discover/1_discover.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook showing DLHub SDK Search Capabilities\n",
    "\n",
    "A Web UI to this search functionality is available at <a href=\"https://petreldata.net\">Petrel Data</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dlhub_sdk import DLHubClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl = DLHubClient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List Servables in ~Human Readable Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['akoripelly_gmail/semCNN',\n",
       " 'akoripelly_gmail/SEM_CNN_Model',\n",
       " 'akoripelly_gmail/SEM',\n",
       " 'akoripelly_gmail/neural_network',\n",
       " 'akoripelly_gmail/FileLength',\n",
       " 'akoripelly_gmail/CNNModel',\n",
       " 'akoripelly_gmail/braincountTF',\n",
       " 'akoripelly_gmail/brainCNN',\n",
       " 'blaiszik_globusid/mnist_tiny_example',\n",
       " 'blaiszik_globusid/iris_svm',\n",
       " 'blaiszik_globusid/cherukara_phase',\n",
       " 'chenru_xsede/static_classifier_geo',\n",
       " 'dlhub.test_gmail/1d_norm',\n",
       " 'etruelove_uchicago/FileLength',\n",
       " 'loganw_globusid/tomo_test',\n",
       " 'loganw_globusid/mnist_example',\n",
       " 'loganw_globusid/g4mp2_delta_schnet',\n",
       " 'loganw_globusid/g4mp2_delta_fchl',\n",
       " 'loganw_globusid/candle_p1_combo_demo1',\n",
       " 'lzhengchun_globusid/TomoGAN',\n",
       " 'maxtuecke_gmail/test_servable_upload',\n",
       " 'maxtuecke_gmail/galaxy_image_classifier',\n",
       " 'mschwarting_anl/Thin_Films_BG_RF',\n",
       " 'mschwarting_anl/Thin_Film_Autoencoder',\n",
       " 'mschwarting_anl/mnist',\n",
       " 'mschwarting_anl/galaxy_catalog_des',\n",
       " 'mschwarting_anl/FileLength',\n",
       " 'mschwarting_anl/dejong_model_ridgereg',\n",
       " 'mschwarting_anl/dejong_model_rforestreg',\n",
       " 'mschwarting_anl/dejong_model_linreg',\n",
       " 'mschwarting_anl/dejong_model_gboost',\n",
       " 'mschwarting_anl/dejong_model_etreereg',\n",
       " 'mschwarting_anl/cifar10_standardizer',\n",
       " 'mschwarting_anl/cifar10_model',\n",
       " 'mschwarting_anl/cherukara_structure',\n",
       " 'mschwarting_anl/cherukara_phase',\n",
       " 'mschwarting_anl/BlurryBrain',\n",
       " 'ryan_globusid/repo2docker_test',\n",
       " 'ryan_globusid/Noop',\n",
       " 'ryan_globusid/FileLength',\n",
       " 'ryan_globusid/candle_p1b1_demo2',\n",
       " 'ryan_globusid/candle_p1b1_demo1',\n",
       " 'ryan_globusid/candle_p1_combo_demo1',\n",
       " 'zhuozhao_uchicago/mnist_tiny_example',\n",
       " 'zhuozhao_uchicago/iris_svm']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl.list_servables()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Search by Owner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dl.search_by_servable(owner=\"blaiszik_globusid\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full Text Search All Fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/rhnvrm/galaxy-image-classifier-tensorflow',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'creators': [{'affiliations': ['Globus'],\n",
       "     'familyName': 'Tuecke',\n",
       "     'givenName': 'Max'}],\n",
       "   'descriptions': [{'description': 'Test upload of a simple galaxy image classifier',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Galaxy Image Classifier'}]},\n",
       "  'dlhub': {'domains': ['astronomy'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/7b8f5273-2e1c-4836-b778-30efce4cf912',\n",
       "   'files': {'other': ['repo2docker/environment.yml',\n",
       "     'app.py',\n",
       "     'model/retrained_labels.txt',\n",
       "     'model/retrained_graph.pb']},\n",
       "   'funcx_id': 'c1de4909-e8f8-443e-b9b7-8dc2f3f72d3e',\n",
       "   'id': '7b8f5273-2e1c-4836-b778-30efce4cf912',\n",
       "   'name': 'galaxy_image_classifier',\n",
       "   'owner': 'maxtuecke_gmail',\n",
       "   'publication_date': '1550171926331',\n",
       "   'shorthand_name': 'maxtuecke_gmail/galaxy_image_classifier',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '9',\n",
       "   'version': '0.5.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Image byte read in data',\n",
       "      'type': 'string'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'run',\n",
       "      'module': 'app'},\n",
       "     'output': {'description': 'What type of galaxy it is and python version running',\n",
       "      'type': 'string'},\n",
       "     'parameters': {'data': 'None'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'creators': [{'familyName': 'Asad', 'givenName': 'Khan'},\n",
       "    {'familyName': 'Huerta', 'givenName': 'E.A.'},\n",
       "    {'familyName': 'Wang', 'givenName': 'Sibo'},\n",
       "    {'familyName': 'Gruendl', 'givenName': 'Robert'},\n",
       "    {'familyName': 'Jennings', 'givenName': 'Elise'},\n",
       "    {'familyName': 'Zheng', 'givenName': 'Huiho'}],\n",
       "   'identifier': {'identifier': 'globus:RgyyV2f4DajH',\n",
       "    'identifierType': 'Globus'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.1016/j.physletb.2019.06.009',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/da89565c-a043-4598-879c-3ccce9783fac-1564166980',\n",
       "   'dependencies': {'python': {'h5py': '2.9.0',\n",
       "     'keras': '2.2.4',\n",
       "     'tensorflow': '1.14.0'}},\n",
       "   'domains': ['astronomy', 'dark energy survey', 'classification'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/da89565c-a043-4598-879c-3ccce9783fac',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/da89565c-a043-4598-879c-3ccce9783fac',\n",
       "   'files': {'model': 'Xception_Final.h5'},\n",
       "   'funcx_id': 'ebc27e84-a282-4e67-b641-6d7597af8150',\n",
       "   'id': 'da89565c-a043-4598-879c-3ccce9783fac',\n",
       "   'identifier': 'globus:RgyyV2f4DajH',\n",
       "   'name': 'galaxy_catalog_des',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1564166980465',\n",
       "   'shorthand_name': 'mschwarting_anl/galaxy_catalog_des',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Image of a galaxy',\n",
       "      'shape': ['None', '224', '224', '3'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'method_name': 'predict'},\n",
       "     'output': {'description': 'Probabilities of being a elliptical or spiral galaxy',\n",
       "      'shape': ['None', '2'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '__________________________________________________________________________________________________\\nLayer (type)                    Output Shape         Param #     Connected to                     \\n==================================================================================================\\ninput_1 (InputLayer)            (None, 224, 224, 3)  0                                            \\n__________________________________________________________________________________________________\\nblock1_conv1 (Conv2D)           (None, 111, 111, 32) 864         input_1[0][0]                    \\n__________________________________________________________________________________________________\\nblock1_conv1_bn (BatchNormaliza (None, 111, 111, 32) 128         block1_conv1[0][0]               \\n__________________________________________________________________________________________________\\nblock1_conv1_act (Activation)   (None, 111, 111, 32) 0           block1_conv1_bn[0][0]            \\n__________________________________________________________________________________________________\\nblock1_conv2 (Conv2D)           (None, 109, 109, 64) 18432       block1_conv1_act[0][0]           \\n__________________________________________________________________________________________________\\nblock1_conv2_bn (BatchNormaliza (None, 109, 109, 64) 256         block1_conv2[0][0]               \\n__________________________________________________________________________________________________\\nblock1_conv2_act (Activation)   (None, 109, 109, 64) 0           block1_conv2_bn[0][0]            \\n__________________________________________________________________________________________________\\nblock2_sepconv1 (SeparableConv2 (None, 109, 109, 128 8768        block1_conv2_act[0][0]           \\n__________________________________________________________________________________________________\\nblock2_sepconv1_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv1[0][0]            \\n__________________________________________________________________________________________________\\nblock2_sepconv2_act (Activation (None, 109, 109, 128 0           block2_sepconv1_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock2_sepconv2 (SeparableConv2 (None, 109, 109, 128 17536       block2_sepconv2_act[0][0]        \\n__________________________________________________________________________________________________\\nblock2_sepconv2_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv2[0][0]            \\n__________________________________________________________________________________________________\\nconv2d_1 (Conv2D)               (None, 55, 55, 128)  8192        block1_conv2_act[0][0]           \\n__________________________________________________________________________________________________\\nblock2_pool (MaxPooling2D)      (None, 55, 55, 128)  0           block2_sepconv2_bn[0][0]         \\n__________________________________________________________________________________________________\\nbatch_normalization_1 (BatchNor (None, 55, 55, 128)  512         conv2d_1[0][0]                   \\n__________________________________________________________________________________________________\\nadd_1 (Add)                     (None, 55, 55, 128)  0           block2_pool[0][0]                \\n                                                                 batch_normalization_1[0][0]      \\n__________________________________________________________________________________________________\\nblock3_sepconv1_act (Activation (None, 55, 55, 128)  0           add_1[0][0]                      \\n__________________________________________________________________________________________________\\nblock3_sepconv1 (SeparableConv2 (None, 55, 55, 256)  33920       block3_sepconv1_act[0][0]        \\n__________________________________________________________________________________________________\\nblock3_sepconv1_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv1[0][0]            \\n__________________________________________________________________________________________________\\nblock3_sepconv2_act (Activation (None, 55, 55, 256)  0           block3_sepconv1_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock3_sepconv2 (SeparableConv2 (None, 55, 55, 256)  67840       block3_sepconv2_act[0][0]        \\n__________________________________________________________________________________________________\\nblock3_sepconv2_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv2[0][0]            \\n__________________________________________________________________________________________________\\nconv2d_2 (Conv2D)               (None, 28, 28, 256)  32768       add_1[0][0]                      \\n__________________________________________________________________________________________________\\nblock3_pool (MaxPooling2D)      (None, 28, 28, 256)  0           block3_sepconv2_bn[0][0]         \\n__________________________________________________________________________________________________\\nbatch_normalization_2 (BatchNor (None, 28, 28, 256)  1024        conv2d_2[0][0]                   \\n__________________________________________________________________________________________________\\nadd_2 (Add)                     (None, 28, 28, 256)  0           block3_pool[0][0]                \\n                                                                 batch_normalization_2[0][0]      \\n__________________________________________________________________________________________________\\nblock4_sepconv1_act (Activation (None, 28, 28, 256)  0           add_2[0][0]                      \\n__________________________________________________________________________________________________\\nblock4_sepconv1 (SeparableConv2 (None, 28, 28, 728)  188672      block4_sepconv1_act[0][0]        \\n__________________________________________________________________________________________________\\nblock4_sepconv1_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv1[0][0]            \\n__________________________________________________________________________________________________\\nblock4_sepconv2_act (Activation (None, 28, 28, 728)  0           block4_sepconv1_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock4_sepconv2 (SeparableConv2 (None, 28, 28, 728)  536536      block4_sepconv2_act[0][0]        \\n__________________________________________________________________________________________________\\nblock4_sepconv2_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv2[0][0]            \\n__________________________________________________________________________________________________\\nconv2d_3 (Conv2D)               (None, 14, 14, 728)  186368      add_2[0][0]                      \\n__________________________________________________________________________________________________\\nblock4_pool (MaxPooling2D)      (None, 14, 14, 728)  0           block4_sepconv2_bn[0][0]         \\n__________________________________________________________________________________________________\\nbatch_normalization_3 (BatchNor (None, 14, 14, 728)  2912        conv2d_3[0][0]                   \\n__________________________________________________________________________________________________\\nadd_3 (Add)                     (None, 14, 14, 728)  0           block4_pool[0][0]                \\n                                                                 batch_normalization_3[0][0]      \\n__________________________________________________________________________________________________\\nblock5_sepconv1_act (Activation (None, 14, 14, 728)  0           add_3[0][0]                      \\n__________________________________________________________________________________________________\\nblock5_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv1_act[0][0]        \\n__________________________________________________________________________________________________\\nblock5_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv1[0][0]            \\n__________________________________________________________________________________________________\\nblock5_sepconv2_act (Activation (None, 14, 14, 728)  0           block5_sepconv1_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock5_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv2_act[0][0]        \\n__________________________________________________________________________________________________\\nblock5_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv2[0][0]            \\n__________________________________________________________________________________________________\\nblock5_sepconv3_act (Activation (None, 14, 14, 728)  0           block5_sepconv2_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock5_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv3_act[0][0]        \\n__________________________________________________________________________________________________\\nblock5_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv3[0][0]            \\n__________________________________________________________________________________________________\\nadd_4 (Add)                     (None, 14, 14, 728)  0           block5_sepconv3_bn[0][0]         \\n                                                                 add_3[0][0]                      \\n__________________________________________________________________________________________________\\nblock6_sepconv1_act (Activation (None, 14, 14, 728)  0           add_4[0][0]                      \\n__________________________________________________________________________________________________\\nb<<TRUNCATED>>',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}}]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl.search(\"astronomy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Info on a Specific Servable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'datacite': {'creators': [{'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Schwarting',\n",
       "     'givenName': 'Marcus'}],\n",
       "   'descriptions': [{'description': 'A function to determine blurry or clear SEM images.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:1AZZVf9LBOLwY',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Brain Image Blurriness Function'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/9c1b435e-3e75-4376-ad5b-88317705afea-1564177444',\n",
       "   'domains': ['general', 'test'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/9c1b435e-3e75-4376-ad5b-88317705afea',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/9c1b435e-3e75-4376-ad5b-88317705afea',\n",
       "   'files': {'other': ['EM_Focus_Canny.py', 'Blurry_SVClassifier.pkl']},\n",
       "   'funcx_id': '4b89ed76-0fdc-40ae-8765-1a3af15bba11',\n",
       "   'id': '9c1b435e-3e75-4376-ad5b-88317705afea',\n",
       "   'identifier': 'globus:1AZZVf9LBOLwY',\n",
       "   'name': 'BlurryBrain',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1564177444648',\n",
       "   'shorthand_name': 'mschwarting_anl/BlurryBrain',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'A list of files',\n",
       "      'type': 'file'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'is_blurry',\n",
       "      'module': 'EM_Focus_Canny'},\n",
       "     'output': {'description': 'Output', 'type': 'float'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl.search_by_servable(\"mschwarting_anl/BlurryBrain\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Servable Metadata for All Servables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'datacite': {'creators': [{'affiliations': 'University of Chicago',\n",
       "     'familyName': 'Koripelly',\n",
       "     'givenName': 'Aarthi'}],\n",
       "   'descriptions': [{'description': 'A function and CNN model to predict if SEM image is focused or blurry.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:7WKVDWQfOB7D',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'SEM_CNN_Model'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/d400a239-c252-4fec-bf17-a9ded50729ba-1565109436',\n",
       "   'domains': ['general', 'machinelearning'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/d400a239-c252-4fec-bf17-a9ded50729ba',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/d400a239-c252-4fec-bf17-a9ded50729ba',\n",
       "   'files': {'other': ['model_hough.h5',\n",
       "     'model_hough.json',\n",
       "     'main.py',\n",
       "     'requirements.txt']},\n",
       "   'funcx_id': '624e050a-806b-46cc-a5c0-5e3ad114b015',\n",
       "   'id': 'd400a239-c252-4fec-bf17-a9ded50729ba',\n",
       "   'identifier': 'globus:7WKVDWQfOB7D',\n",
       "   'name': 'semCNN',\n",
       "   'owner': 'akoripelly_gmail',\n",
       "   'publication_date': '1565109435658',\n",
       "   'shorthand_name': 'akoripelly_gmail/semCNN',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '18',\n",
       "   'version': '0.8.0',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'A file',\n",
       "      'type': 'file'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'analyze_image',\n",
       "      'module': 'main'},\n",
       "     'output': {'description': 'Output', 'type': 'string'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'creators': [{'affiliations': 'University of Chicago',\n",
       "     'familyName': 'Koripelly',\n",
       "     'givenName': 'Aarthi'}],\n",
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       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\nconv2d_7 (Conv2D)            (None, 26, 26, 32)        320       \\n_________________________________________________________________\\nconv2d_8 (Conv2D)            (None, 24, 24, 64)        18496     \\n_________________________________________________________________\\nmax_pooling2d_4 (MaxPooling2 (None, 12, 12, 64)        0         \\n_________________________________________________________________\\ndropout_7 (Dropout)          (None, 12, 12, 64)        0         \\n_________________________________________________________________\\nflatten_4 (Flatten)          (None, 9216)              0         \\n_________________________________________________________________\\ndense_7 (Dense)              (None, 128)               1179776   \\n_________________________________________________________________\\ndropout_8 (Dropout)          (None, 128)               0         \\n_________________________________________________________________\\ndense_8 (Dense)              (None, 10)                1290      \\n=================================================================\\nTotal params: 1,199,882\\nTrainable params: 1,199,882\\nNon-trainable params: 0\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'identifier': {'identifier': '10.YET/UNASSIGNED',\n",
       "    'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:oykGwHLXQuLp',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Example Scikit-Learn Model'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/db5dcb47-0618-470a-9c1d-03cf1151ca74-1567782399',\n",
       "   'dependencies': {'python': {'scikit-learn': '0.19.2'}},\n",
       "   'domains': ['biology'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/db5dcb47-0618-470a-9c1d-03cf1151ca74',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/db5dcb47-0618-470a-9c1d-03cf1151ca74',\n",
       "   'files': {'model': 'model.pkl'},\n",
       "   'funcx_id': '066a2499-9360-461e-b3eb-944fa6d55e08',\n",
       "   'id': 'db5dcb47-0618-470a-9c1d-03cf1151ca74',\n",
       "   'identifier': 'globus:oykGwHLXQuLp',\n",
       "   'name': 'iris_svm',\n",
       "   'owner': 'blaiszik_globusid',\n",
