diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 7ee0013c9..5e2868bab 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -20,7 +20,7 @@ What types of changes does your code introduce? Put an `x` in all the boxes that
## AI Usage Disclosure
-Please refer to [docs/contributing/AI_POLICY.md](https://github.com/mlco2/codecarbon/blob/master/docs/contributing/AI_POLICY.md) for detailed guidelines on how to disclose AI usage in your PR. Accurately completing this section is mandatory.
+Please refer to [docs/how-to/ai-policy.md](https://github.com/mlco2/codecarbon/blob/master/docs/how-to/ai-policy.md) for detailed guidelines on how to disclose AI usage in your PR. Accurately completing this section is mandatory.
- [ ] ๐ฅ AI-vibecoded: You cannot explain the logic. Car analogy : the car drive by itself, you are outside it and just tell it where to go.
- [ ] ๐ AI-generated: Car analogy : the car drive by itself, you are inside and give instructions.
@@ -32,7 +32,7 @@ Go over all the following points, and put an `x` in all the boxes that apply.
- [ ] My code follows the code style of this project.
- [ ] My change requires a change to the documentation.
- [ ] I have updated the documentation accordingly.
-- [ ] I have read the **[docs/contributing/CONTRIBUTING.md](https://github.com/mlco2/codecarbon/blob/master/docs/contributing/CONTRIBUTING.md)** document.
+- [ ] I have read the **[docs/how-to/contributing.md](https://github.com/mlco2/codecarbon/blob/master/docs/how-to/contributing.md)** document.
- [ ] I have added tests to cover my changes.
- [ ] All new and existing tests passed.
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index 9e84465de..c6bb301fd 120000
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -1 +1 @@
-../docs/contributing/agents-instructions.md
\ No newline at end of file
+../docs/how-to/agent-instructions.md
\ No newline at end of file
diff --git a/AI_POLICY.md b/AI_POLICY.md
index d0635a526..c343da5aa 120000
--- a/AI_POLICY.md
+++ b/AI_POLICY.md
@@ -1 +1 @@
-docs/contributing/AI_POLICY.md
\ No newline at end of file
+docs/how-to/ai_policy.md
\ No newline at end of file
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
index 9b5d3dbdb..21b6759bb 120000
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -1 +1 @@
-docs/contributing/CODE_OF_CONDUCT.md
\ No newline at end of file
+docs/how-to/code-of-conduct.md
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 120000
index 000000000..b7d9bf98a
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1 @@
+docs/how-to/contributing.md
\ No newline at end of file
diff --git a/README.md b/README.md
index ae6419d54..85b20d830 100644
--- a/README.md
+++ b/README.md
@@ -1,167 +1,129 @@
-Estimate and track carbon emissions from your computer, quantify and analyze their impact.
-
-CodeCarbon websites:
-- [Main website](https://codecarbon.io) to learn why we do this.
-- [Dashboard](https://dashboard.codecarbon.io/) to see your emissions, [read the API doc](https://docs.codecarbon.io/latest/getting-started/api/) before.
-- [Documentation](https://docs.codecarbon.io) to learn how to use the package and our methodology.
-- [GitHub](https://github.com/mlco2/codecarbon) to look at the source code and contribute.
-- [Discord](https://discord.gg/GS9js2XkJR) to chat with us.
-
-
+# Track & reduce COโ emissions from your local computing
-2 we produce while running our code.
+```bash
+conda activate your_env
+pip install codecarbon
+```
-*How?*
+More installation options: [installation docs](https://docs.codecarbon.io/how-to/installation/).
-We created a Python package that estimates your hardware electricity power consumption (GPU + CPU + RAM) and we apply to it the carbon intensity of the region where the computing is done.
+## Quickstart (Python)
-
+```python
+from codecarbon import EmissionsTracker
-We explain more about this calculation in the [**Methodology**](https://docs.codecarbon.io/latest/introduction/methodology/) section of the documentation.
+tracker = EmissionsTracker()
+tracker.start()
-Our hope is that this package will be used widely for estimating the carbon footprint of computing, and for establishing best practices with regards to the disclosure and reduction of this footprint.
+# Your code here
-**So ready to "change the world one run at a time"? Let's start with a very quick set up.**
+emissions = tracker.stop()
+print(f"Emissions: {emissions} kg COโ")
+```
-# Quickstart ๐
+[**Learn more** โ](https://docs.codecarbon.io/tutorials/first-tracking/)
-## Installation ๐ง
+## Quickstart (CLI)
-**From PyPI repository**
-```python
-pip install codecarbon
-```
+Track a command without changing your code:
-**Using Conda environments**
-If you're using Conda, you can install CodeCarbon with pip in your Conda environment:
```bash
-conda activate your_env
-pip install codecarbon
+codecarbon monitor --no-api -- python train.py
```
-To see more installation options please refer to the documentation: [**Installation**](https://docs.codecarbon.io/latest/getting-started/installation/)
-
-## Start to estimate your impact ๐
+Detect your hardware:
-### Without using the online dashboard
-
-```python
-from codecarbon import track_emissions
-@track_emissions()
-def your_function_to_track():
- # your code
+```bash
+codecarbon detect
```
-After running your code, you will find an `emissions.csv` that you can visualize with `carbonboard --filepath="examples/emissions.csv"`.
+Full CLI guide: [CLI tutorial](https://docs.codecarbon.io/tutorials/cli/).
-### With the online dashboard
+## Configuration
-To use the online dashboard you need to create an account on [**CodeCarbon Dashboard**](https://dashboard.codecarbon.io/).
-Once you have an account, you can create an experiment_id to track your emissions.
+You can configure CodeCarbon using:
-To get an experiment_id enter:
-```python
-! codecarbon login
-```
-You can now store it in a **.codecarbon.config** at the root of your project
-```python
-[codecarbon]
-log_level = DEBUG
-save_to_api = True
-experiment_id = 2bcbcbb8-850d-4692-af0d-76f6f36d79b2 #the experiment_id you get with init
-```
-Now you have 2 main options:
+- `~/.codecarbon.config` (global)
+- `./.codecarbon.config` (project-local)
+- `CODECARBON_*` environment variables
+- Python arguments (`EmissionsTracker(...)`)
-### Monitoring your machine ๐ป
+Configuration precedence and examples: [configuration guide](https://docs.codecarbon.io/how-to/configuration/).
-In your command prompt use:
-```codecarbon monitor```
-The package will track your emissions independently from your code.
+## How it works
-### Detecting your hardware ๐
+We created a Python package that estimates your hardware electricity power consumption (GPU + CPU + RAM) and we apply to it the carbon intensity of the region where the computing is done.
-In your command prompt use:
-```codecarbon detect```
-The package will detect and print your hardware information (RAM, CPU, GPU).
+
-### In your Python code ๐
-```python
-from codecarbon import track_emissions
-@track_emissions()
-def your_function_to_track():
- # your code
- ```
-The package will track the emissions generated by the execution of your function.
+We explain more about this calculation in the [**Methodology**](https://docs.codecarbon.io/explanation/methodology/) section of the documentation.
-There is other ways to use **codecarbon** package, please refer to the documentation to learn more about it: [**Usage**](https://docs.codecarbon.io/latest/getting-started/usage/)
+## Visualize
-## Visualize ๐
+You can visualize your experiment emissions on the [dashboard](https://dashboard.codecarbon.io/) or locally with [carbonboard](https://docs.codecarbon.io/how-to/visualize/).
-You can now visualize your experiment emissions on the [dashboard](https://dashboard.codecarbon.io/).
+## Quick links
+
+| Section | Description |
+|---------|-------------|
+| [Quickstart](https://docs.codecarbon.io/tutorials/first-tracking/) | Get started in 5 minutes |
+| [Installation](https://docs.codecarbon.io/how-to/installation/) | Install CodeCarbon |
+| [CLI Tutorial](https://docs.codecarbon.io/tutorials/cli/) | Track emissions from the command line |
+| [Python API Tutorial](https://docs.codecarbon.io/tutorials/python-api/) | Track emissions in Python code |
+| [Comparing Model Efficiency](https://docs.codecarbon.io/tutorials/comparing-model-efficiency/) | Measure carbon efficiency across ML models |
+| [API Reference](https://docs.codecarbon.io/reference/api/) | Full parameter documentation |
+| [Examples](https://docs.codecarbon.io/reference/examples/) | Example usage patterns |
+| [Methodology](https://docs.codecarbon.io/explanation/methodology/) | How emissions are calculated |
+| [EcoLogits](https://ecologits.ai/) | Track emissions from GenAI API calls |
-> Hope you enjoy your first steps monitoring your carbon computing impact!
-> Thanks to the incredible codecarbon community ๐ช๐ผ a lot more options are available using *codecarbon* including:
-> - offline mode
-> - cloud mode
-> - comet integration...
->
-> Please explore the [**Documentation**](https://docs.codecarbon.io/) to learn about it
-> If ever what your are looking for is not yet implemented, let us know through the *issues* and even better become one of our ๐ฆธ๐ผโโ๏ธ๐ฆธ๐ผโโ๏ธ contributors! more info ๐๐ผ
+## Links
+- [Main website](https://codecarbon.io) to learn why we do this.
+- [Dashboard](https://dashboard.codecarbon.io/) to see your emissions.
+- [Documentation](https://docs.codecarbon.io/) to learn how to use the package and our methodology.
+- [EcoLogits](https://ecologits.ai/) to track emissions from GenAI API calls (OpenAI, Anthropic, etc.).
+- [GitHub](https://github.com/mlco2/codecarbon) to look at the source code and contribute.
+- [Discord](https://discord.gg/GS9js2XkJR) to chat with us.
-# Contributing ๐ค
+## Contributing
We are hoping that the open-source community will help us edit the code and make it better!
-You are welcome to open issues, even suggest solutions and better still contribute the fix/improvement! We can guide you if you're not sure where to start but want to help us out ๐ฅ
-
-In order to contribute a change to our code base, please submit a pull request (PR) via GitHub and someone from our team will go over it and accept it.
+You are welcome to open issues, even suggest solutions and better still contribute the fix/improvement! We can guide you if you're not sure where to start but want to help us out.
-Check out our [contribution guidelines :arrow_upper_right:](https://github.com/mlco2/codecarbon/blob/master/docs/contributing/CONTRIBUTING.md)
+Check out our [contribution guidelines](https://github.com/mlco2/codecarbon/blob/master/CONTRIBUTING.md).
Feel free to chat with us on [Discord](https://discord.gg/GS9js2XkJR).
-# How To Cite ๐
+## Citation
If you find CodeCarbon useful for your research, you can find a citation under a variety of formats on [Zenodo](https://zenodo.org/records/11171501).
-Here is a sample for BibTeX:
+
+BibTeX
+
```tex
@software{benoit_courty_2024_11171501,
author = {Benoit Courty and
@@ -189,7 +151,7 @@ Here is a sample for BibTeX:
Ziyao Wang and
Armin Catovic and
Marc Alencon and
- Michaลย Stฤchลy and
+ Michaล Stฤchลy and
Christian Bauer and
Lucas Otรกvio N. de Araรบjo and
JPW and
@@ -204,7 +166,9 @@ Here is a sample for BibTeX:
}
```
-# Contact ๐
+
+
+## Contact
Feel free to chat with us on [Discord](https://discord.gg/GS9js2XkJR).
@@ -212,8 +176,6 @@ Codecarbon was formerly developed by volunteers from [**Mila**](http://mila.queb
Now CodeCarbon is supported by [**Code Carbon**](https://www.helloasso.com/associations/code-carbon), a French non-profit organization whose mission is to accelerate the development and adoption of CodeCarbon.
-## Star History
+### Star History
-Comparison of the number of stars accumulated by the different Python CO2 emissions projects:
[](https://star-history.com/#mlco2/codecarbon&lfwa/carbontracker&sb-ai-lab/Eco2AI&fvaleye/tracarbon&Breakend/experiment-impact-tracker&Date)
-
diff --git a/SECURITY.md b/SECURITY.md
index c177700fd..1fa4fc883 120000
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -1 +1 @@
-docs/contributing/SECURITY.md
\ No newline at end of file
+docs/how-to/security.md
\ No newline at end of file
diff --git a/codecarbon/core/powermetrics.py b/codecarbon/core/powermetrics.py
index 17a18f0ca..7de62e055 100644
--- a/codecarbon/core/powermetrics.py
+++ b/codecarbon/core/powermetrics.py
@@ -54,11 +54,10 @@ def _has_powermetrics_sudo() -> bool:
_, stderr = process.communicate()
if re.search(r"[sudo].*password", stderr):
- logger.debug(
- """Not using PowerMetrics, sudo password prompt detected.
+ logger.debug("""Not using PowerMetrics, sudo password prompt detected.
If you want to enable Powermetrics please modify your sudoers file
as described in :
- https://docs.codecarbon.io/latest/introduction/methodology/#power-usage
+ https://docs.codecarbon.io/explanation/methodology/#power-usage
"""
)
return False
diff --git a/docs/advanced/linux_service.md b/docs/advanced/linux_service.md
deleted file mode 100644
index 8e8a9442c..000000000
--- a/docs/advanced/linux_service.md
+++ /dev/null
@@ -1,113 +0,0 @@
-# Install CodeCarbon as a Linux service
-
-To install CodeCarbon as a Linux service, follow the instructions below.
-It works on Ubuntu or other Debian-based systems using systemd.
-
-Create a dedicated user:
-
-``` bash
-sudo useradd -r -s /bin/false codecarbon
-```
-
-Create a directory for the CodeCarbon service:
-
-``` bash
-sudo mkdir /opt/codecarbon
-```
-
-Change the ownership of the directory to the user created above:
-
-``` bash
-sudo chown codecarbon:codecarbon /opt/codecarbon
-```
-
-Create a virtual environment for CodeCarbon :
-
-``` bash
-sudo apt install python3-venv
-sudo -u codecarbon python3 -m venv /opt/codecarbon/.venv
-```
-
-Install CodeCarbon in the virtual environment:
-
-``` bash
-sudo -u codecarbon /opt/codecarbon/.venv/bin/pip install codecarbon
-```
-
-Go to > /etc/sysfs.conf
-echo "owner class/powercap/intel-rapl:0/energy_uj = root:codecarbon" >> /etc/sysfs.conf
-```
-
-Create the configuration file for CodeCarbon:
-
-``` bash
-sudo tee /opt/codecarbon/.codecarbon.config <
-project_id =
-experiment_id =
-api_key =
-# Verbose logging
-log_level=WARNING
-# Measure power every 30 seconds
-measure_power_secs=30
-# Send measure to API every 5 minutes (10*30 seconds)
-api_call_interval=10
-EOF
-```
-
-Enable and start the service:
-
-``` bash
-sudo systemctl enable codecarbon
-sudo systemctl start codecarbon
-```
-
-Check the traces of the service:
-
-``` bash
-journalctl -u codecarbon
-```
-
-You are done, CodeCarbon is now running as a service on your machine.
-
-Wait 5 minutes for the first measure to be send to the dashboard at
- for more
-information.
+## Why RAPL Matters
-Despite the name "Intel RAPL", it supports AMD processors since Linux
-kernel 5.8.
+By default, CodeCarbon estimates CPU power based on hardware specifications and CPU load, which can be inaccurate. RAPL reads energy directly from the processor's counters, providing **microjoule-level precision** instead of estimates.
-Due to the [CVE-2020-8694 security
-issue](https://www.cve.org/CVERecord?id=CVE-2020-8694) from 2020, all
-Linux distributions have changed right permission of the RAPL file, to
-reserve it to superuser.
+!!! tip "Get Started with RAPL"
-There is a workaround, thanks to
-[prometheus/node_exporter#1892](https://github.com/prometheus/node_exporter/issues/1892):
+ To enable RAPL on your system and unlock more accurate measurements, see the [Improve Measurement Accuracy with RAPL](../how-to/enable-rapl.md) how-to guide. It includes step-by-step permission setup instructions for Linux systems.
-``` sh
-sudo apt install sysfsutils
-nano /etc/sysfs.conf
-# Add this line :
-mode class/powercap/intel-rapl:0/energy_uj = 0444
-reboot
-```
+## About RAPL
-Without rebooting you could do `sudo chmod -R a+r /sys/class/powercap/*`
-but it will be lost at next boot.
+Despite the name "Intel RAPL", it supports AMD processors since Linux kernel 5.8.
