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Adding codecarbon detect CLI and the ability to report on hardware without running measurements. (#1031)
* Add `detect` command to CLI to report system hardware without starting a tracker. * Create `EmissionsTracker.get_detected_hardware()` to return basic hardware information. * Update internal logging to use `get_detected_hardware()`. * Create `examples/print_hardware.py` and unit tests. * Add documentation for the `detect` command in README and usage guide.
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‎README.md‎

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@@ -107,6 +107,12 @@ In your command prompt use:
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```codecarbon monitor```
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The package will track your emissions independently from your code.
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### Detecting your hardware 🔍
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In your command prompt use:
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```codecarbon detect```
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The package will detect and print your hardware information (RAM, CPU, GPU).
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### In your Python code 🐍
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```python
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from codecarbon import track_emissions

‎codecarbon/cli/main.py‎

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raise e
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@codecarbon.command("detect", short_help="Detect hardware and print information.")
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def detect():
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"""
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Detects hardware and prints information without running any measurements.
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"""
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print("Detecting hardware...")
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tracker = EmissionsTracker(save_to_file=False)
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hardware_info = tracker.get_detected_hardware()
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print("\nDetected Hardware and System Information:")
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print(f"- Available RAM: {hardware_info['ram_total_size']:.3f} GB")
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print(
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f"- CPU count: {hardware_info['cpu_count']} thread(s) in {hardware_info['cpu_physical_count']} physical CPU(s)"
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)
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print(f"- CPU model: {hardware_info['cpu_model']}")
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print(f"- GPU count: {hardware_info['gpu_count']}")
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gpu_model_str = hardware_info["gpu_model"]
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if hardware_info.get("gpu_ids"):
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gpu_model_str += (
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f" BUT only tracking these GPU ids : {hardware_info['gpu_ids']}"
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)
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print(f"- GPU model: {gpu_model_str}")
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def questionary_prompt(prompt, list_options, default):
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value = questionary.select(
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prompt,

‎codecarbon/emissions_tracker.py‎

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self._active_task_emissions_at_start: Optional[EmissionsData] = None
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# Tracking mode detection
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ressource_tracker = ResourceTracker(self)
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ressource_tracker.set_CPU_GPU_ram_tracking()
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self._hardware = []
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resource_tracker = ResourceTracker(self)
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resource_tracker.set_CPU_GPU_ram_tracking()
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self._conf["hardware"] = list(map(lambda x: x.description(), self._hardware))
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logger.info(">>> Tracker's metadata:")
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logger.info(f" Platform system: {self._conf.get('os')}")
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logger.info(f" Python version: {self._conf.get('python_version')}")
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logger.info(f" CodeCarbon version: {self._conf.get('codecarbon_version')}")
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logger.info(f" Available RAM : {self._conf.get('ram_total_size'):.3f} GB")
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hardware_info = self.get_detected_hardware()
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logger.info(f" Available RAM : {hardware_info['ram_total_size']:.3f} GB")
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logger.info(
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f" CPU count: {self._conf.get('cpu_count')} thread(s) in {self._conf.get('cpu_physical_count')} physical CPU(s)"
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f" CPU count: {hardware_info['cpu_count']} thread(s) in {hardware_info['cpu_physical_count']} physical CPU(s)"
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)
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logger.info(f" CPU model: {self._conf.get('cpu_model')}")
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logger.info(f" GPU count: {self._conf.get('gpu_count')}")
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logger.info(f" CPU model: {hardware_info['cpu_model']}")
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logger.info(f" GPU count: {hardware_info['gpu_count']}")
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if self._gpu_ids:
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logger.info(
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f" GPU model: {self._conf.get('gpu_model')} BUT only tracking these GPU ids : {self._conf.get('gpu_ids')}"
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f" GPU model: {hardware_info['gpu_model']} BUT only tracking these GPU ids : {hardware_info['gpu_ids']}"
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)
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else:
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logger.info(f" GPU model: {self._conf.get('gpu_model')}")
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logger.info(f" GPU model: {hardware_info['gpu_model']}")
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# Run `self._measure_power_and_energy` every `measure_power_secs` seconds in a
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# background thread
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if self._save_to_logfire:
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self._output_handlers.append(LogfireOutput())
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def get_detected_hardware(self) -> Dict[str, Any]:
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"""
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Get the detected hardware.
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:return: A dictionary containing hardware data.
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"""
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hardware_info = {
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"ram_total_size": self._conf.get("ram_total_size"),
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"cpu_count": self._conf.get("cpu_count"),
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"cpu_physical_count": self._conf.get("cpu_physical_count"),
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"cpu_model": self._conf.get("cpu_model"),
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"gpu_count": self._conf.get("gpu_count"),
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"gpu_model": self._conf.get("gpu_model"),
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"gpu_ids": self._conf.get("gpu_ids"),
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}
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return hardware_info
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def service_shutdown(self, signum, frame):
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logger.warning("service_shutdown - Caught signal %d" % signum)
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self.stop()

‎docs/_sources/installation.rst.txt‎

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Installing CodeCarbon
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=====================
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Create a virtual environment using `conda` for easier management of dependencies and packages.
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For installing conda, follow the instructions on the
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`official conda website <https://docs.conda.io/projects/conda/en/latest/user-guide/install>`__
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.. code-block:: bash
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conda create --name codecarbon
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conda activate codecarbon
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From PyPi repository
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--------------------
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pip install codecarbon
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From conda repository
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---------------------
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The package is hosted on the conda repository `here <https://anaconda.org/codecarbon/codecarbon>`__.
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Using Conda environments
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------------------------
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To install the package, run the following command in your terminal.
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If you're using Conda for environment management, you can install CodeCarbon with pip in your Conda environment:
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.. code-block:: bash
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conda install -c codecarbon -c conda-forge codecarbon
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conda create --name codecarbon
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conda activate codecarbon
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pip install codecarbon
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.. note::
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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.
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.. note::
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‎docs/_sources/usage.rst.txt‎

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You have to stop the monitoring manually with ``Ctrl+C``.
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If you want to detect the hardware of your computer without starting any measurement, you can use:
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.. code-block:: console
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codecarbon detect
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It will print the detected RAM, CPU and GPU information.
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In the following example you will see how to use the CLI to monitor all the emissions of you computer and sending everything
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to an API running on "localhost:8008" (Or you can start a private local API with "docker-compose up"). Using the public API with
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this is not supported yet (coming soon!)