       "   'publication_date': '1567782398888',\n",
       "   'shorthand_name': 'blaiszik_globusid/iris_svm',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '6',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'List of records to evaluate with model. Each record is a list of 4 variables.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '4'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'method_name': '_predict_proba'},\n",
       "     'output': {'description': 'Probabilities for membership in each of 3 classes',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '3'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': \"SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\\n  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\\n  max_iter=-1, probability=True, random_state=None, shrinking=True,\\n  tol=0.001, verbose=False)\",\n",
       "   'model_type': 'SVC',\n",
       "   'options': {'classes': ['setosa', 'versicolor', 'virginica'],\n",
       "    'is_classifier': True,\n",
       "    'serialization_method': 'pickle'},\n",
       "   'shim': 'sklearn.ScikitLearnServable',\n",
       "   'type': 'Scikit-learn estimator'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/mcherukara/CDI_NN',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'creators': [{'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Cherukara',\n",
       "     'givenName': 'Mathew J'},\n",
       "    {'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Nashed',\n",
       "     'givenName': 'Youssef SG'},\n",
       "    {'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Harder',\n",
       "     'givenName': 'Ross J'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.1038/s41598-018-34525-1',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Coherent Diffraction Phase Prediction'}]},\n",
       "  'dlhub': {'domains': ['materials science'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/fb58d121-3d85-40de-8ec6-98b15d0e9b71',\n",
       "   'files': {'model': 'phase_weights.hdf5'},\n",
       "   'funcx_id': '5ada69eb-dcd2-4cb6-a9cc-0913171b9d56',\n",
       "   'id': 'fb58d121-3d85-40de-8ec6-98b15d0e9b71',\n",
       "   'name': 'cherukara_phase',\n",
       "   'owner': 'blaiszik_globusid',\n",
       "   'publication_date': '1550611311873',\n",
       "   'shorthand_name': 'blaiszik_globusid/cherukara_phase',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '6',\n",
       "   'version': '0.5.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'h5py': '2.9.0', 'keras': '2.2.4'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Fourier transformed diffraction intensity',\n",
       "      'shape': ['None', '32', '32', '1'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': ['output phase image'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Output phase image',\n",
       "      'shape': ['None', '32', '32', '1'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\ninput_1 (InputLayer)         (None, 32, 32, 1)         0         \\n_________________________________________________________________\\nconv2d_1 (Conv2D)            (None, 32, 32, 32)        320       \\n_________________________________________________________________\\nconv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \\n_________________________________________________________________\\nmax_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \\n_________________________________________________________________\\nconv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \\n_________________________________________________________________\\nconv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \\n_________________________________________________________________\\nmax_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \\n_________________________________________________________________\\nconv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \\n_________________________________________________________________\\nconv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \\n_________________________________________________________________\\nmax_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \\n_________________________________________________________________\\nconv2d_7 (Conv2D)            (None, 4, 4, 128)         147584    \\n_________________________________________________________________\\nconv2d_8 (Conv2D)            (None, 4, 4, 128)         147584    \\n_________________________________________________________________\\nup_sampling2d_1 (UpSampling2 (None, 8, 8, 128)         0         \\n_________________________________________________________________\\nconv2d_9 (Conv2D)            (None, 8, 8, 64)          73792     \\n_________________________________________________________________\\nconv2d_10 (Conv2D)           (None, 8, 8, 64)          36928     \\n_________________________________________________________________\\nup_sampling2d_2 (UpSampling2 (None, 16, 16, 64)        0         \\n_________________________________________________________________\\nconv2d_11 (Conv2D)           (None, 16, 16, 32)        18464     \\n_________________________________________________________________\\nconv2d_12 (Conv2D)           (None, 16, 16, 32)        9248      \\n_________________________________________________________________\\nup_sampling2d_3 (UpSampling2 (None, 32, 32, 32)        0         \\n_________________________________________________________________\\nconv2d_13 (Conv2D)           (None, 32, 32, 1)         289       \\n=================================================================\\nTotal params: 720,321\\nTrainable params: 720,321\\nNon-trainable params: 0\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'creators': [{'affiliations': ['MIT'],\n",
       "     'familyName': 'Duan',\n",
       "     'givenName': 'Chenru'}],\n",
       "   'descriptions': [{'description': 'Map the connectivity information of a octahedral transition metal complex to a label that describes the success rate of DFT b3lyp geometry optimmization.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.1021/acs.jctc.9b00057',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'ANN static classifier for DFT geometry labels prediction'}]},\n",
       "  'dlhub': {'domains': ['chemistry',\n",
       "    'transition metal complex',\n",
       "    'molecule design'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/88cc0a34-281b-45a8-8b3c-fbbf794ab343',\n",
       "   'files': {'model': 'spectro_all_geo.h5'},\n",
       "   'funcx_id': '984b0075-f8ae-4440-b4aa-ff33f06a09d1',\n",
       "   'id': '88cc0a34-281b-45a8-8b3c-fbbf794ab343',\n",
       "   'name': 'static_classifier_geo',\n",
       "   'owner': 'chenru_xsede',\n",
       "   'publication_date': '1555360653280',\n",
       "   'shorthand_name': 'chenru_xsede/static_classifier_geo',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '14',\n",
       "   'version': '0.7.4',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'h5py': '2.8.0',\n",
       "     'keras': '2.2.2',\n",
       "     'numpy': '1.13.1',\n",
       "     'tensorflow': '1.6.0'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Features: oxstate, spin multiplicity, net ligand charge, RAC155',\n",
       "      'shape': ['None', '155'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': 'geo_label', 'method_name': 'predict'},\n",
       "     'output': {'description': 'Success rate',\n",
       "      'shape': ['None', '1'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\ninput_RAC_plot (InputLayer)  (None, 154)               0         \\n_________________________________________________________________\\ndense_1 (Dense)              (None, 200)               31000     \\n_________________________________________________________________\\nbatch_normalization_1 (Batch (None, 200)               800       \\n_________________________________________________________________\\nactivation_1 (Activation)    (None, 200)               0         \\n_________________________________________________________________\\nalpha_drop_plot_0 (Dropout)  (None, 200)               0         \\n_________________________________________________________________\\ndense_2 (Dense)              (None, 200)               40200     \\n_________________________________________________________________\\nbatch_normalization_2 (Batch (None, 200)               800       \\n_________________________________________________________________\\nactivation_2 (Activation)    (None, 200)               0         \\n_________________________________________________________________\\nalpha_drop_plot_1 (Dropout)  (None, 200)               0         \\n_________________________________________________________________\\noutput_reg (Dense)           (None, 1)                 201       \\n_________________________________________________________________\\nbatch_normalization_3 (Batch (None, 1)                 4         \\n_________________________________________________________________\\nactivation_3 (Activation)    (None, 1)                 0         \\n=================================================================\\nTotal params: 73,005\\nTrainable params: 72,203\\nNon-trainable params: 802\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'identifier': {'identifier': '10.YET/UNASSIGNED',\n",
       "    'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:x10YWme8JpaS',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Norm of a 1D Array'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/34f16da0-41b1-4f8c-9370-24394a3b2df4-1567729465',\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/34f16da0-41b1-4f8c-9370-24394a3b2df4',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/34f16da0-41b1-4f8c-9370-24394a3b2df4',\n",
       "   'funcx_id': '279a0d19-0052-4b1d-962d-3c7632a3b861',\n",
       "   'id': '34f16da0-41b1-4f8c-9370-24394a3b2df4',\n",
       "   'identifier': 'globus:x10YWme8JpaS',\n",
       "   'name': '1d_norm',\n",
       "   'owner': 'dlhub.test_gmail',\n",
       "   'publication_date': '1567729386567',\n",
       "   'shorthand_name': 'dlhub.test_gmail/1d_norm',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '4',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Array to be normed',\n",
       "      'shape': ['None'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'norm',\n",
       "      'module': 'numpy.linalg'},\n",
       "     'output': {'description': 'Norm of the array', 'type': 'number'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'creators': [{'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Chard',\n",
       "     'givenName': 'Ryan'}],\n",
       "   'descriptions': [{'description': 'A function to get the length of a file.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'File Length Function'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/70ca441e-806f-4ff6-9b01-0efb295bd9d5-1561400844',\n",
       "   'domains': ['general', 'test'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/70ca441e-806f-4ff6-9b01-0efb295bd9d5',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/70ca441e-806f-4ff6-9b01-0efb295bd9d5',\n",
       "   'files': {'other': ['file_length.py']},\n",
       "   'funcx_id': '2124d3f4-6cbc-4360-b52b-7104be23f49f',\n",
       "   'id': '70ca441e-806f-4ff6-9b01-0efb295bd9d5',\n",
       "   'name': 'FileLength',\n",
       "   'owner': 'etruelove_uchicago',\n",
       "   'publication_date': '1561400844609',\n",
       "   'shorthand_name': 'etruelove_uchicago/FileLength',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '16',\n",
       "   'version': '0.8.0',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'A file',\n",
       "      'type': 'file'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'get_file_length',\n",
       "      'module': 'file_length'},\n",
       "     'output': {'description': 'Output', 'type': 'float'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'identifier': {'identifier': '10.YET/UNASSIGNED',\n",
       "    'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'TomoGAN: A tool for denoising computed tomography images'}]},\n",
       "  'dlhub': {'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/01e8f1ed-9cbc-489f-be72-d048630deca8',\n",
       "   'files': {'other': ['saved_model.pb',\n",
       "     'variables\\\\variables.data-00000-of-00001',\n",
       "     'variables\\\\variables.index']},\n",
       "   'funcx_id': '131d615e-8026-4ee4-86a7-db26969828f1',\n",
       "   'id': '01e8f1ed-9cbc-489f-be72-d048630deca8',\n",
       "   'name': 'tomo_test',\n",
       "   'owner': 'loganw_globusid',\n",
       "   'publication_date': '1552666161182',\n",
       "   'shorthand_name': 'loganw_globusid/tomo_test',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '3',\n",
       "   'version': '0.7.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'tensorflow': '1.8.0'}},\n",
       "   'methods': {'run': {'input': {'description': 'input_image',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '1024', '1024', '3'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'input_nodes': ['Placeholder:0'],\n",
       "      'output_nodes': ['generator_model/conv2d_14/BiasAdd:0']},\n",
       "     'output': {'description': 'denoised_image',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '1024', '1024', '1'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'shim': 'tensorflow.TensorFlowServable',\n",
       "   'type': 'TensorFlow Model'}},\n",
       " {'datacite': {'creators': [{'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Ward',\n",
       "     'givenName': 'Logan'}],\n",
       "   'descriptions': [{'description': 'Model that detects which digit is present in an image',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'MNIST Demo'}]},\n",
       "  'dlhub': {'domains': ['ocr'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/fed5a211-4c5d-4ca6-8b5f-f772f7ff4fc2',\n",
       "   'files': {'model': 'model.hd5'},\n",
       "   'funcx_id': '2538a810-cd18-4d9e-99bd-63e9a4d9900d',\n",
       "   'id': 'fed5a211-4c5d-4ca6-8b5f-f772f7ff4fc2',\n",
       "   'name': 'mnist_example',\n",
       "   'owner': 'loganw_globusid',\n",
       "   'publication_date': '1555001662898',\n",
       "   'shorthand_name': 'loganw_globusid/mnist_example',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '3',\n",
       "   'version': '0.7.4',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'h5py': '2.8.0', 'keras': '2.1.3'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Tensor',\n",
       "      'shape': ['None', '28', '28', '1'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': ['0',\n",
       "       '1',\n",
       "       '2',\n",
       "       '3',\n",
       "       '4',\n",
       "       '5',\n",
       "       '6',\n",
       "       '7',\n",
       "       '8',\n",
       "       '9'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Tensor',\n",
       "      'shape': ['None', '10'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\nconv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \\n_________________________________________________________________\\nconv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \\n_________________________________________________________________\\nmax_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \\n_________________________________________________________________\\ndropout_1 (Dropout)          (None, 12, 12, 64)        0         \\n_________________________________________________________________\\nflatten_1 (Flatten)          (None, 9216)              0         \\n_________________________________________________________________\\ndense_1 (Dense)              (None, 128)               1179776   \\n_________________________________________________________________\\ndropout_2 (Dropout)          (None, 128)               0         \\n_________________________________________________________________\\ndense_2 (Dense)              (None, 10)                1290      \\n=================================================================\\nTotal params: 1,199,882\\nTrainable params: 1,199,882\\nNon-trainable params: 0\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'creators': [{'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Ward',\n",
       "     'givenName': 'Logan'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Blaiszik',\n",
       "     'givenName': 'Ben'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Foster',\n",
       "     'givenName': 'Ian'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Assary',\n",
       "     'givenName': 'Rajeev'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Naranayan',\n",
       "     'givenName': 'Badri'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Curtis',\n",
       "     'givenName': 'Larry'}],\n",
       "   'descriptions': [{'description': 'This model predicts the G4MP2 atomization energy of a molecule given the relaxed atomic coordinates and total energy from a B3LYP calculation. It was trained on a set of 134k organic molecules smaller than 9 heavy atoms (i.e., the GDB9 dataset) plus 66 molecules larger than 9 heavy atoms.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.1021/acs.jctc.8b00908',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'Requires'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'SchNet Model to Predict G4MP2 Activation Energy from B3LYP Energy and Coordinates'}]},\n",
       "  'dlhub': {'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/d637593b-e085-4bfe-a054-447af32137a5',\n",
       "   'files': {'other': ['model/architecture.pth',\n",
       "     'model/best_model',\n",
       "     'jcesr_ml/atom_environment.py',\n",
       "     'jcesr_ml/benchmark.py',\n",
       "     'jcesr_ml/coordinates.py',\n",
       "     'jcesr_ml/qml.py',\n",
       "     'jcesr_ml/schnetpack.py',\n",
       "     'jcesr_ml/style.py',\n",
       "     'jcesr_ml/utils.py',\n",
       "     'jcesr_ml/__init__.py',\n",
       "     'dlhub_app.py',\n",
       "     'train_dataset.pkl',\n",
       "     'requirements.txt']},\n",
       "   'funcx_id': '86b425d4-c489-496c-8979-f8f9f6eafabd',\n",
       "   'id': 'd637593b-e085-4bfe-a054-447af32137a5',\n",
       "   'name': 'g4mp2_delta_schnet',\n",
       "   'owner': 'loganw_globusid',\n",
       "   'publication_date': '1554227017046',\n",
       "   'shorthand_name': 'loganw_globusid/g4mp2_delta_schnet',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '3',\n",
       "   'version': '0.7.4',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Structures and B3LYP energies of moelcule sto be evaluated',\n",
       "      'element_types': [{'description': 'Structures of molecules in XYZ format',\n",
       "        'item_type': {'type': 'string'},\n",
       "        'type': 'list'},\n",
       "       {'description': 'B3LYP total energies of Energies of molecules in Ha',\n",
       "        'item_type': {'type': 'float'},\n",
       "        'type': 'list'}],\n",
       "      'type': 'tuple'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'evaluate_molecules',\n",
       "      'module': 'dlhub_app',\n",
       "      'unpack': True},\n",
       "     'output': {'description': 'Estimate of G4MP2 atomization energy',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'type': 'list'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'creators': [{'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Ward',\n",
       "     'givenName': 'Logan'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Blaiszik',\n",
       "     'givenName': 'Ben'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Foster',\n",
       "     'givenName': 'Ian'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Assary',\n",
       "     'givenName': 'Rajeev'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Narayanan',\n",
       "     'givenName': 'Badri'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Curtis',\n",
       "     'givenName': 'Larry'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.5281/zenodo.817332',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'Requires'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'FCHL Model to Predict G4MP2 Activation Energy from B3LYP Energy and Coordinates'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/cc5ca5a8-5a8b-4248-bf16-992ce43861a5-1562702975',\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/cc5ca5a8-5a8b-4248-bf16-992ce43861a5',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/cc5ca5a8-5a8b-4248-bf16-992ce43861a5',\n",
       "   'files': {'other': ['dlhub_app.py',\n",
       "     'model.pkl.gz',\n",
       "     'jcesr_ml/atom_environment.py',\n",
       "     'jcesr_ml/benchmark.py',\n",
       "     'jcesr_ml/coordinates.py',\n",
       "     'jcesr_ml/qml.py',\n",
       "     'jcesr_ml/schnetpack.py',\n",
       "     'jcesr_ml/style.py',\n",
       "     'jcesr_ml/utils.py',\n",
       "     'jcesr_ml/__init__.py',\n",
       "     'environment.yml',\n",
       "     'apt.txt']},\n",
       "   'funcx_id': '0a7da9d0-5c20-4d9e-96a6-13bb89559484',\n",
       "   'id': 'cc5ca5a8-5a8b-4248-bf16-992ce43861a5',\n",
       "   'name': 'g4mp2_delta_fchl',\n",
       "   'owner': 'loganw_globusid',\n",
       "   'publication_date': '1562702975664',\n",
       "   'shorthand_name': 'loganw_globusid/g4mp2_delta_fchl',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '3',\n",
       "   'version': '0.8.0',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Structures and B3LYP energies of moelcule sto be evaluated',\n",
       "      'element_types': [{'description': 'Structures of molecules in XYZ format',\n",
       "        'item_type': {'type': 'string'},\n",
       "        'type': 'list'},\n",
       "       {'description': 'B3LYP total energies of Energies of molecules in Ha',\n",
       "        'item_type': {'type': 'float'},\n",
       "        'type': 'list'}],\n",
       "      'type': 'tuple'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'evaluate_molecules',\n",
       "      'module': 'dlhub_app',\n",
       "      'unpack': True},\n",