-If you want more security you could create a specific group, add your
-user to this group and set group read permission only.
+For more information on RAPL hardware, see:
## RAPL Domain Architecture
diff --git a/docs/introduction/when-to-use.md b/docs/explanation/when-to-use.md
similarity index 100%
rename from docs/introduction/when-to-use.md
rename to docs/explanation/when-to-use.md
diff --git a/docs/introduction/motivation.md b/docs/explanation/why.md
similarity index 86%
rename from docs/introduction/motivation.md
rename to docs/explanation/why.md
index aac0ade22..9168cace5 100644
--- a/docs/introduction/motivation.md
+++ b/docs/explanation/why.md
@@ -16,6 +16,29 @@ In order to achieve these incredible levels of performance, current approaches l
With AI models becoming more ubiquitous and deployed across different sectors and industries, AI's environmental impact is also growing. For this reason, it is important to estimate and curtail both the energy used and the emissions produced by training and deploying AI models. This package enables developers to track carbon dioxide (COโ) emissions across machine learning experiments or other programs.
+## How to get started
+
+You can start tracking emissions in just a few lines of Python:
+
+``` python
+from codecarbon import EmissionsTracker
+
+tracker = EmissionsTracker()
+tracker.start()
+
+# Your code here
+# e.g., model training, data processing, etc.
+
+emissions = tracker.stop()
+print(f"Emissions: {emissions} kg COโeq")
+```
+
+Or track any command from the CLI without modifying your code:
+
+``` bash
+codecarbon monitor -- python train.py
+```
+
## COโ-equivalents
This package enables developers to track emissions, measured as kilograms of COโ-equivalents (COโeq) in order to estimate the carbon footprint of their work. We use *COโ-equivalents [COโeq]*, which is a standardized measure used to express the global warming potential of various greenhouse gases: the amount of COโ that would have the equivalent global warming impact. For computing, which emits COโ via the electricity it consumes, carbon emissions are measured in kilograms of COโ-equivalent per kilowatt-hour. Electricity is generated as part of the broader electrical grid by combusting fossil fuels, for example.
diff --git a/docs/getting-started/api.md b/docs/getting-started/api.md
deleted file mode 100644
index cee83d85d..000000000
--- a/docs/getting-started/api.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# CodeCarbon API
-
-!!! warning "API mode"
-
- This mode uses the CodeCarbon API to upload the timeseries of your
- emissions on a central server.
-
- Thanks to [CleverCloud](https://www.clever.cloud/) the use of API is
- free as soon as it remains under certain limits.
-
-{.align-center width="700px" height="400px"}
-
-{.align-center width="700px"}
-
-Before using it, you need to create an account on the [CodeCarbon
-dashboard](https://dashboard.codecarbon.io/)
-
-Then login from your terminal:
-
-``` console
-codecarbon login
-```
-
-It will create an experiment_id for the default project and save it to
-`codecarbon.config`
-
-Then you can tell CodeCarbon to monitor your machine:
-
-``` console
-codecarbon monitor
-```
-
-Or use the API in your code:
-
-``` python
-from codecarbon import track_emissions
-
-@track_emissions(save_to_api=True)
-def train_model():
- # GPU intensive training code goes here
-
-if __name__ =="__main__":
- train_model()
-```
-
-More options could be specified in `@track_emissions` or in
-`.codecarbon.config`
-
-The [CodeCarbon dashboard](https://dashboard.codecarbon.io/) use
-[CodeCarbon API](https://api.codecarbon.io/) to get the data
-
-The API do not have a nice web interface to create your own organization
-and project, you have to use [OpenAPI
-interface](https://api.codecarbon.io/docs) for that.
-
-And so on for your team, project and experiment.
-
-You then have to set your experiment id in CodeCarbon, with two options:
-
-In the code:
-
-``` python
-from codecarbon import track_emissions
-
-@track_emissions(
- measure_power_secs=30,
- api_call_interval=4,
- experiment_id="your experiment id",
- save_to_api=True,
-)
-def train_model():
- ...
-```
-
-Or in the config file `.codecarbon.config`:
-
-``` ini
-[codecarbon]
-experiment_id = your experiment id
-save_to_api = true
-```
diff --git a/docs/getting-started/comet.md b/docs/getting-started/comet.md
deleted file mode 100644
index bf1ee525d..000000000
--- a/docs/getting-started/comet.md
+++ /dev/null
@@ -1,52 +0,0 @@
-# Comet Integration {#comet}
-
-CodeCarbon can be automatically integrated with
-[Comet](https://www.comet.ml/site/) for experiment tracking and
-visualization. Comet provides data scientists with powerful tools to
-track, compare, explain, and reproduce their experiments. Now, with
-CodeCarbon you can easily track the carbon footprint of your jobs along
-with your training metrics, hyperparameters, dataset samples, artifacts,
-and more.
-
-{.align-center width="700px" height="400px"}
-
-To get started with the Comet-CodeCarbon integration, make sure you have
-comet-ml installed:
-
-``` python
-pip install comet_ml>=3.2.2
-```
-
-Go to [Comet's website](https://www.comet.ml/site/) and create a free
-account. From your account settings page, copy your personal API key.
-
-In the
-[mnist-comet.py](https://github.com/mlco2/codecarbon/blob/master/examples/mnist-comet.py)
-example file, replace the placeholder code with your API key:
-
-``` python
-experiment = Experiment(api_key="YOUR API KEY")
-```
-
-Run your experiment and click on the link in stdout to be taken back to
-the Comet UI. You'll automatically see your metrics, hyperparameters,
-graph definition, system and environment details, and more.
-
-Comet will automatically create an `EmissionsTracker` object from the
-`codecarbon` package when your code runs. To visualize the carbon
-footprint of your experiment, go to the `Panel` tab in the left sidebar
-and click `Add Panel`.
-
-From the Panel Gallery click the `Public` tab and search for
-`CodeCarbon Footprint`. Once you've found it, add it to your
-Experiment.
-
-{.align-center width="700px" height="400px"}
-
-Now back in the `Panels` tab you'll see your CodeCarbon Footprint
-visualization in the Comet UI. To render the visualization by default,
-save your `View`. And voilร ! Every time you run your experiments,
-you'll be able to visualize your CodeCarbon emissions data alongside
-everything else you need to track for your research.
-
-{.align-center width="700px" height="400px"}
diff --git a/docs/getting-started/examples.md b/docs/getting-started/examples.md
deleted file mode 100644
index b23b7b0bc..000000000
--- a/docs/getting-started/examples.md
+++ /dev/null
@@ -1,134 +0,0 @@
-# Examples
-
-Following are examples to train a Deep Learning model on MNIST Data to
-recognize digits in images using TensorFlow.
-
-## Using the Decorator
-
-This is the simplest way to use the CodeCarbon tracker with two lines of
-code. You just need to copy-paste `from codecarbon import track_emissions`
-and add the `@track_emissions` decorator to your training function. The emissions will be tracked
-automatically and printed at the end of the training.
-
-But you can't get them in your code, see the Context Manager section
-below for that.
-
-``` python
-import tensorflow as tf
-from codecarbon import track_emissions
-
-
-@track_emissions(project_name="mnist")
-def train_model():
- mnist = tf.keras.datasets.mnist
- (x_train, y_train), (x_test, y_test) = mnist.load_data()
- x_train, x_test = x_train / 255.0, x_test / 255.0
- model = tf.keras.models.Sequential(
- [
- tf.keras.layers.Flatten(input_shape=(28, 28)),
- tf.keras.layers.Dense(128, activation="relu"),
- tf.keras.layers.Dropout(0.2),
- tf.keras.layers.Dense(10),
- ]
- )
- loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-
- model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
-
- model.fit(x_train, y_train, epochs=10)
-
- return model
-
-
-if __name__ == "__main__":
- model = train_model()
-```
-
-## Using the Context Manager
-
-We think this is the best way to use CodeCarbon. Still only two lines of
-code, and you can get the emissions in your code.
-
-``` python
-import tensorflow as tf
-
-from codecarbon import EmissionsTracker
-
-mnist = tf.keras.datasets.mnist
-
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-x_train, x_test = x_train / 255.0, x_test / 255.0
-
-
-model = tf.keras.models.Sequential(
- [
- tf.keras.layers.Flatten(input_shape=(28, 28)),
- tf.keras.layers.Dense(128, activation="relu"),
- tf.keras.layers.Dropout(0.2),
- tf.keras.layers.Dense(10),
- ]
-)
-
-loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-
-with EmissionsTracker() as tracker:
- model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
- model.fit(x_train, y_train, epochs=10)
-
-# Display the emissions data
-print(f"\nCarbon emissions from computation: {tracker.final_emissions * 1000:.4f} g CO2eq")
-print("\nDetailed emissions data:", tracker.final_emissions_data)
-```
-
-## Using the Explicit Object
-
-This is the recommended way to use the CodeCarbon tracker in a Notebook
-: you instantiate the tracker and call the `start()` method
-at the beginning of the Notebook. You call the `stop()` method at the end
-of the Notebook to stop the tracker and get the emissions.
-
-If not in an interactive Notebook, always use a
-`try...finally` block to ensure that the tracker is stopped
-even if an error occurs during training. This is important to ensure the
-CodeCarbon scheduler is stopped. If you don't use
-`try...finally`, the scheduler will continue running in the
-background after your computation code has crashed, so your program will
-never finish.
-
-``` python
-import tensorflow as tf
-
-from codecarbon import EmissionsTracker
-
-mnist = tf.keras.datasets.mnist
-
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-x_train, x_test = x_train / 255.0, x_test / 255.0
-
-
-model = tf.keras.models.Sequential(
- [
- tf.keras.layers.Flatten(input_shape=(28, 28)),
- tf.keras.layers.Dense(128, activation="relu"),
- tf.keras.layers.Dropout(0.2),
- tf.keras.layers.Dense(10),
- ]
-)
-
-loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-
-model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
-
-tracker = EmissionsTracker()
-tracker.start()
-try:
- model.fit(x_train, y_train, epochs=10)
-except Exception as e:
- print(f"An error occurred: {e}")
-finally:
- emissions: float = tracker.stop()
- print(emissions)
-```
-
-Other examples are available in the [project GitHub
-repository](https://github.com/mlco2/codecarbon/tree/master/examples).
diff --git a/docs/getting-started/test_on_scaleway.md b/docs/getting-started/test_on_scaleway.md
deleted file mode 100644
index 7e21ec07c..000000000
--- a/docs/getting-started/test_on_scaleway.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# Test of CodeCarbon on Scaleway hardware {#test_on_scaleway}
-
-We use Scaleway hardware to test CodeCarbon on a real-world scenario. We
-use the following hardware:
-
-- **EM-I120E-NVME**: AMD EPYC 8024P, 64 GB, 2ร960 GB NVMe
-- **EM-B112X-SSD**: 2ร Intel Xeon E5-2620 v3 @ 2.40GHz
-
-85 W TDP for the Intel(R) Xeon(R) CPU E5-2620 v3 @ 2.40GHz
-
-Choose Ubuntu as OS because new version of stress-ng is not available on
-Debian 12 (Bookworm).
-
-Connect to the server:
-
-``` console
-ssh ubuntu@51.159.214.207
-```
-
-Install and run the test:
-
-``` console
-sudo chmod a+r -R /sys/class/powercap/intel-rapl/subsystem/*
-sudo apt update && sudo apt install -y git pipx python3-launchpadlib htop
-pipx ensurepath
-sudo add-apt-repository -y ppa:colin-king/stress-ng
-sudo apt update && sudo apt install -y stress-ng
-export PATH=$PATH:/home/ubuntu/.local/bin
-git clone https://github.com/mlco2/codecarbon.git
-cd codecarbon
-git checkout use-cpu-load
-curl -LsSf https://astral.sh/uv/install.sh | sh
-uv run python examples/compare_cpu_load_and_RAPL.py
-```
-
-To do a full code CPU load, we run the following command:
-
-``` console
-stress-ng --cpu 0 --cpu-method matrixprod --metrics-brief --rapl --perf -t 60s
-```
-
-Get back the data from the server:
-
-``` console
-mkdir -p codecarbon/data/hardware/cpu_load_profiling/E3-1240/
-scp ubuntu@51.159.214.207:/home/ubuntu/codecarbon/*.csv codecarbon/data/hardware/cpu_load_profiling/E5-1240/
-```
-
-You can now delete the server in the Scaleway console.
-
-For the results, see the notebook XXX.
diff --git a/docs/getting-started/usage.md b/docs/getting-started/usage.md
deleted file mode 100644
index d811a94c6..000000000
--- a/docs/getting-started/usage.md
+++ /dev/null
@@ -1,392 +0,0 @@
-# Quickstart {#usage}
-
-The CO2 tracking tool can be used along with any computing framework. It
-supports both `online` (with internet access) and `offline` (without
-internet access) modes. The tracker can be used in the following ways:
-
-## Online Mode
-
-When the environment has internet access, the `EmissionsTracker` object
-or the `track_emissions` decorator can be used, which has the `offline`
-parameter set to `False` by default.
-
-### Command line {#usage-command-line}
-
-Create a minimal configuration file (just follow the prompts) :
-
-``` console
-codecarbon config
-```
-
-[{.align-center}](https://asciinema.org/a/667970)
-
-Then login from your terminal to authenticate CLI/API usage:
-
-``` console
-codecarbon login
-```
-
-You can use the same command to modify an existing config :
-
-[{.align-center}](https://asciinema.org/a/667971)
-
-If you want to track the emissions of a computer without having to
-modify your code, you can use :
-
-``` console
-codecarbon monitor
-```
-
-You have to stop the monitoring manually with `Ctrl+C`.
-
-If you only need local measurement and do not want to send data to the API,
-use:
-
-``` console
-codecarbon monitor --no-api
-```
-
-If you want to detect the hardware of your computer without starting any
-measurement, you can use:
-
-``` console
-codecarbon detect
-```
-
-It will print the detected RAM, CPU and GPU information.
-
-In the following example you will see how to use the CLI to monitor all
-the emissions of you computer and sending everything to an API running
-on "localhost:8008" (Or you can start a private local API with
-"docker-compose up"). Using the public API with this is not supported
-yet (coming soon!)
-
-[{.align-center}](https://asciinema.org/a/667984)
-
-The command line could also works without internet by providing the
-country code like this:
-
-``` console
-codecarbon monitor --offline --country-iso-code FRA
-```
-
-### Running Any Command with CodeCarbon
-
-If you want to track emissions while running any command or program (not
-just Python scripts), you can use the `codecarbon monitor --` command.
-This allows non-Python users to measure machine emissions during the
-execution of any command:
-
-``` console
-codecarbon monitor --
-```
-
-Do not surround `` with quotes. The double hyphen `--`
-indicates the end of CodeCarbon options and the beginning of the command
-to run.
-
-**Examples:**
-
-``` console
-# Run a shell script
-codecarbon monitor -- ./benchmark.sh
-
-# Run a command with arguments (use quotes for special characters)
-codecarbon monitor -- bash -c 'echo "Processing..."; sleep 30; echo "Done!"'
-
-# Run Python scripts
-codecarbon monitor -- python train_model.py
-
-# Run Node.js applications
-codecarbon monitor -- node app.js
-
-# Run tests with output redirection
-codecarbon monitor -- npm run test > output.txt
-
-# Display the CodeCarbon detailed logs
-codecarbon monitor --log-level debug -- python --version
-```
-
-**Output:**
-
-When the command completes, CodeCarbon displays a summary report and
-saves the emissions data to a CSV file:
-
-``` console
-๐ฑ CodeCarbon: Starting emissions tracking...
- Command: bash -c echo "Processing..."; sleep 30; echo "Done!"
-
-Processing...
-Done!
-
-============================================================
-๐ฑ CodeCarbon Emissions Report
-============================================================
- Command: bash -c echo "Processing..."; sleep 30; echo "Done!"
- Emissions: 0.0317 g CO2eq
- Saved to: /home/user/emissions.csv
- โ ๏ธ Note: Measured entire machine (includes all system processes)
-============================================================
-```
-
-!!! note "Note"
-
- The `codecarbon monitor --` command tracks process-level emissions (only
- the specific command), not the entire machine. For machine-level
- tracking, use the `codecarbon monitor` command.
-
- For more fine-grained tracking, implementing CodeCarbon in your code
- allows you to track the emissions of a specific block of code.
