Eco-CI is a project aimed at estimating energy consumption in continuous integration (CI) environments. It provides functionality to calculate the energy consumption of CI jobs based on the power consumption characteristics of the underlying hardware.
- Requirements / Dependencies
- How Does It Work?
- Usage
- Note on the integration / Auto-Updates
- Limitations / Compatibility
Following packages are expected:
curljqawkdatewith microsecond support. On alpine and macOS this means installingcoreutilsbash> 4.0gitonly if you use GitLab
- The Eco-CI at its core makes its energy estimations based on pre-calculated power curves from Cloud Energy
- When you initialize the Eco-CI, starts a small bash script to track the cpu utilization over a period of time. This tracking begins when you call the start-measurement function. Then, each time you call get-measurement, it will take the cpu-utilization data collected (either from the start, or since the last get-measurement call) and make an energy estimation based on the detected hardware and CPU utilization.
Eco-CI supports both GitHub and GitLab as CI platforms. When you integrate it into your pipeline, you must call the start-measurement script to begin collecting power consumption data, then call the get-measurement script each time you wish to make a spot measurement. When you call get-measurment, you can also assign a label to it to more easily identify the measurement. At the end, call the display-results to see all the measurement results, overall total usage, and export the data.
Follow the instructions below to integrate Eco-CI into your CI pipeline.
To use Eco-CI in your GitHub workflow, call it with the relevant task name (start-measurement, get-measurement, or display-results). Here is a sample workflow that runs some python tests with eco-ci integrated.
name: Daily Tests with Energy Measurement
run-name: Scheduled - DEV Branch
on:
schedule:
- cron: '0 0 * * *'
permissions:
read-all
jobs:
run-tests:
runs-on: ubuntu-latest
steps:
- name: Start Measurement
uses: green-coding-solutions/eco-ci-energy-estimation@v4 # use hash or @vX here (See note below)
with:
task: start-measurement
# continue-on-error: true # recommended setting for production. See notes below.
- name: 'Checkout repository'
uses: actions/checkout@v4
with:
ref: 'dev'
submodules: 'true'
- name: Checkout Repo Measurement
uses: green-coding-solutions/eco-ci-energy-estimation@v4 # use hash or @vX here (See note below)
with:
task: get-measurement
label: 'repo checkout'
# continue-on-error: true # recommended setting for production. See notes below.
- name: setup python
uses: actions/setup-python@v5
with:
python-version: '3.10'
cache: 'pip'
- name: pip install
shell: bash
run: |
pip install -r requirements.txt
- name: Setup Python Measurment
uses: green-coding-solutions/eco-ci-energy-estimation@v4 # use hash or @vX here (See note below)
with:
task: get-measurement
label: 'python setup'
# continue-on-error: true # recommended setting for production. See notes below.
- name: Run Tests
shell: bash
run: |
pytest
- name: Tests measurement
uses: green-coding-solutions/eco-ci-energy-estimation@v4 # use hash or @vX here (See note below)
with:
task: get-measurement
label: 'pytest'
# continue-on-error: true # recommended setting for production. See notes below.
- name: Show Energy Results
uses: green-coding-solutions/eco-ci-energy-estimation@v4 # use hash or @vX here (See note below)
with:
task: display-results
# continue-on-error: true # recommended setting for production. See notes below.
-
task: (required) (options arestart-measurement,get-measurement,display-results)start-measurement: Initialize the action and starts the measurement. This must be called, and only once per job. If called again data will be reset.branch: (optional) (default: ${{ github.ref_name }})- Used to correctly identify this CI run for the Badge. Especially in PRs this will be very cryptic like
merge/72and you might want to set this to something nicer
- Used to correctly identify this CI run for the Badge. Especially in PRs this will be very cryptic like
label: (optional) (default: 'measurement ##')send-data: (optional) (default: true)- Send metrics data to metrics.green-coding.io to create and display badge, and see an overview of the energy of your CI runs. Set to false to send no data. The data we send are: the energy value and duration of measurement; cpu model; repository name/branch/workflow_id/run_id; commit_hash; source (GitHub or GitLab). We use this data to display in our green-metrics-tool front-end here: https://metrics.green-coding.io/ci-index.html
calculate-co2: (optional) (default: true)- You might typically always want this value to be shown unless you are in a restricted network and cannot make outbound requests
- Gets the location using https://ipapi.co/
- Get the CO2 grid intensity for the location from https://www.electricitymaps.com/
- Estimates the amount of carbon the measurement has produced
gh-api-base: (optional) (default: 'api.github.com')- Eco-CI uses the github api to post/edit PR comments and get the workflow id
- set to github's default api, but can be changed if you are using github enterprise
type: (optional)- If you want filter data in the GMT Dashboard or in CarbonDB you can here manually set a type for drill-down later. Defaults to "machine.ci". Cannot be empty.CarbonDB
project: (optional)- If you want filter data in the GMT Dashboard or in CarbonDB you can here manually set a type for drill-down later. Defaults to "CI/CD". Cannot be empty.CarbonDB
machine: (optional)- If you want filter data in the GMT Dashboard or in CarbonDB you can here manually set a type for drill-down later. Defaults to "ubuntu-latest". Cannot be empty.CarbonDB
tags: (optional)- If you want filter data in the GMT Dashboard or in CarbonDB you can here manually set tags for drill-down later. Please supply comma separated. Tags cannot have commas itself or contain quotes. Defaults to empty.CarbonDB
gmt-api-token: (optional)- If you are not using the default user for the GMT API supply your auth token. We recommend to have this as a GitHub Secret.
api-endpoint-add: (optional)- When using the GMT Dashboard and / or CarbonDB specify the endpoint URL to send to. Defaults to "https://api.green-coding.io/v2/ci/measurement/add"
api-endpoint-badge-get: (optional)- When using the GMT Dashboard and / or CarbonDB specify the endpoint URL to get the badge from to. Defaults to "https://api.green-coding.io//v1/ci/badge/get
electricitymaps-api-token: (optional)- API token for electricitymaps in case you get rate-limited. See details below.
-
get-measurement: Measures the energy at this point in time since either the start-measurement or last get-measurement action call.label: (optional) (default: 'measurement ##')
-
display-results: Outputs the energy results to the$GITHUB_STEP_SUMMARY. Creates a table that shows the energy results of all theget-measurements, and then a final row for the entire run. Displays the average cpu utilization, the total Joules used, and average wattage for each measurement+total run. This badge will always be updated to display the total energy of the most recent run of the workflow that generated this badge. The total measurement of this task is provided as outputdata-total-jsonin json format (see example below).pr-comment: (optional) (default: false)- if on, will post a comment on the PR issue with the Eco-CI results. only occurs if the triggering event is a pull_request
- remember to set
pull-requests: writeto true in your workflow file
display-table: (optional) (default: true)display-badge: (optional) (default: true)- used with display-results
- Shows the badge for the ci run during display-results step
- automatically false if `send-data is also false
json-output: (optional) (default: false)- will output data to JSON to
/tmp/eco-ci/lap-data.jsonand/tmp/eco-ci/total-data.json
- will output data to JSON to
We use https://app.electricitymaps.com/ to get the grid intensity for a given location. This service currently works without specifying a token but we recommend to still get one under https://api-portal.electricitymaps.com/
You will need to set this token as a secret and pass it in the initalization. To learn how to create a secret see the GitHub documentation: https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions
You will then need to pass it in your workflow files in the initialization. See documentation above.
Once you have initially set up Eco-CI and have given it a test spin we recommend running our action
with continue-on-error:true, as energy and CO2 metrics is not critical to the success of your workflow, but rather a nice feature to have.