       "     'output': {'description': 'Estimate of G4MP2 atomization energy',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'type': 'list'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/Combo',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'creators': [{'affiliations': 'CANDLE',\n",
       "     'familyName': 'Team',\n",
       "     'givenName': 'CANDLE'}],\n",
       "   'descriptions': [{'description': 'CANDLE pilot 1 combo model.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'CANDLE Pilot1 Combo Demo1'}]},\n",
       "  'dlhub': {'domains': ['genomics', 'cancer research'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/b82c8643-ffd5-4a55-a02c-5b12982ac2ca',\n",
       "   'files': {'arch': 'saved.model.h5',\n",
       "    'model': 'saved.weights.h5',\n",
       "    'other': ['dropout_layer.py']},\n",
       "   'funcx_id': '0a30913d-31be-4c5e-a36b-38da78a5abfc',\n",
       "   'id': 'b82c8643-ffd5-4a55-a02c-5b12982ac2ca',\n",
       "   'name': 'candle_p1_combo_demo1',\n",
       "   'owner': 'loganw_globusid',\n",
       "   'publication_date': '1551207070916',\n",
       "   'shorthand_name': 'loganw_globusid/candle_p1_combo_demo1',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '3',\n",
       "   'version': '0.6.1',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'h5py': '2.8.0',\n",
       "     'keras': '2.1.3',\n",
       "     'numpy': '1.15.4'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Input',\n",
       "      'element_types': [{'description': 'Tensor',\n",
       "        'shape': ['None', '942'],\n",
       "        'type': 'ndarray'},\n",
       "       {'description': 'Tensor', 'shape': ['None', '3820'], 'type': 'ndarray'},\n",
       "       {'description': 'Tensor',\n",
       "        'shape': ['None', '3820'],\n",
       "        'type': 'ndarray'}],\n",
       "      'type': 'tuple'},\n",
       "     'method_details': {'classes': ['0',\n",
       "       '1',\n",
       "       '2',\n",
       "       '3',\n",
       "       '4',\n",
       "       '5',\n",
       "       '6',\n",
       "       '7',\n",
       "       '8',\n",
       "       '9'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Output',\n",
       "      'shape': ['None', '1'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '__________________________________________________________________________________________________\\nLayer (type)                    Output Shape         Param #     Connected to                     \\n==================================================================================================\\ninput.cell.expression (InputLay (None, 942)          0                                            \\n__________________________________________________________________________________________________\\ninput.drug1.descriptors (InputL (None, 3820)         0                                            \\n__________________________________________________________________________________________________\\ninput.drug2.descriptors (InputL (None, 3820)         0                                            \\n__________________________________________________________________________________________________\\ncell.expression (Model)         (None, 1000)         2945000     input.cell.expression[0][0]      \\n__________________________________________________________________________________________________\\ndrug.descriptors (Model)        (None, 1000)         5823000     input.drug1.descriptors[0][0]    \\n                                                                 input.drug2.descriptors[0][0]    \\n__________________________________________________________________________________________________\\nconcatenate_2 (Concatenate)     (None, 3000)         0           cell.expression[1][0]            \\n                                                                 drug.descriptors[1][0]           \\n                                                                 drug.descriptors[2][0]           \\n__________________________________________________________________________________________________\\ndense_17 (Dense)                (None, 1000)         3001000     concatenate_2[0][0]              \\n__________________________________________________________________________________________________\\npermanent_dropout_16 (Permanent (None, 1000)         0           dense_17[0][0]                   \\n__________________________________________________________________________________________________\\ndense_18 (Dense)                (None, 1000)         1001000     permanent_dropout_16[0][0]       \\n__________________________________________________________________________________________________\\npermanent_dropout_17 (Permanent (None, 1000)         0           dense_18[0][0]                   \\n__________________________________________________________________________________________________\\nadd_5 (Add)                     (None, 1000)         0           permanent_dropout_17[0][0]       \\n                                                                 permanent_dropout_16[0][0]       \\n__________________________________________________________________________________________________\\ndense_19 (Dense)                (None, 1000)         1001000     add_5[0][0]                      \\n__________________________________________________________________________________________________\\npermanent_dropout_18 (Permanent (None, 1000)         0           dense_19[0][0]                   \\n__________________________________________________________________________________________________\\nadd_6 (Add)                     (None, 1000)         0           permanent_dropout_18[0][0]       \\n                                                                 add_5[0][0]                      \\n__________________________________________________________________________________________________\\ndense_20 (Dense)                (None, 1)            1001        add_6[0][0]                      \\n==================================================================================================\\nTotal params: 13,772,001\\nTrainable params: 13,772,001\\nNon-trainable params: 0\\n__________________________________________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'options': {'custom_objects': {'PermanentDropout': 'dropout_layer.PermanentDropout'}},\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://arxiv.org/abs/1902.07582',\n",
       "     'alternateIdentifierType': 'arXiv'}],\n",
       "   'creators': [{'affiliations': 'Data Science and Learning Division, Argonne National Laboratory',\n",
       "     'familyName': 'Liu',\n",
       "     'givenName': 'Zhengchun'},\n",
       "    {'affiliations': 'X-ray Science Division, Argonne National Laboratory',\n",
       "     'familyName': 'Bicer',\n",
       "     'givenName': 'Tekin'},\n",
       "    {'affiliations': 'Data Science and Learning Division, Argonne National Laboratory',\n",
       "     'familyName': 'Kettimuthu',\n",
       "     'givenName': 'Rajkumar'},\n",
       "    {'affiliations': 'X-ray Science Division, Argonne National Laboratory',\n",
       "     'familyName': 'Gursoy',\n",
       "     'givenName': 'Doga'},\n",
       "    {'affiliations': 'X-ray Science Division, Argonne National Laboratory',\n",
       "     'familyName': 'Carlo',\n",
       "     'givenName': 'Francesco'},\n",
       "    {'affiliations': 'Data Science and Learning Division, Argonne National Laboratory',\n",
       "     'familyName': 'Foster',\n",
       "     'givenName': 'Ian'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'TomoGAN: Low-Dose X-Ray Tomography with Generative Adversarial Networks'}]},\n",
       "  'dlhub': {'domains': ['Computed tomography', 'Computer Vision'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/37607ccb-c097-4668-867a-e0aab2df53d7',\n",
       "   'files': {'other': ['saved_model.pb',\n",
       "     'variables/variables.index',\n",
       "     'variables/variables.data-00000-of-00001']},\n",
       "   'funcx_id': '8e5597c8-ad81-4ae8-a80a-eb199bfe02b7',\n",
       "   'id': '37607ccb-c097-4668-867a-e0aab2df53d7',\n",
       "   'name': 'TomoGAN',\n",
       "   'owner': 'lzhengchun_globusid',\n",
       "   'publication_date': '1554908897061',\n",
       "   'shorthand_name': 'lzhengchun_globusid/TomoGAN',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '12',\n",
       "   'version': '0.7.4',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'tensorflow': '1.13.1'}},\n",
       "   'methods': {'run': {'input': {'description': 'input_image',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '1024', '1024', '3'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'input_nodes': ['Placeholder:0'],\n",
       "      'output_nodes': ['generator_model/conv2d_14/BiasAdd:0']},\n",
       "     'output': {'description': 'denoised_image',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '1024', '1024', '1'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'shim': 'tensorflow.TensorFlowServable',\n",
       "   'type': 'TensorFlow Model'}},\n",
       " {'datacite': {'creators': [{'affiliations': ['Globus'],\n",
       "     'familyName': 'Tuecke',\n",
       "     'givenName': 'Max'}],\n",
       "   'descriptions': [{'description': 'Test Upload',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Test Servable Upload'}]},\n",
       "  'dlhub': {'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/fa097248-a5db-413c-b055-c40408233b83',\n",
       "   'files': {'other': ['app.py']},\n",
       "   'funcx_id': 'a91bb4d7-a39c-4fee-b030-42930460ea5c',\n",
       "   'id': 'fa097248-a5db-413c-b055-c40408233b83',\n",
       "   'name': 'test_servable_upload',\n",
       "   'owner': 'maxtuecke_gmail',\n",
       "   'publication_date': '1550594783854',\n",
       "   'shorthand_name': 'maxtuecke_gmail/test_servable_upload',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '9',\n",
       "   'version': '0.5.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'numpy': '1.14.3'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Input Data',\n",
       "      'type': 'string'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'run',\n",
       "      'module': 'app'},\n",
       "     'output': {'description': 'Output data', 'type': 'string'},\n",
       "     'parameters': {'check_version': False}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/rhnvrm/galaxy-image-classifier-tensorflow',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'creators': [{'affiliations': ['Globus'],\n",
       "     'familyName': 'Tuecke',\n",
       "     'givenName': 'Max'}],\n",
       "   'descriptions': [{'description': 'Test upload of a simple galaxy image classifier',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Galaxy Image Classifier'}]},\n",
       "  'dlhub': {'domains': ['astronomy'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/7b8f5273-2e1c-4836-b778-30efce4cf912',\n",
       "   'files': {'other': ['repo2docker/environment.yml',\n",
       "     'app.py',\n",
       "     'model/retrained_labels.txt',\n",
       "     'model/retrained_graph.pb']},\n",
       "   'funcx_id': 'c1de4909-e8f8-443e-b9b7-8dc2f3f72d3e',\n",
       "   'id': '7b8f5273-2e1c-4836-b778-30efce4cf912',\n",
       "   'name': 'galaxy_image_classifier',\n",
       "   'owner': 'maxtuecke_gmail',\n",
       "   'publication_date': '1550171926331',\n",
       "   'shorthand_name': 'maxtuecke_gmail/galaxy_image_classifier',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '9',\n",
       "   'version': '0.5.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Image byte read in data',\n",
       "      'type': 'string'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'run',\n",
       "      'module': 'app'},\n",
       "     'output': {'description': 'What type of galaxy it is and python version running',\n",
       "      'type': 'string'},\n",
       "     'parameters': {'data': 'None'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'descriptions': [{'description': 'Gregoire Band Gaps Dataset',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:ieLGpiaEL2MX',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Thin_Films_BG_RF'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/4f3d2dfc-e774-4431-9ea6-23003c2a7d8d-1567707332',\n",
       "   'dependencies': {'python': {'scikit-learn': '0.20.2'}},\n",
       "   'domains': ['materials'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/4f3d2dfc-e774-4431-9ea6-23003c2a7d8d',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/4f3d2dfc-e774-4431-9ea6-23003c2a7d8d',\n",
       "   'files': {'model': 'model.pkl'},\n",
       "   'funcx_id': 'db712b0e-70da-41b8-96d6-8f08b9c885ad',\n",
       "   'id': '4f3d2dfc-e774-4431-9ea6-23003c2a7d8d',\n",
       "   'identifier': 'globus:ieLGpiaEL2MX',\n",
       "   'name': 'Thin_Films_BG_RF',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1567707332279',\n",
       "   'shorthand_name': 'mschwarting_anl/Thin_Films_BG_RF',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'List of records to evaluate with model. Each record is a list of 51 variables.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '51'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'method_name': 'predict'},\n",
       "     'output': {'description': 'Predictions of the machine learning model.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': \"ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\\n          max_features='auto', max_leaf_nodes=None,\\n          min_impurity_decrease=0.0, min_impurity_split=None,\\n          min_samples_leaf=1, min_samples_split=2,\\n          min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\\n          oob_score=False, random_state=None, verbose=0, warm_start=False)\",\n",
       "   'model_type': 'ExtraTreesRegressor',\n",
       "   'options': {'classes': ['Fe',\n",
       "     'Sn',\n",
       "     'Ti',\n",
       "     'Zn',\n",
       "     'Ce',\n",
       "     'Co',\n",
       "     'Ni',\n",
       "     'Bi',\n",
       "     'V',\n",
       "     'Cu',\n",
       "     'Mn',\n",
       "     'W',\n",
       "     'Mo',\n",
       "     'La',\n",
       "     'Cr',\n",
       "     'Y',\n",
       "     'Er',\n",
       "     'Sr',\n",
       "     'Gd',\n",
       "     'Sm',\n",
       "     'Sb',\n",
       "     'Nb',\n",
       "     'Ga',\n",
       "     'In',\n",
       "     'Al',\n",
       "     'Pb',\n",
       "     'Mg',\n",
       "     'Ca',\n",
       "     'Pr',\n",
       "     'Eu',\n",
       "     'Zr',\n",
       "     'Lu',\n",
       "     'Ba',\n",
       "     'Yb',\n",
       "     'P',\n",
       "     'Sc',\n",
       "     'Ag',\n",
       "     'Tb',\n",
       "     'Nd',\n",
       "     'Pd',\n",
       "     'Hf',\n",
       "     'Rb',\n",
       "     'R_channel avg',\n",
       "     'G_channel avg',\n",
       "     'B_channel avg',\n",
       "     'R_channel 1',\n",
       "     'G_channel 1',\n",
       "     'B_channel 1',\n",
       "     'R_channel 2',\n",
       "     'G_channel 2',\n",
       "     'B_channel 2'],\n",
       "    'is_classifier': False,\n",
       "    'serialization_method': 'pickle'},\n",
       "   'shim': 'sklearn.ScikitLearnServable',\n",
       "   'type': 'Scikit-learn estimator'}},\n",
       " {'datacite': {'descriptions': [{'description': 'Gregoire Band Gaps Dataset',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:AkHgR8tyWJpg',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Thin_Film_Autoencoder'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/f4420c7f-bce8-4077-88b0-87291ce254b7-1567544416',\n",
       "   'dependencies': {'python': {'h5py': '2.9.0',\n",
       "     'keras': '2.2.4',\n",
       "     'tensorflow': '1.14.0'}},\n",
       "   'domains': ['materials science'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/f4420c7f-bce8-4077-88b0-87291ce254b7',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/f4420c7f-bce8-4077-88b0-87291ce254b7',\n",
       "   'files': {'model': 'vae-aws-encoder_l100.h5'},\n",
       "   'funcx_id': 'a8f0326e-4b54-41b8-8507-1cabf3eef7f9',\n",
       "   'id': 'f4420c7f-bce8-4077-88b0-87291ce254b7',\n",
       "   'identifier': 'globus:AkHgR8tyWJpg',\n",
       "   'name': 'Thin_Film_Autoencoder',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1567544416040',\n",
       "   'shorthand_name': 'mschwarting_anl/Thin_Film_Autoencoder',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Dataset Image',\n",
       "      'shape': ['None', '32', '32', '3'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': ['output phase image'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Latent Space',\n",
       "      'element_types': [{'description': 'Tensor',\n",
       "        'shape': ['None', '100'],\n",
       "        'type': 'ndarray'},\n",
       "       {'description': 'Tensor', 'shape': ['None', '100'], 'type': 'ndarray'},\n",
       "       {'description': 'Tensor', 'shape': ['None', '100'], 'type': 'ndarray'}],\n",
       "      'type': 'tuple'}}},\n",
       "   'model_summary': '__________________________________________________________________________________________________\\nLayer (type)                    Output Shape         Param #     Connected to                     \\n==================================================================================================\\nencoder_input (InputLayer)      (None, 32, 32, 3)    0                                            \\n__________________________________________________________________________________________________\\nconv2d_10 (Conv2D)              (None, 16, 16, 32)   896         encoder_input[0][0]              \\n__________________________________________________________________________________________________\\nconv2d_11 (Conv2D)              (None, 8, 8, 64)     18496       conv2d_10[0][0]                  \\n__________________________________________________________________________________________________\\nflatten_2 (Flatten)             (None, 4096)         0           conv2d_11[0][0]                  \\n__________________________________________________________________________________________________\\ndense_2 (Dense)                 (None, 16)           65552       flatten_2[0][0]                  \\n__________________________________________________________________________________________________\\nz_mean (Dense)                  (None, 100)          1700        dense_2[0][0]                    \\n__________________________________________________________________________________________________\\nz_log_var (Dense)               (None, 100)          1700        dense_2[0][0]                    \\n__________________________________________________________________________________________________\\nz (Lambda)                      (None, 100)          0           z_mean[0][0]                     \\n                                                                 z_log_var[0][0]                  \\n==================================================================================================\\nTotal params: 88,344\\nTrainable params: 88,344\\nNon-trainable params: 0\\n__________________________________________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'MNIST Digit Classifier'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/cf555897-34a5-49bd-afd1-8527db02287f-1564077013',\n",
       "   'dependencies': {'python': {'h5py': '2.9.0',\n",
       "     'keras': '2.2.4',\n",
       "     'tensorflow': '1.14.0'}},\n",
       "   'domains': ['digit recognition'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/cf555897-34a5-49bd-afd1-8527db02287f',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/cf555897-34a5-49bd-afd1-8527db02287f',\n",
       "   'files': {'model': 'model.h5'},\n",
       "   'funcx_id': '8a453f62-978d-432e-8525-31faaa124897',\n",
       "   'id': 'cf555897-34a5-49bd-afd1-8527db02287f',\n",
       "   'name': 'mnist',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1564077012947',\n",
       "   'shorthand_name': 'mschwarting_anl/mnist',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.1',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Image of a digit',\n",
       "      'shape': ['None', '28', '28', '1'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': ['0',\n",
       "       '1',\n",
       "       '2',\n",
       "       '3',\n",
       "       '4',\n",
       "       '5',\n",
       "       '6',\n",
       "       '7',\n",
       "       '8',\n",
       "       '9'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Probabilities of being 0-9',\n",
       "      'shape': ['None', '10'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\nconv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \\n_________________________________________________________________\\nconv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \\n_________________________________________________________________\\nmax_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \\n_________________________________________________________________\\ndropout_1 (Dropout)          (None, 12, 12, 64)        0         \\n_________________________________________________________________\\nflatten_1 (Flatten)          (None, 9216)              0         \\n_________________________________________________________________\\ndense_1 (Dense)              (None, 128)               1179776   \\n_________________________________________________________________\\ndropout_2 (Dropout)          (None, 128)               0         \\n_________________________________________________________________\\ndense_2 (Dense)              (None, 10)                1290      \\n=================================================================\\nTotal params: 1,199,882\\nTrainable params: 1,199,882\\nNon-trainable params: 0\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'creators': [{'familyName': 'Asad', 'givenName': 'Khan'},\n",
       "    {'familyName': 'Huerta', 'givenName': 'E.A.'},\n",
       "    {'familyName': 'Wang', 'givenName': 'Sibo'},\n",
       "    {'familyName': 'Gruendl', 'givenName': 'Robert'},\n",
