Adding codecarbon detect CLI and the ability to report on hardware without running measurements

README.md

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

codecarbon/cli/main.py

@@ -413,6 +413,31 @@ def signal_handler(signum, frame):
         raise e
 
 
+@codecarbon.command("detect", short_help="Detect hardware and print information.")
+def detect():
+    """
+    Detects hardware and prints information without running any measurements.
+    """
+    print("Detecting hardware...")
+    tracker = EmissionsTracker(save_to_file=False)
+    hardware_info = tracker.get_detected_hardware()
+
+    print("\nDetected Hardware and System Information:")
+    print(f"- Available RAM: {hardware_info['ram_total_size']:.3f} GB")
+    print(
+        f"- CPU count: {hardware_info['cpu_count']} thread(s) in {hardware_info['cpu_physical_count']} physical CPU(s)"
+    )
+    print(f"- CPU model: {hardware_info['cpu_model']}")
+    print(f"- GPU count: {hardware_info['gpu_count']}")
+
+    gpu_model_str = hardware_info["gpu_model"]
+    if hardware_info.get("gpu_ids"):
+        gpu_model_str += (
+            f" BUT only tracking these GPU ids : {hardware_info['gpu_ids']}"
+        )
+    print(f"- GPU model: {gpu_model_str}")
+
+
 def questionary_prompt(prompt, list_options, default):
     value = questionary.select(
         prompt,

codecarbon/emissions_tracker.py

@@ -369,27 +369,30 @@ def __init__(
         self._active_task_emissions_at_start: Optional[EmissionsData] = None
 
         # Tracking mode detection
-        ressource_tracker = ResourceTracker(self)
-        ressource_tracker.set_CPU_GPU_ram_tracking()
+        self._hardware = []
+        resource_tracker = ResourceTracker(self)
+        resource_tracker.set_CPU_GPU_ram_tracking()
 
         self._conf["hardware"] = list(map(lambda x: x.description(), self._hardware))
 
         logger.info(">>> Tracker's metadata:")
         logger.info(f"  Platform system: {self._conf.get('os')}")
         logger.info(f"  Python version: {self._conf.get('python_version')}")
         logger.info(f"  CodeCarbon version: {self._conf.get('codecarbon_version')}")
-        logger.info(f"  Available RAM : {self._conf.get('ram_total_size'):.3f} GB")
+
+        hardware_info = self.get_detected_hardware()
+        logger.info(f"  Available RAM : {hardware_info['ram_total_size']:.3f} GB")
         logger.info(
-            f"  CPU count: {self._conf.get('cpu_count')} thread(s) in {self._conf.get('cpu_physical_count')} physical CPU(s)"
+            f"  CPU count: {hardware_info['cpu_count']} thread(s) in {hardware_info['cpu_physical_count']} physical CPU(s)"
         )
-        logger.info(f"  CPU model: {self._conf.get('cpu_model')}")
-        logger.info(f"  GPU count: {self._conf.get('gpu_count')}")
+        logger.info(f"  CPU model: {hardware_info['cpu_model']}")
+        logger.info(f"  GPU count: {hardware_info['gpu_count']}")
         if self._gpu_ids:
             logger.info(
-                f"  GPU model: {self._conf.get('gpu_model')} BUT only tracking these GPU ids : {self._conf.get('gpu_ids')}"
+                f"  GPU model: {hardware_info['gpu_model']} BUT only tracking these GPU ids : {hardware_info['gpu_ids']}"
             )
         else:
-            logger.info(f"  GPU model: {self._conf.get('gpu_model')}")
+            logger.info(f"  GPU model: {hardware_info['gpu_model']}")
 
         # Run `self._measure_power_and_energy` every `measure_power_secs` seconds in a
         # background thread
@@ -469,6 +472,22 @@ def _init_output_methods(self, *, api_key: str = None):
         if self._save_to_logfire:
             self._output_handlers.append(LogfireOutput())
 
+    def get_detected_hardware(self) -> Dict[str, Any]:
+        """
+        Get the detected hardware.
+        :return: A dictionary containing hardware data.
+        """
+        hardware_info = {
+            "ram_total_size": self._conf.get("ram_total_size"),
+            "cpu_count": self._conf.get("cpu_count"),
+            "cpu_physical_count": self._conf.get("cpu_physical_count"),
+            "cpu_model": self._conf.get("cpu_model"),
+            "gpu_count": self._conf.get("gpu_count"),
+            "gpu_model": self._conf.get("gpu_model"),
+            "gpu_ids": self._conf.get("gpu_ids"),
+        }
+        return hardware_info
+
     def service_shutdown(self, signum, frame):
         logger.warning("service_shutdown - Caught signal %d" % signum)
         self.stop()

docs/_sources/installation.rst.txt

@@ -3,15 +3,6 @@
 Installing CodeCarbon
 =====================
 
-Create a virtual environment using `conda` for easier management of dependencies and packages.
-For installing conda, follow the instructions on the
-`official conda website <https://docs.conda.io/projects/conda/en/latest/user-guide/install>`__
-
-.. code-block::  bash
-
-    conda create --name codecarbon
-    conda activate codecarbon
-
 From PyPi repository
 --------------------
 
