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]

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

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

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

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

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

# 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