       "    {'familyName': 'Jennings', 'givenName': 'Elise'},\n",
       "    {'familyName': 'Zheng', 'givenName': 'Huiho'}],\n",
       "   'identifier': {'identifier': 'globus:RgyyV2f4DajH',\n",
       "    'identifierType': 'Globus'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.1016/j.physletb.2019.06.009',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/da89565c-a043-4598-879c-3ccce9783fac-1564166980',\n",
       "   'dependencies': {'python': {'h5py': '2.9.0',\n",
       "     'keras': '2.2.4',\n",
       "     'tensorflow': '1.14.0'}},\n",
       "   'domains': ['astronomy', 'dark energy survey', 'classification'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/da89565c-a043-4598-879c-3ccce9783fac',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/da89565c-a043-4598-879c-3ccce9783fac',\n",
       "   'files': {'model': 'Xception_Final.h5'},\n",
       "   'funcx_id': 'ebc27e84-a282-4e67-b641-6d7597af8150',\n",
       "   'id': 'da89565c-a043-4598-879c-3ccce9783fac',\n",
       "   'identifier': 'globus:RgyyV2f4DajH',\n",
       "   'name': 'galaxy_catalog_des',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1564166980465',\n",
       "   'shorthand_name': 'mschwarting_anl/galaxy_catalog_des',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Image of a galaxy',\n",
       "      'shape': ['None', '224', '224', '3'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'method_name': 'predict'},\n",
       "     'output': {'description': 'Probabilities of being a elliptical or spiral galaxy',\n",
       "      'shape': ['None', '2'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '__________________________________________________________________________________________________\\nLayer (type)                    Output Shape         Param #     Connected to                     \\n==================================================================================================\\ninput_1 (InputLayer)            (None, 224, 224, 3)  0                                            \\n__________________________________________________________________________________________________\\nblock1_conv1 (Conv2D)           (None, 111, 111, 32) 864         input_1[0][0]                    \\n__________________________________________________________________________________________________\\nblock1_conv1_bn (BatchNormaliza (None, 111, 111, 32) 128         block1_conv1[0][0]               \\n__________________________________________________________________________________________________\\nblock1_conv1_act (Activation)   (None, 111, 111, 32) 0           block1_conv1_bn[0][0]            \\n__________________________________________________________________________________________________\\nblock1_conv2 (Conv2D)           (None, 109, 109, 64) 18432       block1_conv1_act[0][0]           \\n__________________________________________________________________________________________________\\nblock1_conv2_bn (BatchNormaliza (None, 109, 109, 64) 256         block1_conv2[0][0]               \\n__________________________________________________________________________________________________\\nblock1_conv2_act (Activation)   (None, 109, 109, 64) 0           block1_conv2_bn[0][0]            \\n__________________________________________________________________________________________________\\nblock2_sepconv1 (SeparableConv2 (None, 109, 109, 128 8768        block1_conv2_act[0][0]           \\n__________________________________________________________________________________________________\\nblock2_sepconv1_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv1[0][0]            \\n__________________________________________________________________________________________________\\nblock2_sepconv2_act (Activation (None, 109, 109, 128 0           block2_sepconv1_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock2_sepconv2 (SeparableConv2 (None, 109, 109, 128 17536       block2_sepconv2_act[0][0]        \\n__________________________________________________________________________________________________\\nblock2_sepconv2_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv2[0][0]            \\n__________________________________________________________________________________________________\\nconv2d_1 (Conv2D)               (None, 55, 55, 128)  8192        block1_conv2_act[0][0]           \\n__________________________________________________________________________________________________\\nblock2_pool (MaxPooling2D)      (None, 55, 55, 128)  0           block2_sepconv2_bn[0][0]         \\n__________________________________________________________________________________________________\\nbatch_normalization_1 (BatchNor (None, 55, 55, 128)  512         conv2d_1[0][0]                   \\n__________________________________________________________________________________________________\\nadd_1 (Add)                     (None, 55, 55, 128)  0           block2_pool[0][0]                \\n                                                                 batch_normalization_1[0][0]      \\n__________________________________________________________________________________________________\\nblock3_sepconv1_act (Activation (None, 55, 55, 128)  0           add_1[0][0]                      \\n__________________________________________________________________________________________________\\nblock3_sepconv1 (SeparableConv2 (None, 55, 55, 256)  33920       block3_sepconv1_act[0][0]        \\n__________________________________________________________________________________________________\\nblock3_sepconv1_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv1[0][0]            \\n__________________________________________________________________________________________________\\nblock3_sepconv2_act (Activation (None, 55, 55, 256)  0           block3_sepconv1_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock3_sepconv2 (SeparableConv2 (None, 55, 55, 256)  67840       block3_sepconv2_act[0][0]        \\n__________________________________________________________________________________________________\\nblock3_sepconv2_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv2[0][0]            \\n__________________________________________________________________________________________________\\nconv2d_2 (Conv2D)               (None, 28, 28, 256)  32768       add_1[0][0]                      \\n__________________________________________________________________________________________________\\nblock3_pool (MaxPooling2D)      (None, 28, 28, 256)  0           block3_sepconv2_bn[0][0]         \\n__________________________________________________________________________________________________\\nbatch_normalization_2 (BatchNor (None, 28, 28, 256)  1024        conv2d_2[0][0]                   \\n__________________________________________________________________________________________________\\nadd_2 (Add)                     (None, 28, 28, 256)  0           block3_pool[0][0]                \\n                                                                 batch_normalization_2[0][0]      \\n__________________________________________________________________________________________________\\nblock4_sepconv1_act (Activation (None, 28, 28, 256)  0           add_2[0][0]                      \\n__________________________________________________________________________________________________\\nblock4_sepconv1 (SeparableConv2 (None, 28, 28, 728)  188672      block4_sepconv1_act[0][0]        \\n__________________________________________________________________________________________________\\nblock4_sepconv1_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv1[0][0]            \\n__________________________________________________________________________________________________\\nblock4_sepconv2_act (Activation (None, 28, 28, 728)  0           block4_sepconv1_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock4_sepconv2 (SeparableConv2 (None, 28, 28, 728)  536536      block4_sepconv2_act[0][0]        \\n__________________________________________________________________________________________________\\nblock4_sepconv2_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv2[0][0]            \\n__________________________________________________________________________________________________\\nconv2d_3 (Conv2D)               (None, 14, 14, 728)  186368      add_2[0][0]                      \\n__________________________________________________________________________________________________\\nblock4_pool (MaxPooling2D)      (None, 14, 14, 728)  0           block4_sepconv2_bn[0][0]         \\n__________________________________________________________________________________________________\\nbatch_normalization_3 (BatchNor (None, 14, 14, 728)  2912        conv2d_3[0][0]                   \\n__________________________________________________________________________________________________\\nadd_3 (Add)                     (None, 14, 14, 728)  0           block4_pool[0][0]                \\n                                                                 batch_normalization_3[0][0]      \\n__________________________________________________________________________________________________\\nblock5_sepconv1_act (Activation (None, 14, 14, 728)  0           add_3[0][0]                      \\n__________________________________________________________________________________________________\\nblock5_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv1_act[0][0]        \\n__________________________________________________________________________________________________\\nblock5_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv1[0][0]            \\n__________________________________________________________________________________________________\\nblock5_sepconv2_act (Activation (None, 14, 14, 728)  0           block5_sepconv1_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock5_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv2_act[0][0]        \\n__________________________________________________________________________________________________\\nblock5_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv2[0][0]            \\n__________________________________________________________________________________________________\\nblock5_sepconv3_act (Activation (None, 14, 14, 728)  0           block5_sepconv2_bn[0][0]         \\n__________________________________________________________________________________________________\\nblock5_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv3_act[0][0]        \\n__________________________________________________________________________________________________\\nblock5_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv3[0][0]            \\n__________________________________________________________________________________________________\\nadd_4 (Add)                     (None, 14, 14, 728)  0           block5_sepconv3_bn[0][0]         \\n                                                                 add_3[0][0]                      \\n__________________________________________________________________________________________________\\nblock6_sepconv1_act (Activation (None, 14, 14, 728)  0           add_4[0][0]                      \\n__________________________________________________________________________________________________\\nb<<TRUNCATED>>',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'creators': [{'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Schwarting',\n",
       "     'givenName': 'Marcus'}],\n",
       "   'descriptions': [{'description': 'A function to determine blurry or clear SEM images.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:1OHG2kKFeTju7',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'File Length Function'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/b2146ad4-f1a1-4fb9-811c-3f59cb73ee9a-1564176554',\n",
       "   'domains': ['general', 'test'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/b2146ad4-f1a1-4fb9-811c-3f59cb73ee9a',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/b2146ad4-f1a1-4fb9-811c-3f59cb73ee9a',\n",
       "   'files': {'other': ['EM_Focus_Canny.py', 'Blurry_SVClassifier.pkl']},\n",
       "   'funcx_id': '66a6f9f8-a629-4705-ab5c-bfca7c91785a',\n",
       "   'id': 'b2146ad4-f1a1-4fb9-811c-3f59cb73ee9a',\n",
       "   'identifier': 'globus:1OHG2kKFeTju7',\n",
       "   'name': 'FileLength',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1564176554204',\n",
       "   'shorthand_name': 'mschwarting_anl/FileLength',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
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       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'A list of files',\n",
       "      'type': 'file'},\n",
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       "      'method_name': 'is_blurry',\n",
       "      'module': 'EM_Focus_Canny'},\n",
       "     'output': {'description': 'Output', 'type': 'float'}}},\n",
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       "   'type': 'Python static method'}},\n",
       " {'datacite': {'descriptions': [{'description': 'GBM-Locfit Supervised Learning Framework, Ridge Regression Model',\n",
       "     'descriptionType': 'Abstract'}],\n",
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       "   'publicationYear': '2019',\n",
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       "     'relationType': 'IsDescribedBy'}],\n",
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       "   'titles': [{'title': 'dejong_model_ridgereg'}]},\n",
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       "   'dependencies': {'python': {'scikit-learn': '0.20.2'}},\n",
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       "   'files': {'model': 'dejong_model_ridgereg.pkl'},\n",
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       "   'identifier': 'globus:8JaNrYj47NTl',\n",
       "   'name': 'dejong_model_ridgereg',\n",
       "   'owner': 'mschwarting_anl',\n",
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       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': \"Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\\n   normalize=False, random_state=None, solver='auto', tol=0.001)\",\n",
       "   'model_type': 'Ridge',\n",
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       "     'holder_mean::4 atomic_mass',\n",
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       "     'std_dev atomic_mass',\n",
       "     'holder_mean::-4 atomic_radius',\n",
       "     'holder_mean::-3 atomic_radius',\n",
       "     'holder_mean::-2 atomic_radius',\n",
       "     'holder_mean::-1 atomic_radius',\n",
       "     'holder_mean::0 atomic_radius',\n",
       "     'holder_mean::1 atomic_radius',\n",
       "     'holder_mean::2 atomic_radius',\n",
       "     'holder_mean::3 atomic_radius',\n",
       "     'holder_mean::4 atomic_radius',\n",
       "     'geom_std_dev atomic_radius',\n",
       "     'std_dev atomic_radius',\n",
       "     'holder_mean::-4 row',\n",
       "     'holder_mean::-3 row',\n",
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       "     'holder_mean::1 row',\n",
       "     'holder_mean::2 row',\n",
       "     'holder_mean::3 row',\n",
       "     'holder_mean::4 row',\n",
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       "     'std_dev row',\n",
       "     'holder_mean::-4 boiling_point',\n",
       "     'holder_mean::-3 boiling_point',\n",
       "     'holder_mean::-2 boiling_point',\n",
       "     'holder_mean::-1 boiling_point',\n",
       "     'holder_mean::0 boiling_point',\n",
       "     'holder_mean::1 boiling_point',\n",
       "     'holder_mean::2 boiling_point',\n",
       "     'holder_mean::3 boiling_point',\n",
       "     'holder_mean::4 boiling_point',\n",
       "     'geom_std_dev boiling_point',\n",
       "     'std_dev boiling_point',\n",
       "     'holder_mean::-4 melting_point',\n",
       "     'holder_mean::-3 melting_point',\n",
       "     'holder_mean::-2 melting_point',\n",
       "     'holder_mean::-1 melting_point',\n",
       "     'holder_mean::0 melting_point',\n",
       "     'holder_mean::1 melting_point',\n",
       "     'holder_mean::2 melting_point',\n",
       "     'holder_mean::3 melting_point',\n",
       "     'holder_mean::4 melting_point',\n",
       "     'geom_std_dev melting_point',\n",
       "     'std_dev melting_point',\n",
       "     'holder_mean::-4 X',\n",
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       "     'holder_mean::-2 X',\n",
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       "     'std_dev X',\n",
       "     'holder_mean::-4 Z',\n",
       "     'holder_mean::-3 Z',\n",
       "     'holder_mean::-2 Z',\n",
       "     'holder_mean::-1 Z',\n",
       "     'holder_mean::0 Z',\n",
       "     'holder_mean::1 Z',\n",
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       "     'holder_mean::3 Z',\n",
       "     'holder_mean::4 Z',\n",
       "     'geom_std_dev Z',\n",
       "     'std_dev Z',\n",
       "     'cohesive energy',\n",
       "     'formation_energy_per_atom',\n",
       "     'e_above_hull',\n",
       "     'band_gap',\n",
       "     'density',\n",
       "     'log volume per atom',\n",
       "     'holder_mean::0 local difference in Number',\n",
       "     'holder_mean::1 local difference in Number',\n",
       "     'holder_mean::2 local difference in Number',\n",
       "     'holder_mean::3 local difference in Number',\n",
       "     'holder_mean::4 local difference in Number',\n",
       "     'std_dev local difference in Number',\n",
       "     'holder_mean::0 local difference in AtomicWeight',\n",
       "     'holder_mean::1 local difference in AtomicWeight',\n",
       "     'holder_mean::2 local difference in AtomicWeight',\n",
       "     'holder_mean::3 local difference in AtomicWeight',\n",
       "     'holder_mean::4 local difference in AtomicWeight',\n",
       "     'std_dev local difference in AtomicWeight',\n",
       "     'holder_mean::0 local difference in Column',\n",
       "     'holder_mean::1 local difference in Column',\n",
       "     'holder_mean::2 local difference in Column',\n",
       "     'holder_mean::3 local difference in Column',\n",
       "     'holder_mean::4 local difference in Column',\n",
       "     'std_dev local difference in Column',\n",
       "     'holder_mean::0 local difference in Row',\n",
       "     'holder_mean::1 local difference in Row',\n",
       "     'holder_mean::2 local difference in Row',\n",
       "     'holder_mean::3 local difference in Row',\n",
       "     'holder_mean::4 local difference in Row',\n",
       "     'std_dev local difference in Row',\n",
       "     'holder_mean::0 local difference in CovalentRadius',\n",
       "     'holder_mean::1 local difference in CovalentRadius',\n",
       "     'holder_mean::2 local difference in CovalentRadius',\n",
       "     'holder_mean::3 local difference in CovalentRadius',\n",
       "     'holder_mean::4 local difference in CovalentRadius',\n",
       "     'std_dev local difference in CovalentRadius',\n",
       "     'holder_mean::0 local difference in Electronegativity',\n",
       "     'holder_mean::1 local difference in Electronegativity',\n",
       "     'holder_mean::2 local difference in Electronegativity',\n",
       "     'holder_mean::3 local difference in Electronegativity',\n",
       "     'holder_mean::4 local difference in Electronegativity',\n",
       "     'std_dev local difference in Electronegativity',\n",
       "     'holder_mean::1 local signed difference in Number',\n",
       "     'holder_mean::2 local signed difference in Number',\n",
       "     'holder_mean::4 local signed difference in Number',\n",
       "     'std_dev local signed difference in Number',\n",
       "     'holder_mean::1 local signed difference in AtomicWeight',\n",
       "     'holder_mean::2 local signed difference in AtomicWeight',\n",
       "     'holder_mean::4 local signed difference in AtomicWeight',\n",
       "     'std_dev local signed difference in AtomicWeight',\n",
       "     'holder_mean::1 local signed difference in Column',\n",
       "     'holder_mean::2 local signed difference in Column',\n",
       "     'holder_mean::4 local signed difference in Column',\n",
       "     'std_dev local signed difference in Column',\n",
       "     'holder_mean::1 local signed difference in Row',\n",
       "     'holder_mean::2 local signed difference in Row',\n",
       "     'holder_mean::4 local signed difference in Row',\n",
       "     'std_dev local signed difference in Row',\n",
       "     'holder_mean::1 local signed difference in CovalentRadius',\n",
       "     'holder_mean::2 local signed difference in CovalentRadius',\n",
       "     'holder_mean::4 local signed difference in CovalentRadius',\n",
       "     'std_dev local signed difference in CovalentRadius',\n",
       "     'holder_mean::1 local signed difference in Electronegativity',\n",
       "     'holder_mean::2 local signed difference in Electronegativity',\n",
       "     'holder_mean::4 local signed difference in Electronegativity',\n",
       "     'std_dev local signed difference in Electronegativity',\n",
       "     'holder_mean::-4 Average bond length',\n",
       "     'holder_mean::-3 Average bond length',\n",
       "     'holder_mean::-2 Average bond length',\n",
       "     'holder_mean::-1 Average bond length',\n",
       "     'holder_mean::0 Average bond length',\n",
       "     'holder_mean::1 Average bond length',\n",
       "     'holder_mean::2 Average bond length',\n",
       "     'holder_mean::3 Average bond length',\n",
       "     'holder_mean::4 Average bond length',\n",
       "     'std_dev Average bond length',\n",
       "     'geom_std_dev Average bond length',\n",
       "     'holder_mean::-4 Average bond angle',\n",
       "     'holder_mean::-3 Average bond angle',\n",
       "     'holder_mean::-2 Average bond angle',\n",
       "     'holder_mean::-1 Average bond angle',\n",
       "     'holder_mean::0 Average bond angle',\n",
       "     'holder_mean::1 Average bond angle',\n",