- name: Eco CI Energy Estimation
uses: green-coding-solutions/eco-ci-energy-estimation@v4
with:
task: final-measurement
continue-on-error: trueFor both tasks get-measurement and display-results the lap measurements and total measurement can be consumed in JSON format.
You can use the outputs data-lap-json or data-total-json respectively.
You must set json-output to true in GitHub or export ECO_CI_JSON_OUTPUT="true" for it to be active.
Here is an example demonstrating how this can be achieved:
# ...
- name: 'Checkout repository'
uses: actions/checkout@v4
with:
ref: 'dev'
submodules: 'true'
- name: Checkout Repo Measurment
uses: green-coding-solutions/eco-ci-energy-estimation@v4
id: checkout-step
with:
task: get-measurement
label: 'repo checkout'
- name: Print checkout data
run: |
echo "total json: ${{ steps.checkout-step.outputs.data-lap-json }}"
- name: Show Energy Results
uses: green-coding-solutions/eco-ci-energy-estimation@v4
id: total-measurement-step
with:
task: display-results
- name: Print total data
run: |
echo "total json: ${{ steps.total-measurement-step.outputs.data-total-json }}"Note that the steps you want to consume the measurements of need to have an id so that you can access the corresponding data from their outputs.
If you are running in a private repo, you must give your job actions read permissions for the GITHUB_TOKEN. This is because we make an api call to get your workflow_id which uses your $GITHUB_TOKEN, and it needs the correct permissions to do so:
jobs:
test:
runs-on: ubuntu-latest
permissions:
actions: read
steps:
- name: Eco CI - Start Measurement
uses: green-coding-solutions/eco-ci-energy-estimation@v4
with:
task: start-measurementThis plugin is primarily designed for the GitHub Shared Runners and comes with their energy values already pre-calculated.
All the values for supported machines are found in the power-data folder.
The heavy work to get this values is done by Cloud Energy (See below for details).
If you want to support a custom machine you need to create one of these files and load it into Eco-CI.
Here is an exemplary command to create the power data for the basic 4 CPU GitHub Shared Runner (at the time of writing 13. June 2024).
python3 xgb.py --tdp 280 --cpu-threads 128 --cpu-cores=64 --cpu-make "amd" --release-year=2021 --ram 512 --cpu-freq=2450 --cpu-chips=1 --vhost-ratio=0.03125 --dump-hashmap > github_EPYC_7763_4_CPU_shared.sh
The following would be the command for Gitlab Shared Runners (at the time of writing 13. June 2024)
python3 xgb.py --tdp 240 --cpu-threads 128 --cpu-cores=64 --cpu-make "amd" --release-year=2021 --ram 512 --cpu-freq=2250 --cpu-chips=1 --vhost-ratio=0.015625 --dump-hashmap > gitlab_EPYC_7B12_saas-linux-small-amd64.txt
Gitlab uses an AMD EPYC 7B12 according to our findings
You can see how the machine specs must be supplied to Cloud Energy and also, since the runners are shared, you need to supply the splitting ratio that is used.
Since GitHub for instance uses an AMD EPYC 7763, which only comes with 64 cores and 128 threads, and gives you 4 CPUs the assumption is
that the splitting factor is 4/128 = 0.03125.
For macOS we used for the macos-14 M1 shared runners:
python3 xgb.py --tdp 10 --cpu-threads=8 --cpu-cores=8 --release-year=2020 --ram 16 --cpu-freq=3200 --cpu-chips=1 --vhost-ratio=0.3 --dump-hashmap > macos-14-mac-mini-m1.sh
Source for full Mac Mini power consumption
Source for Cores and RAM of total machine (assuming only efficiency cores used for hypervisor and performance for runners) (We slightly tuned vhost-ratio to 0.3 instead of 0.4 to adapt to the measured power source from Source #1)
And for the Intel macos-14 shared runners:
python3 xgb.py --tdp 65 --cpu-threads=4 --cpu-cores=4 --release-year=2017 --ram 16 --cpu-freq=3600 --cpu-chips=1 --vhost-ratio=1 --dump-hashmap > macos-13-mac-mini-intel.sh
GitHub specs
Source for hardware specs
Source for CPU specs
It seems GitHub is not sharing this machine for the runners and is just running some virtualization layer, as some memory is reserved ...?