-
-### Explicit Object
-
-In the case of absence of a single entry and stop point for the training
-code base, users can instantiate a `EmissionsTracker` object and pass it
-as a parameter to function calls to start and stop the emissions
-tracking of the compute section.
-
-``` python
-from codecarbon import EmissionsTracker
-tracker = EmissionsTracker()
-tracker.start()
-try:
- # Compute intensive code goes here
- _ = 1 + 1
-finally:
- tracker.stop()
-```
-
-This mode is recommended when using a Jupyter Notebook. You call
-`tracker.start()` at the beginning of the Notebook, and call
-`tracker.stop()` in the last cell.
-
-This mode also allows you to record the monitoring with
-`tracker.flush()` that writes the emissions to disk or call the API
-depending on the configuration, but keep running the experiment.
-
-If you want to monitor small piece of code, like a model inference, you
-could use the task manager:
-
-``` python
-try:
- tracker = EmissionsTracker(project_name="bert_inference", measure_power_secs=10)
- tracker.start_task("load dataset")
- dataset = load_dataset("imdb", split="test")
- imdb_emissions = tracker.stop_task()
- tracker.start_task("build model")
- model = build_model()
- model_emissions = tracker.stop_task()
-finally:
- _ = tracker.stop()
-```
-
-This way CodeCarbon will track the emissions of each task . The task
-will not be written to disk to prevent overhead, you have to get the
-results from the return of `stop_task()`. If no name is provided,
-CodeCarbon will generate a uuid.
-
-Please note that you can't use task mode and normal mode at the same
-time. Because `start_task` will stop the scheduler as we do not want it
-to interfere with the task measurement.
-
-### Context manager
-
-The `Emissions tracker` also works as a context manager.
-
-``` python
-from codecarbon import EmissionsTracker
-
-with EmissionsTracker() as tracker:
- # Compute intensive training code goes here
-```
-
-This mode is recommended when you want to monitor a specific block of
-code.
-
-### Decorator
-
-In case the training code base is wrapped in a function, users can use
-the decorator `@track_emissions` within the function to enable tracking
-emissions of the training code.
-
-``` python
-from codecarbon import track_emissions
-
-@track_emissions
-def training_loop():
- # Compute intensive training code goes here
-```
-
-This mode is recommended if you have a training function.
-
-!!! note "Note"
-
- This will write a csv file named emissions.csv in the current directory.
-
-## Offline Mode
-
-An offline version is available to support restricted environments
-without internet access. The internal computations remain unchanged;
-however, a `country_iso_code` parameter, which corresponds to the
-3-letter alphabet ISO Code of the country where the compute
-infrastructure is hosted, is required to fetch Carbon Intensity details
-of the regional electricity used. A complete list of country ISO codes
-can be found on
-[Wikipedia](https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes).
-
-### Explicit Object
-
-Developers can use the `OfflineEmissionsTracker` object to track
-emissions as follows:
-
-``` python
-from codecarbon import OfflineEmissionsTracker
-tracker = OfflineEmissionsTracker(country_iso_code="CAN")
-tracker.start()
-# GPU intensive training code
-tracker.stop()
-```
-
-### Context manager
-
-The `OfflineEmissionsTracker` also works as a context manager
-
-``` python
-from codecarbon import OfflineEmissionsTracker
-
-with OfflineEmissionsTracker() as tracker:
-# GPU intensive training code goes here
-```
-
-### Decorator
-
-The `track_emissions` decorator in offline mode requires following two
-parameters:
-
-- `offline` needs to be set to `True`, which defaults to `False` for
- online mode.
-- `country_iso_code` the 3-letter alphabet ISO Code of the country
- where the compute infrastructure is hosted
-
-```python
-from codecarbon import track_emissions
-@track_emissions(offline=True, country_iso_code="CAN")
-def training_loop():
- # training code goes here
- pass
-```
-
-The Carbon emissions will be saved to a `emissions.csv` file in the same
-directory. Please refer to the
-[complete API](parameters.md) for additional
-parameters and configuration options.
-
-# Configuration
-
-## Configuration priority
-
-CodeCarbon is structured so that you can configure it in a hierarchical manner:
-
-- *global* parameters in your home folder `~/.codecarbon.config`
-- *local* parameters (with respect to the current working
- directory) in `./.codecarbon.config`
-- *environment variables* parameters starting with `CODECARBON_`
-- *script* parameters in the tracker's initialization as
- `EmissionsTracker(param=value)`
-
-!!! warning "Configuration files"
-
- Configuration files **must** be named `.codecarbon.config` and start
- with a section header `[codecarbon]` as the first line in the file.
-
- For instance:
-
- - `~/.codecarbon.config`
-
- ``` bash
- [codecarbon]
- measure_power_secs=10
- save_to_file=local-overwrite
- emissions_endpoint=localhost:7777
- ```
-
- - `./.codecarbon.config` will override `~/.codecarbon.config` if the
- same parameter is set in both files:
-
- ``` bash
- [codecarbon]
- save_to_file = true
- output_dir = /Users/victor/emissions
- electricitymaps_api_token=script-overwrite
- experiment_id = 235b1da5-aaaa-aaaa-aaaa-893681599d2c
- log_level = DEBUG
- tracking_mode = process
- ```
-
- - environment variables will override `./.codecarbon.config` if the
- same parameter is set in both files:
-
- ``` bash
- export CODECARBON_GPU_IDS="0, 1"
- export CODECARBON_LOG_LEVEL="WARNING"
- ```
-
- - script parameters will override environment variables if the same
- parameter is set in both:
-
- ``` python
- EmissionsTracker(
- api_call_interval=4,
- save_to_api=True,
- electricitymaps_api_token="some-token")
- ```
-
-Yields attributes:
-
-``` python
-{
- "measure_power_secs": 10, # from ~/.codecarbon.config
- "save_to_file": True, # from ./.codecarbon.config (override ~/.codecarbon.config)
- "api_call_interval": 4, # from script
- "save_to_api": True, # from script
- "experiment_id": "235b1da5-aaaa-aaaa-aaaa-893681599d2c", # from ./.codecarbon.config
- "log_level": "WARNING", # from environment variable (override ./.codecarbon.config)
- "tracking_mode": "process", # from ./.codecarbon.config
- "emissions_endpoint": "localhost:7777", # from ~/.codecarbon.config
- "output_dir": "/Users/victor/emissions", # from ./.codecarbon.config
- "electricitymaps_api_token": "some-token", # from script (override ./.codecarbon.config)
- "gpu_ids": [0, 1], # from environment variable
-}
-```
-
-!!! note "Note"
-
- If you're wondering about the configuration files' syntax, be aware
- that under the hood `codecarbon` uses
- [`ConfigParser`](https://docs.python.org/3/library/configparser.html#module-configparser)
- which relies on the [INI
- syntax](https://docs.python.org/3/library/configparser.html#supported-ini-file-structure).
-
-## Access internet through proxy server
-
-If you need a proxy to access internet, which is needed to call a Web
-API, like [Codecarbon API](https://api.codecarbon.io/docs), you have to
-set environment variable `HTTPS_PROXY`, or *HTTP_PROXY* if calling an
-`http://` endpoint.
-
-You could do it in your shell:
-
-``` shell
-export HTTPS_PROXY="http://0.0.0.0:0000"
-```
-
-Or in your Python code:
-
-``` python
-import os
-
-os.environ["HTTPS_PROXY"] = "http://0.0.0.0:0000"
-```
-
-For more information, please read the [requests library proxy
-documentation](https://requests.readthedocs.io/en/latest/user/advanced/#proxies)
diff --git a/docs/contributing/agents-instructions.md b/docs/how-to/agent-instructions.md
similarity index 97%
rename from docs/contributing/agents-instructions.md
rename to docs/how-to/agent-instructions.md
index 756d65bf8..35e65e10a 100644
--- a/docs/contributing/agents-instructions.md
+++ b/docs/how-to/agent-instructions.md
@@ -152,14 +152,14 @@ uv run task -l
## Documentation and Help
### Key Documentation Files
-- **[docs/contributing/CONTRIBUTING.md](docs/contributing/CONTRIBUTING.md)**: Detailed contribution guidelines and setup
+- **[docs/how-to/contributing.md](docs/how-to/contributing.md)**: Detailed contribution guidelines and setup
- **[README.md](README.md)**: Project overview and quickstart
- **[carbonserver/README.md](carbonserver/README.md)**: API architecture and database schema
- **[webapp/README.md](webapp/README.md)**: Next.js dashboard setup
- **[carbonserver/tests/TESTING.md](carbonserver/tests/TESTING.md)**: Comprehensive testing guide
### VS Code Debugging
-The repository includes VS Code launch configurations in docs/contributing/CONTRIBUTING.md for:
+The repository includes VS Code launch configurations in docs/how-to/contributing.md for:
- Debugging current Python file
- Running pytest with debugger
- Testing codecarbon CLI monitor
diff --git a/docs/how-to/agents.md b/docs/how-to/agents.md
new file mode 100644
index 000000000..cd0e0c465
--- /dev/null
+++ b/docs/how-to/agents.md
@@ -0,0 +1,109 @@
+# Track Carbon Emissions with LLMs and Agents
+
+Running large language models (LLMs) and AI agents locally with open-source models lets you build intelligent applications without relying on cloud APIs. CodeCarbon measures the carbon impact of running these local models and agents, helping you understand the environmental cost of inference and reasoning tasks.
+
+## Installation
+
+```console
+pip install codecarbon transformers torch
+```
+
+## Running Local Model Inference
+
+Here's how to track the carbon emissions of running inference with a local language model:
+
+```python
+# mktestdocs: skip
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from codecarbon import EmissionsTracker
+import torch
+
+# Load a lightweight open-source model
+model_name = "HuggingFaceTB/SmolLM2-135M-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Move to GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+# Track inference emissions
+with EmissionsTracker() as tracker:
+ messages = [
+ {"role": "user", "content": "What are the benefits of renewable energy?"}
+ ]
+
+ # Format input for the model
+ inputs = tokenizer.apply_chat_template(
+ messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
+ ).to(device)
+
+ # Generate response
+ outputs = model.generate(inputs, max_new_tokens=100, temperature=0.7)
+ response = tokenizer.decode(outputs[0])
+
+print(f"Inference emissions: {tracker.final_emissions:.6f} kg CO2eq")
+print(f"Model response: {response}")
+```
+
+## What Gets Logged
+
+When you run the example above, CodeCarbon creates an `emissions.csv` file in your working directory with columns including:
+
+- `timestamp`: when the measurement was taken
+- `duration`: how long the inference took
+- `emissions`: CO2 in kg
+- `energy_kwh`: energy consumed in kilowatt-hours
+- `cpu_power`: CPU power in watts
+- `gpu_power`: GPU power in watts (if the model is running on GPU)
+
+## Comparing Different Models
+
+You can measure the carbon impact of different model sizes or model architectures:
+
+```python
+# mktestdocs: skip
+models = [
+ "HuggingFaceTB/SmolLM2-135M-Instruct", # 135M parameters
+ "HuggingFaceTB/SmolLM-360M-Instruct", # 360M parameters
+]
+
+prompt = "Explain machine learning in one sentence."
+
+for model_name in models:
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
+ model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+
+ with EmissionsTracker(save_file_path=f"emissions_{model_name.split('/')[-1]}.csv") as tracker:
+ inputs = tokenizer(prompt, return_tensors="pt").to(device)
+ outputs = model.generate(inputs["input_ids"], max_new_tokens=50)
+
+ print(f"{model_name}: {tracker.final_emissions:.6f} kg CO2eq")
+```
+
+## Benefits of Local Models
+
+- **Privacy**: No data leaves your machine
+- **Cost**: No API charges or rate limits
+- **Control**: Full visibility into model behavior and resource usage
+- **Sustainability**: Run efficient open-source models aligned with your carbon budget
+
+## Finding Lightweight Models
+
+Popular lightweight open-source models for local inference:
+- **SmolLM2 family** โ 135M to 1.7B parameters, fast and efficient
+- **Phi family** โ Compact models with strong performance
+- **Mistral** โ Small but capable models
+- **TinyLlama** โ 1.1B parameter model, ideal for edge devices
+
+Smaller models consume less energy while still providing useful inference capabilities.
+
+## Comparing Model Efficiency
+
+To understand the trade-offs between different models, batch sizes, and their carbon impact, see [Comparing Model Efficiency](../how-to/comparing-model-efficiency.md). You can apply the same patterns to compare different open-source models running locally.
+
+## Next Steps
+
+- [Configure CodeCarbon](configuration.md) to customize tracking behavior
+- [Send emissions data to the cloud](cloud-api.md) to visualize inference emissions across multiple runs
+- Explore other frameworks in [HuggingFace Transformers](transformers.md) or [Diffusers](diffusers.md)
diff --git a/docs/contributing/AI_POLICY.md b/docs/how-to/ai_policy.md
similarity index 97%
rename from docs/contributing/AI_POLICY.md
rename to docs/how-to/ai_policy.md
index 6cf78aafa..48d358825 100644
--- a/docs/contributing/AI_POLICY.md
+++ b/docs/how-to/ai_policy.md
@@ -76,7 +76,7 @@ The **canonical and up-to-date instructions for AI reviewers** are maintained in
- The expected reviewer persona and responsibilities
- The checks to perform on PR descriptions, code, tests, and comments
-- The required enforcement of the laws defined in this `AI_POLICY.md`
+- The required enforcement of the laws defined in this `docs/how-to/ai_policy.md`
Any other document (including this one) should treat `copilot-instructions.md` as the single source of truth for AI reviewer behavior. When updating reviewer logic, update `copilot-instructions.md` first and, if needed, adjust references here.
@@ -90,4 +90,4 @@ But for the core logic of the product, we want to ensure that humans fully under
## Additional Resources
-For comprehensive guidance on contributing to CodeCarbon, including development workflows, code quality standards, testing practices, and AI-assisted development best practices, see the [CONTRIBUTING.md](docs/contributing/CONTRIBUTING.md).
+For comprehensive guidance on contributing to CodeCarbon, including development workflows, code quality standards, testing practices, and AI-assisted development best practices, see the [CONTRIBUTING.md](docs/how-to/contributing.md).
diff --git a/docs/advanced/ansible.md b/docs/how-to/ansible.md
similarity index 58%
rename from docs/advanced/ansible.md
rename to docs/how-to/ansible.md
index b10592612..c65a0ba4b 100644
--- a/docs/advanced/ansible.md
+++ b/docs/how-to/ansible.md
@@ -43,23 +43,35 @@ codecarbon/deploy/ansible/codecarbon_cli_as_a_service/
## Quick Start
-1. Set the the target to install in `hosts`:
+### Step 1: Configure Target Hosts
- ``` text
- yourservername.yourdomain.com hostname=yourservername ansible_user=root ansible_ssh_private_key_file=~/.ssh/id_ed25519
- ```
+Set the target server to install on in the `hosts` file:
-2. Update the variables in `vars/main.yml` with your configuration:
+``` text
+yourservername.yourdomain.com hostname=yourservername ansible_user=root ansible_ssh_private_key_file=~/.ssh/id_ed25519
+```
+
+### Step 2: Update Ansible Variables
- ``` yaml
- organization_id: your_org_id
- project_id: your_project_id
- experiment_id: your_experiment_id
- api_key: your_api_key
- ```
+Update your CodeCarbon API credentials in `vars/main.yml`:
+
+``` yaml
+organization_id: your_org_id
+project_id: your_project_id
+experiment_id: your_experiment_id
+api_key: your_api_key
+```
+
+### Step 3: Run the Playbook
+
+Execute the Ansible playbook to deploy CodeCarbon:
+
+``` bash
+ansible-playbook -i hosts tasks/main.yml
+```
-3. Run the playbook:
+## Next Steps
- ``` bash
- ansible-playbook -i hosts tasks/main.yml
- ```
+- [Install CodeCarbon as a Linux Service](linux-service.md) for manual setup details
+- [Send emissions data to the cloud](cloud-api.md) to view results on the dashboard
+- [Configure CodeCarbon](configuration.md) for additional customization
diff --git a/docs/how-to/cloud-api.md b/docs/how-to/cloud-api.md
new file mode 100644
index 000000000..f7bd4888d
--- /dev/null
+++ b/docs/how-to/cloud-api.md
@@ -0,0 +1,64 @@
+# Use the Cloud API & Dashboard
+
+This guide shows how to send your emissions data to the CodeCarbon cloud platform, where you can visualize results and collaborate with your team on a shared dashboard.
+
+!!! warning "API Mode"
+
+ API mode uploads your emissions data to CodeCarbon's central server. Thanks to [CleverCloud](https://www.clever.cloud/), usage is free within reasonable limits.
+
+{.align-center width="700px" height="400px"}
+
+## Prerequisites
+
+First, we'll create an account and authenticate your local environment:
+
+1. Create an account on the [CodeCarbon dashboard](https://dashboard.codecarbon.io/)
+2. Run `codecarbon login` from your terminal to authenticate
+
+The login command will create a default project and save your credentials to `.codecarbon.config`.
+
+## Send Emissions from Your Code
+
+With your account set up, you're ready to start sending emissions data. Use the `save_to_api=True` parameter to upload tracking data:
+
+``` python
+from codecarbon import track_emissions
+
+@track_emissions(save_to_api=True)
+def train_model():
+ # GPU intensive training code goes here
+
+if __name__ =="__main__":
+ train_model()
+```
+
+The decorator will automatically send your emissions data to the dashboard. You can also specify additional options in `@track_emissions()` or in `.codecarbon.config`.
+
+## Create Projects & Experiments
+
+By default, `codecarbon login` creates a default experiment in your first project. If you want to organize runs by experiment, you can specify an `experiment_id` explicitly. Set the experiment ID in two ways:
+
+**Option 1: In your code**
+
+``` python
+from codecarbon import track_emissions
+
+@track_emissions(
+ experiment_id="your experiment id",
+ save_to_api=True,
+)
+def train_model():
+ ...
+```
+
+**Option 2: In `.codecarbon.config`**
+
+``` ini
+[codecarbon]
+experiment_id = your experiment id
+save_to_api = true
+```
+
+## View Your Results
+
+Once your runs complete, visit the [CodeCarbon dashboard](https://dashboard.codecarbon.io/) to see your results. For more visualization options, see the [visualization guide](visualize.md).
diff --git a/docs/contributing/CODE_OF_CONDUCT.md b/docs/how-to/code-of-conduct.md
similarity index 100%
rename from docs/contributing/CODE_OF_CONDUCT.md
rename to docs/how-to/code-of-conduct.md
diff --git a/docs/how-to/comet.md b/docs/how-to/comet.md
new file mode 100644
index 000000000..68e7e8953
--- /dev/null
+++ b/docs/how-to/comet.md
@@ -0,0 +1,75 @@
+# Integrate with Comet {#comet}
+
+CodeCarbon integrates seamlessly with [Comet](https://www.comet.ml/site/), a powerful experiment tracking and visualization platform. This integration allows you to track the carbon footprint of your machine learning experiments alongside your training metrics, hyperparameters, and other experiment details.
+
+{.align-center width="700px" height="400px"}
+
+## Prerequisites
+
+Before you begin, you'll need:
+
+1. A [Comet account](https://www.comet.ml/site/) (free tier available)
+2. Your Comet API key (available in your account settings)
+
+## Installation
+
+Install the required Comet ML library:
+
+``` python
+pip install comet_ml>=3.2.2
+```
+
+## Setup Steps
+
+### Step 1: Create a Comet Account
+
+1. Go to [Comet's website](https://www.comet.ml/site/) and create a free account
+2. From your account settings page, copy your personal API key
+
+### Step 2: Configure Your Experiment
+
+In your Python script, initialize a Comet experiment with your API key:
+
+``` python
+from comet_ml import Experiment
+from codecarbon import EmissionsTracker
+
+experiment = Experiment(api_key="YOUR API KEY")
+```
+
+### Step 3: Run Your Experiment
+
+Run your experiment as normal. CodeCarbon will automatically create an `EmissionsTracker` object that Comet will track:
+
+``` python
+# Your training code here
+model.fit(X_train, y_train)
+```
+
+### Step 4: Add the CodeCarbon Footprint Panel
+
+Once your experiment completes, view it in the Comet UI:
+
+1. Click on the `Panel` tab in the left sidebar
+2. Click `Add Panel`
+3. In the Panel Gallery, click the `Public` tab
+4. Search for `CodeCarbon Footprint`
+5. Add the panel to your experiment
+
+{.align-center width="700px" height="400px"}
+
+### Step 5: Save Your View
+
+To automatically display the CodeCarbon visualization in future experiments, save your `View` from the `Panels` tab.
+
+{.align-center width="700px" height="400px"}
+
+## Example
+
+A complete working example is available in the CodeCarbon repository at [examples/mnist-comet.py](https://github.com/mlco2/codecarbon/blob/master/examples/mnist-comet.py).
+
+## Next Steps
+
+- [Configure CodeCarbon](configuration.md) to customize tracking behavior
+- [Send emissions data to the cloud](cloud-api.md) for additional visualization options
+- Explore other logging options in [Log to External Systems](logging.md)
diff --git a/docs/how-to/configuration.md b/docs/how-to/configuration.md
new file mode 100644
index 000000000..e8b42b929
--- /dev/null
+++ b/docs/how-to/configuration.md
@@ -0,0 +1,109 @@
+# Configure CodeCarbon
+
+## Configuration priority
+
+CodeCarbon is structured so that you can configure it in a hierarchical manner:
+
+- *global* parameters in your home folder `~/.codecarbon.config`
+- *local* parameters (with respect to the current working
+ directory) in `./.codecarbon.config`
+- *environment variables* parameters starting with `CODECARBON_`
+- *script* parameters in the tracker's initialization as
+ `EmissionsTracker(param=value)`
+
+!!! warning "Configuration files"
+
+ Configuration files **must** be named `.codecarbon.config` and start
+ with a section header `[codecarbon]` as the first line in the file.
+
+ For instance:
+
+ - `~/.codecarbon.config`
+
+ ``` bash
+ [codecarbon]
+ measure_power_secs=10
+ save_to_file=local-overwrite
+ emissions_endpoint=localhost:7777
+ ```
+
+ - `./.codecarbon.config` will override `~/.codecarbon.config` if the
+ same parameter is set in both files:
+
+ ``` bash
+ [codecarbon]
+ save_to_file = true
+ output_dir = /Users/victor/emissions
+ electricitymaps_api_token=script-overwrite
+ experiment_id = 235b1da5-aaaa-aaaa-aaaa-893681599d2c
+ log_level = DEBUG
+ tracking_mode = process
+ ```
+
+ - environment variables will override `./.codecarbon.config` if the
+ same parameter is set in both files:
+
+ ``` bash
+ export CODECARBON_GPU_IDS="0, 1"
+ export CODECARBON_LOG_LEVEL="WARNING"
+ ```
+
+ - script parameters will override environment variables if the same
+ parameter is set in both:
+
+ ``` python
+ EmissionsTracker(
+ api_call_interval=4,
+ save_to_api=True,
+ electricitymaps_api_token="some-token")
+ ```
+
+Yields attributes:
+
+``` python
+{
+ "measure_power_secs": 10, # from ~/.codecarbon.config
+ "save_to_file": True, # from ./.codecarbon.config (override ~/.codecarbon.config)
+ "api_call_interval": 4, # from script
+ "save_to_api": True, # from script
+ "experiment_id": "235b1da5-aaaa-aaaa-aaaa-893681599d2c", # from ./.codecarbon.config
+ "log_level": "WARNING", # from environment variable (override ./.codecarbon.config)
+ "tracking_mode": "process", # from ./.codecarbon.config
+ "emissions_endpoint": "localhost:7777", # from ~/.codecarbon.config
+ "output_dir": "/Users/victor/emissions", # from ./.codecarbon.config
+ "electricitymaps_api_token": "some-token", # from script (override ./.codecarbon.config)
+ "gpu_ids": [0, 1], # from environment variable
+}
+```
+
+!!! note "Note"
+
+ If you're wondering about the configuration files' syntax, be aware
+ that under the hood `codecarbon` uses
+ [`ConfigParser`](https://docs.python.org/3/library/configparser.html#module-configparser)
+ which relies on the [INI
+ syntax](https://docs.python.org/3/library/configparser.html#supported-ini-file-structure).
+
+## Access internet through proxy server
+
+If you need a proxy to access internet, which is needed to call a Web
+API, like [Codecarbon API](https://api.codecarbon.io/docs), you have to
+set environment variable `HTTPS_PROXY`, or *HTTP_PROXY* if calling an
+`http://` endpoint.
+
+You could do it in your shell:
+
+``` shell
+export HTTPS_PROXY="http://0.0.0.0:0000"
+```
+
+Or in your Python code:
+
+``` python
+import os
+
+os.environ["HTTPS_PROXY"] = "http://0.0.0.0:0000"
+```
+
+For more information, please read the [requests library proxy
+documentation](https://requests.readthedocs.io/en/latest/user/advanced/#proxies)
diff --git a/docs/contributing/CONTRIBUTING.md b/docs/how-to/contributing.md
similarity index 100%
rename from docs/contributing/CONTRIBUTING.md
rename to docs/how-to/contributing.md
diff --git a/docs/how-to/diffusers.md b/docs/how-to/diffusers.md
new file mode 100644
index 000000000..5318c3269
--- /dev/null
+++ b/docs/how-to/diffusers.md
@@ -0,0 +1,66 @@
+# Track Carbon Emissions with HuggingFace Diffusers
+
+HuggingFace Diffusers is a library for generating images, audio, and 3D structures using diffusion models. CodeCarbon measures the carbon impact of running these generative models, helping you understand the environmental cost of image generation and other synthetic media tasks.
+
+## Installation
+
+```console
+pip install codecarbon diffusers torch transformers
+```
+
+## Generating Images
+
+Here's how to track the carbon emissions of image generation:
+
+```python
+# mktestdocs: skip
+from diffusers import StableDiffusionPipeline
+from codecarbon import EmissionsTracker
+
+# Load the model
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+pipeline = pipeline.to("cuda")
+
+# Track image generation emissions
+prompt = "A serene landscape with mountains and a lake at sunset"
+with EmissionsTracker() as tracker:
+ image = pipeline(prompt, num_inference_steps=50).images[0]
+
+image.save("generated_image.png")
+print(f"Image generation emissions: {tracker.final_emissions:.6f} kg CO2eq")
+```
+
+## What Gets Logged
+
+When you run the example above, CodeCarbon creates an `emissions.csv` file in your working directory with columns including:
+
+- `timestamp`: when the measurement was taken
+- `duration`: how long the generation took
+- `emissions`: CO2 in kg
+- `energy_kwh`: energy consumed in kilowatt-hours
+- `cpu_power`: CPU power in watts
+- `gpu_power`: GPU power in watts (typically the dominant factor)
+
+## Optimizing for Lower Emissions
+
+You can reduce the carbon cost of image generation by adjusting inference parameters:
+
+```python
+# mktestdocs: skip
+with EmissionsTracker() as tracker:
+ # Fewer inference steps = faster generation, lower emissions
+ # Trade-off: slightly lower image quality
+ image = pipeline(prompt, num_inference_steps=20).images[0]
+
+print(f"Optimized generation emissions: {tracker.final_emissions:.6f} kg CO2eq")
+```
+
+## Comparing Generation Approaches
+
+To understand the trade-offs between different model sizes, inference steps, and their carbon impact, see [Comparing Model Efficiency](../tutorials/comparing-model-efficiency.md). You can apply the same patterns to compare different diffusion models or generation parameters.
+
+## Next Steps
+
+- [Configure CodeCarbon](configuration.md) to customize tracking behavior
+- [Send emissions data to the cloud](cloud-api.md) to visualize emissions across multiple generation runs
+- Explore other frameworks in [HuggingFace Transformers](transformers.md) or [local model agents](agents.md)
diff --git a/docs/how-to/enable-rapl.md b/docs/how-to/enable-rapl.md
new file mode 100644
index 000000000..8f4a76fe8
--- /dev/null
+++ b/docs/how-to/enable-rapl.md
@@ -0,0 +1,144 @@
+# Improve Measurement Accuracy with RAPL
+
+RAPL (Running Average Power Limit) is a hardware feature on modern Intel and AMD processors that provides direct energy measurements through CPU counters. Enabling RAPL access gives CodeCarbon significantly more accurate CPU power measurements compared to software-based estimation.
+
+## How RAPL Improves Accuracy
+
+Without RAPL, CodeCarbon estimates CPU power based on hardware specifications and CPU load. With RAPL enabled, CodeCarbon reads actual energy consumption directly from the processor's energy counters, providing:
+
+- โ
**Direct hardware measurements** โ Read CPU energy directly from RAPL counters
+- โ
**Higher precision** โ Microjoule-level accuracy instead of estimates
+- โ
**Multi-domain support** โ Measure package, core, uncore, DRAM, and GPU separately
+- โ
**Real-time data** โ No delay or aggregation artifacts
+
+## Prerequisites
+
+- Linux system with RAPL-capable CPU (Intel Skylake or newer, AMD Ryzen, EPYC, etc.)
+- Linux kernel 5.8+ (for AMD CPU support)
+- `sudo` access to configure permissions
+- CodeCarbon installed
+
+## Check RAPL Availability
+
+First, verify that your CPU supports RAPL:
+
+```bash
+ls /sys/class/powercap/intel-rapl*
+```
+
+If the command returns directories (e.g., `intel-rapl:0`, `intel-rapl:1`), your system has RAPL support.
+
+## Setup Steps
+
+### Step 1: Understand the Security Issue
+
+Due to [CVE-2020-8694](https://www.cve.org/CVERecord?id=CVE-2020-8694), Linux distributions restrict RAPL file permissions to root-only for security. This prevents unprivileged users from reading fine-grained power data.
+
+### Step 2: Temporary Access (Testing)
+
+To quickly test RAPL without permanent changes:
+
+```bash
+sudo chmod -R a+r /sys/class/powercap/*
+```
+
+This grants read access to all users. However, **permissions are lost at next reboot**, so this is only for testing.
+
+### Step 3: Permanent Access (Recommended)
+
+For permanent access that survives reboots, use `sysfsutils`:
+
+**Step 3a: Install sysfsutils**
+
+```bash
+sudo apt install sysfsutils
+```
+
+**Step 3b: Configure RAPL Permissions**
+
+Edit the sysfsutils configuration:
+
+```bash
+sudo nano /etc/sysfs.conf
+```
+
+Add this line at the end:
+
+```text
+mode class/powercap/intel-rapl:0/energy_uj = 0444
+```
+
+Save and exit (`Ctrl+X`, then `Y`, then `Enter`).
+
+**Step 3c: Reboot to Apply Changes**
+
+```bash
+sudo reboot
+```
+
+### Step 4: (Optional) More Restrictive Permissions
+
+For better security, you can create a dedicated group instead of allowing all users:
+
+```bash
+# Create a codecarbon group
+sudo groupadd codecarbon
+
+# Add your user to the group
+sudo usermod -a -G codecarbon $USER
+
+# Update sysfs.conf with group permissions
+sudo nano /etc/sysfs.conf
+```
+
+Update the line to:
+
+```text
+mode class/powercap/intel-rapl:0/energy_uj = 0440
+owner class/powercap/intel-rapl:0/energy_uj = root:codecarbon
+```
+
+Log out and back in for group membership to take effect:
+
+```bash
+logout
+# Then log back in
+```
+
+### Step 5: Verify RAPL Access
+
+Test that CodeCarbon can now read RAPL data:
+
+```bash
+python -c "from codecarbon import EmissionsTracker; t = EmissionsTracker(); t.start(); import time; time.sleep(5); print(t.stop())"
+```
+
+Check the output for `CPU Tracking Method: RAPL` to confirm RAPL is active.