       "     'holder_mean::2 Average bond angle',\n",
       "     'holder_mean::3 Average bond angle',\n",
       "     'holder_mean::4 Average bond angle',\n",
       "     'std_dev Average bond angle',\n",
       "     'geom_std_dev Average bond angle',\n",
       "     'holder_mean::-4 CN_VoronoiNN',\n",
       "     'holder_mean::-3 CN_VoronoiNN',\n",
       "     'holder_mean::-2 CN_VoronoiNN',\n",
       "     'holder_mean::-1 CN_VoronoiNN',\n",
       "     'holder_mean::0 CN_VoronoiNN',\n",
       "     'holder_mean::1 CN_VoronoiNN',\n",
       "     'holder_mean::2 CN_VoronoiNN',\n",
       "     'holder_mean::3 CN_VoronoiNN',\n",
       "     'holder_mean::4 CN_VoronoiNN',\n",
       "     'std_dev CN_VoronoiNN',\n",
       "     'geom_std_dev CN_VoronoiNN'],\n",
       "    'is_classifier': False,\n",
       "    'serialization_method': 'pickle'},\n",
       "   'shim': 'sklearn.ScikitLearnServable',\n",
       "   'type': 'Scikit-learn estimator'}},\n",
       " {'datacite': {'descriptions': [{'description': 'GBM-Locfit Supervised Learning Framework, Random Forest Regression Model',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
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       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'dejong_model_rforestreg'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/3bfcecc1-b882-46a0-a754-4c1be222be00-1566331848',\n",
       "   'dependencies': {'python': {'scikit-learn': '0.20.2'}},\n",
       "   'domains': ['materials'],\n",
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       "   'files': {'model': 'dejong_model_rforestreg.pkl'},\n",
       "   'funcx_id': '06cc1707-fa12-4d9b-81c7-37e9202f8400',\n",
       "   'id': '3bfcecc1-b882-46a0-a754-4c1be222be00',\n",
       "   'identifier': 'globus:1GcLaO0rXw63d',\n",
       "   'name': 'dejong_model_rforestreg',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1566331798200',\n",
       "   'shorthand_name': 'mschwarting_anl/dejong_model_rforestreg',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'List of records to evaluate with model. Each record is a list of 187 variables.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '187'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'method_name': 'predict'},\n",
       "     'output': {'description': 'Predictions of the machine learning model.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n           max_features='auto', max_leaf_nodes=None,\\n           min_impurity_decrease=0.0, min_impurity_split=None,\\n           min_samples_leaf=1, min_samples_split=2,\\n           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\\n           oob_score=False, random_state=None, verbose=0, warm_start=False)\",\n",
       "   'model_type': 'RandomForestRegressor',\n",
       "   'options': {'classes': ['holder_mean::-4 group',\n",
       "     'holder_mean::-3 group',\n",
       "     'holder_mean::-2 group',\n",
       "     'holder_mean::-1 group',\n",
       "     'holder_mean::0 group',\n",
       "     'holder_mean::1 group',\n",
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       "     'holder_mean::3 group',\n",
       "     'holder_mean::4 group',\n",
       "     'geom_std_dev group',\n",
       "     'std_dev group',\n",
       "     'holder_mean::-4 atomic_mass',\n",
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       "     'holder_mean::-2 atomic_mass',\n",
       "     'holder_mean::-1 atomic_mass',\n",
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       "     'holder_mean::3 atomic_mass',\n",
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       "     'geom_std_dev atomic_mass',\n",
       "     'std_dev atomic_mass',\n",
       "     'holder_mean::-4 atomic_radius',\n",
       "     'holder_mean::-3 atomic_radius',\n",
       "     'holder_mean::-2 atomic_radius',\n",
       "     'holder_mean::-1 atomic_radius',\n",
       "     'holder_mean::0 atomic_radius',\n",
       "     'holder_mean::1 atomic_radius',\n",
       "     'holder_mean::2 atomic_radius',\n",
       "     'holder_mean::3 atomic_radius',\n",
       "     'holder_mean::4 atomic_radius',\n",
       "     'geom_std_dev atomic_radius',\n",
       "     'std_dev atomic_radius',\n",
       "     'holder_mean::-4 row',\n",
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       "     'holder_mean::1 row',\n",
       "     'holder_mean::2 row',\n",
       "     'holder_mean::3 row',\n",
       "     'holder_mean::4 row',\n",
       "     'geom_std_dev row',\n",
       "     'std_dev row',\n",
       "     'holder_mean::-4 boiling_point',\n",
       "     'holder_mean::-3 boiling_point',\n",
       "     'holder_mean::-2 boiling_point',\n",
       "     'holder_mean::-1 boiling_point',\n",
       "     'holder_mean::0 boiling_point',\n",
       "     'holder_mean::1 boiling_point',\n",
       "     'holder_mean::2 boiling_point',\n",
       "     'holder_mean::3 boiling_point',\n",
       "     'holder_mean::4 boiling_point',\n",
       "     'geom_std_dev boiling_point',\n",
       "     'std_dev boiling_point',\n",
       "     'holder_mean::-4 melting_point',\n",
       "     'holder_mean::-3 melting_point',\n",
       "     'holder_mean::-2 melting_point',\n",
       "     'holder_mean::-1 melting_point',\n",
       "     'holder_mean::0 melting_point',\n",
       "     'holder_mean::1 melting_point',\n",
       "     'holder_mean::2 melting_point',\n",
       "     'holder_mean::3 melting_point',\n",
       "     'holder_mean::4 melting_point',\n",
       "     'geom_std_dev melting_point',\n",
       "     'std_dev melting_point',\n",
       "     'holder_mean::-4 X',\n",
       "     'holder_mean::-3 X',\n",
       "     'holder_mean::-2 X',\n",
       "     'holder_mean::-1 X',\n",
       "     'holder_mean::0 X',\n",
       "     'holder_mean::1 X',\n",
       "     'holder_mean::2 X',\n",
       "     'holder_mean::3 X',\n",
       "     'holder_mean::4 X',\n",
       "     'geom_std_dev X',\n",
       "     'std_dev X',\n",
       "     'holder_mean::-4 Z',\n",
       "     'holder_mean::-3 Z',\n",
       "     'holder_mean::-2 Z',\n",
       "     'holder_mean::-1 Z',\n",
       "     'holder_mean::0 Z',\n",
       "     'holder_mean::1 Z',\n",
       "     'holder_mean::2 Z',\n",
       "     'holder_mean::3 Z',\n",
       "     'holder_mean::4 Z',\n",
       "     'geom_std_dev Z',\n",
       "     'std_dev Z',\n",
       "     'cohesive energy',\n",
       "     'formation_energy_per_atom',\n",
       "     'e_above_hull',\n",
       "     'band_gap',\n",
       "     'density',\n",
       "     'log volume per atom',\n",
       "     'holder_mean::0 local difference in Number',\n",
       "     'holder_mean::1 local difference in Number',\n",
       "     'holder_mean::2 local difference in Number',\n",
       "     'holder_mean::3 local difference in Number',\n",
       "     'holder_mean::4 local difference in Number',\n",
       "     'std_dev local difference in Number',\n",
       "     'holder_mean::0 local difference in AtomicWeight',\n",
       "     'holder_mean::1 local difference in AtomicWeight',\n",
       "     'holder_mean::2 local difference in AtomicWeight',\n",
       "     'holder_mean::3 local difference in AtomicWeight',\n",
       "     'holder_mean::4 local difference in AtomicWeight',\n",
       "     'std_dev local difference in AtomicWeight',\n",
       "     'holder_mean::0 local difference in Column',\n",
       "     'holder_mean::1 local difference in Column',\n",
       "     'holder_mean::2 local difference in Column',\n",
       "     'holder_mean::3 local difference in Column',\n",
       "     'holder_mean::4 local difference in Column',\n",
       "     'std_dev local difference in Column',\n",
       "     'holder_mean::0 local difference in Row',\n",
       "     'holder_mean::1 local difference in Row',\n",
       "     'holder_mean::2 local difference in Row',\n",
       "     'holder_mean::3 local difference in Row',\n",
       "     'holder_mean::4 local difference in Row',\n",
       "     'std_dev local difference in Row',\n",
       "     'holder_mean::0 local difference in CovalentRadius',\n",
       "     'holder_mean::1 local difference in CovalentRadius',\n",
       "     'holder_mean::2 local difference in CovalentRadius',\n",
       "     'holder_mean::3 local difference in CovalentRadius',\n",
       "     'holder_mean::4 local difference in CovalentRadius',\n",
       "     'std_dev local difference in CovalentRadius',\n",
       "     'holder_mean::0 local difference in Electronegativity',\n",
       "     'holder_mean::1 local difference in Electronegativity',\n",
       "     'holder_mean::2 local difference in Electronegativity',\n",
       "     'holder_mean::3 local difference in Electronegativity',\n",
       "     'holder_mean::4 local difference in Electronegativity',\n",
       "     'std_dev local difference in Electronegativity',\n",
       "     'holder_mean::1 local signed difference in Number',\n",
       "     'holder_mean::2 local signed difference in Number',\n",
       "     'holder_mean::4 local signed difference in Number',\n",
       "     'std_dev local signed difference in Number',\n",
       "     'holder_mean::1 local signed difference in AtomicWeight',\n",
       "     'holder_mean::2 local signed difference in AtomicWeight',\n",
       "     'holder_mean::4 local signed difference in AtomicWeight',\n",
       "     'std_dev local signed difference in AtomicWeight',\n",
       "     'holder_mean::1 local signed difference in Column',\n",
       "     'holder_mean::2 local signed difference in Column',\n",
       "     'holder_mean::4 local signed difference in Column',\n",
       "     'std_dev local signed difference in Column',\n",
       "     'holder_mean::1 local signed difference in Row',\n",
       "     'holder_mean::2 local signed difference in Row',\n",
       "     'holder_mean::4 local signed difference in Row',\n",
       "     'std_dev local signed difference in Row',\n",
       "     'holder_mean::1 local signed difference in CovalentRadius',\n",
       "     'holder_mean::2 local signed difference in CovalentRadius',\n",
       "     'holder_mean::4 local signed difference in CovalentRadius',\n",
       "     'std_dev local signed difference in CovalentRadius',\n",
       "     'holder_mean::1 local signed difference in Electronegativity',\n",
       "     'holder_mean::2 local signed difference in Electronegativity',\n",
       "     'holder_mean::4 local signed difference in Electronegativity',\n",
       "     'std_dev local signed difference in Electronegativity',\n",
       "     'holder_mean::-4 Average bond length',\n",
       "     'holder_mean::-3 Average bond length',\n",
       "     'holder_mean::-2 Average bond length',\n",
       "     'holder_mean::-1 Average bond length',\n",
       "     'holder_mean::0 Average bond length',\n",
       "     'holder_mean::1 Average bond length',\n",
       "     'holder_mean::2 Average bond length',\n",
       "     'holder_mean::3 Average bond length',\n",
       "     'holder_mean::4 Average bond length',\n",
       "     'std_dev Average bond length',\n",
       "     'geom_std_dev Average bond length',\n",
       "     'holder_mean::-4 Average bond angle',\n",
       "     'holder_mean::-3 Average bond angle',\n",
       "     'holder_mean::-2 Average bond angle',\n",
       "     'holder_mean::-1 Average bond angle',\n",
       "     'holder_mean::0 Average bond angle',\n",
       "     'holder_mean::1 Average bond angle',\n",
       "     'holder_mean::2 Average bond angle',\n",
       "     'holder_mean::3 Average bond angle',\n",
       "     'holder_mean::4 Average bond angle',\n",
       "     'std_dev Average bond angle',\n",
       "     'geom_std_dev Average bond angle',\n",
       "     'holder_mean::-4 CN_VoronoiNN',\n",
       "     'holder_mean::-3 CN_VoronoiNN',\n",
       "     'holder_mean::-2 CN_VoronoiNN',\n",
       "     'holder_mean::-1 CN_VoronoiNN',\n",
       "     'holder_mean::0 CN_VoronoiNN',\n",
       "     'holder_mean::1 CN_VoronoiNN',\n",
       "     'holder_mean::2 CN_VoronoiNN',\n",
       "     'holder_mean::3 CN_VoronoiNN',\n",
       "     'holder_mean::4 CN_VoronoiNN',\n",
       "     'std_dev CN_VoronoiNN',\n",
       "     'geom_std_dev CN_VoronoiNN'],\n",
       "    'is_classifier': False,\n",
       "    'serialization_method': 'pickle'},\n",
       "   'shim': 'sklearn.ScikitLearnServable',\n",
       "   'type': 'Scikit-learn estimator'}},\n",
       " {'datacite': {'descriptions': [{'description': 'GBM-Locfit Supervised Learning Framework, Linear Regression Model',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:Q3YzF3k9f81d',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'dejong_model_linreg'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/8ad2a167-4454-4f6a-bb3f-143fde2b8594-1566331789',\n",
       "   'dependencies': {'python': {'scikit-learn': '0.20.2'}},\n",
       "   'domains': ['materials'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/8ad2a167-4454-4f6a-bb3f-143fde2b8594',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/8ad2a167-4454-4f6a-bb3f-143fde2b8594',\n",
       "   'files': {'model': 'dejong_model_linreg.pkl'},\n",
       "   'funcx_id': '1ddba022-8ed4-4810-9f1e-f3e970d6e3d4',\n",
       "   'id': '8ad2a167-4454-4f6a-bb3f-143fde2b8594',\n",
       "   'identifier': 'globus:Q3YzF3k9f81d',\n",
       "   'name': 'dejong_model_linreg',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1566331789483',\n",
       "   'shorthand_name': 'mschwarting_anl/dejong_model_linreg',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'List of records to evaluate with model. Each record is a list of 187 variables.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '187'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'method_name': 'predict'},\n",
       "     'output': {'description': 'Predictions of the machine learning model.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': 'LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\\n         normalize=False)',\n",
       "   'model_type': 'LinearRegression',\n",
       "   'options': {'classes': ['holder_mean::-4 group',\n",
       "     'holder_mean::-3 group',\n",
       "     'holder_mean::-2 group',\n",
       "     'holder_mean::-1 group',\n",
       "     'holder_mean::0 group',\n",
       "     'holder_mean::1 group',\n",
       "     'holder_mean::2 group',\n",
       "     'holder_mean::3 group',\n",
       "     'holder_mean::4 group',\n",
       "     'geom_std_dev group',\n",
       "     'std_dev group',\n",
       "     'holder_mean::-4 atomic_mass',\n",
       "     'holder_mean::-3 atomic_mass',\n",
       "     'holder_mean::-2 atomic_mass',\n",
       "     'holder_mean::-1 atomic_mass',\n",
       "     'holder_mean::0 atomic_mass',\n",
       "     'holder_mean::1 atomic_mass',\n",
       "     'holder_mean::2 atomic_mass',\n",
       "     'holder_mean::3 atomic_mass',\n",
       "     'holder_mean::4 atomic_mass',\n",
       "     'geom_std_dev atomic_mass',\n",
       "     'std_dev atomic_mass',\n",
       "     'holder_mean::-4 atomic_radius',\n",
       "     'holder_mean::-3 atomic_radius',\n",
       "     'holder_mean::-2 atomic_radius',\n",
       "     'holder_mean::-1 atomic_radius',\n",
       "     'holder_mean::0 atomic_radius',\n",
       "     'holder_mean::1 atomic_radius',\n",
       "     'holder_mean::2 atomic_radius',\n",
       "     'holder_mean::3 atomic_radius',\n",
       "     'holder_mean::4 atomic_radius',\n",
       "     'geom_std_dev atomic_radius',\n",
       "     'std_dev atomic_radius',\n",
       "     'holder_mean::-4 row',\n",
       "     'holder_mean::-3 row',\n",
       "     'holder_mean::-2 row',\n",
       "     'holder_mean::-1 row',\n",
       "     'holder_mean::0 row',\n",
       "     'holder_mean::1 row',\n",
       "     'holder_mean::2 row',\n",
       "     'holder_mean::3 row',\n",
       "     'holder_mean::4 row',\n",
       "     'geom_std_dev row',\n",
       "     'std_dev row',\n",
       "     'holder_mean::-4 boiling_point',\n",
       "     'holder_mean::-3 boiling_point',\n",
       "     'holder_mean::-2 boiling_point',\n",
       "     'holder_mean::-1 boiling_point',\n",
       "     'holder_mean::0 boiling_point',\n",
       "     'holder_mean::1 boiling_point',\n",
       "     'holder_mean::2 boiling_point',\n",
       "     'holder_mean::3 boiling_point',\n",
       "     'holder_mean::4 boiling_point',\n",
       "     'geom_std_dev boiling_point',\n",
       "     'std_dev boiling_point',\n",
       "     'holder_mean::-4 melting_point',\n",
       "     'holder_mean::-3 melting_point',\n",
       "     'holder_mean::-2 melting_point',\n",
       "     'holder_mean::-1 melting_point',\n",
       "     'holder_mean::0 melting_point',\n",
       "     'holder_mean::1 melting_point',\n",
       "     'holder_mean::2 melting_point',\n",
       "     'holder_mean::3 melting_point',\n",
       "     'holder_mean::4 melting_point',\n",
       "     'geom_std_dev melting_point',\n",
       "     'std_dev melting_point',\n",
       "     'holder_mean::-4 X',\n",
       "     'holder_mean::-3 X',\n",
       "     'holder_mean::-2 X',\n",
       "     'holder_mean::-1 X',\n",
       "     'holder_mean::0 X',\n",
       "     'holder_mean::1 X',\n",
       "     'holder_mean::2 X',\n",
       "     'holder_mean::3 X',\n",
       "     'holder_mean::4 X',\n",
       "     'geom_std_dev X',\n",
       "     'std_dev X',\n",
       "     'holder_mean::-4 Z',\n",
       "     'holder_mean::-3 Z',\n",
       "     'holder_mean::-2 Z',\n",
       "     'holder_mean::-1 Z',\n",
       "     'holder_mean::0 Z',\n",
       "     'holder_mean::1 Z',\n",
       "     'holder_mean::2 Z',\n",
       "     'holder_mean::3 Z',\n",
       "     'holder_mean::4 Z',\n",
       "     'geom_std_dev Z',\n",
       "     'std_dev Z',\n",
       "     'cohesive energy',\n",
       "     'formation_energy_per_atom',\n",
       "     'e_above_hull',\n",
       "     'band_gap',\n",
       "     'density',\n",
       "     'log volume per atom',\n",
       "     'holder_mean::0 local difference in Number',\n",
       "     'holder_mean::1 local difference in Number',\n",
       "     'holder_mean::2 local difference in Number',\n",
       "     'holder_mean::3 local difference in Number',\n",
       "     'holder_mean::4 local difference in Number',\n",
       "     'std_dev local difference in Number',\n",
       "     'holder_mean::0 local difference in AtomicWeight',\n",
       "     'holder_mean::1 local difference in AtomicWeight',\n",
       "     'holder_mean::2 local difference in AtomicWeight',\n",
       "     'holder_mean::3 local difference in AtomicWeight',\n",
       "     'holder_mean::4 local difference in AtomicWeight',\n",
       "     'std_dev local difference in AtomicWeight',\n",
       "     'holder_mean::0 local difference in Column',\n",
       "     'holder_mean::1 local difference in Column',\n",
       "     'holder_mean::2 local difference in Column',\n",
       "     'holder_mean::3 local difference in Column',\n",
       "     'holder_mean::4 local difference in Column',\n",
       "     'std_dev local difference in Column',\n",
       "     'holder_mean::0 local difference in Row',\n",
       "     'holder_mean::1 local difference in Row',\n",
       "     'holder_mean::2 local difference in Row',\n",
       "     'holder_mean::3 local difference in Row',\n",
       "     'holder_mean::4 local difference in Row',\n",
       "     'std_dev local difference in Row',\n",
       "     'holder_mean::0 local difference in CovalentRadius',\n",
       "     'holder_mean::1 local difference in CovalentRadius',\n",
       "     'holder_mean::2 local difference in CovalentRadius',\n",
       "     'holder_mean::3 local difference in CovalentRadius',\n",
       "     'holder_mean::4 local difference in CovalentRadius',\n",
       "     'std_dev local difference in CovalentRadius',\n",
       "     'holder_mean::0 local difference in Electronegativity',\n",
       "     'holder_mean::1 local difference in Electronegativity',\n",
       "     'holder_mean::2 local difference in Electronegativity',\n",
       "     'holder_mean::3 local difference in Electronegativity',\n",
       "     'holder_mean::4 local difference in Electronegativity',\n",
       "     'std_dev local difference in Electronegativity',\n",
       "     'holder_mean::1 local signed difference in Number',\n",
       "     'holder_mean::2 local signed difference in Number',\n",
       "     'holder_mean::4 local signed difference in Number',\n",
       "     'std_dev local signed difference in Number',\n",
       "     'holder_mean::1 local signed difference in AtomicWeight',\n",
       "     'holder_mean::2 local signed difference in AtomicWeight',\n",
       "     'holder_mean::4 local signed difference in AtomicWeight',\n",
       "     'std_dev local signed difference in AtomicWeight',\n",
       "     'holder_mean::1 local signed difference in Column',\n",
       "     'holder_mean::2 local signed difference in Column',\n",
       "     'holder_mean::4 local signed difference in Column',\n",
       "     'std_dev local signed difference in Column',\n",
       "     'holder_mean::1 local signed difference in Row',\n",
       "     'holder_mean::2 local signed difference in Row',\n",
       "     'holder_mean::4 local signed difference in Row',\n",
       "     'std_dev local signed difference in Row',\n",
       "     'holder_mean::1 local signed difference in CovalentRadius',\n",
       "     'holder_mean::2 local signed difference in CovalentRadius',\n",
       "     'holder_mean::4 local signed difference in CovalentRadius',\n",
       "     'std_dev local signed difference in CovalentRadius',\n",
       "     'holder_mean::1 local signed difference in Electronegativity',\n",
       "     'holder_mean::2 local signed difference in Electronegativity',\n",
       "     'holder_mean::4 local signed difference in Electronegativity',\n",
       "     'std_dev local signed difference in Electronegativity',\n",