An uncertainty for all Intel runners is if Hyper-Threading / SMT is turned on or off, but we believe it is reasonable to assume that for Shared runners they will turn it on as it generally increases throughput and performance in shared environments.
If you have trouble finding out the splitting factor for your system: Open an issue! We are happy to help!!
Once you have the file ready we are happy to merge it in through a PR! In future versions we also plan to include a loading mechanism, where you can just ingest a file from your repository without having to upstream it with us. But since this is a community open source plugin upstream is preferred, right :)
To use Eco-CI in your GitLab pipeline, you must first include a reference to the eco-ci-gitlab.yml file as such:
include:
remote: 'https://raw.githubusercontent.com/green-coding-solutions/eco-ci-energy-estimation/main/eco-ci-gitlab.yml'
and you call the various scripts in your pipeline with call like this:
- !reference [.<function-name>, script]
where function name is one of the following:
start_measurement - begin the measurment
get_measurement - make a spot measurment here. If you wish to label the measurement, you need to set the ECO_CI_LABEL environment variable right before this call.
display_results - will print all the measurement values to the jobs-output and prepare the artifacts, which must be exported in the normal GitLab way.
By default, we send data to our API, which will allow us to present you with a badge, and a front-end display to review your results. The data we send are: the energy value and duration of measurement; cpu model; repository name/branch/workflow_id/run_id; commit_hash; source (GitHub or GitLab). We use this data to display in our green-metrics-tool front-end here: https://metrics.green-coding.io/ci-index.html
If you do not wish to send us data, you can set this global variable in your pipeline:
variables:
ECO_CI_SEND_DATA: "false"
For each job you can export the artifacts. We currently export the pipeline data as a regular artifact, as well as make use of GitLab's Metric Report artifact (which we output to the default metrics.txt):
artifacts:
paths:
- eco-ci-output.txt
- eco-ci-total-data.json
reports:
metrics: metrics.txt
Please look at .gitlab-ci.yml.example
macOS requires the GNU date tool so it can properly create a microsecond timestamp.
Install it with the package manager of your choice and then add its binary first in the PATH variable, so that it precedes the BSD date.
Example for using in local CI with homebrew:
brew install coreutils
export PATH="/opt/homebrew/opt/coreutils/libexec/gnubin:/usr/local/opt/coreutils/libexec/gnubin:$PATH"
# then you can run:
bash local_ci.example.shExample for using in GitHub Actions with homebrew:
- name: Install dependencies (macos only)
run: |
brew install coreutils
echo "/opt/homebrew/opt/coreutils/libexec/gnubin:/usr/local/opt/coreutils/libexec/gnubin:$PATH" >> $GITHUB_PATHAlthough initially designed for use in GitHub Actions and GitLab Pipelines the Eco-CI tool works everywhere where bash works.
This means you can just use it locally by following it's general 3-step interface:
- Start
- Measure (optionally repeat if you want to lap multiple steps)
- End & Display
As an example we have set up a full example pipeline in the form of a bash file under local_ci.example.sh.
In this file you find the needed calls along with some fake activity like calls to sleep and ls etc.
You just need to slice the file to you needs and bring the code that you want to encapsulate with Eco-CI into the positions where currently the sleep and ls calls are.
For local testing you can just run in the docker container of your choice, directly from the root of the repository.
Here is an example with the Circle-CI base image:
docker run --network host --rm -it -v ./:/tmp/data:ro cimg/base:current bash /tmp/data/local_ci.example.shIn case you are testing with a local installation of the GMT append --network host to access api.green-coding.internal
docker run --rm -it -v ./:/tmp/data:ro invent-registry.kde.org/sysadmin/ci-images/suse-qt67:latest bash /tmp/data/local_ci.example.shFor Jenkins Eco-CI can be easily used in combination with the Execute Shell plugin.
