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.
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: Initialize Energy Estimation
uses: green-coding-solutions/eco-ci-energy-estimation@v2 # use hash or @vX here (See note below)
with:
task: start-measurement
- name: 'Checkout repository'
uses: actions/checkout@v3
with:
ref: 'dev'
submodules: 'true'
- name: Checkout Repo Measurement
uses: green-coding-solutions/eco-ci-energy-estimation@v2 # use hash or @vX here (See note below)
with:
task: get-measurement
label: 'repo checkout'
- name: setup python
uses: actions/setup-python@v4
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@v2 # use hash or @vX here (See note below)
with:
task: get-measurement
label: 'python setup'
- name: Run Tests
shell: bash
run: |
pytest
- name: Tests measurement
uses: green-coding-solutions/eco-ci-energy-estimation@v2 # use hash or @vX here (See note below)
with:
task: get-measurement
label: 'pytest'
- name: Show Energy Results
uses: green-coding-solutions/eco-ci-energy-estimation@v2 # use hash or @vX here (See note below)
with:
task: display-results
task
: (required) (options arestart-measurement
,get-measurement
,display-results
)start-measurement
- Initialize the action starts the measurement. This must be called, and only once per job.get-measurement
- Measures the energy at this point in time since either the start-measurement or last get-measurement action call.display-results
- Outputs the energy results to the$GITHUB_STEP_SUMMARY
. Creates a table that shows the energy results of all the get-measurements, and then a final row for the entire run. Displays the avergae cpu utilization, the total Joules used, and average wattage for each measurment+total run. It will also display a graph of the energy used, and a badge for you to display.- 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 output
data-total-json
in json format (see example below). - Can be used with
pr-comment
flag (see below) to post the results as a comment on the PR.
branch
: (optional) (default: ${{ github.ref_name }})- Used with
get_measurement
anddisplay_results
to correctly identify this CI run for the Badge.
- Used with
label
: (optional) (default: 'measurement ##')- Used with
get_measurement
anddisplay_results
to identify the measurement
- Used with
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
display-table
: (optional) (default: true)- call during the
display-graph
step to either show/hide the energy reading table results in the output
- call during the
display-graph
: (optional) (default: true)- We use an ascii charting library written in go (https://github.com/guptarohit/asciigraph). For GitHub hosted runners their images come with go so we do not install it. If you are using a private runner instance however, your machine may not have go installed, and this will not work. As we want to minimize what we install on private runner machines to not intefere with your setup, we will not install go. Therefore, you will need to call
start-measurement
with thedisplay-graph
flag set to false, and that will skip the installation of this go library.
- We use an ascii charting library written in go (https://github.com/guptarohit/asciigraph). For GitHub hosted runners their images come with go so we do not install it. If you are using a private runner instance however, your machine may not have go installed, and this will not work. As we want to minimize what we install on private runner machines to not intefere with your setup, we will not install go. Therefore, you will need to call
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
pr-comment
: (optional) (default: false)- used with display-results
- 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: write
to true in your workflow file
api-base
: (optional) (default: 'api.github.com')- Eco-CI uses the github api to post/edit PR comments
- set to github's default api, but can be changed if you are using github enterprise
We recommend running our action with continue-on-error:true
, as it 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@v2
with:
task: final-measurement
continue-on-error: true
For 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.
Here is an example demonstrating how this can be achieved:
# ...
- name: 'Checkout repository'
uses: actions/checkout@v3
with:
ref: 'dev'
submodules: 'true'
- name: Checkout Repo Measurment
uses: green-coding-solutions/eco-ci-energy-estimation@v2
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@v2
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 - Initialize
uses: green-coding-solutions/eco-ci-energy-estimation@v2
with:
task: start-measurement
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:
initialize_energy_estimator
- used to setup the machine for measurement. Needs to be called once per VM job.
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"
Then, for each job you need to 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
Here is a sample .gitlab-ci.yml example file to illustrate:
image: ubuntu:22.04
include:
remote: 'https://raw.githubusercontent.com/green-coding-solutions/eco-ci-energy-estimation/main/eco-ci-gitlab.yml'
stages:
- test
test-job:
stage: test
script:
- !reference [.initialize_energy_estimator, script]
- !reference [.start_measurement, script]
- sleep 10s # Your main pipeline logic here
- export ECO_CI_LABEL="measurement 1"
- !reference [.get_measurement, script]
- sleep 3s # more of your pipeline logic here
- export ECO_CI_LABEL="measurement 2"
- !reference [.get_measurement, script]
- !reference [.display_results, script]
artifacts:
paths:
- eco-ci-output.txt
reports:
metrics: metrics.txt
- The Eco-CI at its core makes its energy estimations based on an XGBoost Machine Learning model we have created based on the SpecPower database. The model and further information can be found here: https://github.com/green-coding-solutions/spec-power-model
- When you initialize the Eco-CI, it downloads the XGBoost model onto the machine, as well as a small program 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 (mainly cpu data) and utilization.
-
At the moment this will only work with linux based pipelines, mainly tested on ubuntu images.
-
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 XGBoost model requires the CPU to have a fixed frequency setting. This is typical for cloud testing, but not always the case.
-
The XGBoost 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.
-
If you use dependabot and want to get updates, we recommend using the hash notation
uses: green-coding-solutions/eco-ci-energy-estimation@06837b0b3b393a04d055979e1305852bda82f044 #v2.2
- Note that this hash is just an example. You find the latest current hash under Tags
-
If you want the extension to automatically update within a version number, use the convenient @v2 form
uses: green-coding-solutions/eco-ci-energy-estimation@v2 # will pick the latest minor v2. for example v2.2