Docs partial mdformat improvements (#7378)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/en/integrations/amazon-sagemaker.md

@@ -34,9 +34,9 @@ First, ensure you have the following prerequisites in place:
 
 - Configured IAM Roles: You’ll need an IAM role with the necessary permissions for Amazon SageMaker, AWS CloudFormation, and Amazon S3. This role should have policies that allow it to access these services.
 
-- AWS CLI:  If not already installed, download and install the AWS Command Line Interface (CLI) and configure it with your account details. Follow [the AWS CLI instructions](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) for installation.
+- AWS CLI: If not already installed, download and install the AWS Command Line Interface (CLI) and configure it with your account details. Follow [the AWS CLI instructions](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) for installation.
 
-- AWS CDK: If not already installed, install the AWS Cloud Development Kit (CDK), which will be used for scripting the deployment. Follow [the AWS CDK  instructions](https://docs.aws.amazon.com/cdk/v2/guide/getting_started.html#getting_started_install) for installation.
+- AWS CDK: If not already installed, install the AWS Cloud Development Kit (CDK), which will be used for scripting the deployment. Follow [the AWS CDK instructions](https://docs.aws.amazon.com/cdk/v2/guide/getting_started.html#getting_started_install) for installation.
 
 - Adequate Service Quota: Confirm that you have sufficient quotas for two separate resources in Amazon SageMaker: one for ml.m5.4xlarge for endpoint usage and another for ml.m5.4xlarge for notebook instance usage. Each of these requires a minimum of one quota value. If your current quotas are below this requirement, it's important to request an increase for each. You can request a quota increase by following the detailed instructions in the [AWS Service Quotas documentation](https://docs.aws.amazon.com/servicequotas/latest/userguide/request-quota-increase.html#quota-console-increase).
 

docs/en/integrations/clearml.md

@@ -119,22 +119,27 @@ By clicking on the URL link to the ClearML results page in the output of the usa
 #### Key Features of the ClearML Results Page
 
 - **Real-Time Metrics Tracking**
+
     - Track critical metrics like loss, accuracy, and validation scores as they occur.
     - Provides immediate feedback for timely model performance adjustments.
 
 - **Experiment Comparison**
+
     - Compare different training runs side-by-side.
     - Essential for hyperparameter tuning and identifying the most effective models.
 
 - **Detailed Logs and Outputs**
+
     - Access comprehensive logs, graphical representations of metrics, and console outputs.
     - Gain a deeper understanding of model behavior and issue resolution.
 
 - **Resource Utilization Monitoring**
+
     - Monitor the utilization of computational resources, including CPU, GPU, and memory.
     - Key to optimizing training efficiency and costs.
 
 - **Model Artifacts Management**
+
     - View, download, and share model artifacts like trained models and checkpoints.
     - Enhances collaboration and streamlines model deployment and sharing.
 

docs/en/integrations/mlflow.md

@@ -63,24 +63,31 @@ Make sure that MLflow logging is enabled in Ultralytics settings. Usually, this
 ### Commands
 
 1. **Set a Project Name**: You can set the project name via an environment variable:
+
     ```bash
     export MLFLOW_EXPERIMENT_NAME=<your_experiment_name>
     ```
+
    Or use the `project=<project>` argument when training a YOLO model, i.e. `yolo train project=my_project`.
 
 2. **Set a Run Name**: Similar to setting a project name, you can set the run name via an environment variable:
+
     ```bash
     export MLFLOW_RUN=<your_run_name>
     ```
+
    Or use the `name=<name>` argument when training a YOLO model, i.e. `yolo train project=my_project name=my_name`.
 
 3. **Start Local MLflow Server**: To start tracking, use:
+
     ```bash
     mlflow server --backend-store-uri runs/mlflow'
     ```
+
    This will start a local server at http://127.0.0.1:5000 by default and save all mlflow logs to the 'runs/mlflow' directory. To specify a different URI, set the `MLFLOW_TRACKING_URI` environment variable.
 
 4. **Kill MLflow Server Instances**: To stop all running MLflow instances, run:
+
     ```bash
     ps aux | grep 'mlflow' | grep -v 'grep' | awk '{print $2}' | xargs kill -9
     ```
@@ -93,11 +100,9 @@ The logging is taken care of by the `on_pretrain_routine_end`, `on_fit_epoch_end
 
 1. **Logging Custom Metrics**: You can add custom metrics to be logged by modifying the `trainer.metrics` dictionary before `on_fit_epoch_end` is called.
 
