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features = torch.eye(node_count)
print(features[2])

tensor([[0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])

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import dgl
import torch
import numpy as np
import pickle

node_count = 34
epochs = 30

# Load edges
with open('edge_list.pickle', 'rb') as f:

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edge_list =  []
for e in g.E().toList():
  edge_list.append((e.inV.id, e.outV.id))
print(edge_list)

[('0', '8'), ('1', '17'), ('24', '31'), ('13', '33'), ('0', '1'), ('2', '8'), ('0', '19'), 
 ('25', '31'), ('14', '33'), ('0', '2'), ('2', '9'), ('1', '19'), ('28', '31'), ('15', '33'), 
 ('1', '2'), ('0', '10'), ('0', '21'), ('2', '32'), ('18', '33'), ('0', '3'), ('4', '10'), 
 ('1', '21'), ('8', '32'), ('19', '33'), ('1', '3'), ('5', '10'), ('23', '25'), ('14', '32'), 
 ('20', '33'), ('2', '3'), ('0', '11'), ('24', '25'), ('15', '32'), ('22', '33'), ('0', '4'), 

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# Print all vertices (don't run this on large database!)
print(g.V().toList())

[v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7], v[8], v[9], v[10], v[11], v[12], v[13], v[14], v[15], v[16], v[17], v[18], v[19], v[20], v[21], v[22], v[23], v[24], v[25], v[26], v[27], v[28], v[29], v[30], v[31], v[32], v[33]]

# Print all edges (don't run this on large database!)
print(g.E().toList())

[e[edge15][8-edge->0], e[edge31][17-edge->1], e[edge47][31-edge->24], e[edge63][33-edge->13], e[edge0][1-edge->0], e[edge16][8-edge->2], e[edge32][19-edge->0], e[edge48][31-edge->25], e[edge64][33-edge->14], e[edge1][2-edge->0], e[edge17][9-edge->2], e[edge33][19-edge->1], e[edge49][31-edge->28], e[edge65][33-edge->15], e[edge2][2-edge->1], e[edge18][10-edge->0], e[edge34][21-edge->0], e[edge50][32-edge->2], e[edge66][33-edge->18], e[edge3][3-edge->0], e[edge19][10-edge->4], e[edge35][21-edge->1], e[edge51][32-edge->8], e[edge67][33-edge->19], e[edge4][3-edge->1], e[edge20][10-edge->5], e[edge36][25-edge->23], e[edge52][32-edge->

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from gremlin_python import statics
from gremlin_python.structure.graph import Graph
from gremlin_python.process.graph_traversal import __
from gremlin_python.process.strategies import *
from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection

graph = Graph()
remoteConn = DriverRemoteConnection('ws://ENDPOINT:PORT/gremlin','g')
g = graph.traversal().withRemote(remoteConn)

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AWSTemplateFormatVersion: 2010-09-09

Parameters:
  ModelName:
    Description: First model name 
    Type: String
  ModelDataUrl:
    Description: Location of first model artefact
    Type: String
  ModelName2:

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import boto3

trainingJobName = "xgboost-2019-05-09-15-20-51-276"

print(trainingJobName)

sm = boto3.client("sagemaker")

job = sm.describe_training_job(TrainingJobName=trainingJobName)
trainingImage = job['AlgorithmSpecification']['TrainingImage']

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AWSTemplateFormatVersion: 2010-09-09

Parameters:
  ModelName:
    Description: Model name
    Type: String
  ModelDataUrl:
    Description: Location of model artefact
    Type: String
  TrainingImage: