Experiments: Reproduce experiments

content/guides/models/track/reproduce_experiments.md

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+---
+menu:
+  default:
+    identifier: reproduce_experiments
+    parent: track
+title: Reproduce experiments
+weight: 7
+---
+
+Use W&B to reproduce an experiment that a team member creates. Reproducing a machine learning experiment is a crucial part in verifying and validating the results of that experiment. 
+
+Before you reproduce an experiment, make note of the:
+
+* Name of the project the run was logged to
+* Name of the run you want to reproduce
+
+{{% alert %}}
+This section assumes you know the name of the project that contains the run you want to reproduce.
+{{% /alert %}}
+
+Once you have the name of the project and run you want to reproduce, you can reproduce an experiment that a team member created by:
+
+1. Navigate to the project where the run is logged to.
+2. Select the **Workspace** tab in the left sidebar.
+3. From the list of runs, select the run that you want to reproduce.
+4. Click **Overview**.
+
+Depending on how your team member configured their project, you can reproduce an experiment by either checking out their GitHub repository or downloading their code.
+
+{{< tabpane text=true >}}
+{{% tab "Download Python script or notebook" %}}
+
+Download a Python script or notebook that your teammate used to create the experiment:
+
+1. Note of the Python script or notebook in the **Command** field. This is the script that your teammate used to create the experiment.
+2. Select the **Code** tab in the left navigation bar.
+3. Download the Python script or notebook specified in the **Command** field. Click on the **Download** button next to the name of the file.
+
+
+{{% /tab %}}
+{{% tab "GitHub" %}}
+
+{{% alert %}}
+Before you continue, ensure you have access to the GitHub repository that your teammate used to create the experiment.
+{{% /alert %}}
+
+Clone the GitHub repository your teammate used when creating the experiment. To do this, 
+
+1. Copy and paste the GitHub repository URL specified in the **Git repository** field.
+```bash
+git clone https://github.com/your-repo.git && cd your-repo
+```
+2. Copy and paste **Git state** into your terminal. The Git state is a set of Git commands that will check out the exact commit that your teammate used to create the experiment. Replace values specified in the proceeding code snippet with your own:
+```bash
+git checkout -b "<run-name>" 0123456789012345678901234567890123456789
+```
+
+
+
+{{% /tab %}}
+{{< /tabpane >}}
+
+5. Select **Files** in the left navigation bar.
+6. Download the `requirements.txt` file and store it in your working directory. This directory should contain either the cloned GitHub repository or the downloaded Python script or notebook.
+7. (Recommended) Create a Python virtual environment.
+8. Install the requirements specified in the `requirements.txt` file.
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+Now that you have the code and dependencies, you can run the script or notebook to reproduce the experiment. If you cloned a repository, you might need to navigate to the directory where the script or notebook is located. Otherwise, you can run the script or notebook from your working directory.
+
+{{< tabpane text=true >}}
+{{% tab "Python notebook" %}}
+
+If you downloaded a Python notebook, navigate to the directory where you downloaded the notebook and run the following command in your terminal:
+```bash
+jupyter notebook
+```
+
+{{% /tab %}}
+{{% tab "Python script" %}}
+
+If you downloaded a Python script, navigate to the directory where you downloaded the script and run the following command in your terminal; Replace values enclosed in `<>` with your own:
+
+```bash
+python <your-script-name>.py
+```
+
+
+{{% /tab %}}
+{{< /tabpane >}}