By following Eco-CI's general 3-step you need to create 3 steps in your workflow that:
- Start
- Measure (optionally repeat if you want to lap multiple steps)
- End & Display
You need to have the eco-ci-energy-estimation repository checked out somewhere, where Jenkins can read it.
Replace __PATH_WHERE_YOU_HAVE_THE_REPO__ with the actual repo location.
Start:
bash __PATH_WHERE_YOU_HAVE_THE_REPO__/scripts/examples/jenkins_start.shMeasure Step:
You should set a custom text label for the step, here defined with the variable LABEL
LABEL="This is the step label"
bash __PATH_WHERE_YOU_HAVE_THE_REPO__/scripts/examples/jenkins_measure.sh "${LABEL}"End and display data:
You should set a custom text label for the step, here defined with the variable LABEL
LABEL="This is the step label"
bash __PATH_WHERE_YOU_HAVE_THE_REPO__/scripts/examples/jenkins_end_and_display.sh "${LABEL}"See a full example of a freestyle pipeline with the codeblocks here: 
The data will then show up in the text log. See an example how this looks here: 
If you use Jenkins on macOS you must have coretuils installed and gdate must replace the normal date function.
Append this to every step where you call Eco-CI precede the linux date tool before the macOS native one:
set +x # if macOS to reduce noise
export PATH="/opt/homebrew/opt/coreutils/libexec/gnubin:$PATH" # if macOS-
If you want the extension to automatically update within a version number, use the convenient @vX form.
uses: green-coding-solutions/eco-ci-energy-estimation@v4 # will pick the latest minor v4.x- In case of a major change from @v4 to @v5 you need to upgrade manually. The upside is: If you use dependabot it will create a PR for you as it understands the notation
-
If you want to pin the dependency and want to audit every release we recommend using the hash notation
uses: green-coding-solutions/eco-ci-energy-estimation@06837b0b3b393a04d055979e1305852bda82f044 #resolves to v2.2- Note that this hash is just an example. You find the latest current hash under Tags
- Dependabot also understands this notation so it will create an update with the changelog for you
-
If you want the bleeding edge features use the @main notation.
uses: green-coding-solutions/eco-ci-energy-estimation@main- We do not recommend this as it might contain beta features. We recommend using the releases and tagged versions only
-
At the moment this will only work with linux based pipelines, mainly tested on ubuntu images.
- The plugin is tested on:
- GitHub
ubuntu-latest(GitHub - 22.04 at the time of writing)ubuntu-24.04(GitHub)ubuntu-20.04(GitHub)- Autoscaling Github Runners are not supported
- The plugin technically supports large runners, but they will need extra pre-calculated power curved. Contact us if you need them and we are happy to bring them in!
- GitLab
saas-linux-small-amd64(GitLab)
- Generic
alpine(Install dependencies before - See above)
- macOS is working on our local test machines (see install details below).
- Runners on GitHub are untested, but should work. You need to create a power profile though (see Cloud Energy below). We are happy for beta testers! contact us :)
- Windows is currently only supported with WSL2
-
If you use Alpine, you must install coreutils so that time logging with date is possible with an accuracy of microseconds (
apk add coreutils) -
If you have your pipelines split over multiple VM's (often the case with many jobs) ,you have to treat each VM as a seperate machine for the purposes of measuring and setting up Eco-CI.
-
The underlying Cloud Energy model requires the CPU to have a fixed frequency setting. This is typical for cloud testing and is the case for instance on GitHub, but not always the case in different CIs.
See also our work on analysing fixed frequency in Cloud Providers and CI/CD
- The Cloud Energy model data is trained via the SPECpower database, which was mostly collected on compute machines. Results will be off for non big cloud servers and also for machines that are memory heavy or machines which rely more heavily on their GPU's for computations.