       "     'holder_mean::-4 Average bond length',\n",
       "     'holder_mean::-3 Average bond length',\n",
       "     'holder_mean::-2 Average bond length',\n",
       "     'holder_mean::-1 Average bond length',\n",
       "     'holder_mean::0 Average bond length',\n",
       "     'holder_mean::1 Average bond length',\n",
       "     'holder_mean::2 Average bond length',\n",
       "     'holder_mean::3 Average bond length',\n",
       "     'holder_mean::4 Average bond length',\n",
       "     'std_dev Average bond length',\n",
       "     'geom_std_dev Average bond length',\n",
       "     'holder_mean::-4 Average bond angle',\n",
       "     'holder_mean::-3 Average bond angle',\n",
       "     'holder_mean::-2 Average bond angle',\n",
       "     'holder_mean::-1 Average bond angle',\n",
       "     'holder_mean::0 Average bond angle',\n",
       "     'holder_mean::1 Average bond angle',\n",
       "     'holder_mean::2 Average bond angle',\n",
       "     'holder_mean::3 Average bond angle',\n",
       "     'holder_mean::4 Average bond angle',\n",
       "     'std_dev Average bond angle',\n",
       "     'geom_std_dev Average bond angle',\n",
       "     'holder_mean::-4 CN_VoronoiNN',\n",
       "     'holder_mean::-3 CN_VoronoiNN',\n",
       "     'holder_mean::-2 CN_VoronoiNN',\n",
       "     'holder_mean::-1 CN_VoronoiNN',\n",
       "     'holder_mean::0 CN_VoronoiNN',\n",
       "     'holder_mean::1 CN_VoronoiNN',\n",
       "     'holder_mean::2 CN_VoronoiNN',\n",
       "     'holder_mean::3 CN_VoronoiNN',\n",
       "     'holder_mean::4 CN_VoronoiNN',\n",
       "     'std_dev CN_VoronoiNN',\n",
       "     'geom_std_dev CN_VoronoiNN'],\n",
       "    'is_classifier': False,\n",
       "    'serialization_method': 'pickle'},\n",
       "   'shim': 'sklearn.ScikitLearnServable',\n",
       "   'type': 'Scikit-learn estimator'}},\n",
       " {'datacite': {'descriptions': [{'description': 'GBM-Locfit Supervised Learning Framework, Gradient Boosting Model',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:17ddn9S633cTy',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'dejong_model_gboost'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/1a1730a2-07ec-4dc5-843a-f0353afe4663-1566331958',\n",
       "   'dependencies': {'python': {'scikit-learn': '0.20.2'}},\n",
       "   'domains': ['materials'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/1a1730a2-07ec-4dc5-843a-f0353afe4663',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/1a1730a2-07ec-4dc5-843a-f0353afe4663',\n",
       "   'files': {'model': 'dejong_model_gboost.pkl'},\n",
       "   'funcx_id': '1247699b-5da9-432e-b693-eccb2caf3686',\n",
       "   'id': '1a1730a2-07ec-4dc5-843a-f0353afe4663',\n",
       "   'identifier': 'globus:17ddn9S633cTy',\n",
       "   'name': 'dejong_model_gboost',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1566331807391',\n",
       "   'shorthand_name': 'mschwarting_anl/dejong_model_gboost',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'List of records to evaluate with model. Each record is a list of 187 variables.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '187'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'method_name': 'predict'},\n",
       "     'output': {'description': 'Predictions of the machine learning model.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': \"GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\\n             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\\n             max_leaf_nodes=None, min_impurity_decrease=0.0,\\n             min_impurity_split=None, min_samples_leaf=1,\\n             min_samples_split=2, min_weight_fraction_leaf=0.0,\\n             n_estimators=100, n_iter_no_change=None, presort='auto',\\n             random_state=None, subsample=1.0, tol=0.0001,\\n             validation_fraction=0.1, verbose=0, warm_start=False)\",\n",
       "   'model_type': 'GradientBoostingRegressor',\n",
       "   'options': {'classes': ['holder_mean::-4 group',\n",
       "     'holder_mean::-3 group',\n",
       "     'holder_mean::-2 group',\n",
       "     'holder_mean::-1 group',\n",
       "     'holder_mean::0 group',\n",
       "     'holder_mean::1 group',\n",
       "     'holder_mean::2 group',\n",
       "     'holder_mean::3 group',\n",
       "     'holder_mean::4 group',\n",
       "     'geom_std_dev group',\n",
       "     'std_dev group',\n",
       "     'holder_mean::-4 atomic_mass',\n",
       "     'holder_mean::-3 atomic_mass',\n",
       "     'holder_mean::-2 atomic_mass',\n",
       "     'holder_mean::-1 atomic_mass',\n",
       "     'holder_mean::0 atomic_mass',\n",
       "     'holder_mean::1 atomic_mass',\n",
       "     'holder_mean::2 atomic_mass',\n",
       "     'holder_mean::3 atomic_mass',\n",
       "     'holder_mean::4 atomic_mass',\n",
       "     'geom_std_dev atomic_mass',\n",
       "     'std_dev atomic_mass',\n",
       "     'holder_mean::-4 atomic_radius',\n",
       "     'holder_mean::-3 atomic_radius',\n",
       "     'holder_mean::-2 atomic_radius',\n",
       "     'holder_mean::-1 atomic_radius',\n",
       "     'holder_mean::0 atomic_radius',\n",
       "     'holder_mean::1 atomic_radius',\n",
       "     'holder_mean::2 atomic_radius',\n",
       "     'holder_mean::3 atomic_radius',\n",
       "     'holder_mean::4 atomic_radius',\n",
       "     'geom_std_dev atomic_radius',\n",
       "     'std_dev atomic_radius',\n",
       "     'holder_mean::-4 row',\n",
       "     'holder_mean::-3 row',\n",
       "     'holder_mean::-2 row',\n",
       "     'holder_mean::-1 row',\n",
       "     'holder_mean::0 row',\n",
       "     'holder_mean::1 row',\n",
       "     'holder_mean::2 row',\n",
       "     'holder_mean::3 row',\n",
       "     'holder_mean::4 row',\n",
       "     'geom_std_dev row',\n",
       "     'std_dev row',\n",
       "     'holder_mean::-4 boiling_point',\n",
       "     'holder_mean::-3 boiling_point',\n",
       "     'holder_mean::-2 boiling_point',\n",
       "     'holder_mean::-1 boiling_point',\n",
       "     'holder_mean::0 boiling_point',\n",
       "     'holder_mean::1 boiling_point',\n",
       "     'holder_mean::2 boiling_point',\n",
       "     'holder_mean::3 boiling_point',\n",
       "     'holder_mean::4 boiling_point',\n",
       "     'geom_std_dev boiling_point',\n",
       "     'std_dev boiling_point',\n",
       "     'holder_mean::-4 melting_point',\n",
       "     'holder_mean::-3 melting_point',\n",
       "     'holder_mean::-2 melting_point',\n",
       "     'holder_mean::-1 melting_point',\n",
       "     'holder_mean::0 melting_point',\n",
       "     'holder_mean::1 melting_point',\n",
       "     'holder_mean::2 melting_point',\n",
       "     'holder_mean::3 melting_point',\n",
       "     'holder_mean::4 melting_point',\n",
       "     'geom_std_dev melting_point',\n",
       "     'std_dev melting_point',\n",
       "     'holder_mean::-4 X',\n",
       "     'holder_mean::-3 X',\n",
       "     'holder_mean::-2 X',\n",
       "     'holder_mean::-1 X',\n",
       "     'holder_mean::0 X',\n",
       "     'holder_mean::1 X',\n",
       "     'holder_mean::2 X',\n",
       "     'holder_mean::3 X',\n",
       "     'holder_mean::4 X',\n",
       "     'geom_std_dev X',\n",
       "     'std_dev X',\n",
       "     'holder_mean::-4 Z',\n",
       "     'holder_mean::-3 Z',\n",
       "     'holder_mean::-2 Z',\n",
       "     'holder_mean::-1 Z',\n",
       "     'holder_mean::0 Z',\n",
       "     'holder_mean::1 Z',\n",
       "     'holder_mean::2 Z',\n",
       "     'holder_mean::3 Z',\n",
       "     'holder_mean::4 Z',\n",
       "     'geom_std_dev Z',\n",
       "     'std_dev Z',\n",
       "     'cohesive energy',\n",
       "     'formation_energy_per_atom',\n",
       "     'e_above_hull',\n",
       "     'band_gap',\n",
       "     'density',\n",
       "     'log volume per atom',\n",
       "     'holder_mean::0 local difference in Number',\n",
       "     'holder_mean::1 local difference in Number',\n",
       "     'holder_mean::2 local difference in Number',\n",
       "     'holder_mean::3 local difference in Number',\n",
       "     'holder_mean::4 local difference in Number',\n",
       "     'std_dev local difference in Number',\n",
       "     'holder_mean::0 local difference in AtomicWeight',\n",
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       "     'std_dev local difference in CovalentRadius',\n",
       "     'holder_mean::0 local difference in Electronegativity',\n",
       "     'holder_mean::1 local difference in Electronegativity',\n",
       "     'holder_mean::2 local difference in Electronegativity',\n",
       "     'holder_mean::3 local difference in Electronegativity',\n",
       "     'holder_mean::4 local difference in Electronegativity',\n",
       "     'std_dev local difference in Electronegativity',\n",
       "     'holder_mean::1 local signed difference in Number',\n",
       "     'holder_mean::2 local signed difference in Number',\n",
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       "     'std_dev local signed difference in Number',\n",
       "     'holder_mean::1 local signed difference in AtomicWeight',\n",
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       "     'holder_mean::4 local signed difference in AtomicWeight',\n",
       "     'std_dev local signed difference in AtomicWeight',\n",
       "     'holder_mean::1 local signed difference in Column',\n",
       "     'holder_mean::2 local signed difference in Column',\n",
       "     'holder_mean::4 local signed difference in Column',\n",
       "     'std_dev local signed difference in Column',\n",
       "     'holder_mean::1 local signed difference in Row',\n",
       "     'holder_mean::2 local signed difference in Row',\n",
       "     'holder_mean::4 local signed difference in Row',\n",
       "     'std_dev local signed difference in Row',\n",
       "     'holder_mean::1 local signed difference in CovalentRadius',\n",
       "     'holder_mean::2 local signed difference in CovalentRadius',\n",
       "     'holder_mean::4 local signed difference in CovalentRadius',\n",
       "     'std_dev local signed difference in CovalentRadius',\n",
       "     'holder_mean::1 local signed difference in Electronegativity',\n",
       "     'holder_mean::2 local signed difference in Electronegativity',\n",
       "     'holder_mean::4 local signed difference in Electronegativity',\n",
       "     'std_dev local signed difference in Electronegativity',\n",
       "     'holder_mean::-4 Average bond length',\n",
       "     'holder_mean::-3 Average bond length',\n",
       "     'holder_mean::-2 Average bond length',\n",
       "     'holder_mean::-1 Average bond length',\n",
       "     'holder_mean::0 Average bond length',\n",
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       "     'holder_mean::3 Average bond length',\n",
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       "     'std_dev Average bond length',\n",
       "     'geom_std_dev Average bond length',\n",
       "     'holder_mean::-4 Average bond angle',\n",
       "     'holder_mean::-3 Average bond angle',\n",
       "     'holder_mean::-2 Average bond angle',\n",
       "     'holder_mean::-1 Average bond angle',\n",
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       "     'holder_mean::3 Average bond angle',\n",
       "     'holder_mean::4 Average bond angle',\n",
       "     'std_dev Average bond angle',\n",
       "     'geom_std_dev Average bond angle',\n",
       "     'holder_mean::-4 CN_VoronoiNN',\n",
       "     'holder_mean::-3 CN_VoronoiNN',\n",
       "     'holder_mean::-2 CN_VoronoiNN',\n",
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       "     'holder_mean::3 CN_VoronoiNN',\n",
       "     'holder_mean::4 CN_VoronoiNN',\n",
       "     'std_dev CN_VoronoiNN',\n",
       "     'geom_std_dev CN_VoronoiNN'],\n",
       "    'is_classifier': False,\n",
       "    'serialization_method': 'pickle'},\n",
       "   'shim': 'sklearn.ScikitLearnServable',\n",
       "   'type': 'Scikit-learn estimator'}},\n",
       " {'datacite': {'descriptions': [{'description': 'GBM-Locfit Supervised Learning Framework, Extra Tree Regression Model',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
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       "     'relatedIdentifierType': 'Globus',\n",
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       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/b24d7eba-1ae8-462f-8910-003dd0a79cb8-1566331928',\n",
       "   'dependencies': {'python': {'scikit-learn': '0.20.2'}},\n",
       "   'domains': ['materials'],\n",
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       "   'files': {'model': 'dejong_model_etreereg.pkl'},\n",
       "   'funcx_id': '5bbf8f17-9b57-4bf9-9302-95a39df59b41',\n",
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       "   'name': 'dejong_model_etreereg',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1566331803533',\n",
       "   'shorthand_name': 'mschwarting_anl/dejong_model_etreereg',\n",
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       "      'type': 'ndarray'},\n",
       "     'method_details': {'method_name': 'predict'},\n",
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       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': \"ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\\n          max_features='auto', max_leaf_nodes=None,\\n          min_impurity_decrease=0.0, min_impurity_split=None,\\n          min_samples_leaf=1, min_samples_split=2,\\n          min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\\n          oob_score=False, random_state=None, verbose=0, warm_start=False)\",\n",
       "   'model_type': 'ExtraTreesRegressor',\n",
       "   'options': {'classes': ['holder_mean::-4 group',\n",
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       "     'cohesive energy',\n",
       "     'formation_energy_per_atom',\n",
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       "     'holder_mean::0 local difference in Number',\n",
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       "     'holder_mean::2 local difference in Number',\n",
       "     'holder_mean::3 local difference in Number',\n",
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       "     'holder_mean::4 local difference in Electronegativity',\n",
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       "     'holder_mean::1 local signed difference in Number',\n",
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       "     'holder_mean::1 local signed difference in Row',\n",
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       "     'std_dev local signed difference in Row',\n",
       "     'holder_mean::1 local signed difference in CovalentRadius',\n",
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       "     'holder_mean::4 local signed difference in CovalentRadius',\n",
       "     'std_dev local signed difference in CovalentRadius',\n",
       "     'holder_mean::1 local signed difference in Electronegativity',\n",
       "     'holder_mean::2 local signed difference in Electronegativity',\n",
       "     'holder_mean::4 local signed difference in Electronegativity',\n",
       "     'std_dev local signed difference in Electronegativity',\n",
       "     'holder_mean::-4 Average bond length',\n",
       "     'holder_mean::-3 Average bond length',\n",
       "     'holder_mean::-2 Average bond length',\n",
       "     'holder_mean::-1 Average bond length',\n",
       "     'holder_mean::0 Average bond length',\n",
       "     'holder_mean::1 Average bond length',\n",
       "     'holder_mean::2 Average bond length',\n",
       "     'holder_mean::3 Average bond length',\n",
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       "     'std_dev Average bond length',\n",
       "     'geom_std_dev Average bond length',\n",
       "     'holder_mean::-4 Average bond angle',\n",
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       "     'holder_mean::-2 Average bond angle',\n",
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       "     'std_dev Average bond angle',\n",
       "     'geom_std_dev Average bond angle',\n",
       "     'holder_mean::-4 CN_VoronoiNN',\n",
       "     'holder_mean::-3 CN_VoronoiNN',\n",
       "     'holder_mean::-2 CN_VoronoiNN',\n",
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       "     'holder_mean::3 CN_VoronoiNN',\n",
       "     'holder_mean::4 CN_VoronoiNN',\n",
       "     'std_dev CN_VoronoiNN',\n",
       "     'geom_std_dev CN_VoronoiNN'],\n",
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       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/geifmany/cifar-vgg/blob/master/cifar10vgg.py',\n",
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       "     'givenName': 'Yonatan'}],\n",
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       "   'publicationYear': '2019',\n",
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       "   'files': {'pickle': 'img_normalizer.pkl'},\n",
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       "   'id': '53b4abdb-55ba-49e8-b879-b30cf2177281',\n",
       "   'name': 'cifar10_standardizer',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1564080454918',\n",
       "   'shorthand_name': 'mschwarting_anl/cifar10_standardizer',\n",
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       "      'method_name': 'standardize'},\n",
       "     'output': {'description': 'List of images. Standardized from the training set',\n",
       "      'item_type': {'description': 'Image',\n",
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       "   'funcx_id': '51294b08-78c0-40c5-81e8-b1f0576657cd',\n",
       "   'id': 'e8afa0b3-c867-414a-aad3-ef7a9f07a9b9',\n",
       "   'identifier': 'globus:8lvSwa2V8mZB',\n",
       "   'name': 'cifar10_model',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1566330980626',\n",
       "   'shorthand_name': 'mschwarting_anl/cifar10_model',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'List of images. Each image must be standardized by the mean and standard deviation of the training set',\n",
       "      'item_type': {'description': 'Image',\n",
       "       'shape': ['32', '32', '3'],\n",
       "       'type': 'ndarray'},\n",
       "      'type': 'list'},\n",
       "     'method_details': {'classes': ['airplane',\n",
       "       'automobile',\n",
       "       'bird',\n",
       "       'cat',\n",
       "       'deer',\n",
       "       'dog',\n",
       "       'frog',\n",
       "       'horse',\n",
       "       'ship',\n",
       "       'truck'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Probabilities of being in each of the cifar classes',\n",