-2. **View Experiment**: To view your logs, navigate to your MLflow server (usually http://127.0.0.1:5000) and select your experiment and run.
-   <img width="1024" src="https://user-images.githubusercontent.com/26833433/274933329-3127aa8c-4491-48ea-81df-ed09a5837f2a.png" alt="YOLO MLflow Experiment">
+2. **View Experiment**: To view your logs, navigate to your MLflow server (usually http://127.0.0.1:5000) and select your experiment and run. <img width="1024" src="https://user-images.githubusercontent.com/26833433/274933329-3127aa8c-4491-48ea-81df-ed09a5837f2a.png" alt="YOLO MLflow Experiment">
 
-3. **View Run**: Runs are individual models inside an experiment. Click on a Run and see the Run details, including uploaded artifacts and model weights.
-   <img width="1024" src="https://user-images.githubusercontent.com/26833433/274933337-ac61371c-2867-4099-a733-147a2583b3de.png" alt="YOLO MLflow Run">
+3. **View Run**: Runs are individual models inside an experiment. Click on a Run and see the Run details, including uploaded artifacts and model weights. <img width="1024" src="https://user-images.githubusercontent.com/26833433/274933337-ac61371c-2867-4099-a733-147a2583b3de.png" alt="YOLO MLflow Run">
 
 ## Disabling MLflow
 

docs/en/integrations/roboflow.md

@@ -37,7 +37,6 @@ Universe is an online repository with over 250,000 vision datasets totalling ove
 
 With a [free Roboflow account](https://app.roboflow.com/?ref=ultralytics), you can export any dataset available on Universe. To export a dataset, click the "Download this Dataset" button on any dataset.
 
-
 <p align="center">
 <img src="https://media.roboflow.com/ultralytics/rf_dataset.png" alt="Roboflow Universe dataset export" width="800">
 </p>

docs/en/integrations/weights-biases.md

@@ -126,7 +126,7 @@ After running the usage code snippet, you can access the Weights & Biases (W&B)
 
 - **Real-Time Metrics Tracking**: Observe metrics like loss, accuracy, and validation scores as they evolve during the training, offering immediate insights for model tuning.
 
-  <div style="text-align:center;"><blockquote class="imgur-embed-pub" lang="en" data-id="a/TB76U9O"><a href="//imgur.com/D6NVnmN">Take a look at how the experiments are tracked using Weights & Biases.</a></blockquote></div><script async src="//s.imgur.com/min/embed.js" charset="utf-8"></script>
+    <div style="text-align:center;"><blockquote class="imgur-embed-pub" lang="en" data-id="a/TB76U9O"><a href="//imgur.com/D6NVnmN">Take a look at how the experiments are tracked using Weights & Biases.</a></blockquote></div><script async src="//s.imgur.com/min/embed.js" charset="utf-8"></script>
 
 - **Hyperparameter Optimization**: Weights & Biases aids in fine-tuning critical parameters such as learning rate, batch size, and more, enhancing the performance of YOLOv8.
 
@@ -134,7 +134,7 @@ After running the usage code snippet, you can access the Weights & Biases (W&B)
 
 - **Visualization of Training Progress**: Graphical representations of key metrics provide an intuitive understanding of the model's performance across epochs.
 
-  <div style="text-align:center;"><blockquote class="imgur-embed-pub" lang="en" data-id="a/kU5h7W4" data-context="false" ><a href="//imgur.com/a/kU5h7W4">Take a look at how Weights & Biases helps you visualize validation results.</a></blockquote></div><script async src="//s.imgur.com/min/embed.js" charset="utf-8"></script>
+    <div style="text-align:center;"><blockquote class="imgur-embed-pub" lang="en" data-id="a/kU5h7W4" data-context="false" ><a href="//imgur.com/a/kU5h7W4">Take a look at how Weights & Biases helps you visualize validation results.</a></blockquote></div><script async src="//s.imgur.com/min/embed.js" charset="utf-8"></script>
 
 - **Resource Monitoring**: Keep track of CPU, GPU, and memory usage to optimize the efficiency of the training process.
 
@@ -142,7 +142,7 @@ After running the usage code snippet, you can access the Weights & Biases (W&B)
 
 - **Viewing Inference Results with Image Overlay**: Visualize the prediction results on images using interactive overlays in Weights & Biases, providing a clear and detailed view of model performance on real-world data. For more detailed information on Weights & Biases’ image overlay capabilities, check out this [link](https://docs.wandb.ai/guides/track/log/media#image-overlays).
 
-  <div style="text-align:center;"><blockquote class="imgur-embed-pub" lang="en" data-id="a/UTSiufs" data-context="false" ><a href="//imgur.com/a/UTSiufs">Take a look at how Weights & Biases’ image overlays helps visualize model inferences.</a></blockquote></div><script async src="//s.imgur.com/min/embed.js" charset="utf-8"></script>
+    <div style="text-align:center;"><blockquote class="imgur-embed-pub" lang="en" data-id="a/UTSiufs" data-context="false" ><a href="//imgur.com/a/UTSiufs">Take a look at how Weights & Biases’ image overlays helps visualize model inferences.</a></blockquote></div><script async src="//s.imgur.com/min/embed.js" charset="utf-8"></script>
 
 By using these features, you can effectively track, analyze, and optimize your YOLOv8 model's training, ensuring the best possible performance and efficiency.