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Merged
merged 5 commits into from
Nov 25, 2017
Merged

Kmeans regional images #37

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e4b34b5
Use regional images for Kmeans notebooks
winstonaws Nov 24, 2017
017c3df
Remove debuggin cell
winstonaws Nov 24, 2017
3d99ef4
Make delete optional
winstonaws Nov 24, 2017
33a0549
Fix image to use different account per region
winstonaws Nov 24, 2017
d0a5bf1
Make beginning cells identical
winstonaws Nov 24, 2017
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19 changes: 7 additions & 12 deletions sagemaker-python-sdk/1P_kmeans_highlevel/kmeans_mnist.ipynb
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@
"\n",
"Here we set up the linkage and authentication to AWS services. There are three parts to this:\n",
"\n",
"1. The credentials and region for the account that's running training. Upload the credentials in the normal AWS credentials file format using the jupyter upload feature. The region must always be `us-west-2` during the Beta program.\n",
"1. The credentials and region for the account that's running training. Upload the credentials in the normal AWS credentials file format using the jupyter upload feature.\n",
"2. The roles used to give learning and hosting access to your data. See the documentation for how to specify these.\n",
"3. The S3 bucket that you want to use for training and model data.\n",
"\n",
Expand All @@ -68,8 +68,9 @@
"metadata": {},
"outputs": [],
"source": [
"output_location = 's3://{}/kmeans_highlevel_example/output'.format(bucket)\n",
"data_location = 's3://{}/kmeans_highlevel_example/data'.format(bucket)\n",
"data_key = 'kmeans_example/data'\n",
"data_location = 's3://{}/{}'.format(bucket, data_key)\n",
"output_location = 's3://{}/kmeans_example/output'.format(bucket)\n",
"\n",
"print('training data will be uploaded to: {}'.format(data_location))\n",
"print('training artifacts will be uploaded to: {}'.format(output_location))"
Expand Down Expand Up @@ -130,13 +131,6 @@
"show_digit(train_set[0][30], 'This is a {}'.format(train_set[1][30]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload training data"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -292,9 +286,10 @@
"metadata": {},
"outputs": [],
"source": [
"import sagemaker\n",
"# Uncomment and run to delete\n",
"\n",
"sagemaker.Session().delete_endpoint(kmeans_predictor.endpoint)"
"#import sagemaker\n",
"#sagemaker.Session().delete_endpoint(kmeans_predictor.endpoint)"
]
},
{
Expand Down
53 changes: 31 additions & 22 deletions sagemaker-python-sdk/1P_kmeans_lowlevel/kmeans_mnist_lowlevel.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -62,6 +62,20 @@
"bucket='<bucket-name>'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_key = 'kmeans_example/data'\n",
"data_location = 's3://{}/{}'.format(bucket, data_key)\n",
"output_location = 's3://{}/kmeans_example/output'.format(bucket)\n",
"\n",
"print('training data will be uploaded to: {}'.format(data_location))\n",
"print('training artifacts will be uploaded to: {}'.format(output_location))"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -121,7 +135,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data conversion\n",
"### Data conversion and upload\n",
"\n",
"Since algorithms have particular input and output requirements, converting the dataset is also part of the process that a data scientist goes through prior to initiating training. In this particular case, the hosted implementation of k-means takes recordio-wrapped protobuf, where the data we have today is a pickle-ized numpy array on disk.\n",
"\n",
Expand All @@ -140,27 +154,14 @@
"%%time\n",
"from sagemaker.amazon.common import write_numpy_to_dense_tensor\n",
"import io\n",
"import boto3\n",
"\n",
"# Convert the training data into the format required by the SageMaker KMeans algorithm\n",
"buf = io.BytesIO()\n",
"write_numpy_to_dense_tensor(buf, train_set[0], train_set[1])\n",
"buf.seek(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"\n",
"import boto3\n",
"buf.seek(0)\n",
"\n",
"key = 'kmeans_lowlevel_example/data'\n",
"boto3.resource('s3').Bucket(bucket).Object(key).upload_fileobj(buf)\n",
"s3_train_data = 's3://{}/{}'.format(bucket, key)\n",
"print('uploaded training data location: {}'.format(s3_train_data))"
"boto3.resource('s3').Bucket(bucket).Object(data_key).upload_fileobj(buf)"
]
},
{
Expand All @@ -187,15 +188,21 @@
"job_name = 'kmeans-lowlevel-' + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
"print(\"Training job\", job_name)\n",
"\n",
"images = {'us-west-2': '174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:latest',\n",
" 'us-east-1': '382416733822.dkr.ecr.us-east-1.amazonaws.com/kmeans:latest',\n",
" 'us-east-2': '404615174143.dkr.ecr.us-east-2.amazonaws.com/kmeans:latest',\n",
" 'eu-west-1': '438346466558.dkr.ecr.eu-west-1.amazonaws.com/kmeans:latest'}\n",
"image = images[boto3.Session().region_name]\n",
"\n",
"create_training_params = \\\n",
"{\n",
" \"AlgorithmSpecification\": {\n",
" \"TrainingImage\": \"174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1\",\n",
" \"TrainingImage\": image,\n",
" \"TrainingInputMode\": \"File\"\n",
" },\n",
" \"RoleArn\": role,\n",
" \"OutputDataConfig\": {\n",
" \"S3OutputPath\": \"s3://{}/kmeans_lowlevel_example/output\".format(bucket)\n",
" \"S3OutputPath\": output_location\n",
" },\n",
" \"ResourceConfig\": {\n",
" \"InstanceCount\": 2,\n",
Expand All @@ -218,7 +225,7 @@
" \"DataSource\": {\n",
" \"S3DataSource\": {\n",
" \"S3DataType\": \"S3Prefix\",\n",
" \"S3Uri\": s3_train_data,\n",
" \"S3Uri\": data_location,\n",
" \"S3DataDistributionType\": \"FullyReplicated\"\n",
" }\n",
" },\n",
Expand Down Expand Up @@ -278,7 +285,7 @@
"model_data = info['ModelArtifacts']['S3ModelArtifacts']\n",
"\n",
"primary_container = {\n",
" 'Image': \"174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1\",\n",
" 'Image': image,\n",
" 'ModelDataUrl': model_data\n",
"}\n",
"\n",
Expand Down Expand Up @@ -474,7 +481,9 @@
"metadata": {},
"outputs": [],
"source": [
"sagemaker.delete_endpoint(EndpointName=endpoint_name)"
"# Uncomment and run to delete\n",
"\n",
"# sagemaker.delete_endpoint(EndpointName=endpoint_name)"
]
},
{
Expand Down