       "      'shape': ['None', '10'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\nconv2d_1 (Conv2D)            (None, 32, 32, 64)        1792      \\n_________________________________________________________________\\nactivation_1 (Activation)    (None, 32, 32, 64)        0         \\n_________________________________________________________________\\nbatch_normalization_1 (Batch (None, 32, 32, 64)        256       \\n_________________________________________________________________\\ndropout_1 (Dropout)          (None, 32, 32, 64)        0         \\n_________________________________________________________________\\nconv2d_2 (Conv2D)            (None, 32, 32, 64)        36928     \\n_________________________________________________________________\\nactivation_2 (Activation)    (None, 32, 32, 64)        0         \\n_________________________________________________________________\\nbatch_normalization_2 (Batch (None, 32, 32, 64)        256       \\n_________________________________________________________________\\nmax_pooling2d_1 (MaxPooling2 (None, 16, 16, 64)        0         \\n_________________________________________________________________\\nconv2d_3 (Conv2D)            (None, 16, 16, 128)       73856     \\n_________________________________________________________________\\nactivation_3 (Activation)    (None, 16, 16, 128)       0         \\n_________________________________________________________________\\nbatch_normalization_3 (Batch (None, 16, 16, 128)       512       \\n_________________________________________________________________\\ndropout_2 (Dropout)          (None, 16, 16, 128)       0         \\n_________________________________________________________________\\nconv2d_4 (Conv2D)            (None, 16, 16, 128)       147584    \\n_________________________________________________________________\\nactivation_4 (Activation)    (None, 16, 16, 128)       0         \\n_________________________________________________________________\\nbatch_normalization_4 (Batch (None, 16, 16, 128)       512       \\n_________________________________________________________________\\nmax_pooling2d_2 (MaxPooling2 (None, 8, 8, 128)         0         \\n_________________________________________________________________\\nconv2d_5 (Conv2D)            (None, 8, 8, 256)         295168    \\n_________________________________________________________________\\nactivation_5 (Activation)    (None, 8, 8, 256)         0         \\n_________________________________________________________________\\nbatch_normalization_5 (Batch (None, 8, 8, 256)         1024      \\n_________________________________________________________________\\ndropout_3 (Dropout)          (None, 8, 8, 256)         0         \\n_________________________________________________________________\\nconv2d_6 (Conv2D)            (None, 8, 8, 256)         590080    \\n_________________________________________________________________\\nactivation_6 (Activation)    (None, 8, 8, 256)         0         \\n_________________________________________________________________\\nbatch_normalization_6 (Batch (None, 8, 8, 256)         1024      \\n_________________________________________________________________\\ndropout_4 (Dropout)          (None, 8, 8, 256)         0         \\n_________________________________________________________________\\nconv2d_7 (Conv2D)            (None, 8, 8, 256)         590080    \\n_________________________________________________________________\\nactivation_7 (Activation)    (None, 8, 8, 256)         0         \\n_________________________________________________________________\\nbatch_normalization_7 (Batch (None, 8, 8, 256)         1024      \\n_________________________________________________________________\\nmax_pooling2d_3 (MaxPooling2 (None, 4, 4, 256)         0         \\n_________________________________________________________________\\nconv2d_8 (Conv2D)            (None, 4, 4, 512)         1180160   \\n_________________________________________________________________\\nactivation_8 (Activation)    (None, 4, 4, 512)         0         \\n_________________________________________________________________\\nbatch_normalization_8 (Batch (None, 4, 4, 512)         2048      \\n_________________________________________________________________\\ndropout_5 (Dropout)          (None, 4, 4, 512)         0         \\n_________________________________________________________________\\nconv2d_9 (Conv2D)            (None, 4, 4, 512)         2359808   \\n_________________________________________________________________\\nactivation_9 (Activation)    (None, 4, 4, 512)         0         \\n_________________________________________________________________\\nbatch_normalization_9 (Batch (None, 4, 4, 512)         2048      \\n_________________________________________________________________\\ndropout_6 (Dropout)          (None, 4, 4, 512)         0         \\n_________________________________________________________________\\nconv2d_10 (Conv2D)           (None, 4, 4, 512)         2359808   \\n_________________________________________________________________\\nactivation_10 (Activation)   (None, 4, 4, 512)         0         \\n_________________________________________________________________\\nbatch_normalization_10 (Batc (None, 4, 4, 512)         2048      \\n_________________________________________________________________\\nmax_pooling2d_4 (MaxPooling2 (None, 2, 2, 512)         0         \\n_________________________________________________________________\\nconv2d_11 (Conv2D)           (None, 2, 2, 512)         2359808   \\n_________________________________________________________________\\nactivation_11 (Activation)   (None, 2, 2, 512)         0         \\n_________________________________________________________________\\nbatch_normalization_11 (Batc (None, 2, 2, 512)         2048      \\n_________________________________________________________________\\ndropout_7 (Dropout)          (None, 2, 2, 512)         0         \\n_________________________________________________________________\\nconv2d_12 (Conv2D)           (None, 2, 2, 512)         2359808   \\n_________________________________________________________________\\nactivation_12 (Activation)   (None, 2, 2, 512)         0         \\n_________________________________________________________________\\nbatch_normalization_12 (Batc (None, 2, 2, 512)         2048      \\n_________________________________________________________________\\ndropout_8 (Dropout)          (None, 2, 2, 512)         0         \\n_________________________________________________________________\\nconv2d_13 (Conv2D)           (None, 2, 2, 512)         2359808   \\n_________________________________________________________________\\nactivation_13 (Activation)   (None, 2, 2, 512)         0         \\n_________________________________________________________________\\nbatch_normalization_13 (Batc (None, 2, 2, 512)         2048      \\n_________________________________________________________________\\nmax_pooling2d_5 (MaxPooling2 (None, 1, 1, 512)         0         \\n_________________________________________________________________\\ndropout_9 (Dropout)          (None, 1, 1, 512)         0         \\n_________________________________________________________________\\nflatten_1 (Flatten)          (None, 512)               0         \\n_________________________________________________________________\\ndense_1 (Dense)              (None, 512)               262656    \\n_________________________________________________________________\\nactivation_14 (Activation)   (None, 512)               0         \\n_________________________________________________________________\\nbatch_normalization_14 (Batc (None, 512)               2048      \\n_________________________________________________________________\\ndropout_10 (Dropout)         (None, 512)               0         \\n_________________________________________________________________\\ndense_2 (Dense)              (None, 10)                5130      \\n_________________________________________________________________\\nactivation_15 (Activation)   (None, 10)                0         \\n=================================================================\\nTotal params: 15,001,418\\nTrainable params: 14,991,946\\nNon-trainable params: 9,472\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/mcherukara/CDI_NN',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'creators': [{'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Cherukara',\n",
       "     'givenName': 'Mathew J'},\n",
       "    {'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Nashed',\n",
       "     'givenName': 'Youssef SG'},\n",
       "    {'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Harder',\n",
       "     'givenName': 'Ross J'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.1038/s41598-018-34525-1',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'IsDescribedBy'},\n",
       "    {'relatedIdentifier': 'globus:iDnjbEKilPqn',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Coherent Diffraction Structure Prediction'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/208a0f88-b197-4782-9cc4-a62319da973f-1566334417',\n",
       "   'dependencies': {'python': {'h5py': '2.9.0',\n",
       "     'keras': '2.2.4',\n",
       "     'tensorflow': '1.14.0'}},\n",
       "   'domains': ['materials science'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/208a0f88-b197-4782-9cc4-a62319da973f',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/208a0f88-b197-4782-9cc4-a62319da973f',\n",
       "   'files': {'model': 'structure_weights.hdf5'},\n",
       "   'funcx_id': '8d4fdfc7-53c1-49d4-adf0-65e13c6b6815',\n",
       "   'id': '208a0f88-b197-4782-9cc4-a62319da973f',\n",
       "   'identifier': 'globus:iDnjbEKilPqn',\n",
       "   'name': 'cherukara_structure',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1566334345073',\n",
       "   'shorthand_name': 'mschwarting_anl/cherukara_structure',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Fourier transformed diffraction intensity',\n",
       "      'shape': ['None', '32', '32', '1'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': ['output structure image'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Output structure image',\n",
       "      'shape': ['None', '32', '32', '1'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\ninput_1 (InputLayer)         (None, 32, 32, 1)         0         \\n_________________________________________________________________\\nconv2d_1 (Conv2D)            (None, 32, 32, 32)        320       \\n_________________________________________________________________\\nconv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \\n_________________________________________________________________\\nmax_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \\n_________________________________________________________________\\nconv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \\n_________________________________________________________________\\nconv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \\n_________________________________________________________________\\nmax_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \\n_________________________________________________________________\\nconv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \\n_________________________________________________________________\\nconv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \\n_________________________________________________________________\\nmax_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \\n_________________________________________________________________\\nconv2d_7 (Conv2D)            (None, 4, 4, 128)         147584    \\n_________________________________________________________________\\nconv2d_8 (Conv2D)            (None, 4, 4, 128)         147584    \\n_________________________________________________________________\\nup_sampling2d_1 (UpSampling2 (None, 8, 8, 128)         0         \\n_________________________________________________________________\\nconv2d_9 (Conv2D)            (None, 8, 8, 64)          73792     \\n_________________________________________________________________\\nconv2d_10 (Conv2D)           (None, 8, 8, 64)          36928     \\n_________________________________________________________________\\nup_sampling2d_2 (UpSampling2 (None, 16, 16, 64)        0         \\n_________________________________________________________________\\nconv2d_11 (Conv2D)           (None, 16, 16, 32)        18464     \\n_________________________________________________________________\\nconv2d_12 (Conv2D)           (None, 16, 16, 32)        9248      \\n_________________________________________________________________\\nup_sampling2d_3 (UpSampling2 (None, 32, 32, 32)        0         \\n_________________________________________________________________\\nconv2d_13 (Conv2D)           (None, 32, 32, 1)         289       \\n=================================================================\\nTotal params: 720,321\\nTrainable params: 720,321\\nNon-trainable params: 0\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/mcherukara/CDI_NN',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'creators': [{'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Cherukara',\n",
       "     'givenName': 'Mathew J'},\n",
       "    {'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Nashed',\n",
       "     'givenName': 'Youssef SG'},\n",
       "    {'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Harder',\n",
       "     'givenName': 'Ross J'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.1038/s41598-018-34525-1',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'IsDescribedBy'},\n",
       "    {'relatedIdentifier': 'globus:1243GCbt14yGv',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Coherent Diffraction Phase Prediction'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/85a55fe7-ad66-4dd0-9d67-2ac21bd6b6df-1566334343',\n",
       "   'dependencies': {'python': {'h5py': '2.9.0',\n",
       "     'keras': '2.2.4',\n",
       "     'tensorflow': '1.14.0'}},\n",
       "   'domains': ['materials science'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/85a55fe7-ad66-4dd0-9d67-2ac21bd6b6df',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/85a55fe7-ad66-4dd0-9d67-2ac21bd6b6df',\n",
       "   'files': {'model': 'phase_weights.hdf5'},\n",
       "   'funcx_id': 'b405463f-47f8-411f-99d6-52bef3268de8',\n",
       "   'id': '85a55fe7-ad66-4dd0-9d67-2ac21bd6b6df',\n",
       "   'identifier': 'globus:1243GCbt14yGv',\n",
       "   'name': 'cherukara_phase',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1566334343058',\n",
       "   'shorthand_name': 'mschwarting_anl/cherukara_phase',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Fourier transformed diffraction intensity',\n",
       "      'shape': ['None', '32', '32', '1'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': ['output phase image'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Output phase image',\n",
       "      'shape': ['None', '32', '32', '1'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\ninput_1 (InputLayer)         (None, 32, 32, 1)         0         \\n_________________________________________________________________\\nconv2d_1 (Conv2D)            (None, 32, 32, 32)        320       \\n_________________________________________________________________\\nconv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \\n_________________________________________________________________\\nmax_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \\n_________________________________________________________________\\nconv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \\n_________________________________________________________________\\nconv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \\n_________________________________________________________________\\nmax_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \\n_________________________________________________________________\\nconv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \\n_________________________________________________________________\\nconv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \\n_________________________________________________________________\\nmax_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \\n_________________________________________________________________\\nconv2d_7 (Conv2D)            (None, 4, 4, 128)         147584    \\n_________________________________________________________________\\nconv2d_8 (Conv2D)            (None, 4, 4, 128)         147584    \\n_________________________________________________________________\\nup_sampling2d_1 (UpSampling2 (None, 8, 8, 128)         0         \\n_________________________________________________________________\\nconv2d_9 (Conv2D)            (None, 8, 8, 64)          73792     \\n_________________________________________________________________\\nconv2d_10 (Conv2D)           (None, 8, 8, 64)          36928     \\n_________________________________________________________________\\nup_sampling2d_2 (UpSampling2 (None, 16, 16, 64)        0         \\n_________________________________________________________________\\nconv2d_11 (Conv2D)           (None, 16, 16, 32)        18464     \\n_________________________________________________________________\\nconv2d_12 (Conv2D)           (None, 16, 16, 32)        9248      \\n_________________________________________________________________\\nup_sampling2d_3 (UpSampling2 (None, 32, 32, 32)        0         \\n_________________________________________________________________\\nconv2d_13 (Conv2D)           (None, 32, 32, 1)         289       \\n=================================================================\\nTotal params: 720,321\\nTrainable params: 720,321\\nNon-trainable params: 0\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'creators': [{'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Schwarting',\n",
       "     'givenName': 'Marcus'}],\n",
       "   'descriptions': [{'description': 'A function to determine blurry or clear SEM images.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:1AZZVf9LBOLwY',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Brain Image Blurriness Function'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/9c1b435e-3e75-4376-ad5b-88317705afea-1564177444',\n",
       "   'domains': ['general', 'test'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/9c1b435e-3e75-4376-ad5b-88317705afea',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/9c1b435e-3e75-4376-ad5b-88317705afea',\n",
       "   'files': {'other': ['EM_Focus_Canny.py', 'Blurry_SVClassifier.pkl']},\n",
       "   'funcx_id': '4b89ed76-0fdc-40ae-8765-1a3af15bba11',\n",
       "   'id': '9c1b435e-3e75-4376-ad5b-88317705afea',\n",
       "   'identifier': 'globus:1AZZVf9LBOLwY',\n",
       "   'name': 'BlurryBrain',\n",
       "   'owner': 'mschwarting_anl',\n",
       "   'publication_date': '1564177444648',\n",
       "   'shorthand_name': 'mschwarting_anl/BlurryBrain',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '5',\n",
       "   'version': '0.8.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'A list of files',\n",
       "      'type': 'file'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'is_blurry',\n",
       "      'module': 'EM_Focus_Canny'},\n",
       "     'output': {'description': 'Output', 'type': 'float'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'creators': [{'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Ward',\n",
       "     'givenName': 'Logan'}],\n",
       "   'descriptions': [{'description': 'A servable that will only work if repo2docker is properly used',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Test servable for repo2docker'}]},\n",
       "  'dlhub': {'domains': ['testing'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/290a41b8-c3eb-4cb5-bfca-6a60e83777fb',\n",
       "   'files': {'other': ['environment.yml', 'dlhub_app.py']},\n",
       "   'funcx_id': 'b981d41d-4693-4df0-963d-8dc2ba8b2049',\n",
       "   'id': '290a41b8-c3eb-4cb5-bfca-6a60e83777fb',\n",
       "   'name': 'repo2docker_test',\n",
       "   'owner': 'ryan_globusid',\n",
       "   'publication_date': '1556038243428',\n",
       "   'shorthand_name': 'ryan_globusid/repo2docker_test',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '1',\n",
       "   'version': '0.8.0',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Nothing useful',\n",
       "      'type': 'string'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'app',\n",
       "      'module': 'dlhub_app'},\n",
       "     'output': {'description': 'ASE version', 'type': 'string'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'creators': [{'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Chard',\n",
       "     'givenName': 'Ryan'}],\n",
       "   'descriptions': [{'description': 'A function to test accessibility.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Noop Function'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/d49453db-1b6a-446d-a49a-c1017e5f963e-1560806253',\n",
       "   'domains': ['general', 'test'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/d49453db-1b6a-446d-a49a-c1017e5f963e',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/d49453db-1b6a-446d-a49a-c1017e5f963e',\n",
       "   'files': {'other': ['noop.py']},\n",
       "   'funcx_id': '5b23ec26-e994-4bf0-a4ac-4587492026a0',\n",
       "   'id': 'd49453db-1b6a-446d-a49a-c1017e5f963e',\n",
       "   'name': 'Noop',\n",
       "   'owner': 'ryan_globusid',\n",
       "   'publication_date': '1560806252992',\n",
       "   'shorthand_name': 'ryan_globusid/Noop',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '1',\n",
       "   'version': '0.8.0',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'string',\n",
       "      'type': 'string'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'noop_function',\n",
       "      'module': 'noop'},\n",
       "     'output': {'description': 'Output', 'type': 'string'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'creators': [{'affiliations': 'Argonne National Laboratory',\n",
       "     'familyName': 'Chard',\n",
       "     'givenName': 'Ryan'}],\n",
       "   'descriptions': [{'description': 'A function to get the length of a file.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': 'globus:ad9PPC86Olli',\n",
       "     'relatedIdentifierType': 'Globus',\n",
       "     'relationType': 'IsDescribedBy'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'File Length Function'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/478f64e0-8174-431b-8d52-586fa69d35b3-1564169623',\n",
       "   'domains': ['general', 'test'],\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/478f64e0-8174-431b-8d52-586fa69d35b3',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/478f64e0-8174-431b-8d52-586fa69d35b3',\n",
       "   'files': {'other': ['file_length.py']},\n",
       "   'funcx_id': '7a59e0fc-2ce7-4e5b-af29-828ecb48e7ed',\n",
       "   'id': '478f64e0-8174-431b-8d52-586fa69d35b3',\n",
       "   'identifier': 'globus:ad9PPC86Olli',\n",
       "   'name': 'FileLength',\n",
       "   'owner': 'ryan_globusid',\n",
       "   'publication_date': '1564169623679',\n",
       "   'shorthand_name': 'ryan_globusid/FileLength',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '1',\n",
       "   'version': '0.8.0',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'A file',\n",
       "      'type': 'file'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'get_file_length',\n",
       "      'module': 'file_length'},\n",
       "     'output': {'description': 'Output', 'type': 'float'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/ECP-CANDLE/Benchmarks/tree/release_01/Pilot1/P1B1',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'creators': [{'affiliations': 'CANDLE',\n",
       "     'familyName': 'Team',\n",
       "     'givenName': 'CANDLE'}],\n",
       "   'descriptions': [{'description': 'CANDLE pilot 1 benchmark 1 model.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'CANDLE P1B1 Demo2'}]},\n",
       "  'dlhub': {'domains': ['genomics', 'cancer research'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/99876c88-e867-11e8-8d97-001e67d43688',\n",
       "   'files': {'model': 'p1b1.h5'},\n",
       "   'funcx_id': 'ab1005d8-fa04-455d-9386-f5c96c7f2074',\n",
       "   'id': '99876c88-e867-11e8-8d97-001e67d43688',\n",
       "   'name': 'candle_p1b1_demo2',\n",
       "   'owner': 'ryan_globusid',\n",
       "   'publication_date': '1551323499987',\n",
       "   'shorthand_name': 'ryan_globusid/candle_p1b1_demo2',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '1',\n",
       "   'version': '0.6.1',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'h5py': '2.9.0',\n",
       "     'keras': '2.2.4',\n",
       "     'numpy': '1.16.1'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Input',\n",
       "      'shape': ['None', '60483'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': ['0',\n",
       "       '1',\n",
       "       '2',\n",
       "       '3',\n",
       "       '4',\n",
       "       '5',\n",
       "       '6',\n",
       "       '7',\n",
       "       '8',\n",
       "       '9'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Output',\n",
