConfigName = endpoint_config_name,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType':'ml.p2.xlarge',\n",
+    "        'InstanceType':'ml.m4.xlarge',\n",
     "        'InitialInstanceCount':1,\n",
     "        'ModelName':model_name,\n",
     "        'VariantName':'AllTraffic'}])\n",
diff --git a/introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning.ipynb b/introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning.ipynb
@@ -409,7 +409,7 @@
     "endpoint_config_response = sage.create_endpoint_config(\n",
     "    EndpointConfigName = endpoint_config_name,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType':'ml.p2.xlarge',\n",
+    "        'InstanceType':'ml.m4.xlarge',\n",
     "        'InitialInstanceCount':1,\n",
     "        'ModelName':model_name,\n",
     "        'VariantName':'AllTraffic'}])\n",
diff --git a/introduction_to_amazon_algorithms/lda_topic_modeling/LDA-Introduction.ipynb b/introduction_to_amazon_algorithms/lda_topic_modeling/LDA-Introduction.ipynb
@@ -396,7 +396,7 @@
    "source": [
     "lda_inference = lda.deploy(\n",
     "    initial_instance_count=1,\n",
-    "    instance_type='ml.c4.xlarge',  # LDA inference works best on ml.c4 instances\n",
+    "    instance_type='ml.m4.xlarge',  # LDA inference may work better at scale on ml.c4 instances\n",
     ")"
    ]
   },
diff --git a/introduction_to_amazon_algorithms/linear_learner_mnist/linear_learner_mnist.ipynb b/introduction_to_amazon_algorithms/linear_learner_mnist/linear_learner_mnist.ipynb
@@ -273,7 +273,7 @@
    "outputs": [],
    "source": [
     "linear_predictor = linear.deploy(initial_instance_count=1,\n",
-    "                                 instance_type='ml.c4.xlarge')"
+    "                                 instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/introduction_to_amazon_algorithms/ntm_synthetic/ntm_synthetic.ipynb b/introduction_to_amazon_algorithms/ntm_synthetic/ntm_synthetic.ipynb
@@ -293,7 +293,7 @@
    "outputs": [],
    "source": [
     "ntm_predictor = ntm.deploy(initial_instance_count=1,\n",
-    "                           instance_type='ml.c4.xlarge')"
+    "                           instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/introduction_to_amazon_algorithms/pca_mnist/pca_mnist.ipynb b/introduction_to_amazon_algorithms/pca_mnist/pca_mnist.ipynb
@@ -279,7 +279,7 @@
    "outputs": [],
    "source": [
     "pca_predictor = pca.deploy(initial_instance_count=1,\n",
-    "                           instance_type='ml.c4.xlarge')"
+    "                           instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/introduction_to_amazon_algorithms/seq2seq_translation_en-de/SageMaker-Seq2Seq-Translation-English-German.ipynb b/introduction_to_amazon_algorithms/seq2seq_translation_en-de/SageMaker-Seq2Seq-Translation-English-German.ipynb
@@ -461,7 +461,7 @@
     "### Create endpoint configuration\n",
     "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",
     "\n",
-    "Since SageMaker Seq2Seq is based on Neural Nets, we will use an ml.p2.xlarge (GPU) instance in this example."
+    "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."
    ]
   },
   {
@@ -477,7 +477,7 @@
     "create_endpoint_config_response = sage.create_endpoint_config(\n",
     "    EndpointConfigName = endpoint_config_name,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType':'ml.p2.xlarge',\n",
+    "        'InstanceType':'ml.m4.xlarge',\n",
     "        'InitialInstanceCount':1,\n",
     "        'ModelName':model_name,\n",
     "        'VariantName':'AllTraffic'}])\n",
diff --git a/introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist.ipynb b/introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist.ipynb
@@ -464,7 +464,7 @@
     "create_endpoint_config_response = sm.create_endpoint_config(\n",
     "    EndpointConfigName = endpoint_config_name,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType':'ml.c4.xlarge',\n",
+    "        'InstanceType':'ml.m4.xlarge',\n",
     "        'InitialVariantWeight':1,\n",
     "        'InitialInstanceCount':1,\n",
     "        'ModelName':model_name,\n",
diff --git a/introduction_to_applying_machine_learning/breast_cancer_prediction/Breast Cancer Prediction.ipynb b/introduction_to_applying_machine_learning/breast_cancer_prediction/Breast Cancer Prediction.ipynb
@@ -472,7 +472,7 @@
     "create_endpoint_config_response = sm.create_endpoint_config(\n",
