rjurney

# The DGL network has sentence encoded JSON for node features.
In [26]: g
Out[26]:
[Graph(num_nodes=27770, num_edges=352807,
       ndata_schemes={'x': Scheme(shape=(384,), dtype=torch.float64)}
       edata_schemes={})]

# The networkx network parsed the records and has inidividual fields for analysis
In [27]: G
Out[27]: <networkx.classes.digraph.DiGraph at 0x14fe5c4c0>

rjurney

# ChatGPT says:

1) The NVIDIA module is having trouble retrieving NUMA (Non-Uniform Memory Access) node information for a specific device. NUMA is a method for setting up memory in multiprocessor systems to better match memory access times with processor cores. This might be related to the multi-GPU setup you mentioned.
2) The NVIDIA kernel module for your graphics cards is being loaded. The version is 530.30.02, and it was compiled on Wed Feb 22 04:11:39 UTC 2023.
3) This is a warning from the ACPI subsystem in the kernel, related to device configuration and power management. The specific warning pertains to an argument type mismatch for a given method. Such ACPI warnings can result from inconsistencies between the computer's BIOS/UEFI firmware and the OS's expectations.
4) Lastly, the nvidia-drm, nvidia_uvm, and other NVIDIA related entries refer to the NVIDIA graphics driver's components being initialized. The Direct Rendering Manager (drm) is used in graphics pipelines, while nvidia_uvm refers to NVIDIA'

rjurney

import logging
import os

from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory
from langchain.vectorstores import Chroma

rjurney

// Use this to start up a session
conf = new BaseConfiguration()
conf.setProperty("storage.directory", "/Users/rjurney/Software/marketing/titan/data")
conf.setProperty("storage.backend", "berkeleyje")
graph = TitanFactory.open(conf)

// Get a graph traverser
g = graph.traversal()

// Various centralities to use as features - JSONize and save

rjurney

# Start from a Jupyter Docker Stacks version
FROM jupyter/scipy-notebook:python-3.10.11

# Work in the jovyan user's home directory
WORKDIR "/home/${NB_USER}"

# Needed for poetry package management: no venv, latest poetry, GRANT_SUDO don't work :(
ENV POETRY_VIRTUALENVS_CREATE=false \
    POETRY_VERSION=1.4.2 \
    GRANT_SUDO=yes

rjurney

Assortativity

Assortativity in networks refers to a correlation pattern observed in real-world networks where nodes are preferentially connected to other nodes that are like (or unlike) them in some way. This is essentially a bias in connection preference.

--ChatGPT4

A related term is assortative mixing:

In the study of complex networks, assortative mixing, or assortativity, is a bias in favor of connections between network nodes with similar characteristics. In the specific case of social networks, assortative mixing is also known as homophily. The rarer disassortative mixing is a bias in favor of connections between dissimilar nodes.

rjurney

var colorPalette = [“#fdfdfd”, “#1d1d1d”, “#ebce2b”, “#702c8c”, “#db6917”, “#96cde6”, “#ba1c30”, “#c0bd7f”, “#7f7e80”, “#5fa641”, “#d485b2”, “#4277b6”, “#df8461”, “#463397”, “#e1a11a”, “#91218c”, “#e8e948”, “#7e1510”, “#92ae31”, “#6f340d”, “#d32b1e”, “#2b3514”];

rjurney

from sklearn.model_selection import train_test_split

X_train, X_test_dev, y_train, y_test_dev = train_test_split(
    df['_Body'], 
    df['_Index'], 
    test_size=0.3,
    random_state=1337,
)
X_dev, X_test, y_dev, y_test = train_test_split(
    X_test_dev,

rjurney

[virtualenvs]
create = false

rjurney

#!/bin/bash

# Launch the scheduler
nohup dask scheduler --host 127.0.0.1 --port 9000 --protocol tcp --dashboard --no-show 2>&1 >> /tmp/dask.log &

nohup nohup dask worker --nworkers=-1 tcp://127.0.0.1:9000 2>&1 >> /tmp/dask.log &