‎docs/_static/basic.css‎

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cursor: help;
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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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‎docs/_static/searchtools.js‎

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// perform the search on the required terms
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searchTerms.forEach((word) => {
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const files = [];
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// find documents, if any, containing the query word in their text/title term indices
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// use Object.hasOwnProperty to avoid mismatching against prototype properties
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const arr = [
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{ files: terms[word], score: Scorer.term },
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{ files: titleTerms[word], score: Scorer.title },
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{ files: terms.hasOwnProperty(word) ? terms[word] : undefined, score: Scorer.term },
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{ files: titleTerms.hasOwnProperty(word) ? titleTerms[word] : undefined, score: Scorer.title },
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recordFiles.forEach((file) => {
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if (!scoreMap.has(file)) scoreMap.set(file, {});
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scoreMap.get(file)[word] = record.score;
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if (!scoreMap.has(file)) scoreMap.set(file, new Map());
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const fileScores = scoreMap.get(file);
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fileScores.set(word, record.score);
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// select one (max) score for the file.
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const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
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const score = Math.max(...wordList.map((w) => scoreMap.get(file).get(w)));
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// add result to the result list
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‎docs/edit/usage.rst‎

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You have to stop the monitoring manually with ``Ctrl+C``.
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If you want to detect the hardware of your computer without starting any measurement, you can use:
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.. code-block:: console
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codecarbon detect
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It will print the detected RAM, CPU and GPU information.
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In the following example you will see how to use the CLI to monitor all the emissions of you computer and sending everything
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to an API running on "localhost:8008" (Or you can start a private local API with "docker-compose up"). Using the public API with
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this is not supported yet (coming soon!)

‎docs/index.html‎

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<script src="_static/documentation_options.js?v=eb155f5e"></script>
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<script src="_static/doctools.js?v=9bcbadda"></script>
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<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
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<script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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<script src="_static/js/theme.js"></script>
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<link rel="index" title="Index" href="genindex.html" />
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="installation.html">Installing CodeCarbon</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="installation.html#from-pypi-repository">From PyPi repository</a></li>
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<li class="toctree-l2"><a class="reference internal" href="installation.html#from-conda-repository">From conda repository</a></li>
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<li class="toctree-l2"><a class="reference internal" href="installation.html#using-conda-environments">Using Conda environments</a></li>
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<li class="toctree-l2"><a class="reference internal" href="installation.html#dependencies">Dependencies</a></li>
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</ul>
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</li>

‎docs/installation.html‎

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<ul class="current">
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Installing CodeCarbon</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="#from-pypi-repository">From PyPi repository</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#from-conda-repository">From conda repository</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#using-conda-environments">Using Conda environments</a></li>
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<section id="installing-codecarbon">
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<span id="installation"></span><h1>Installing CodeCarbon<a class="headerlink" href="#installing-codecarbon" title="Link to this heading"></a></h1>
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<p>Create a virtual environment using <cite>conda</cite> for easier management of dependencies and packages.
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For installing conda, follow the instructions on the
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<a class="reference external" href="https://docs.conda.io/projects/conda/en/latest/user-guide/install">official conda website</a></p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>create<span class="w"> </span>--name<span class="w"> </span>codecarbon
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conda<span class="w"> </span>activate<span class="w"> </span>codecarbon
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</pre></div>
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</div>
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<section id="from-pypi-repository">
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<h2>From PyPi repository<a class="headerlink" href="#from-pypi-repository" title="Link to this heading"></a></h2>
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<p>The package is hosted on the pip repository <a class="reference external" href="https://pypi.org/project/codecarbon/">here</a>.</p>
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<section id="from-conda-repository">
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<h2>From conda repository<a class="headerlink" href="#from-conda-repository" title="Link to this heading"></a></h2>
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<p>The package is hosted on the conda repository <a class="reference external" href="https://anaconda.org/codecarbon/codecarbon">here</a>.</p>
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<p>To install the package, run the following command in your terminal.</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>install<span class="w"> </span>-c<span class="w"> </span>codecarbon<span class="w"> </span>-c<span class="w"> </span>conda-forge<span class="w"> </span>codecarbon
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<section id="using-conda-environments">
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<h2>Using Conda environments<a class="headerlink" href="#using-conda-environments" title="Link to this heading"></a></h2>
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<p>If you’re using Conda for environment management, you can install CodeCarbon with pip in your Conda environment:</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>create<span class="w"> </span>--name<span class="w"> </span>codecarbon
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conda<span class="w"> </span>activate<span class="w"> </span>codecarbon
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pip<span class="w"> </span>install<span class="w"> </span>codecarbon
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</pre></div>
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</div>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>While CodeCarbon can be used in Conda environments, we no longer maintain Conda packages. We recommend using <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">codecarbon</span></code> within your Conda environment, which works seamlessly with Conda.</p>
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</div>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>We recommend using Python 3.8 or above.</p>
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</div>
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</section>

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