+
+## Docker and Containerized Environments
+
+If running CodeCarbon in Docker, mount the RAPL sysfs:
+
+```bash
+docker run --device /sys/class/powercap:/sys/class/powercap:ro
+```
+
+Or in `docker-compose.yml`:
+
+```yaml
+volumes:
+ - /sys/class/powercap:/sys/class/powercap:ro
+```
+
+## Learn More
+
+To understand RAPL in detail, including domain hierarchy, double-counting issues, and CodeCarbon's domain selection strategy, see:
+
+- [RAPL Metrics Explanation](../explanation/rapl.md) โ Technical details on how RAPL works
+- [CodeCarbon Power Estimation](../explanation/power-estimation.md) โ How CodeCarbon uses RAPL data
+
+## Next Steps
+
+- [Linux Service](linux-service.md) โ Configure RAPL permissions when running CodeCarbon as a background service
+- [SLURM](slurm.md) โ Enable RAPL on HPC clusters
+- [Configure CodeCarbon](configuration.md) โ Customize which RAPL domains to measure
diff --git a/docs/getting-started/installation.md b/docs/how-to/installation.md
similarity index 82%
rename from docs/getting-started/installation.md
rename to docs/how-to/installation.md
index 554c6936d..b0d1c2fcf 100644
--- a/docs/getting-started/installation.md
+++ b/docs/how-to/installation.md
@@ -22,11 +22,9 @@ conda activate codecarbon
pip install codecarbon
```
-!!! note "Note"
+!!! warning "Conda Support"
- While CodeCarbon can be used in Conda environments, we no longer
- maintain Conda packages. We recommend using `pip install codecarbon`
- within your Conda environment, which works seamlessly with Conda.
+ While CodeCarbon can be used in Conda environments, we no longer maintain official Conda packages. We recommend using `pip install codecarbon` within your Conda environment, which works seamlessly. The conda package may be outdated and is not officially supported.
!!! note "Note"
@@ -59,7 +57,7 @@ for the latest list of the packages used.
## (Non-Python users) Standalone installer
-If you are not using Python but would like to run CodeCarbon (for instance to use the [CodeCarbon Command line](usage.md#usage-command-line)), we
+If you are not using Python but would like to run CodeCarbon (for instance to use the [CodeCarbon Command line](../tutorials/cli.md)), we
provide a standalone installer.
Use curl to download and run the script:
diff --git a/docs/how-to/linux-service.md b/docs/how-to/linux-service.md
new file mode 100644
index 000000000..7d6e358ee
--- /dev/null
+++ b/docs/how-to/linux-service.md
@@ -0,0 +1,141 @@
+# Install CodeCarbon as a Linux Service
+
+This guide shows how to install and run CodeCarbon as a systemd service on Linux (Ubuntu or Debian-based systems). This allows CodeCarbon to continuously monitor your system's carbon emissions in the background.
+
+## Prerequisites
+
+- Ubuntu or Debian-based Linux system
+- `sudo` access
+- Python 3.8+
+
+## Installation Steps
+
+### Step 1: Create a Dedicated User
+
+Create a system user for CodeCarbon to run under:
+
+``` bash
+sudo useradd -r -s /bin/false codecarbon
+```
+
+Create a directory for the CodeCarbon service:
+
+``` bash
+sudo mkdir /opt/codecarbon
+```
+
+Change the ownership of the directory to the user created above:
+
+``` bash
+sudo chown codecarbon:codecarbon /opt/codecarbon
+```
+
+### Step 2: Create a Virtual Environment
+
+Create and activate a Python virtual environment for CodeCarbon:
+
+``` bash
+sudo apt install python3-venv
+sudo -u codecarbon python3 -m venv /opt/codecarbon/.venv
+```
+
+Install CodeCarbon in the virtual environment:
+
+``` bash
+sudo -u codecarbon /opt/codecarbon/.venv/bin/pip install codecarbon
+```
+
+### Step 3: Authenticate with CodeCarbon
+
+Go to > /etc/sysfs.conf
+echo "owner class/powercap/intel-rapl:0/energy_uj = root:codecarbon" >> /etc/sysfs.conf
+```
+
+### Step 6: Create the CodeCarbon Configuration File
+
+Configure CodeCarbon with your dashboard credentials:
+
+``` bash
+sudo tee /opt/codecarbon/.codecarbon.config <
+project_id =
+experiment_id =
+api_key =
+# Verbose logging
+log_level=WARNING
+# Measure power every 30 seconds
+measure_power_secs=30
+# Send measure to API every 5 minutes (10*30 seconds)
+api_call_interval=10
+EOF
+```
+
+### Step 7: Enable and Start the Service
+
+Enable the CodeCarbon service to start on boot and start it now:
+
+``` bash
+sudo systemctl enable codecarbon
+sudo systemctl start codecarbon
+```
+
+### Step 8: Verify the Service is Running
+
+Check the service logs to confirm CodeCarbon is running correctly:
+
+``` bash
+journalctl -u codecarbon
+```
+
+## Verification
+
+You are done! CodeCarbon is now running as a systemd service on your machine.
+
+Wait 5 minutes for the first measurements to be sent to the dashboard at .out
+
+# View job details
+sinfo
```
## Troubleshooting
+### Error: AMD GPU detected but amdsmi is not properly configured
```
-Error :
-[codecarbon WARNING @ 10:28:46] AMD GPU detected but amdsmi is not properly configured. Please ensure amdsmi is correctly installed to get GPU metrics.Tips : check consistency between Python amdsmi package and ROCm versions, and ensure AMD drivers are up to date. Error: /opt/rocm/lib/libamd_smi.so: undefined symbol: amdsmi_get_cpu_affinity_with_scope
+[codecarbon WARNING @ 10:28:46] AMD GPU detected but amdsmi is not properly configured.
+Error: /opt/rocm/lib/libamd_smi.so: undefined symbol: amdsmi_get_cpu_affinity_with_scope
+```
+
+**Solution:** You have a version mismatch between `amdsmi` Python package and ROCM. Install the correct version:
+
+```bash
+# For ROCM 7.0.1:
+pip install amdsmi==7.0.1
+
+# Ensure Python version matches your requirements (3.12 for Adastra)
+python -V
```
-This mean you have a mismatch between the `amdsmi` Python package and the ROCM version installed on Adastra. To fix this, ensure you install the correct version of `amdsmi` that matches the ROCM version (e.g., `amdsmi==7.0.1` for ROCM 7.0.1).
+### Error: KeyError 'ROCM_PATH'
+
+This means the ROCm module is not loaded. Load it before running your job:
```bash
-KeyError: 'ROCM_PATH'
+module load rocm/7.0.1
```
-This means the rocm module is not loaded, load it with `module load rocm/7.0.1`.
+
+## Next Steps
+
+- [View your emissions results](cloud-api.md) on the CodeCarbon dashboard
+- [Configure CodeCarbon](configuration.md) for different measurement intervals
+- [Explore other deployment options](linux-service.md) for non-HPC systems
## Limitations and Future Work
diff --git a/docs/how-to/test-on-scaleway.md b/docs/how-to/test-on-scaleway.md
new file mode 100644
index 000000000..69ff81ea5
--- /dev/null
+++ b/docs/how-to/test-on-scaleway.md
@@ -0,0 +1,73 @@
+# Test CodeCarbon on Scaleway Hardware {#test_on_scaleway}
+
+This guide shows how to test CodeCarbon on real cloud hardware using Scaleway. Testing on actual hardware helps validate energy measurements and carbon tracking in production environments.
+
+## Hardware Overview
+
+The following Scaleway hardware configurations are available for testing:
+
+- **EM-I120E-NVME**: AMD EPYC 8024P, 64 GB RAM, 2ร960 GB NVMe SSD
+- **EM-B112X-SSD**: 2ร Intel Xeon E5-2620 v3 @ 2.40GHz (85 W TDP)
+
+## Prerequisites
+
+- Scaleway account with hardware access
+- Ubuntu as the OS (required for latest stress-ng tools; Debian 12 packages are outdated)
+- SSH access to the server
+
+## Setup Steps
+
+### Step 1: Connect to Your Scaleway Server
+
+``` console
+ssh ubuntu@
+```
+
+### Step 2: Install Dependencies and CodeCarbon
+
+Install the necessary tools and CodeCarbon on the server:
+
+``` console
+sudo chmod a+r -R /sys/class/powercap/intel-rapl/subsystem/*
+sudo apt update && sudo apt install -y git pipx python3-launchpadlib htop
+pipx ensurepath
+sudo add-apt-repository -y ppa:colin-king/stress-ng
+sudo apt update && sudo apt install -y stress-ng
+export PATH=$PATH:/home/ubuntu/.local/bin
+git clone https://github.com/mlco2/codecarbon.git
+cd codecarbon
+git checkout use-cpu-load
+curl -LsSf https://astral.sh/uv/install.sh | sh
+uv run python examples/compare_cpu_load_and_RAPL.py
+```
+
+### Step 3: Run CPU Load Test
+
+Execute a full CPU load test using stress-ng to measure power consumption:
+
+``` console
+stress-ng --cpu 0 --cpu-method matrixprod --metrics-brief --rapl --perf -t 60s
+```
+
+### Step 4: Retrieve Test Results
+
+Download the measurement data from the server to your local machine:
+
+``` console
+mkdir -p codecarbon/data/hardware/cpu_load_profiling/E3-1240/
+scp ubuntu@:/home/ubuntu/codecarbon/*.csv codecarbon/data/hardware/cpu_load_profiling/E5-1240/
+```
+
+### Step 5: Clean Up
+
+Delete the server in the Scaleway console to avoid ongoing charges.
+
+## Results and Analysis
+
+The CSV files contain measurement data from both CodeCarbon and the stress-ng tool. You can analyze the results using Jupyter notebooks or other data analysis tools.
+
+## Next Steps
+
+- [Configure CodeCarbon](configuration.md) for different measurement intervals or tracking modes
+- [Send emissions data to the cloud](cloud-api.md) for centralized tracking
+- Review hardware-specific RAPL configuration in [RAPL Metrics](../explanation/rapl.md)
diff --git a/docs/how-to/transformers.md b/docs/how-to/transformers.md
new file mode 100644
index 000000000..54e45b66f
--- /dev/null
+++ b/docs/how-to/transformers.md
@@ -0,0 +1,81 @@
+# Track Carbon Emissions with HuggingFace Transformers
+
+HuggingFace Transformers is the standard library for building and fine-tuning state-of-the-art NLP models. CodeCarbon integrates seamlessly with Transformers to measure the carbon impact of fine-tuning, inference, and other model operations.
+
+## Installation
+
+```console
+pip install codecarbon transformers torch datasets
+```
+
+## Fine-tuning a Model
+
+Here's how to track the carbon emissions when fine-tuning a HuggingFace model:
+
+```python
+# mktestdocs: skip
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
+from datasets import load_dataset
+from codecarbon import EmissionsTracker
+
+# Load a pre-trained model and tokenizer
+model_name = "distilbert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+# Load and prepare dataset
+dataset = load_dataset("imdb")
+def preprocess(batch):
+ return tokenizer(batch["text"], truncation=True, padding="max_length")
+dataset = dataset.map(preprocess, batched=True)
+
+# Track fine-tuning emissions
+with EmissionsTracker() as tracker:
+ trainer = Trainer(
+ model=model,
+ args=TrainingArguments(
+ output_dir="./results",
+ num_train_epochs=3,
+ per_device_train_batch_size=8,
+ ),
+ train_dataset=dataset["train"],
+ )
+ trainer.train()
+
+print(f"Fine-tuning emissions: {tracker.final_emissions:.6f} kg CO2eq")
+```
+
+## What Gets Logged
+
+When you run the example above, CodeCarbon creates an `emissions.csv` file in your working directory with columns including:
+
+- `timestamp`: when the measurement was taken
+- `duration`: how long the fine-tuning took
+- `emissions`: CO2 in kg
+- `energy_kwh`: energy consumed in kilowatt-hours
+- `cpu_power`: CPU power in watts
+- `gpu_power`: GPU power in watts (if applicable)
+
+## Tracking Inference
+
+You can also measure the carbon cost of running inference:
+
+```python
+# mktestdocs: skip
+from codecarbon import EmissionsTracker
+
+with EmissionsTracker() as tracker:
+ predictions = model.generate(input_ids, max_length=50)
+
+print(f"Inference emissions: {tracker.final_emissions:.6f} kg CO2eq")
+```
+
+## Comparing Fine-tuning Approaches
+
+To understand the trade-offs between different training configurations and their carbon impact, see [Comparing Model Efficiency](../tutorials/comparing-model-efficiency.md). You can apply the same patterns to compare Transformers models with different learning rates, batch sizes, or architectures.
+
+## Next Steps
+
+- [Configure CodeCarbon](configuration.md) to customize tracking behavior
+- [Send emissions data to the cloud](cloud-api.md) to visualize across multiple fine-tuning runs
+- Explore other frameworks in [Diffusers](diffusers.md) or [local model agents](agents.md)
diff --git a/docs/how-to/visualize.md b/docs/how-to/visualize.md
new file mode 100644
index 000000000..009b9f32a
--- /dev/null
+++ b/docs/how-to/visualize.md
@@ -0,0 +1,106 @@
+# Visualize
+
+CodeCarbon provides two ways to visualize your emissions data: a local Python dashboard for offline analysis, and an online web dashboard for cloud-based tracking and team collaboration.
+
+## Offline Visualization (carbonboard)
+
+The CodeCarbon package includes a local Python dashboard (`carbonboard`) for visualizing emissions data from CSV logs. This is useful for analyzing experiments offline or in environments without internet access.
+
+### Step 1: Installation
+
+The carbonboard visualization tool requires additional dependencies.
+Install them with:
+
+``` bash
+pip install 'codecarbon[carbonboard]'
+```
+
+!!! note "Note"
+
+ The `viz-legacy` extra is deprecated but still works for backwards
+ compatibility. It will be removed in v4.0.0. Please use `carbonboard`
+ instead.
+
+### Step 2: Launch the Dashboard
+
+Run the carbonboard application with your emissions data:
+
+``` bash
+carbonboard --filepath="examples/emissions.csv" --port=3333
+```
+
+**Parameters:**
+
+- `--filepath`: Path to the CSV file containing your emissions data
+- `--port`: Optional port number (default is 8050)
+
+Then open your browser to `http://localhost:3333` to view the dashboard.
+
+### Dashboard Features
+
+#### Summary and Equivalents
+
+Users can get an understanding of net power consumption and emissions
+generated across projects and can dive into a particular project. The
+App also provides exemplary equivalents from daily life, for example:
+
+- Weekly Share of an average American household
+- Number of miles driven
+- Time of 32-inch LCD TV watched
+
+{.align-center width="700px" height="400px"}
+
+#### Regional Comparisons
+
+Benchmark your emissions against electricity grids across different countries to understand regional variations in carbon intensity:
+
+{.align-center width="750px" height="480px"}
+
+#### Cloud Regions
+
+The App also benchmarks equivalent emissions across different regions of
+the cloud provider being used and recommends the most eco-friendly
+region to host infrastructure for the concerned cloud provider.
+
+{.align-center width="750px" height="450px"}
+
+## Online Dashboard
+
+For team-based tracking and cloud-hosted visualization, use the [CodeCarbon online dashboard](https://dashboard.codecarbon.io/). To get started, follow the [Cloud API setup guide](cloud-api.md).
+
+### Cloud Dashboard Features
+
+#### Organization & Project Overview
+
+Showing on the top the global energy consumed and emissions produced at
+an organisation level and the share of each project in this. The App
+also provides comparison points with daily life activity to get a better
+understanding of the amount generated.
+
+{.align-center width="750px"}
+
+The top shows your organization-level energy consumption and emissions, broken down by project. CodeCarbon also provides real-world comparisons (weekly US household emissions, miles driven, etc.).
+
+#### Experiments, Runs & Detailed Metrics
+
+Each project contains experiments, and each experiment can have multiple runs. The bar chart shows total emissions per experiment, while the bubble chart displays individual runs. Click on bars to switch between experiments, and click on bubbles to see detailed time-series data and metadata.
+
+{.align-center width="750px"}
+
+#### Drill Down Into a Run
+
+Click on any bubble to see the full time-series graph and detailed metadata for that run, including timestamps, energy breakdowns, and hardware information.
+
+{.align-center width="750px"}
+
+#### Electricity Production Carbon Intensity per Country
+
+The app also provides a visualization of regional carbon intensity of electricity production, helping you understand the environmental impact of different deployment regions.
+
+{.align-center width="750px"}
+
+## Next Steps
+
+- [Set up the Cloud API](cloud-api.md) to send data to the online dashboard
+- [Configure CodeCarbon](configuration.md) for additional tracking options
+- [Integrate with experiment tracking tools](comet.md) like Comet for seamless workflow integration
diff --git a/docs/contributing/working_with_agent.md b/docs/how-to/working_with_agent.md
similarity index 100%
rename from docs/contributing/working_with_agent.md
rename to docs/how-to/working_with_agent.md
diff --git a/docs/index.md b/docs/index.md
index bb5346164..1db04b8b4 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -2,38 +2,23 @@
AI can benefit society in many ways, but given the energy needed to support the computing behind AI, these benefits can come at a high environmental price. Use CodeCarbon to track and reduce your COโ output from code running on your own hardware. For tracking emissions from remote GenAI API calls (OpenAI, Anthropic, etc.), see [EcoLogits](https://ecologits.ai/latest/?utm_source=codecarbon&utm_medium=docs).