     "    EndpointConfigName=linear_endpoint_config,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType': 'ml.c4.2xlarge',\n",
+    "        'InstanceType': 'ml.m4.xlarge',\n",
     "        'InitialInstanceCount': 1,\n",
     "        'ModelName': linear_job,\n",
     "        'VariantName': 'AllTraffic'}])\n",
diff --git a/introduction_to_applying_machine_learning/ensemble_modeling/EnsembleLearnerCensusIncome.ipynb b/introduction_to_applying_machine_learning/ensemble_modeling/EnsembleLearnerCensusIncome.ipynb
@@ -942,7 +942,7 @@
     "create_endpoint_config_response = sm.create_endpoint_config(\n",
     "    EndpointConfigName=linear_endpoint_config,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType': 'ml.c4.2xlarge',\n",
+    "        'InstanceType': 'ml.m4.xlarge',\n",
     "        'InitialInstanceCount': 1,\n",
     "        'ModelName': linear_job,\n",
     "        'VariantName': 'AllTraffic'}])\n",
diff --git a/introduction_to_applying_machine_learning/linear_time_series_forecast/linear_time_series_forecast.ipynb b/introduction_to_applying_machine_learning/linear_time_series_forecast/linear_time_series_forecast.ipynb
@@ -374,7 +374,7 @@
    "outputs": [],
    "source": [
     "linear_predictor = linear.deploy(initial_instance_count=1,\n",
-    "                                 instance_type='ml.c4.xlarge')"
+    "                                 instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb b/introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb
@@ -420,7 +420,7 @@
    "outputs": [],
    "source": [
     "xgb_predictor = xgb.deploy(initial_instance_count=1,\n",
-    "                           instance_type='ml.c4.xlarge')"
+    "                           instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/introduction_to_applying_machine_learning/xgboost_direct_marketing/xgboost_direct_marketing_sagemaker.ipynb b/introduction_to_applying_machine_learning/xgboost_direct_marketing/xgboost_direct_marketing_sagemaker.ipynb
@@ -483,7 +483,7 @@
    "outputs": [],
    "source": [
     "xgb_predictor = xgb.deploy(initial_instance_count=1,\n",
-    "                           instance_type='ml.c4.xlarge')"
+    "                           instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/sagemaker-python-sdk/1P_kmeans_highlevel/kmeans_mnist.ipynb b/sagemaker-python-sdk/1P_kmeans_highlevel/kmeans_mnist.ipynb
@@ -176,7 +176,7 @@
     "%%time\n",
     "\n",
     "kmeans_predictor = kmeans.deploy(initial_instance_count=1,\n",
-    "                                 instance_type='ml.c4.xlarge')"
+    "                                 instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/sagemaker-python-sdk/1P_kmeans_lowlevel/kmeans_mnist_lowlevel.ipynb b/sagemaker-python-sdk/1P_kmeans_lowlevel/kmeans_mnist_lowlevel.ipynb
@@ -307,8 +307,8 @@
     "create_endpoint_config_response = sagemaker.create_endpoint_config(\n",
     "    EndpointConfigName = endpoint_config_name,\n",
     "    ProductionVariants=[{\n",
-    "        'InstanceType':'ml.c4.xlarge',\n",
-    "        'InitialInstanceCount':3,\n",
+    "        'InstanceType':'ml.m4.xlarge',\n",
+    "        'InitialInstanceCount':1,\n",
     "        'ModelName':model_name,\n",
     "        'VariantName':'AllTraffic'}])\n",
     "\n",
diff --git a/sagemaker-python-sdk/mxnet_gluon_cifar10/cifar10.ipynb b/sagemaker-python-sdk/mxnet_gluon_cifar10/cifar10.ipynb
@@ -141,7 +141,7 @@
    "source": [
     "## Prediction\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "The predictor object returned by `deploy` lets us call the new endpoint and perform inference on our sample images. "
    ]
@@ -152,7 +152,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "predictor = m.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
+    "predictor = m.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/sagemaker-python-sdk/mxnet_gluon_mnist/mnist_with_gluon.ipynb b/sagemaker-python-sdk/mxnet_gluon_mnist/mnist_with_gluon.ipynb
@@ -144,7 +144,7 @@
    },
    "outputs": [],
    "source": [
-    "predictor = m.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
+    "predictor = m.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/sagemaker-python-sdk/mxnet_mnist/mxnet_mnist.ipynb b/sagemaker-python-sdk/mxnet_mnist/mxnet_mnist.ipynb
@@ -132,7 +132,7 @@
     "\n",
     "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",
     "\n",
-    "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."
+    "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."