       "      'shape': ['None', '60483'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\ninput_1 (InputLayer)         (None, 60483)             0         \\n_________________________________________________________________\\ndense_1 (Dense)              (None, 2000)              120968000 \\n_________________________________________________________________\\ndense_2 (Dense)              (None, 600)               1200600   \\n_________________________________________________________________\\ndense_3 (Dense)              (None, 2000)              1202000   \\n_________________________________________________________________\\ndense_4 (Dense)              (None, 60483)             121026483 \\n=================================================================\\nTotal params: 244,397,083\\nTrainable params: 244,397,083\\nNon-trainable params: 0\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/ECP-CANDLE/Benchmarks/tree/release_01/Pilot1/P1B1',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'creators': [{'affiliations': 'CANDLE',\n",
       "     'familyName': 'Team',\n",
       "     'givenName': 'CANDLE'}],\n",
       "   'descriptions': [{'description': 'CANDLE pilot 1 benchmark 1 model.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'CANDLE P1B1 Demo1'}]},\n",
       "  'dlhub': {'domains': ['genomics', 'cancer research'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/037c8768-45fd-4466-aa53-95d2044ae14f',\n",
       "   'files': {'model': 'p1b1.h5'},\n",
       "   'funcx_id': '642e2668-ea2c-4fef-b52c-0f47341c74b0',\n",
       "   'id': '037c8768-45fd-4466-aa53-95d2044ae14f',\n",
       "   'name': 'candle_p1b1_demo1',\n",
       "   'owner': 'ryan_globusid',\n",
       "   'publication_date': '1551124925604',\n",
       "   'shorthand_name': 'ryan_globusid/candle_p1b1_demo1',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '1',\n",
       "   'version': '0.6.0',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'h5py': '2.9.0',\n",
       "     'keras': '2.2.4',\n",
       "     'numpy': '1.16.1'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Input',\n",
       "      'shape': ['None', '60483'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': ['0',\n",
       "       '1',\n",
       "       '2',\n",
       "       '3',\n",
       "       '4',\n",
       "       '5',\n",
       "       '6',\n",
       "       '7',\n",
       "       '8',\n",
       "       '9'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Output',\n",
       "      'shape': ['None', '60483'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\ninput_1 (InputLayer)         (None, 60483)             0         \\n_________________________________________________________________\\ndense_1 (Dense)              (None, 2000)              120968000 \\n_________________________________________________________________\\ndense_2 (Dense)              (None, 600)               1200600   \\n_________________________________________________________________\\ndense_3 (Dense)              (None, 2000)              1202000   \\n_________________________________________________________________\\ndense_4 (Dense)              (None, 60483)             121026483 \\n=================================================================\\nTotal params: 244,397,083\\nTrainable params: 244,397,083\\nNon-trainable params: 0\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'alternateIdentifiers': [{'alternateIdentifier': 'https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/Combo',\n",
       "     'alternateIdentifierType': 'URL'},\n",
       "    {'alternateIdentifier': 'https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/Combo',\n",
       "     'alternateIdentifierType': 'URL'}],\n",
       "   'creators': [{'affiliations': 'CANDLE',\n",
       "     'familyName': 'Team',\n",
       "     'givenName': 'CANDLE'},\n",
       "    {'affiliations': 'CANDLE', 'familyName': 'Team', 'givenName': 'CANDLE'}],\n",
       "   'descriptions': [{'description': 'CANDLE pilot 1 combo model.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'CANDLE Pilot1 Combo Demo1'}]},\n",
       "  'dlhub': {'domains': ['genomics', 'cancer research'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/b32bcc2e-8a8a-47e7-837d-483eb2219717',\n",
       "   'files': {'arch': 'saved.model.h5',\n",
       "    'model': 'saved.weights.h5',\n",
       "    'other': ['dropout_layer.py']},\n",
       "   'funcx_id': '968c0aa9-626b-4532-904f-9ceeffee9756',\n",
       "   'id': 'b32bcc2e-8a8a-47e7-837d-483eb2219717',\n",
       "   'name': 'candle_p1_combo_demo1',\n",
       "   'owner': 'ryan_globusid',\n",
       "   'publication_date': '1551128773858',\n",
       "   'shorthand_name': 'ryan_globusid/candle_p1_combo_demo1',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '1',\n",
       "   'version': '0.6.1',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'h5py': '2.9.0',\n",
       "     'keras': '2.2.4',\n",
       "     'numpy': '1.16.1'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Input',\n",
       "      'element_types': [{'description': 'Tensor',\n",
       "        'shape': ['None', '942'],\n",
       "        'type': 'ndarray'},\n",
       "       {'description': 'Tensor', 'shape': ['None', '3820'], 'type': 'ndarray'},\n",
       "       {'description': 'Tensor',\n",
       "        'shape': ['None', '3820'],\n",
       "        'type': 'ndarray'}],\n",
       "      'type': 'tuple'},\n",
       "     'method_details': {'classes': ['0',\n",
       "       '1',\n",
       "       '2',\n",
       "       '3',\n",
       "       '4',\n",
       "       '5',\n",
       "       '6',\n",
       "       '7',\n",
       "       '8',\n",
       "       '9'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Output',\n",
       "      'shape': ['None', '1'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '__________________________________________________________________________________________________\\nLayer (type)                    Output Shape         Param #     Connected to                     \\n==================================================================================================\\ninput.cell.expression (InputLay (None, 942)          0                                            \\n__________________________________________________________________________________________________\\ninput.drug1.descriptors (InputL (None, 3820)         0                                            \\n__________________________________________________________________________________________________\\ninput.drug2.descriptors (InputL (None, 3820)         0                                            \\n__________________________________________________________________________________________________\\ncell.expression (Model)         (None, 1000)         2945000     input.cell.expression[0][0]      \\n__________________________________________________________________________________________________\\ndrug.descriptors (Model)        (None, 1000)         5823000     input.drug1.descriptors[0][0]    \\n                                                                 input.drug2.descriptors[0][0]    \\n__________________________________________________________________________________________________\\nconcatenate_2 (Concatenate)     (None, 3000)         0           cell.expression[1][0]            \\n                                                                 drug.descriptors[1][0]           \\n                                                                 drug.descriptors[2][0]           \\n__________________________________________________________________________________________________\\ndense_17 (Dense)                (None, 1000)         3001000     concatenate_2[0][0]              \\n__________________________________________________________________________________________________\\npermanent_dropout_16 (Permanent (None, 1000)         0           dense_17[0][0]                   \\n__________________________________________________________________________________________________\\ndense_18 (Dense)                (None, 1000)         1001000     permanent_dropout_16[0][0]       \\n__________________________________________________________________________________________________\\npermanent_dropout_17 (Permanent (None, 1000)         0           dense_18[0][0]                   \\n__________________________________________________________________________________________________\\nadd_5 (Add)                     (None, 1000)         0           permanent_dropout_17[0][0]       \\n                                                                 permanent_dropout_16[0][0]       \\n__________________________________________________________________________________________________\\ndense_19 (Dense)                (None, 1000)         1001000     add_5[0][0]                      \\n__________________________________________________________________________________________________\\npermanent_dropout_18 (Permanent (None, 1000)         0           dense_19[0][0]                   \\n__________________________________________________________________________________________________\\nadd_6 (Add)                     (None, 1000)         0           permanent_dropout_18[0][0]       \\n                                                                 add_5[0][0]                      \\n__________________________________________________________________________________________________\\ndense_20 (Dense)                (None, 1)            1001        add_6[0][0]                      \\n==================================================================================================\\nTotal params: 13,772,001\\nTrainable params: 13,772,001\\nNon-trainable params: 0\\n__________________________________________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'options': {'custom_objects': {'PermanentDropout': 'dropout_layer.PermanentDropout'}},\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'identifier': {'identifier': '10.YET/UNASSIGNED',\n",
       "    'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'MNIST Digit Classifier'}]},\n",
       "  'dlhub': {'domains': ['general', 'digit recognition'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/c38a49a5-27f4-496c-9144-f5b7ce1151dd',\n",
       "   'files': {'model': 'model.hd5'},\n",
       "   'funcx_id': '7438865a-783c-4437-8945-fdcb3a33b187',\n",
       "   'id': 'c38a49a5-27f4-496c-9144-f5b7ce1151dd',\n",
       "   'name': 'mnist_tiny_example',\n",
       "   'owner': 'zhuozhao_uchicago',\n",
       "   'publication_date': '1555538491944',\n",
       "   'shorthand_name': 'zhuozhao_uchicago/mnist_tiny_example',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '10',\n",
       "   'version': '0.7.4',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'h5py': '2.9.0', 'keras': '2.2.4'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Image of a digit',\n",
       "      'shape': ['None', '28', '28', '1'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'classes': ['0',\n",
       "       '1',\n",
       "       '2',\n",
       "       '3',\n",
       "       '4',\n",
       "       '5',\n",
       "       '6',\n",
       "       '7',\n",
       "       '8',\n",
       "       '9'],\n",
       "      'method_name': 'predict'},\n",
       "     'output': {'description': 'Probabilities of being 0-9',\n",
       "      'shape': ['None', '10'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': '_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\nconv2d_5 (Conv2D)            (None, 26, 26, 32)        320       \\n_________________________________________________________________\\nconv2d_6 (Conv2D)            (None, 24, 24, 64)        18496     \\n_________________________________________________________________\\nmax_pooling2d_3 (MaxPooling2 (None, 12, 12, 64)        0         \\n_________________________________________________________________\\ndropout_5 (Dropout)          (None, 12, 12, 64)        0         \\n_________________________________________________________________\\nflatten_3 (Flatten)          (None, 9216)              0         \\n_________________________________________________________________\\ndense_5 (Dense)              (None, 128)               1179776   \\n_________________________________________________________________\\ndropout_6 (Dropout)          (None, 128)               0         \\n_________________________________________________________________\\ndense_6 (Dense)              (None, 10)                1290      \\n=================================================================\\nTotal params: 1,199,882\\nTrainable params: 1,199,882\\nNon-trainable params: 0\\n_________________________________________________________________\\n',\n",
       "   'model_type': 'Deep NN',\n",
       "   'shim': 'keras.KerasServable',\n",
       "   'type': 'Keras Model'}},\n",
       " {'datacite': {'identifier': {'identifier': '10.YET/UNASSIGNED',\n",
       "    'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'Example Scikit-Learn Model'}]},\n",
       "  'dlhub': {'domains': ['biology'],\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/e0d67403-f7c2-422d-87a5-5e8998b737b3',\n",
       "   'files': {'model': 'model.pkl'},\n",
       "   'funcx_id': '231f19e2-c5ec-46e0-99ee-e14b4e7754c5',\n",
       "   'id': 'e0d67403-f7c2-422d-87a5-5e8998b737b3',\n",
       "   'name': 'iris_svm',\n",
       "   'owner': 'zhuozhao_uchicago',\n",
       "   'publication_date': '1553210574532',\n",
       "   'shorthand_name': 'zhuozhao_uchicago/iris_svm',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '10',\n",
       "   'version': '0.7.2',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'dependencies': {'python': {'scikit-learn': '0.20.3'}},\n",
       "   'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'List of records to evaluate with model. Each record is a list of 4 variables.',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '4'],\n",
       "      'type': 'ndarray'},\n",
       "     'method_details': {'method_name': '_predict_proba'},\n",
       "     'output': {'description': 'Probabilities for membership in each of 3 classes',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'shape': ['None', '3'],\n",
       "      'type': 'ndarray'}}},\n",
       "   'model_summary': \"SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\\n  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\\n  kernel='linear', max_iter=-1, probability=True, random_state=None,\\n  shrinking=True, tol=0.001, verbose=False)\",\n",
       "   'model_type': 'SVC',\n",
       "   'options': {'classes': ['setosa', 'versicolor', 'virginica'],\n",
       "    'is_classifier': True,\n",
       "    'serialization_method': 'pickle'},\n",
       "   'shim': 'sklearn.ScikitLearnServable',\n",
       "   'type': 'Scikit-learn estimator'}}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl.get_servables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'datacite': {'creators': [{'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Ward',\n",
       "     'givenName': 'Logan'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Blaiszik',\n",
       "     'givenName': 'Ben'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Foster',\n",
       "     'givenName': 'Ian'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Assary',\n",
       "     'givenName': 'Rajeev'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Naranayan',\n",
       "     'givenName': 'Badri'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Curtis',\n",
       "     'givenName': 'Larry'}],\n",
       "   'descriptions': [{'description': 'This model predicts the G4MP2 atomization energy of a molecule given the relaxed atomic coordinates and total energy from a B3LYP calculation. It was trained on a set of 134k organic molecules smaller than 9 heavy atoms (i.e., the GDB9 dataset) plus 66 molecules larger than 9 heavy atoms.',\n",
       "     'descriptionType': 'Abstract'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.1021/acs.jctc.8b00908',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'Requires'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'SchNet Model to Predict G4MP2 Activation Energy from B3LYP Energy and Coordinates'}]},\n",
       "  'dlhub': {'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/d637593b-e085-4bfe-a054-447af32137a5',\n",
       "   'files': {'other': ['model/architecture.pth',\n",
       "     'model/best_model',\n",
       "     'jcesr_ml/atom_environment.py',\n",
       "     'jcesr_ml/benchmark.py',\n",
       "     'jcesr_ml/coordinates.py',\n",
       "     'jcesr_ml/qml.py',\n",
       "     'jcesr_ml/schnetpack.py',\n",
       "     'jcesr_ml/style.py',\n",
       "     'jcesr_ml/utils.py',\n",
       "     'jcesr_ml/__init__.py',\n",
       "     'dlhub_app.py',\n",
       "     'train_dataset.pkl',\n",
       "     'requirements.txt']},\n",
       "   'funcx_id': '86b425d4-c489-496c-8979-f8f9f6eafabd',\n",
       "   'id': 'd637593b-e085-4bfe-a054-447af32137a5',\n",
       "   'name': 'g4mp2_delta_schnet',\n",
       "   'owner': 'loganw_globusid',\n",
       "   'publication_date': '1554227017046',\n",
       "   'shorthand_name': 'loganw_globusid/g4mp2_delta_schnet',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '3',\n",
       "   'version': '0.7.4',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'language': 'python',\n",
       "   'methods': {'run': {'input': {'description': 'Structures and B3LYP energies of moelcule sto be evaluated',\n",
       "      'element_types': [{'description': 'Structures of molecules in XYZ format',\n",
       "        'item_type': {'type': 'string'},\n",
       "        'type': 'list'},\n",
       "       {'description': 'B3LYP total energies of Energies of molecules in Ha',\n",
       "        'item_type': {'type': 'float'},\n",
       "        'type': 'list'}],\n",
       "      'type': 'tuple'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'evaluate_molecules',\n",
       "      'module': 'dlhub_app',\n",
       "      'unpack': True},\n",
       "     'output': {'description': 'Estimate of G4MP2 atomization energy',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'type': 'list'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}},\n",
       " {'datacite': {'creators': [{'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Ward',\n",
       "     'givenName': 'Logan'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Blaiszik',\n",
       "     'givenName': 'Ben'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Foster',\n",
       "     'givenName': 'Ian'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Assary',\n",
       "     'givenName': 'Rajeev'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Narayanan',\n",
       "     'givenName': 'Badri'},\n",
       "    {'affiliations': ['Argonne National Laboratory'],\n",
       "     'familyName': 'Curtis',\n",
       "     'givenName': 'Larry'}],\n",
       "   'identifier': {'identifier': '10.YET/UNASSIGNED', 'identifierType': 'DOI'},\n",
       "   'publicationYear': '2019',\n",
       "   'publisher': 'DLHub',\n",
       "   'relatedIdentifiers': [{'relatedIdentifier': '10.5281/zenodo.817332',\n",
       "     'relatedIdentifierType': 'DOI',\n",
       "     'relationType': 'Requires'}],\n",
       "   'resourceType': {'resourceTypeGeneral': 'InteractiveResource'},\n",
       "   'titles': [{'title': 'FCHL Model to Predict G4MP2 Activation Energy from B3LYP Energy and Coordinates'}]},\n",
       "  'dlhub': {'build_location': '/mnt/dlhub_ingest/cc5ca5a8-5a8b-4248-bf16-992ce43861a5-1562702975',\n",
       "   'ecr_arn': 'arn:aws:ecr:us-east-1:039706667969:repository/cc5ca5a8-5a8b-4248-bf16-992ce43861a5',\n",
       "   'ecr_uri': '039706667969.dkr.ecr.us-east-1.amazonaws.com/cc5ca5a8-5a8b-4248-bf16-992ce43861a5',\n",
       "   'files': {'other': ['dlhub_app.py',\n",
       "     'model.pkl.gz',\n",
       "     'jcesr_ml/atom_environment.py',\n",
       "     'jcesr_ml/benchmark.py',\n",
       "     'jcesr_ml/coordinates.py',\n",
       "     'jcesr_ml/qml.py',\n",
       "     'jcesr_ml/schnetpack.py',\n",
       "     'jcesr_ml/style.py',\n",
       "     'jcesr_ml/utils.py',\n",
       "     'jcesr_ml/__init__.py',\n",
       "     'environment.yml',\n",
       "     'apt.txt']},\n",
       "   'funcx_id': '0a7da9d0-5c20-4d9e-96a6-13bb89559484',\n",
       "   'id': 'cc5ca5a8-5a8b-4248-bf16-992ce43861a5',\n",
       "   'name': 'g4mp2_delta_fchl',\n",
       "   'owner': 'loganw_globusid',\n",
       "   'publication_date': '1562702975664',\n",
       "   'shorthand_name': 'loganw_globusid/g4mp2_delta_fchl',\n",
       "   'transfer_method': {'POST': 'file', 'path': '/mnt/tmp/servable.zip'},\n",
       "   'type': 'servable',\n",
       "   'user_id': '3',\n",
       "   'version': '0.8.0',\n",
       "   'visible_to': ['public']},\n",
       "  'servable': {'methods': {'run': {'input': {'description': 'Structures and B3LYP energies of moelcule sto be evaluated',\n",
       "      'element_types': [{'description': 'Structures of molecules in XYZ format',\n",
       "        'item_type': {'type': 'string'},\n",
       "        'type': 'list'},\n",
       "       {'description': 'B3LYP total energies of Energies of molecules in Ha',\n",
       "        'item_type': {'type': 'float'},\n",
       "        'type': 'list'}],\n",
       "      'type': 'tuple'},\n",
       "     'method_details': {'autobatch': False,\n",
       "      'method_name': 'evaluate_molecules',\n",
       "      'module': 'dlhub_app',\n",
       "      'unpack': True},\n",
       "     'output': {'description': 'Estimate of G4MP2 atomization energy',\n",
       "      'item_type': {'type': 'float'},\n",
       "      'type': 'list'}}},\n",
       "   'shim': 'python.PythonStaticMethodServable',\n",
       "   'type': 'Python static method'}}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl.search_by_authors([\"Blaiszik\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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