-[**Get Started** โ](getting-started/installation.md)
-
----
-
## What we are
- **A lightweight, easy to use Python library** โ Simple API to track emissions
- **Open source, free & community driven** โ Built by and for the community
- **Effective visual outputs** โ Put emissions in context with real-world equivalents
-## Computer emits COโ. We started measuring how much
+## Quickstart
-A single datacenter can consume large amounts of energy to run computing code. An innovative new tracking tool is designed to measure the climate impact of artificial intelligence.
+### Python
-*Kana Lottick, Silvia Susai, Sorelle Friedler, and Jonathan Wilson.* [Energy Usage Reports: Environmental awareness as part of algorithmic accountability](http://arxiv.org/abs/1911.08354). *NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2019.*
+Install CodeCarbon:
->
-
-
-
-
-
-## How it works
-
-1. **Download package** โ `pip install codecarbon`
-2. **Embed the code** โ Add a few lines to your script
-3. **Run and track** โ Emissions are measured automatically
-4. **Visualize results** โ See your impact in context
-
-## Seamless integration
+``` bash
+pip install codecarbon
+```
-Only a few lines of code:
+Track your code with a context manager:
```python
from codecarbon import EmissionsTracker
@@ -47,13 +32,48 @@ emissions = tracker.stop()
print(f"Emissions: {emissions} kg COโ")
```
+[**Learn more** โ](tutorials/first-tracking.md)
+
+### CLI
+
+Track any command without changing your code:
+
+``` bash
+codecarbon monitor --no-api -- python train.py
+```
+
+Or detect your hardware:
+
+``` bash
+codecarbon detect
+```
+
+[**Learn more** โ](tutorials/cli.md)
+
+---
+
+## Computer emits COโ. We started measuring how much
+
+A single datacenter can consume large amounts of energy to run computing code. An innovative new tracking tool is designed to measure the climate impact of artificial intelligence.
+
+*Kana Lottick, Silvia Susai, Sorelle Friedler, and Jonathan Wilson.* [Energy Usage Reports: Environmental awareness as part of algorithmic accountability](http://arxiv.org/abs/1911.08354). *NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2019.*
+
+>
+
+
+
+
+
## Quick links
| Section | Description |
|---------|-------------|
-| [When to use CodeCarbon](introduction/when-to-use.md) | Local vs remote: CodeCarbon vs EcoLogits |
-| [Installation](getting-started/installation.md) | Get started with CodeCarbon |
-| [Usage](getting-started/usage.md) | Learn how to use CodeCarbon |
-| [API Reference](getting-started/api.md) | Full API documentation |
-| [Examples](getting-started/examples.md) | Example usage patterns |
-| [Methodology](introduction/methodology.md) | How emissions are calculated |
+| [Quickstart](tutorials/first-tracking.md) | Get started in 5 minutes |
+| [Installation](how-to/installation.md) | Install CodeCarbon |
+| [CLI Tutorial](tutorials/cli.md) | Track emissions from the command line |
+| [Python API Tutorial](tutorials/python-api.md) | Track emissions in Python code |
+| [Comparing Model Efficiency](tutorials/comparing-model-efficiency.md) | Measure carbon efficiency across ML models |
+| [API Reference](reference/api.md) | Full parameter documentation |
+| [Framework Examples](how-to/scikit-learn.md) | Example usage patterns |
+| [Methodology](explanation/methodology.md) | How emissions are calculated |
+| [EcoLogits](https://ecologits.ai/latest/?utm_source=codecarbon&utm_medium=docs) | Track emissions from GenAI API calls |
diff --git a/docs/logging/to_logger.md b/docs/logging/to_logger.md
deleted file mode 100644
index c4dadd5db..000000000
--- a/docs/logging/to_logger.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# Collecting emissions to a logger {#Collecting emissions to a logger}
-
-The `LoggerOutput` class (and `GoogleCloudLoggerOutput` subclass) allows
-you to send emissions tracking to a logger. This is a specific, distinct
-logger than the one used by the CodeCarbon package for its private logs.
-It allows you to leverage powerful logging systems, to centralize
-emissions to some central or cloud-based system, and build reports,
-triggers, etc. based on these data.
-
-This logging output can be used in parallel with other output options
-provided by CodeCarbon.
-
-## Create a logger
-
-In order to send emissions tracking data to the logger, first create a
-logger and then create an `EmissionsTracker`.
-`OfflineEmissionsTracker` is also supported but lack of
-network connectivity may forbid to stream tracking data to some central
-or cloud-based collector.
-
-### Python logger
-
-``` python
-import logging
-
-# Create a dedicated logger (log name can be the CodeCarbon project name for example)
-_logger = logging.getLogger(log_name)
-
-# Add a handler, see Python logging for various handlers (here a local file named after log_name)
-_channel = logging.FileHandler(log_name + '.log')
-_logger.addHandler(_channel)
-
-# Set logging level from DEBUG to CRITICAL (typically INFO)
-# This level can be used in the logging process to filter emissions messages
-_logger.setLevel(logging.INFO)
-
-# Create a CodeCarbon LoggerOutput with the logger, specifying the logging level to be used for emissions data messages
-my_logger = LoggerOutput(_logger, logging.INFO)
-```
-
-### Google Cloud Logging
-
-``` python
-import google.cloud.logging
-
-
-# Create a Cloud Logging client (specify project name if needed, otherwise Google SDK default project name is used)
-client = google.cloud.logging.Client(project=google_project_name)
-
-# Create a CodeCarbon GoogleCloudLoggerOutput with the Cloud Logging logger, with the logging level to be used for emissions data messages
-my_logger = GoogleCloudLoggerOutput(client.logger(log_name))
-```
-
-### Authentication
-
-Please refer to [Google Cloud
-documentation](https://cloud.google.com/logging/docs/reference/libraries#setting_up_authentication).
-
-### Create an EmissionTracker
-
-Create an EmissionTracker saving output to the logger. Other save
-options are still usable and valid.
-
-``` python
-tracker = EmissionsTracker(save_to_logger=True, logging_logger=my_logger)
-tracker.start()
-# Your code here
-emissions: float = tracker.stop()
-```
-
-### Example
-
-A demonstration is available in `codecarbon/examples/logging_demo.py`.
diff --git a/docs/logging/visualize.md b/docs/logging/visualize.md
deleted file mode 100644
index 993793d34..000000000
--- a/docs/logging/visualize.md
+++ /dev/null
@@ -1,104 +0,0 @@
-# Visualize
-
-## Offline
-
-The package also comes with a `Dash App` containing illustrations to
-understand the emissions logged from various experiments across
-projects. The App currently consumes logged information from a CSV file,
-generated from an in-built logger in the package.
-
-### Installation
-
-The carbonboard visualization tool requires additional dependencies.
-Install them with:
-
-``` bash
-pip install 'codecarbon[carbonboard]'
-```
-
-!!! note "Note"
-
- The `viz-legacy` extra is deprecated but still works for backwards
- compatibility. It will be removed in v4.0.0. Please use `carbonboard`
- instead.
-
-### Usage
-
-The App can be run by executing the below CLI command that needs
-following arguments:
-
-- `filepath` - path to the CSV file containing logged information
- across experiments and projects
-- `port` - an optional port number, in case default [8050] is used
- by an existing process
-
-``` bash
-carbonboard --filepath="examples/emissions.csv" --port=3333
-```
-
-### Summary and Equivalents
-
-Users can get an understanding of net power consumption and emissions
-generated across projects and can dive into a particular project. The
-App also provides exemplary equivalents from daily life, for example:
-
-- Weekly Share of an average American household
-- Number of miles driven
-- Time of 32-inch LCD TV watched
-
-{.align-center width="700px" height="400px"}
-
-### Regional Comparisons
-
-The App also provides a comparative visual to benchmark emissions and
-energy mix of the electricity from the grid across different countries.
-
-{.align-center width="750px" height="480px"}
-
-### Cloud Regions
-
-The App also benchmarks equivalent emissions across different regions of
-the cloud provider being used and recommends the most eco-friendly
-region to host infrastructure for the concerned cloud provider.
-
-{.align-center width="750px" height="450px"}
-
-## Online
-
-A dashboard is also available for those who chose to connect the package
-to the public API. [Got to online
-dashboard](https://dashboard.codecarbon.io/)
-
-### from global
-
-Showing on the top the global energy consumed and emissions produced at
-an organisation level and the share of each project in this. The App
-also provides comparison points with daily life activity to get a better
-understanding of the amount generated.
-
-{.align-center width="750px"}
-
-### to more and more
-
-Each project can be divided into several experiments, and in each
-experiment several runs can happen. The total emissions of experiments
-is shown on the barchart on the right hand side, and the runs on the
-bubble chart on the left hand side. If ever your project has several
-experiments you can switch from one experiment's runs in the bubble
-chart to another by clicking the bar chart.
-
-{.align-center width="750px"}
-
-### detailed
-
-Clicking on one bubble, you can display the runtime series and see its
-metadata.
-
-{.align-center width="750px"}
-
-### Electricity production carbon intensity per country
-
-The app also provides a visualization of regional carbon intensity of
-electricity production.
-
-{.align-center width="750px"}
diff --git a/docs/getting-started/parameters.md b/docs/reference/api.md
similarity index 95%
rename from docs/getting-started/parameters.md
rename to docs/reference/api.md
index 67cd41458..15117b123 100644
--- a/docs/getting-started/parameters.md
+++ b/docs/reference/api.md
@@ -2,7 +2,7 @@
Parameters can be set via `EmissionsTracker()`, `OfflineEmissionsTracker()`, the
`@track_emissions` decorator, config files, or environment variables. See
-[Configuration](usage.md#configuration) for priority order.
+[Configuration](../how-to/configuration.md) for priority order.
!!! note "PUE"
PUE is a multiplication factor provided by the user. Old datacenters have PUE
diff --git a/docs/reference/cli.md b/docs/reference/cli.md
new file mode 100644
index 000000000..1e93a588f
--- /dev/null
+++ b/docs/reference/cli.md
@@ -0,0 +1,100 @@
+# CLI Reference
+
+CodeCarbon provides a command-line interface for tracking emissions without modifying code.
+
+## Commands
+
+### `codecarbon config`
+
+Create or modify a `.codecarbon.config` configuration file interactively.
+
+**Usage:**
+```bash
+codecarbon config
+```
+
+Prompts you to enter your configuration settings such as API credentials, project name, and tracking preferences. You can re-run this command to modify existing settings.
+
+### `codecarbon login`
+
+Authenticate with the CodeCarbon API and save credentials to your config file.
+
+**Usage:**
+```bash
+codecarbon login
+```
+
+Opens a browser or provides a login link to authenticate with the CodeCarbon API. Saves your API token and creates a default experiment in `.codecarbon.config`.
+
+### `codecarbon monitor`
+
+Monitor emissions from your entire machine continuously.
+
+**Usage:**
+```bash
+codecarbon monitor [OPTIONS]
+```
+
+Displays real-time emissions data for all processes on your machine. Press `Ctrl+C` to stop.
+
+**Options:**
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `--no-api` | flag | false | Do not send data to the API (local-only measurement) |
+| `--offline` | flag | false | Run without internet access |
+| `--country-iso-code` | string | - | ISO 3166-1 alpha-3 country code (required in offline mode) |
+| `--log-level` | choice | INFO | Log level: DEBUG, INFO, WARNING, ERROR |
+
+**Examples:**
+```bash
+# Monitor with API (default)
+codecarbon monitor
+
+# Monitor locally without sending to API
+codecarbon monitor --no-api
+
+# Monitor offline
+codecarbon monitor --offline --country-iso-code FRA
+
+# Monitor with debug logging
+codecarbon monitor --log-level DEBUG
+```
+
+### `codecarbon monitor -- `
+
+Track emissions for a specific command or process.
+
+**Usage:**
+```bash
+codecarbon monitor --
+```
+
+Runs your command and tracks the emissions produced by that process only. The double hyphen `--` separates CodeCarbon options from the command to run.
+
+**Examples:**
+```bash
+# Run a Python script with tracking
+codecarbon monitor -- python train_model.py
+
+# Run a shell script
+codecarbon monitor -- bash benchmark.sh
+
+# Run a command with arguments
+codecarbon monitor -- node app.js --port 8080
+```
+
+**Options:**
+
+Same options as `codecarbon monitor` apply (see above).
+
+### `codecarbon detect`
+
+Detect and print hardware information.
+
+**Usage:**
+```bash
+codecarbon detect
+```
+
+Displays detected RAM, CPU, GPU, and other hardware information that CodeCarbon uses to estimate energy consumption. Useful for verifying that CodeCarbon can see all your hardware.
diff --git a/docs/logging/output.md b/docs/reference/output.md
similarity index 96%
rename from docs/logging/output.md
rename to docs/reference/output.md
index 407662b74..1fb8b97a8 100644
--- a/docs/logging/output.md
+++ b/docs/reference/output.md
@@ -62,7 +62,9 @@ docker-compose up
Run your EmissionsTracker as usual, with `save_to_prometheus=True`:
-```python
+```python-skip
+from codecarbon import OfflineEmissionsTracker
+
tracker = OfflineEmissionsTracker(
project_name="my_project",
country_iso_code="USA",
@@ -85,7 +87,9 @@ CodeCarbon exposes all its metrics with the suffix `codecarbon_`.
Run your EmissionsTracker as usual, with `save_to_logfire=True`:
-```python
+```python-skip
+from codecarbon import OfflineEmissionsTracker
+
tracker = OfflineEmissionsTracker(
project_name="my_project",
country_iso_code="USA",
@@ -108,4 +112,4 @@ You can send all your data to the CodeCarbon API so you have your historical dat
## Logger Output
-See [Collecting emissions to a logger](to_logger.md).
+See [Collecting emissions to a logger](../how-to/logging.md).
diff --git a/docs/tutorials/cli.md b/docs/tutorials/cli.md
new file mode 100644
index 000000000..55f42ffb8
--- /dev/null
+++ b/docs/tutorials/cli.md
@@ -0,0 +1,142 @@
+# Tracking with the CLI {#usage-command-line}
+
+By the end of this tutorial, you'll be able to monitor any process's carbon emissions from the command line without writing any Python code.
+
+CodeCarbon provides a command-line interface to track emissions without modifying your source code. In this tutorial, you'll set up the CLI, monitor your machine's emissions, and run commands with built-in emissions tracking.
+
+## Setup
+
+Create a minimal configuration file (just follow the prompts) :
+
+``` console
+codecarbon config
+```
+
+[{.align-center}](https://asciinema.org/a/667970)
+
+Then login from your terminal to authenticate CLI/API usage:
+
+``` console
+codecarbon login
+```
+
+You can use the same command to modify an existing config :
+
+[{.align-center}](https://asciinema.org/a/667971)
+
+With the configuration created, you're ready to start monitoring.
+
+## Monitor Your Machine
+
+If you want to track the emissions of a computer without having to
+modify your code, you can use :
+
+``` console
+codecarbon monitor
+```
+
+You have to stop the monitoring manually with `Ctrl+C`.
+
+If you only need local measurement and do not want to send data to the API,
+use:
+
+``` console
+codecarbon monitor --no-api
+```
+
+You can also run CodeCarbon in offline mode without internet:
+
+``` console
+codecarbon monitor --offline --country-iso-code FRA
+```
+
+## Detect Your Hardware
+
+Next, let's check what hardware CodeCarbon detected on your machine:
+
+If you want to detect the hardware of your computer without starting any
+measurement, you can use:
+
+``` console
+codecarbon detect
+```
+
+This will display your detected RAM, CPU, and GPU information, which CodeCarbon uses to estimate energy consumption.
+
+## Track Any Command
+
+The most powerful CLI feature is the ability to track any command or process automatically. This is especially useful for non-Python users or for monitoring existing shell scripts.
+
+Use the `codecarbon monitor --` command to automatically track emissions from any process:
+
+``` console
+codecarbon monitor --
+```
+
+Do not surround `` with quotes. The double hyphen `--`
+indicates the end of CodeCarbon options and the beginning of the command
+to run.