    ]
   },
   {
@@ -144,7 +144,7 @@
     "%%time\n",
     "\n",
     "predictor = mnist_estimator.deploy(initial_instance_count=1,\n",
-    "                                   instance_type='ml.c4.xlarge')"
+    "                                   instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras/tensorflow_abalone_age_predictor_using_keras.ipynb b/sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras/tensorflow_abalone_age_predictor_using_keras.ipynb
@@ -507,7 +507,7 @@
    },
    "outputs": [],
    "source": [
-    "abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
+    "abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_layers/tensorflow_abalone_age_predictor_using_layers.ipynb b/sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_layers/tensorflow_abalone_age_predictor_using_layers.ipynb
@@ -550,7 +550,7 @@
    },
    "outputs": [],
    "source": [
-    "abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
+    "abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_distributed_mnist.ipynb b/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_distributed_mnist.ipynb
@@ -186,7 +186,7 @@
    "outputs": [],
    "source": [
     "mnist_predictor = mnist_estimator.deploy(initial_instance_count=1,\n",
-    "                                             instance_type='ml.c4.xlarge')"
+    "                                             instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators/tensorflow_iris_dnn_classifier_using_estimators.ipynb b/sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators/tensorflow_iris_dnn_classifier_using_estimators.ipynb
@@ -343,7 +343,7 @@
    "source": [
     "%%time\n",
     "iris_predictor = iris_estimator.deploy(initial_instance_count=1,\n",
-    "                                       instance_type='ml.c4.xlarge')"
+    "                                       instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb b/sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb
@@ -159,7 +159,7 @@
    },
    "outputs": [],
    "source": [
-    "predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
+    "predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
    ]
   },
   {
diff --git a/scientific_details_of_algorithms/lda_topic_modeling/LDA-Science.ipynb b/scientific_details_of_algorithms/lda_topic_modeling/LDA-Science.ipynb
@@ -723,7 +723,7 @@
    "source": [
     "lda_inference = lda.deploy(\n",
     "    initial_instance_count=1,\n",
-    "    instance_type='ml.c4.xlarge',  # LDA inference works best on ml.c4 instances\n",
+    "    instance_type='ml.m4.xlarge',  # LDA inference may work better at scale on ml.c4 instances\n",
     ")"
    ]
   },

Original file line number	Diff line number	Diff line change
`@@ -433,7 +433,7 @@`
`433`	`433`	`"outputs": [],`
`434`	`434`	`"source": [`
`435`	`435`	`"from sagemaker.predictor import csv_serializer\n",`
`436`		`- "predictor = tree.deploy(1, 'ml.c4.xlarge', serializer=csv_serializer)"`
	`436`	`+ "predictor = tree.deploy(1, 'ml.m4.xlarge', serializer=csv_serializer)"`
`437`	`437`	`]`
`438`	`438`	`},`
`439`	`439`	`{`
Original file line number	Diff line number	Diff line change
`@@ -243,7 +243,7 @@`
`243`	`243`	`"source": [`
`244`	`244`	`"### Create endpoint\n",`
`245`	`245`	`"\n",`
`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.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. "
`247`	`247`	`]`
`248`	`248`	`},`
`249`	`249`	`{`
`@@ -254,7 +254,7 @@`
`254`	`254`	`"source": [`
`255`	`255`	`"%%time\n",`
`256`	`256`	`"predictor = sagemaker_model.deploy(initial_instance_count=1,\n",`
`257`		`- " instance_type='ml.c4.xlarge')"`
	`257`	`+ " instance_type='ml.m4.xlarge')"`
`258`	`258`	`]`
`259`	`259`	`},`
`260`	`260`	`{`
Original file line number	Diff line number	Diff line change
`@@ -277,7 +277,7 @@`
`277`	`277`	`"outputs": [],`
`278`	`278`	`"source": [`
`279`	`279`	`"fm_predictor = fm.deploy(initial_instance_count=1,\n",`
`280`		`- " instance_type='ml.c4.xlarge')"`
	`280`	`+ " instance_type='ml.m4.xlarge')"`
`281`	`281`	`]`
`282`	`282`	`},`
`283`	`283`	`{`
Original file line number	Diff line number	Diff line change
`@@ -396,7 +396,7 @@`
`396`	`396`	`"source": [`
`397`	`397`	`"lda_inference = lda.deploy(\n",`
`398`	`398`	`" initial_instance_count=1,\n",`
`399`		`- " instance_type='ml.c4.xlarge', # LDA inference works best on ml.c4 instances\n",`
	`399`	`+ " instance_type='ml.m4.xlarge', # LDA inference may work better at scale on ml.c4 instances\n",`
`400`	`400`	`")"`
`401`	`401`	`]`
`402`	`402`	`},`
Original file line number	Diff line number	Diff line change
`@@ -273,7 +273,7 @@`
`273`	`273`	`"outputs": [],`
`274`	`274`	`"source": [`
`275`	`275`	`"linear_predictor = linear.deploy(initial_instance_count=1,\n",`
`276`		`- " instance_type='ml.c4.xlarge')"`
	`276`	`+ " instance_type='ml.m4.xlarge')"`
`277`	`277`	`]`
`278`	`278`	`},`
`279`	`279`	`{`
Original file line number	Diff line number	Diff line change
`@@ -293,7 +293,7 @@`
`293`	`293`	`"outputs": [],`
`294`	`294`	`"source": [`
`295`	`295`	`"ntm_predictor = ntm.deploy(initial_instance_count=1,\n",`
`296`		`- " instance_type='ml.c4.xlarge')"`
	`296`	`+ " instance_type='ml.m4.xlarge')"`
`297`	`297`	`]`
`298`	`298`	`},`
`299`	`299`	`{`