tomas-wood

import urllib3

http = urllib3.PoolManager()

with open('token', 'r') as f:
    token = f.read()

# Headers are the way we pass through the token.
headers = {}
headers['Content-Type'] = 'application/json'

tomas-wood

Partitioning Graph for Mini-Batch Approximate Graph Convolutional Networks (GCNs)

So we want to be able to create partitions of the graph which we can perform graph convolutions on without having to load the entire dataset into memory.

Terms: A, the adjacency matrix of the graph (normalized and with identity matrix added to it, but still A). With N nodes in the graph, A is N X N.
X, a dense matrix of features such that each feature is of dimension F, then for all N nodes we get a dense matrix of size N X F

In the GCN paper and code, the entire dataset and the entire adjacency matrix are loaded into memory. The weight updates are done by

tomas-wood

def fetch_data():
    urls = [
            "https://visualgenome.org/static/data/dataset/objects.json.zip",
            "https://visualgenome.org/static/data/dataset/relationships.json.zip",
            "https://visualgenome.org/static/data/dataset/object_alias.txt",
            "https://visualgenome.org/static/data/dataset/relationship_alias.txt"
           ]
    if not os.path.exists('data'):
        os.mkdir('data')
    else:

tomas-wood

GraphML Links!!!

Articles:
https://medium.com/octavian-ai/an-introduction-to-machine-learning-on-graph-databases-24ee502fd12e

Code:
https://github.com/Octavian-ai/article-0

tomas-wood

Wed Sep 19 11:48:26 2018       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 396.37                 Driver Version: 396.37                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  TITAN X (Pascal)    Off  | 00000000:04:00.0 Off |                  N/A |
| 23%   40C    P2    50W / 250W |    893MiB / 12196MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

tomas-wood

curl -H "Content-Type:application/json" -X POST --data '{"command":"graph=EywaGraph.open(\"ges_abfe\");g=graph.traversal();g.V().limit(10)"}' http://49.4.88.7/graph/ges/v1.0/1111/1/graphs/222/execute-gremlin-query
{"errorMessage":"Resource not allowed!","errorCode":"GES.8006"}

tomas-wood

import urllib3
import pickle
import json

http = urllib3.PoolManager()

url = "http://localhost:5000/detectron"

img_url="https://cdn7.dissolve.com/p/D1028_98_301/D1028_98_301_1200.jpg"

tomas-wood

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from collections import defaultdict
import argparse
import cv2  # NOQA (Must import before importing caffe2 due to bug in cv2)
import glob
import logging

tomas-wood

#!/usr/bin/env python2
# -*- coding: utf-8 -*-

################################################################
#
# This script takes an image as input and outputs the
# region proposals 
#
# Author: Aniket Adnaik ([email protected])
################################################################

tomas-wood

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      5760      C   /usr/bin/python2.7                         11739MiB |
|    1      5760      C   /usr/bin/python2.7                         11589MiB |
|    2      5760      C   /usr/bin/python2.7                         11589MiB |
|    3      5760      C   /usr/bin/python2.7                         11589MiB |
|    4      5760      C   /usr/bin/python2.7                         11589MiB |
|    5      5760      C   /usr/bin/python2.7                         11589MiB |