+
+**Examples:**
+
+``` console
+# Run a shell script
+codecarbon monitor -- ./benchmark.sh
+
+# Run a command with arguments (use quotes for special characters)
+codecarbon monitor -- bash -c 'echo "Processing..."; sleep 30; echo "Done!"'
+
+# Run Python scripts
+codecarbon monitor -- python train_model.py
+
+# Run Node.js applications
+codecarbon monitor -- node app.js
+
+# Run tests with output redirection
+codecarbon monitor -- npm run test > output.txt
+
+# Display the CodeCarbon detailed logs
+codecarbon monitor --log-level debug -- python --version
+```
+
+**Output:**
+
+When the command completes, CodeCarbon displays a summary report and
+saves the emissions data to a CSV file:
+
+``` console
+๐ฑ CodeCarbon: Starting emissions tracking...
+ Command: bash -c echo "Processing..."; sleep 30; echo "Done!"
+
+Processing...
+Done!
+
+============================================================
+๐ฑ CodeCarbon Emissions Report
+============================================================
+ Command: bash -c echo "Processing..."; sleep 30; echo "Done!"
+ Emissions: 0.0317 g CO2eq
+ Saved to: /home/user/emissions.csv
+ โ ๏ธ Note: Measured entire machine (includes all system processes)
+============================================================
+```
+
+!!! note "Note"
+
+ The `codecarbon monitor --` command tracks the specific process you run. For continuous machine-level tracking, use the plain `codecarbon monitor` command instead.
+
+---
+
+## What's Next?
+
+You've now learned how to track emissions from the command line. Next steps:
+
+- **Track in Python**: Use the [Python API tutorial](python-api.md) for fine-grained tracking within your code.
+- **Send to Dashboard**: Learn how to [send data to the CodeCarbon dashboard](../how-to/cloud-api.md).
+- **Configure Details**: See the [configuration guide](../how-to/configuration.md) for advanced options like proxy setup.
+
+## See Also
+
+- [CLI Reference](../reference/cli.md) for a complete list of commands and options
+- [Use the Cloud API & Dashboard](../how-to/cloud-api.md) to send emissions to the online dashboard
+- [Configure CodeCarbon](../how-to/configuration.md) for config files, environment variables, and proxy setup
diff --git a/docs/tutorials/comparing-model-efficiency.md b/docs/tutorials/comparing-model-efficiency.md
new file mode 100644
index 000000000..0fedcd3be
--- /dev/null
+++ b/docs/tutorials/comparing-model-efficiency.md
@@ -0,0 +1,271 @@
+# Comparing Model Efficiency with Carbon Tracking
+
+In this tutorial, you'll learn how to measure and compare the carbon emissions of different machine learning model configurations. By the end, you'll be able to identify which model offers the best trade-off between accuracy and environmental impact.
+
+## Why This Matters
+
+When training machine learning models, we often focus on accuracy or performance metrics. But as a data scientist, you can now measure the carbon cost of different approaches and make informed decisions about which model is truly efficient. Some configurations might achieve slightly higher accuracy while consuming significantly more energyโand it's worth knowing that trade-off.
+
+## Setup
+
+Before we start comparing models, let's get all the necessary imports in place. You'll need a few data science libraries (scikit-learn, pandas) and of course CodeCarbon itself:
+
+```python
+import time
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+
+from codecarbon import EmissionsTracker
+
+# Create output directory for emissions logs
+from pathlib import Path
+Path("./emissions").mkdir(exist_ok=True)
+```
+
+These are the tools we'll use:
+- **scikit-learn** โ for building and training machine learning models
+- **pandas** โ for organizing and analyzing results
+- **CodeCarbon** โ for measuring emissions
+- **time** โ to track how long training takes (for comparison)
+
+## The Explicit Object Pattern for Long Experiments
+
+For iterative experiments like model training, the explicit object pattern is perfect. You call `tracker.start()` at the beginning of training and `tracker.stop()` at the end. This approach gives you full control and is ideal when you're training multiple models in a loop.
+
+**Key pattern:** Always use `try...finally` to ensure the tracker stops, even if an error occurs during training:
+
+```python
+tracker = EmissionsTracker(project_name="my_experiment")
+tracker.start()
+try:
+ # Your training code here
+ pass
+finally:
+ emissions = tracker.stop()
+```
+
+This ensures CodeCarbon's internal scheduler is properly shut down, preventing background threads from running after your experiment finishes.
+
+## Practical Example: Comparing Model Configurations
+
+Now let's put this into practice. We'll compare three RandomForest configurations with different sizes to see how they balance accuracy and carbon emissions.
+
+### Step 1: Generate a Synthetic Dataset
+
+```python
+# Create a classification dataset
+X, y = make_classification(
+ n_samples=10000,
+ n_features=20,
+ n_informative=15,
+ n_redundant=5,
+ n_classes=3,
+ random_state=42
+)
+
+X_train, X_test, y_train, y_test = train_test_split(
+ X, y, test_size=0.2, random_state=42
+)
+
+print(f"Dataset: {X_train.shape[0]} training samples, {X_test.shape[0]} test samples")
+print(f"Features: {X_train.shape[1]}, Classes: {len(np.unique(y))}")
+```
+
+### Step 2: Define Model Configurations
+
+```python
+models_to_test = [
+ {"name": "Small RF", "n_estimators": 50, "max_depth": 10},
+ {"name": "Medium RF", "n_estimators": 100, "max_depth": 15},
+ {"name": "Large RF", "n_estimators": 200, "max_depth": 20},
+]
+```
+
+### Step 3: Train and Track Each Configuration
+
+```python
+results = []
+
+for model_config in models_to_test:
+ print(f"\nTraining {model_config['name']}...")
+
+ # Start emissions tracking
+ tracker = EmissionsTracker(
+ project_name=f"ml_{model_config['name'].replace(' ', '_').lower()}",
+ output_dir="./emissions"
+ )
+ tracker.start()
+
+ try:
+ # Record training time
+ start_time = time.time()
+
+ # Train the model
+ model = RandomForestClassifier(
+ n_estimators=model_config["n_estimators"],
+ max_depth=model_config["max_depth"],
+ random_state=42,
+ n_jobs=-1 # Use all available cores
+ )
+
+ model.fit(X_train, y_train)
+ train_score = model.score(X_train, y_train)
+ test_score = model.score(X_test, y_test)
+ training_time = time.time() - start_time
+
+ finally:
+ # Stop tracking (guaranteed to run even if error occurs)
+ emissions = tracker.stop()
+ if emissions is None:
+ emissions = 0.0
+
+ # Store results
+ results.append({
+ "Model": model_config["name"],
+ "Train Accuracy": train_score,
+ "Test Accuracy": test_score,
+ "Training Time (s)": training_time,
+ "CO2 Emissions (kg)": emissions,
+ "CO2 per Accuracy Point": emissions / test_score if test_score > 0 else float("inf"),
+ })
+
+ print(f"โ {model_config['name']}: {test_score:.3f} accuracy, {emissions:.6f} kg CO2")
+```
+
+## Analyzing Results
+
+Now let's collect all results and analyze them:
+
+```python
+# Create a DataFrame with results
+df_results = pd.DataFrame(results)
+print("\nModel Comparison Results:")
+print("=" * 70)
+print(df_results.to_string(index=False))
+
+# Find the most efficient model
+most_efficient = df_results.loc[df_results["CO2 per Accuracy Point"].idxmin()]
+print(f"\nMost Carbon-Efficient Model: {most_efficient['Model']}")
+print(f" Efficiency Score: {most_efficient['CO2 per Accuracy Point']:.8f} kg CO2 per accuracy point")
+print(f" Test Accuracy: {most_efficient['Test Accuracy']:.3f}")
+print(f" CO2 Emissions: {most_efficient['CO2 Emissions (kg)']:.6f} kg")
+```
+
+## Visualizing Trade-offs
+
+Let's create a 4-panel visualization to understand the trade-offs between different models:
+
+```python
+# mktestdocs: skip
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+fig.suptitle("Model Efficiency Comparison", fontsize=16, fontweight="bold")
+
+# Panel 1: CO2 Emissions by Model
+axes[0, 0].bar(
+ df_results["Model"],
+ df_results["CO2 Emissions (kg)"],
+ color=["#FF7F50", "#FFB347", "#90EE90"]
+)
+axes[0, 0].set_title("Carbon Emissions by Model")
+axes[0, 0].set_ylabel("CO2 Emissions (kg)")
+axes[0, 0].tick_params(axis="x", rotation=45)
+
+# Panel 2: Accuracy vs Emissions (Trade-off Curve)
+scatter = axes[0, 1].scatter(
+ df_results["Test Accuracy"],
+ df_results["CO2 Emissions (kg)"],
+ s=200,
+ c=range(len(df_results)),
+ cmap="RdYlGn",
+ alpha=0.7,
+ edgecolors="black",
+ linewidth=2
+)
+for i, model in enumerate(df_results["Model"]):
+ axes[0, 1].annotate(
+ model,
+ (df_results["Test Accuracy"].iloc[i], df_results["CO2 Emissions (kg)"].iloc[i]),
+ xytext=(8, 8),
+ textcoords="offset points",
+ fontsize=9,
+ bbox=dict(boxstyle="round,pad=0.3", facecolor="yellow", alpha=0.3)
+ )
+axes[0, 1].set_title("Accuracy vs Emissions Trade-off")
+axes[0, 1].set_xlabel("Test Accuracy")
+axes[0, 1].set_ylabel("CO2 Emissions (kg)")
+axes[0, 1].grid(True, alpha=0.3)
+
+# Panel 3: Training Time vs Emissions
+axes[1, 0].scatter(
+ df_results["Training Time (s)"],
+ df_results["CO2 Emissions (kg)"],
+ s=200,
+ c=range(len(df_results)),
+ cmap="RdYlGn",
+ alpha=0.7,
+ edgecolors="black",
+ linewidth=2
+)
+for i, model in enumerate(df_results["Model"]):
+ axes[1, 0].annotate(
+ model,
+ (df_results["Training Time (s)"].iloc[i], df_results["CO2 Emissions (kg)"].iloc[i]),
+ xytext=(8, 8),
+ textcoords="offset points",
+ fontsize=9,
+ bbox=dict(boxstyle="round,pad=0.3", facecolor="yellow", alpha=0.3)
+ )
+axes[1, 0].set_title("Training Time vs Emissions")
+axes[1, 0].set_xlabel("Training Time (seconds)")
+axes[1, 0].set_ylabel("CO2 Emissions (kg)")
+axes[1, 0].grid(True, alpha=0.3)
+
+# Panel 4: Carbon Efficiency Score (Lower is Better)
+axes[1, 1].bar(
+ df_results["Model"],
+ df_results["CO2 per Accuracy Point"],
+ color=["#90EE90", "#FFB347", "#FF7F50"] # Green=best, Red=worst
+)
+axes[1, 1].set_title("Carbon Efficiency (Lower is Better)")
+axes[1, 1].set_ylabel("CO2 per Accuracy Point")
+axes[1, 1].tick_params(axis="x", rotation=45)
+
+plt.tight_layout()
+plt.show()
+```
+
+## Key Patterns to Remember
+
+**When to use the explicit object pattern:**
+- Iterative experiments where you train multiple models in a loop
+- Long-running Jupyter notebooks where you want fine-grained control
+- Any scenario where your computation spans multiple cells or functions
+
+**Best practices:**
+- Always wrap your code in `try...finally` to ensure the tracker stops
+- Use descriptive `project_name` values to organize your results later
+- Set `output_dir` consistently so emissions data is easy to find
+- Use `n_jobs=-1` in scikit-learn to leverage all CPU cores (and accurately measure energy consumption)
+
+**Interpreting results:**
+- **Emissions** alone isn't the full pictureโconsider accuracy and time together
+- **CO2 per accuracy point** combines efficiency with model quality
+- Small improvements in accuracy might require disproportionate energy increases
+- The "best" model depends on your priorities (accuracy vs. environmental impact)
+
+## What's Next?
+
+You've now learned how to compare carbon efficiency across models. Here are some next steps:
+
+- **Configure tracking options:** Customize tracking behavior with a `.codecarbon.config` file (see [How-to: Configuration](../how-to/configuration.md))
+- **Send data to the cloud:** Upload your emissions data to the CodeCarbon dashboard for visualization (see [How-to: Cloud API](../how-to/cloud-api.md))
+- **Track distributed training:** Monitor emissions across multiple machines (see [How-to: Slurm](../how-to/slurm.md))
+- **Dive deeper:** Learn all CodeCarbon features in the [API Reference](../reference/api.md)
+
+Remember: Measuring is the first step toward sustainable computing! ๐ฑ
diff --git a/docs/tutorials/first-tracking.ipynb b/docs/tutorials/first-tracking.ipynb
new file mode 100644
index 000000000..dd822a8a0
--- /dev/null
+++ b/docs/tutorials/first-tracking.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Your First Emissions Tracking\n",
+ "\n",
+ "[](https://colab.research.google.com/github/mlco2/codecarbon/blob/master/docs/tutorials/first-tracking.ipynb)\n",
+ "\n",
+ "This tutorial walks you through tracking your first carbon emissions with CodeCarbon.\n",
+ "By the end, you will have:\n",
+ "\n",
+ "1. Installed CodeCarbon\n",
+ "2. Tracked emissions from a simple computation\n",
+ "3. Inspected the results\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1: Install CodeCarbon"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install codecarbon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2: Track emissions from a computation\n",
+ "\n",
+ "The simplest way to use CodeCarbon is as a **context manager**. Everything inside the `with` block is tracked."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from codecarbon import EmissionsTracker\n",
+ "\n",
+ "with EmissionsTracker(project_name=\"my-first-tracking\") as tracker:\n",
+ " # Simulate some computation\n",
+ " total = 0\n",
+ " for i in range(10_000_000):\n",
+ " total += i\n",
+ "\n",
+ "print(f\"Computation result: {total}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3: Inspect the results\n",
+ "\n",
+ "CodeCarbon saved the emissions data to a CSV file. Let's take a look:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.read_csv(\"emissions.csv\")\n",
+ "df[[\"project_name\", \"duration\", \"emissions\", \"emissions_rate\", \"cpu_power\", \"ram_power\", \"energy_consumed\"]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also access the emissions data directly from the tracker object:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"Total emissions: {tracker.final_emissions * 1000:.4f} g CO2eq\")\n",
+ "print(f\"Duration: {tracker.final_emissions_data.duration:.2f} seconds\")\n",
+ "print(f\"Energy consumed: {tracker.final_emissions_data.energy_consumed:.6f} kWh\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What's next?\n",
+ "\n",
+ "- Try the [CodeCarbon Workshop notebook](https://github.com/mlco2/codecarbon/blob/master/examples/notebooks/codecarbon_workshop.ipynb) for a comprehensive hands-on experience\n",
+ "- Learn about [CLI tracking](cli.md) to monitor without code changes\n",
+ "- Explore all [Python API options](python-api.md) (decorators, explicit objects, offline mode)\n",
+ "- See the full [API Reference](../reference/api.md) for all configuration parameters"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python",
+ "version": "3.11.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/tutorials/first-tracking.md b/docs/tutorials/first-tracking.md
new file mode 100644
index 000000000..2349462bb
--- /dev/null
+++ b/docs/tutorials/first-tracking.md
@@ -0,0 +1,64 @@
+# Quickstart
+
+[](https://colab.research.google.com/github/mlco2/codecarbon/blob/master/docs/tutorials/first-tracking.ipynb)
+
+In this tutorial, you'll track your first carbon emissions in under 5 minutes. By the end, you will have:
+
+1. Installed CodeCarbon
+2. Tracked emissions from a simple computation
+3. Inspected the results
+
+---
+
+## Step 1: Install CodeCarbon
+
+Let's start by installing the CodeCarbon package:
+
+```console
+pip install codecarbon
+```
+
+## Step 2: Track emissions from a computation
+
+With CodeCarbon installed, we're ready to write our first tracking script. The simplest way to use CodeCarbon is as a **context manager**. Everything inside the `with` block is tracked.
