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name: deepseek operational carbon estimate
description: null
tags: null
aggregation:
  metrics:
    - operational-carbon
  type: both
initialize:
  plugins:
    power-to-energy-ws:

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import pandas as pd

root = './neuralwatt/'
in_data = 'inference.nvidia_smi_log.none_h100_llama3.3_20250316_230401.csv'

# clean and prep data
data = pd.read_csv(root+in_data)
data = data[['timestamp', 'power.draw(W)']] # extract relevant features
data.replace(' ', 'T', regex=True, inplace=True) # format timestamp

jmcook1186

name: carbon-intensity plugin demo
description: null
tags: null
aggregation:
  metrics:
    - operational-carbon
  type: both
initialize:
  plugins:
    power-to-energy-ws:

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const tree = {
    name: "Root",
    children: [{

        name: "A",
        energy: [40, 40, 40],
        carbon: [10, 10, 10],
        children: [{

            name: "C",

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/** This function does a depth-first, post-order traversal of the tree and aggregates the named metrics from each child into the parent node all the way up to the root*/

const tree = {
    name: "Root",
    carbon: 0,
    energy: 0,
    children: [{

        name: "A",
        energy: 40,

jmcook1186

name: nesting-demo
description: null
tags:
  kind: web
  complexity: moderate
  category: on-premise
aggregation:
  type: both
  metrics: 
    - 'cpu-util'

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// some input data with gaps in the record
const inputs = [{ timestamp: '2023-07-06T00:00', carbon: 10, energy: 30, duration: 10 },
{ timestamp: '2023-07-06T00:10', carbon: 10, energy: 30, duration: 10 },
{ timestamp: '2023-07-06T00:30', carbon: 10, energy: 30, duration: 5 },
{ timestamp: '2023-07-06T00:50', carbon: 10, energy: 30, duration: 20 }
]

const startTime = '2023-07-06T00:00'
const interval = 60
const endTime = '2023-07-06T10:00'

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name: if-demo
description: pipeline demo
initialize:
  models:
    - name: azure-importer
      kind: builtin
    - name: cloud-instance-metadata
      kind: builtin
    - name: intel
      kind: shell

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name: if-demo
description: pipeline demo
initialize:
  models:
    - name: azure-importer
      kind: builtin
    - name: cloud-instance-metadata
      kind: builtin
    - name: intel
      kind: shell

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# save this alongside snicar-driver.py and run in the terminal using
# `python src/biosnicar/biosnicar-distributed.py`

import numpy as np
from pathlib import Path
import dask