Jupyter notebooks for quick analysis and visualization of cloud-carbon-footprint data
Python >= 3.10
pip >= 22.0
install dependencies:
pip install -r requirements.txt This reporting solution can be integrated in a GitLab CI pipeline.
variables:
# secrets that have to be set
AZURE_CLIENT_ID: ""
AZURE_CLIENT_SECRET: ""
AZURE_TENANT_ID: ""
# azure configuration
AZURE_USE_BILLING_DATA: "true"
AZURE_AUTH_MODE: "default"
AZURE_RESOURCE_TAG_NAMES: '["resourceGroup","project","customer"]'
AZURE_CONSUMPTION_CHUNKS_DAYS:
value: "14"
description: "Perform requests to the Azure API in chunks of X days to avoid rate limit errors"
# settings for run
START_DATE:
value: ""
description: "Estimation range start date, YYYY-MM-DD, default: start of last month"
END_DATE:
value: ""
description: "Estimation range end date, YYYY-MM-DD, default: end of last month"
stages:
- estimate
- report
estimate:
image: node:16
stage: estimate
timeout: 24h
before_script:
- git clone https://github.com/cloud-carbon-footprint/cloud-carbon-footprint.git
- cd cloud-carbon-footprint
- yarn install
script:
# if start date is not set, set to beginning of last month
- START_DATE=${START_DATE:-$(date -d " $(date +\%Y-\%m-01) -1 month" +\%Y-\%m-01)}
# if end date is not set, set to end of last month
- END_DATE=${END_DATE:-$(date -d " $(date +\%Y-\%m-01) -1 day" +\%Y-\%m-\%d)}
- echo estimating for time range from $START_DATE to $END_DATE
- yarn start-cli --startDate $START_DATE --endDate $END_DATE
artifacts:
paths:
- "cloud-carbon-footprint/packages/cli/estimates.cache.day.json"
expire_in: 1 hour
report:
image: python:3.10
stage: report
before_script:
- pip install jupyter
- git clone https://github.com/NicolasBissig/cloud-carbon-footprint-analyzer.git
- cd cloud-carbon-footprint-analyzer
- pip install -r requirements.txt
script:
- jupyter nbconvert --to html --no-input --execute report.ipynb
artifacts:
paths:
- "cloud-carbon-footprint-analyzer/report.html"
expire_in: 1 hour