@@ -23,16 +14,20 @@ To install the package, run the following command in your terminal.
 
     pip install codecarbon
 
-From conda repository
----------------------
-
-The package is hosted on the conda repository `here <https://anaconda.org/codecarbon/codecarbon>`__.
+Using Conda environments
+------------------------
 
-To install the package, run the following command in your terminal.
+If you're using Conda for environment management, you can install CodeCarbon with pip in your Conda environment:
 
 .. code-block::  bash
 
-    conda install -c codecarbon -c conda-forge codecarbon
+    conda create --name codecarbon
+    conda activate codecarbon
+    pip install codecarbon
+
+..  note::
+
+    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.
 
 ..  note::
 

docs/_sources/usage.rst.txt

@@ -42,6 +42,14 @@ If you want to track the emissions of a computer without having to modify your c
 
 You have to stop the monitoring manually with ``Ctrl+C``.
 
+If you want to detect the hardware of your computer without starting any measurement, you can use:
+
+.. code-block:: 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!)

docs/_static/basic.css

@@ -741,14 +741,6 @@ abbr, acronym {
     cursor: help;
 }
 
-.translated {
-    background-color: rgba(207, 255, 207, 0.2)
-}
-
-.untranslated {
-    background-color: rgba(255, 207, 207, 0.2)
-}
-
 /* -- code displays --------------------------------------------------------- */
 
 pre {

docs/_static/searchtools.js

@@ -513,9 +513,11 @@ const Search = {
     // perform the search on the required terms
     searchTerms.forEach((word) => {
       const files = [];
+      // find documents, if any, containing the query word in their text/title term indices
+      // use Object.hasOwnProperty to avoid mismatching against prototype properties
       const arr = [
-        { files: terms[word], score: Scorer.term },
-        { files: titleTerms[word], score: Scorer.title },
+        { files: terms.hasOwnProperty(word) ? terms[word] : undefined, score: Scorer.term },
+        { files: titleTerms.hasOwnProperty(word) ? titleTerms[word] : undefined, score: Scorer.title },
       ];
       // add support for partial matches
       if (word.length > 2) {
@@ -547,8 +549,9 @@ const Search = {
 
         // set score for the word in each file
         recordFiles.forEach((file) => {
-          if (!scoreMap.has(file)) scoreMap.set(file, {});
-          scoreMap.get(file)[word] = record.score;
+          if (!scoreMap.has(file)) scoreMap.set(file, new Map());
+          const fileScores = scoreMap.get(file);
+          fileScores.set(word, record.score);
         });
       });
 
@@ -587,7 +590,7 @@ const Search = {
         break;
 
       // select one (max) score for the file.
-      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file).get(w)));
       // add result to the result list
       results.push([
         docNames[file],

docs/edit/usage.rst

@@ -42,6 +42,14 @@ If you want to track the emissions of a computer without having to modify your c
 
 You have to stop the monitoring manually with ``Ctrl+C``.
 
+If you want to detect the hardware of your computer without starting any measurement, you can use:
+
+.. code-block:: 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!)

docs/index.html

@@ -16,7 +16,6 @@
       <script src="_static/documentation_options.js?v=eb155f5e"></script>
       <script src="_static/doctools.js?v=9bcbadda"></script>
       <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -133,7 +132,7 @@ <h1>CodeCarbon<a class="headerlink" href="#codecarbon" title="Link to this headi
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installing CodeCarbon</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="installation.html#from-pypi-repository">From PyPi repository</a></li>
-<li class="toctree-l2"><a class="reference internal" href="installation.html#from-conda-repository">From conda repository</a></li>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#using-conda-environments">Using Conda environments</a></li>
 <li class="toctree-l2"><a class="reference internal" href="installation.html#dependencies">Dependencies</a></li>
 </ul>
 </li>

docs/installation.html

@@ -54,7 +54,7 @@
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Installing CodeCarbon</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="#from-pypi-repository">From PyPi repository</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#from-conda-repository">From conda repository</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#using-conda-environments">Using Conda environments</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#dependencies">Dependencies</a></li>
 </ul>
 </li>
@@ -99,13 +99,6 @@
 
