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Merge pull request #1016 from mlco2/chore/bump_3.2.1
Chore/bump 3.2.1
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CONTRIBUTING.md

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Before using it, you need an experiment_id, to get one, run:
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```
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codecarbon init
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codecarbon login
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```
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It will ask the API for an experiment_id on the default project and save it to `.codecarbon.config` for you.

codecarbon/_version.py

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__version__ = "3.2.0"
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__version__ = "3.2.1"

docs/.buildinfo

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# Sphinx build info version 1
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# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 1179cfac2a69f75fdb97976cfa9a706e
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config: 6fab6ba8b08b27f1b70031950c82d429
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tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/_sources/api.rst.txt

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~~~~~~~~~~~~~~
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.. warning::
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This mode use the CodeCarbon API to upload the timeseries of your emissions on a central server. All data will be public!
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This mode use the CodeCarbon API to upload the timeseries of your emissions on a central server.
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Thanks to `CleverCloud <https://www.clever.cloud/>`_ the use of API is free as soon as it remains under certain limits.
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.. image:: https://github.com/mlco2/codecarbon/raw/master/carbonserver/Images/code_carbon_archi.png
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:alt: Summary
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:width: 700px
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Before using it, you need an experiment_id, to get one, run:
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Before using it, you need to create on account on the `CodeCarbon dashboard <https://dashboard.codecarbon.io/>`_
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Then login from your terminal:
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.. code-block:: console
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codecarbon init
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codecarbon login
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It will create an experiment_id for the default project and save it to ``codecarbon.config``
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docs/_sources/output.rst.txt

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* - emissions_rate
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- emissions divided per duration, in Kg/s
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* - cpu_power
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- CPU power (W)
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- Mean CPU power (W)
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* - gpu_power
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- GPU power (W)
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- Mean GPU power (W)
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* - ram_power
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- RAM power (W)
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- Mean RAM power (W)
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* - cpu_energy
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- Energy used per CPU (kWh)
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* - gpu_energy
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| This is done for privacy protection.
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* - ram_total_size
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- total RAM available (Go)
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* - Tracking_mode:
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* - tracking_mode:
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- ``machine`` or ``process``(default to ``machine``)
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* - cpu_utilization_percent
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- Average CPU utilization during tracking period (%)
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* - gpu_utilization_percent
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- Average GPU utilization during tracking period (%)
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* - ram_utilization_percent
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- Average RAM utilization during tracking period (%)
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* - ram_used_gb
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- Average RAM used during tracking period (GB)
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.. note::
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Using CodeCarbon with logfire
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`````````````````````````````````
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`Logfire <https://docs.pydantic.dev/logfire/>`_ is an observability platform.
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`Logfire <https://docs.pydantic.dev/logfire/>`_ is an observability platform.
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CodeCarbon exposes all its metrics with the suffix `codecarbon_`.
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docs/_sources/visualize.rst.txt

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The package also comes with a ``Dash App`` containing illustrations to understand the emissions logged from various experiments across projects.
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The App currently consumes logged information from a CSV file, generated from an in-built logger in the package.
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Installation
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~~~~~~~~~~~~
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The carbonboard visualization tool requires additional dependencies. Install them with:
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.. code-block:: bash
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pip install codecarbon[carbonboard]
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.. note::
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The ``viz-legacy`` extra is deprecated but still works for backwards compatibility. It will be removed in v4.0.0. Please use ``carbonboard`` instead.
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Usage
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~~~~~
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The App can be run by executing the below CLI command that needs following arguments:
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- ``filepath`` - path to the CSV file containing logged information across experiments and projects
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:alt: carbon intensity carbon_map
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:width: 750px
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Note that for now, all data sent to CodeCarbon API are public.
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docs/_static/basic.css

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cursor: help;
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}
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.translated {
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background-color: rgba(207, 255, 207, 0.2)
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}
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.untranslated {
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background-color: rgba(255, 207, 207, 0.2)
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}
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/* -- code displays --------------------------------------------------------- */
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pre {

docs/_static/documentation_options.js

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const DOCUMENTATION_OPTIONS = {
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VERSION: '3.1.1',
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VERSION: '3.2.1',
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LANGUAGE: 'en',
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COLLAPSE_INDEX: false,
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BUILDER: 'html',
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