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vrkharevrkhare
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fixing the environment to conda 3
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‎advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.ipynb

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@@ -44,7 +44,6 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true,
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"isConfigCell": true
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},
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"outputs": [],
@@ -60,7 +59,7 @@
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"assumed_role = boto3.client('sts').get_caller_identity()['Arn']\n",
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"role = re.sub(r'^(.+)sts::(\\d+):assumed-role/(.+?)/.*$', r'\\1iam::\\2:role/\\3', assumed_role)\n",
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"\n",
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"bucket='<bucket-name>' # put your s3 bucket name here, and create s3 bucket\n",
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"bucket='<s3 bucket>' # put your s3 bucket name here, and create s3 bucket\n",
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"prefix = 'sagemaker/xgboost-byo'\n",
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"bucket_path = 'https://s3-{}.amazonaws.com/{}'.format(region,bucket)\n",
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"# customize to your bucket where you have stored the data"
@@ -81,9 +80,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"!conda install -y -c conda-forge xgboost scikit-learn"
@@ -99,9 +96,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
@@ -124,9 +119,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
@@ -147,9 +140,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"train_set, valid_set, test_set = get_dataset()\n",
@@ -174,9 +165,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import xgboost as xgb\n",
@@ -202,9 +191,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"model_file_name = \"locally-trained-xgboost-model\"\n",
@@ -214,9 +201,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"!tar czvf model.tar.gz $model_file_name"
@@ -232,9 +217,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"fObj = open(\"model.tar.gz\", 'rb')\n",
@@ -255,9 +238,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"containers = {'us-west-2': '433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest',\n",
@@ -270,9 +251,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from time import gmtime, strftime\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"runtime_client = boto3.client('sagemaker-runtime')"
@@ -399,9 +372,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"arr = result[1:len(result)-1].split(',')\n",
@@ -461,16 +428,15 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Delete Endpoint\n",
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"Once you are done using the endpoint, you can use the following to delete it. "
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"### (Optional) Delete the Endpoint\n",
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"\n",
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"If you're ready to be done with this notebook, please uncomment the delete_endpoint line in the cell below and then run it. This will remove the hosted endpoint you created and avoid any charges from a stray instance being left on."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# sm_client.delete_endpoint(EndpointName=endpoint_name)"
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [conda root]",
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"display_name": "Environment (conda_python3)",
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"language": "python",
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"name": "conda-root-py"
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"name": "conda_python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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},
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"notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
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},

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