   <section id="installing-codecarbon">
 <span id="installation"></span><h1>Installing CodeCarbon<a class="headerlink" href="#installing-codecarbon" title="Link to this heading"></a></h1>
-<p>Create a virtual environment using <cite>conda</cite> for easier management of dependencies and packages.
-For installing conda, follow the instructions on the
-<a class="reference external" href="https://docs.conda.io/projects/conda/en/latest/user-guide/install">official conda website</a></p>
-<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
-conda<span class="w"> </span>activate<span class="w"> </span>codecarbon
-</pre></div>
-</div>
 <section id="from-pypi-repository">
 <h2>From PyPi repository<a class="headerlink" href="#from-pypi-repository" title="Link to this heading"></a></h2>
 <p>The package is hosted on the pip repository <a class="reference external" href="https://pypi.org/project/codecarbon/">here</a>.</p>
@@ -114,15 +107,20 @@ <h2>From PyPi repository<a class="headerlink" href="#from-pypi-repository" title
 </pre></div>
 </div>
 </section>
-<section id="from-conda-repository">
-<h2>From conda repository<a class="headerlink" href="#from-conda-repository" title="Link to this heading"></a></h2>
-<p>The package is hosted on the conda repository <a class="reference external" href="https://anaconda.org/codecarbon/codecarbon">here</a>.</p>
-<p>To install the package, run the following command in your terminal.</p>
-<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
+<section id="using-conda-environments">
+<h2>Using Conda environments<a class="headerlink" href="#using-conda-environments" title="Link to this heading"></a></h2>
+<p>If you’re using Conda for environment management, you can install CodeCarbon with pip in your Conda environment:</p>
+<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
+conda<span class="w"> </span>activate<span class="w"> </span>codecarbon
+pip<span class="w"> </span>install<span class="w"> </span>codecarbon
 </pre></div>
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
+<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>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
 <p>We recommend using Python 3.8 or above.</p>
 </div>
 </section>

docs/usage.html

@@ -135,6 +135,11 @@ <h3>Command line<a class="headerlink" href="#command-line" title="Link to this h
 </pre></div>
 </div>
 <p>You have to stop the monitoring manually with <code class="docutils literal notranslate"><span class="pre">Ctrl+C</span></code>.</p>
+<p>If you want to detect the hardware of your computer without starting any measurement, you can use:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">codecarbon detect</span>
+</pre></div>
+</div>
+<p>It will print the detected RAM, CPU and GPU information.</p>
 <p>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!)</p>

examples/print_hardware.py

@@ -0,0 +1,21 @@
+from codecarbon.emissions_tracker import EmissionsTracker
+
+if __name__ == "__main__":
+    tracker = EmissionsTracker(measure_power_secs=0, save_to_file=False)
+
+    print("Detected Hardware:")
+    hardware_info = tracker.get_detected_hardware()
+
+    print(f"- Available RAM: {hardware_info['ram_total_size']:.3f} GB")
+    print(
+        f"- CPU count: {hardware_info['cpu_count']} thread(s) in {hardware_info['cpu_physical_count']} physical CPU(s)"
+    )
+    print(f"- CPU model: {hardware_info['cpu_model']}")
+    print(f"- GPU count: {hardware_info['gpu_count']}")
+
+    gpu_model_str = hardware_info["gpu_model"]
+    if hardware_info.get("gpu_ids"):
+        gpu_model_str += (
+            f" BUT only tracking these GPU ids : {hardware_info['gpu_ids']}"
+        )
+    print(f"- GPU model: {gpu_model_str}")

tests/test_emissions_tracker.py

@@ -637,3 +637,22 @@ def test_scheduler_warning_shown_when_running(
             self.assertTrue(
                 scheduler_warning_found, "Expected scheduler warning was not found"
             )
+
+    def test_get_detected_hardware(
+        self,
+        mock_cli_setup,
+        mock_log_values,
+        mocked_get_gpu_details,
+        mocked_env_cloud_details,
+        mocked_is_gpu_details_available,
+    ):
+        tracker = EmissionsTracker(save_to_file=False)
+        hardware_info = tracker.get_detected_hardware()
+        self.assertIsInstance(hardware_info, dict)
+        self.assertIn("ram_total_size", hardware_info)
+        self.assertIn("cpu_count", hardware_info)
+        self.assertIn("cpu_physical_count", hardware_info)
+        self.assertIn("cpu_model", hardware_info)
+        self.assertIn("gpu_count", hardware_info)
+        self.assertIn("gpu_model", hardware_info)
+        self.assertIn("gpu_ids", hardware_info)