vejvarm

import json
import os
import re
import sqlite3
import asyncio
import threading
from typing import Tuple, Any, List, Set
from itertools import product
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor

vejvarm

import json
import os
import re
import sqlite3
import asyncio
import multiprocessing
from typing import Tuple, Any, List, Set
from itertools import product, chain
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor  # Use ProcessPoolExecutor instead of ThreadPoolExecutor

vejvarm

# create new local conda env
conda create --prefix ./.conda python=3.11
conda activate ./.conda

# https://azure.github.io/MS-AMP/docs/getting-started/installation/
git clone https://github.com/Azure/MS-AMP.git
cd MS-AMP
git submodule update --init --recursive

cd third_party/msccl

vejvarm

MATCH (T1:ROOT__endowment)
MATCH (T2:ROOT__school)
WHERE (T2_school_name = 'Glenn')
RETURN DISTINCT count(DISTINCT T1.endowment__donator_name) AS aggregation_ T1.endowment__donator_name _106

vejvarm

{
  "sPREFIX": {},
  "RETURN": [
    "T1_school_name"
  ],
  "TRIPLES": {
    "T1": {
      "label": "ROOT__school",
      "school__school_name": "T1_school_name"
    },

vejvarm

@prefix : <http://valuenet/ontop/> .
@prefix budget: <http://valuenet/ontop/budget#> .
@prefix endowment: <http://valuenet/ontop/endowment#> .
@prefix school: <http://valuenet/ontop/school#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<http://valuenet/ontop/budget/school_id=1;year=2003> a :budget ;
    budget:SCHOOL_ID <http://valuenet/ontop/school/school_id=1> ;
    budget:budget_invested_percent "71.3" ;
    budget:budgeted 119527 ;

vejvarm

{
    "RETURN": [
        "DISTINCT",
        "*"
    ],
    "TRIPLES": {
        "T1": {
            "label": "ROOT__projects",
            "projects__project_details": "T1_project_details",
            "projects__project_id": "T1_project_id"

vejvarm

#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check

vejvarm

import pathlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


def read_data(path_to_csv):
    df = pd.read_csv(path_to_csv, skiprows=3)
    unit = df.iloc[:, 1].name.split("(")[-1][:-1]