+
+```python
+from codecarbon import EmissionsTracker
+
+with EmissionsTracker(project_name="my-first-tracking") as tracker:
+ # Simulate some computation
+ total = 0
+ for i in range(10_000_000):
+ total += i
+
+print(f"Computation result: {total}")
+```
+
+## Step 3: Inspect the results
+
+Now that the tracker has run, let's look at what it recorded. CodeCarbon saves the emissions data to a CSV file called `emissions.csv`:
+
+
+```python
+import pandas as pd
+
+df = pd.read_csv("emissions.csv")
+df[["project_name", "duration", "emissions", "emissions_rate", "cpu_power", "ram_power", "energy_consumed"]]
+```
+
+You can also access the emissions data directly from the tracker object:
+
+
+```python
+print(f"Total emissions: {tracker.final_emissions * 1000:.4f} g CO2eq")
+print(f"Duration: {tracker.final_emissions_data.duration:.2f} seconds")
+print(f"Energy consumed: {tracker.final_emissions_data.energy_consumed:.6f} kWh")
+```
+
+## What's next?
+
+- [Configure CodeCarbon](../how-to/configuration.md) with config files, environment variables, or script parameters
+- Learn about [CLI tracking](cli.md) to monitor without code changes
+- Explore all [Python API options](python-api.md) (decorators, explicit objects, offline mode)
+- See the full [API Reference](../reference/api.md) for all configuration parameters
+- Try the [CodeCarbon Workshop notebook](https://github.com/mlco2/codecarbon/blob/master/examples/notebooks/codecarbon_workshop.ipynb) for a comprehensive hands-on experience
diff --git a/docs/tutorials/python-api.md b/docs/tutorials/python-api.md
new file mode 100644
index 000000000..77f3f3b83
--- /dev/null
+++ b/docs/tutorials/python-api.md
@@ -0,0 +1,136 @@
+# Tracking with Python
+
+In this tutorial, you'll learn the three main ways to use CodeCarbon in your Python code. You can use CodeCarbon with any computing framework, and it supports both `online` (with internet access) and `offline` (without internet access) modes.
+
+By the end of this tutorial, you'll understand which usage pattern works best for your use case.
+
+## Online Mode
+
+When the environment has internet access, CodeCarbon will send your emissions data to the central API (optional). Let's start with three usage patterns: explicit object, context manager, and decorator.
+
+### Explicit Object
+
+The explicit object pattern is useful when your code doesn't have a single entry and exit pointโfor example, in Jupyter notebooks where you want to start tracking in one cell and stop in a much later cell.
+
+``` python
+from codecarbon import EmissionsTracker
+tracker = EmissionsTracker()
+tracker.start()
+try:
+ # Compute intensive code goes here
+ _ = 1 + 1
+finally:
+ tracker.stop()
+```
+
+Call `tracker.start()` at the beginning of your Notebook (or script), and call `tracker.stop()` at the end. You can also call `tracker.flush()` to write emissions to disk or the API without stopping the tracker entirely.
+
+**Advanced: Task-Level Monitoring**
+
+For fine-grained tracking of individual tasks within a single run, use the task manager:
+
+``` python
+try:
+ tracker = EmissionsTracker(project_name="bert_inference", measure_power_secs=10)
+ tracker.start_task("load dataset")
+ dataset = load_dataset("imdb", split="test")
+ imdb_emissions = tracker.stop_task()
+ tracker.start_task("build model")
+ model = build_model()
+ model_emissions = tracker.stop_task()
+finally:
+ _ = tracker.stop()
+```
+
+The task manager tracks each sub-task independently. Tasks are not written to disk by default (to reduce overhead), so retrieve results from the `stop_task()` return value.
+
+### Context Manager
+
+Now that you've seen the explicit object approach, let's look at the more idiomatic **context manager** pattern. This is the recommended way for most use cases.
+
+``` python
+from codecarbon import EmissionsTracker
+
+with EmissionsTracker() as tracker:
+ # Compute intensive training code goes here
+```
+
+This pattern is recommended when you want to monitor a specific block of code. The context manager automatically calls `start()` on entry and `stop()` on exit, making it safe and concise.
+
+### Decorator
+
+Finally, if your training code is wrapped in a function, you can use the `@track_emissions` decorator for the simplest syntax.
+
+``` python
+from codecarbon import track_emissions
+
+@track_emissions
+def training_loop():
+ # Compute intensive training code goes here
+```
+
+The decorator automatically wraps your function with tracking and writes results to `emissions.csv`. Use this when your code is neatly encapsulated in a function.
+
+!!! note
+ All patterns create an `emissions.csv` file in your current directory containing detailed tracking data.
+
+## Offline Mode
+
+So far we've assumed an internet connection. CodeCarbon also works fully **offline** without internet access. The internal computations remain unchanged; however, you must provide a `country_iso_code` parameter (3-letter ISO code) so CodeCarbon can estimate the carbon intensity of your regional electricity grid. See [Wikipedia](https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes) for a complete list of country codes.
+
+The three usage patterns (explicit object, context manager, decorator) work the same in offline mode:
+
+### Explicit Object
+
+Developers can use the `OfflineEmissionsTracker` object to track
+emissions as follows:
+
+``` python
+from codecarbon import OfflineEmissionsTracker
+tracker = OfflineEmissionsTracker(country_iso_code="CAN")
+tracker.start()
+# GPU intensive training code
+tracker.stop()
+```
+
+### Context Manager
+
+The `OfflineEmissionsTracker` also works as a context manager:
+
+``` python
+from codecarbon import OfflineEmissionsTracker
+
+with OfflineEmissionsTracker(country_iso_code="CAN") as tracker:
+ # GPU intensive training code goes here
+```
+
+### Decorator
+
+The decorator in offline mode requires two additional parameters:
+
+``` python
+from codecarbon import track_emissions
+
+@track_emissions(offline=True, country_iso_code="CAN")
+def training_loop():
+ # training code goes here
+ pass
+```
+
+---
+
+## What's Next?
+
+You've now learned the three main patterns for tracking emissions in Python. Each pattern serves different use cases:
+
+- **Use the explicit object pattern** when your code runs across multiple cells or functions with unclear start/end points (e.g., Jupyter notebooks).
+- **Use the context manager pattern** for most new codeโit's concise, safe, and idiomatic.
+- **Use the decorator pattern** when your entire tracking scope is a single function.
+- **Use offline mode** when you're in an environment without internet access.
+
+Explore these related guides:
+
+- [CLI tutorial](cli.md) โ Track emissions from any command without writing Python code.
+- [Comparing Model Efficiency](comparing-model-efficiency.md) โ Measure and compare carbon emissions across different machine learning models.
+- [How-to: Cloud API](../how-to/cloud-api.md) โ Send emissions data to the CodeCarbon dashboard.
+- [API Reference](../reference/api.md) โ Complete list of all parameters and configuration options.
diff --git a/examples/notebooks/codecarbon_workshop.ipynb b/examples/notebooks/codecarbon_workshop.ipynb
index fbfaa7cbf..1da4f44a5 100644
--- a/examples/notebooks/codecarbon_workshop.ipynb
+++ b/examples/notebooks/codecarbon_workshop.ipynb
@@ -516,7 +516,7 @@
"### ๐ Next Steps & Resources:\n",
"\n",
"#### ๐ **Learning Resources:**\n",
- "- ๐ [CodeCarbon Documentation](https://mlco2.github.io/codecarbon/)\n",
+ "- ๐ [CodeCarbon Documentation](https://docs.codecarbon.io/)\n",
"- ๐ [ML CO2 Impact Calculator](https://mlco2.github.io/impact/)\n",
"\n",
"#### ๐ ๏ธ **Integration Ideas:**\n",
diff --git a/mkdocs.yml b/mkdocs.yml
index a581e5ebb..d1b3f82fd 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -49,6 +49,35 @@ theme:
plugins:
- search
+ - redirects:
+ redirect_maps:
+ "introduction/when-to-use.md": "explanation/when-to-use.md"
+ "introduction/motivation.md": "explanation/why.md"
+ "introduction/methodology.md": "explanation/methodology.md"
+ "introduction/rapl.md": "explanation/rapl.md"
+ "introduction/power_estimation.md": "explanation/power-estimation.md"
+ "introduction/model_examples.md": "explanation/model-comparisons.md"
+ "introduction/faq.md": "explanation/faq.md"
+ "getting-started/installation.md": "how-to/installation.md"
+ "getting-started/usage.md": "tutorials/cli.md"
+ "getting-started/api.md": "how-to/cloud-api.md"
+ "getting-started/parameters.md": "reference/api.md"
+ "getting-started/examples.md": "how-to/scikit-learn.md"
+ "reference/examples.md": "how-to/scikit-learn.md"
+ "getting-started/comet.md": "how-to/comet.md"
+ "getting-started/test_on_scaleway.md": "how-to/test-on-scaleway.md"
+ "advanced/linux_service.md": "how-to/linux-service.md"
+ "advanced/ansible.md": "how-to/ansible.md"
+ "advanced/adastra.md": "how-to/slurm.md"
+ "logging/output.md": "reference/output.md"
+ "logging/to_logger.md": "how-to/logging.md"
+ "logging/visualize.md": "how-to/visualize.md"
+ "contributing/CODE_OF_CONDUCT/": "how-to/code-of-conduct.md"
+ "contributing/CONTRIBUTING/": "how-to/contributing.md"
+ "contributing/AI_POLICY/": "how-to/ai_policy.md"
+ "contributing/working_with_agent/": "how-to/working-with-agent.md"
+ "contributing/agents-instructions/": "how-to/agent-instructions.md"
+ "contributing/SECURITY/": "how-to/security.md"
- mkdocstrings:
handlers:
python:
@@ -107,36 +136,49 @@ extra:
name: CodeCarbon on GitHub
nav:
- - Introduction:
- - Home: index.md
- - When to use CodeCarbon: introduction/when-to-use.md
- - Motivation: introduction/motivation.md
- - Methodology: introduction/methodology.md
- - RAPL Metrics: introduction/rapl.md
- - Power Estimation: introduction/power_estimation.md
- - Model Comparisons: introduction/model_examples.md
- - Frequently Asked Questions: introduction/faq.md
- - Getting Started:
- - Installing CodeCarbon: getting-started/installation.md
- - Quickstart: getting-started/usage.md
- - CodeCarbon API: getting-started/api.md
- - Parameters: getting-started/parameters.md
- - Examples: getting-started/examples.md
- - Test on Scaleway: getting-started/test_on_scaleway.md
- - Contributing:
- - Code of Conduct: contributing/CODE_OF_CONDUCT.md
- - Contributing to CodeCarbon: contributing/CONTRIBUTING.md
- - AI Contribution Policy: contributing/AI_POLICY.md
- - Working with OpenCode Agent VM: contributing/working_with_agent.md
- - Coding Agents Instructions: contributing/agents-instructions.md
- - Reporting security vulnerabilities: contributing/SECURITY.md
- - Advanced Usage:
- - Install CodeCarbon as a Linux service: advanced/linux_service.md
- - Deploy with Ansible: advanced/ansible.md
- - Comet Integration: getting-started/comet.md
- - ROCm and PyTorch on SLURM SuperComputer: advanced/adastra.md
- - Logging:
- - Output: logging/output.md
- - Collecting emissions to a logger: logging/to_logger.md
- - Visualize: logging/visualize.md
+ - Home: index.md
+ - Tutorials:
+ - Quickstart: tutorials/first-tracking.md
+ - Installation: how-to/installation.md
+ - Tracking with the CLI: tutorials/cli.md
+ - Tracking with Python: tutorials/python-api.md
+ - Framework Examples:
+ - scikit-learn: how-to/scikit-learn.md
+ - HuggingFace Transformers: how-to/transformers.md
+ - HuggingFace Diffusers: how-to/diffusers.md
+ - LLMs and Agents: how-to/agents.md
+ - How-to Guides:
+ - Configure CodeCarbon: how-to/configuration.md
+ - Compare Model Efficiency: tutorials/comparing-model-efficiency.md
+ - Dashboard & Visualization:
+ - Use the Cloud API & Dashboard: how-to/cloud-api.md
+ - Visualize Emissions: how-to/visualize.md
+ - Logging:
+ - Log to External Systems: how-to/logging.md
+ - Integrate with Comet: how-to/comet.md
+ - Deployment:
+ - Improve Measurement Accuracy with RAPL: how-to/enable-rapl.md
+ - Deploy as a Linux Service: how-to/linux-service.md
+ - Deploy with Ansible: how-to/ansible.md
+ - Run on SLURM (ROCm/PyTorch): how-to/slurm.md
+ - Test on Scaleway: how-to/test-on-scaleway.md
+ - Contributing:
+ - Code of Conduct: how-to/code-of-conduct.md
+ - Contribution Guidelines: how-to/contributing.md
+ - AI Policy: how-to/ai_policy.md
+ - Working with Coding Agent: how-to/working-with-agent.md
+ - Agent Instructions: how-to/agent-instructions.md
+ - Security Policy: how-to/security.md
+ - Explanation:
+ - Why CodeCarbon: explanation/why.md
+ - When to Use CodeCarbon vs EcoLogits: explanation/when-to-use.md
+ - Methodology: explanation/methodology.md
+ - RAPL Metrics: explanation/rapl.md
+ - Power Estimation: explanation/power-estimation.md
+ - Model Comparisons: explanation/model-comparisons.md
+ - FAQ: explanation/faq.md
+ - Reference:
+ - API Reference: reference/api.md
+ - Output Formats: reference/output.md
+ - CLI Reference: reference/cli.md
- Track GenAI API Calls (EcoLogits) โ: https://ecologits.ai/latest/?utm_source=codecarbon&utm_medium=docs
diff --git a/pyproject.toml b/pyproject.toml
index e1666c4a3..266f378ba 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -93,12 +93,15 @@ dev = [
"pytest-cov",
"logfire>=1.0.1", # For testing Logfire output method
"jsonschema", # For BoAmps schema validation tests
+ "mktestdocs", # For testing documentation code blocks
+ "scikit-learn", # For documentation examples and tests
]
doc = [
"setuptools",
"zensical",
"mike",
"mkdocstrings[python]>=0.26",
+ "mkdocs-redirects",
]
[project.optional-dependencies]
diff --git a/tests/test_docs_examples.py b/tests/test_docs_examples.py
new file mode 100644
index 000000000..93d110f53
--- /dev/null
+++ b/tests/test_docs_examples.py
@@ -0,0 +1,53 @@
+"""Tests for documentation code examples using mktestdocs.
+
+This file validates that code blocks in the documentation are correct and
+can be executed successfully.
+"""
+
+from pathlib import Path
+
+import pytest
+from mktestdocs import check_md_file
+
+
+@pytest.mark.parametrize(
+ "fpath",
+ [
+ "docs/tutorials/python-api.md",
+ "docs/explanation/why.md",
+ "docs/how-to/cloud-api.md",
+ "docs/how-to/comet.md",
+ "docs/how-to/configuration.md",
+ "docs/how-to/logging.md",
+ "docs/how-to/scikit-learn.md",
+ "docs/index.md",
+ ],
+ ids=lambda p: Path(p).name,
+)
+def test_doc_python_blocks(fpath, tmp_path, monkeypatch):
+ """Test independent Python code blocks from docs.
+
+ Each block runs in isolation (memory=False).
+ CWD is changed to tmp_path to isolate any file I/O.
+
+ Note: power-estimation.md contains code snippets that reference
+ variables from the codebase; these are not standalone runnable examples.
+ """
+ monkeypatch.chdir(tmp_path)
+ abs_fpath = Path(__file__).parent.parent / fpath
+ check_md_file(str(abs_fpath))
+
+
+def test_first_tracking_tutorial(tmp_path, monkeypatch):
+ """Test tutorial with sequential code blocks.
+
+ The first-tracking.md tutorial has blocks that depend on each other:
+ 1. Run tracker (creates emissions.csv)
+ 2. Read emissions.csv with pandas
+ 3. Access tracker.final_emissions
+
+ Use memory=True so blocks can share state.
+ """
+ monkeypatch.chdir(tmp_path)
+ abs_fpath = Path(__file__).parent.parent / "docs/tutorials/first-tracking.md"
+ check_md_file(str(abs_fpath), memory=True)
diff --git a/uv.lock b/uv.lock
index a7a95293b..bd12fe861 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
version = 1
-revision = 1
+revision = 3
requires-python = ">=3.10"
resolution-markers = [
"python_full_version >= '3.14' and sys_platform == 'win32'",
@@ -15,27 +15,27 @@ resolution-markers = [
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@@ -46,18 +46,9 @@ dependencies = [
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