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docs/en/integrations/clearml.md
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@ -1,14 +1,14 @@
---
comments: true
description: Discover how to integrate YOLOv8 with ClearML to streamline your MLOps workflow, automate experiments, and enhance model management effortlessly.
keywords: YOLOv8, ClearML, MLOps, Ultralytics, machine learning, object detection, model training, automation, experiment management
description: Discover how to integrate YOLO11 with ClearML to streamline your MLOps workflow, automate experiments, and enhance model management effortlessly.
keywords: YOLO11, ClearML, MLOps, Ultralytics, machine learning, object detection, model training, automation, experiment management
---
# Training YOLOv8 with ClearML: Streamlining Your MLOps Workflow
# Training YOLO11 with ClearML: Streamlining Your MLOps Workflow
MLOps bridges the gap between creating and deploying [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models in real-world settings. It focuses on efficient deployment, scalability, and ongoing management to ensure models perform well in practical applications.
[Ultralytics YOLOv8](https://www.ultralytics.com/) effortlessly integrates with ClearML, streamlining and enhancing your [object detection](https://www.ultralytics.com/glossary/object-detection) model's training and management. This guide will walk you through the integration process, detailing how to set up ClearML, manage experiments, automate model management, and collaborate effectively.
[Ultralytics YOLO11](https://www.ultralytics.com/) effortlessly integrates with ClearML, streamlining and enhancing your [object detection](https://www.ultralytics.com/glossary/object-detection) model's training and management. This guide will walk you through the integration process, detailing how to set up ClearML, manage experiments, automate model management, and collaborate effectively.
## ClearML
@ -18,9 +18,9 @@ MLOps bridges the gap between creating and deploying [machine learning](https://
[ClearML](https://clear.ml/) is an innovative open-source MLOps platform that is skillfully designed to automate, monitor, and orchestrate machine learning workflows. Its key features include automated logging of all training and inference data for full experiment reproducibility, an intuitive web UI for easy [data visualization](https://www.ultralytics.com/glossary/data-visualization) and analysis, advanced hyperparameter [optimization algorithms](https://www.ultralytics.com/glossary/optimization-algorithm), and robust model management for efficient deployment across various platforms.
## YOLOv8 Training with ClearML
## YOLO11 Training with ClearML
You can bring automation and efficiency to your machine learning workflow by improving your training process by integrating YOLOv8 with ClearML.
You can bring automation and efficiency to your machine learning workflow by improving your training process by integrating YOLO11 with ClearML.
## Installation
@ -31,11 +31,11 @@ To install the required packages, run:
=== "CLI"
```bash
# Install the required packages for YOLOv8 and ClearML
# Install the required packages for YOLO11 and ClearML
pip install ultralytics clearml
```
For detailed instructions and best practices related to the installation process, be sure to check our [YOLOv8 Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, be sure to check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
## Configuring ClearML
@ -56,7 +56,7 @@ After executing this command, visit the [ClearML Settings page](https://app.clea
## Usage
Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
!!! example "Usage"
@ -70,11 +70,11 @@ Before diving into the usage instructions, be sure to check out the range of [YO
# Step 1: Creating a ClearML Task
task = Task.init(project_name="my_project", task_name="my_yolov8_task")
# Step 2: Selecting the YOLOv8 Model
model_variant = "yolov8n"
# Step 2: Selecting the YOLO11 Model
model_variant = "yolo11n"
task.set_parameter("model_variant", model_variant)
# Step 3: Loading the YOLOv8 Model
# Step 3: Loading the YOLO11 Model
model = YOLO(f"{model_variant}.pt")
# Step 4: Setting Up Training Arguments
@ -91,11 +91,11 @@ Let's understand the steps showcased in the usage code snippet above.
**Step 1: Creating a ClearML Task**: A new task is initialized in ClearML, specifying your project and task names. This task will track and manage your model's training.
**Step 2: Selecting the YOLOv8 Model**: The `model_variant` variable is set to 'yolov8n', one of the YOLOv8 models. This variant is then logged in ClearML for tracking.
**Step 2: Selecting the YOLO11 Model**: The `model_variant` variable is set to 'yolo11n', one of the YOLO11 models. This variant is then logged in ClearML for tracking.
**Step 3: Loading the YOLOv8 Model**: The selected YOLOv8 model is loaded using Ultralytics' YOLO class, preparing it for training.
**Step 3: Loading the YOLO11 Model**: The selected YOLO11 model is loaded using Ultralytics' YOLO class, preparing it for training.
**Step 4: Setting Up Training Arguments**: Key training arguments like the dataset (`coco8.yaml`) and the number of [epochs](https://www.ultralytics.com/glossary/epoch) (`16`) are organized in a dictionary and connected to the ClearML task. This allows for tracking and potential modification via the ClearML UI. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
**Step 4: Setting Up Training Arguments**: Key training arguments like the dataset (`coco8.yaml`) and the number of [epochs](https://www.ultralytics.com/glossary/epoch) (`16`) are organized in a dictionary and connected to the ClearML task. This allows for tracking and potential modification via the ClearML UI. For a detailed understanding of the model training process and best practices, refer to our [YOLO11 Model Training guide](../modes/train.md).
**Step 5: Initiating Model Training**: The model training is started with the specified arguments. The results of the training process are captured in the `results` variable.
@ -106,7 +106,7 @@ Upon running the usage code snippet above, you can expect the following output:
- A confirmation message indicating the creation of a new ClearML task, along with its unique ID.
- An informational message about the script code being stored, indicating that the code execution is being tracked by ClearML.
- A URL link to the ClearML results page where you can monitor the training progress and view detailed logs.
- Download progress for the YOLOv8 model and the specified dataset, followed by a summary of the model architecture and training configuration.
- Download progress for the YOLO11 model and the specified dataset, followed by a summary of the model architecture and training configuration.
- Initialization messages for various training components like TensorBoard, Automatic [Mixed Precision](https://www.ultralytics.com/glossary/mixed-precision) (AMP), and dataset preparation.
- Finally, the training process starts, with progress updates as the model trains on the specified dataset. For an in-depth understanding of the performance metrics used during training, read [our guide on performance metrics](../guides/yolo-performance-metrics.md).
@ -151,7 +151,7 @@ For a visual walkthrough of what the ClearML Results Page looks like, watch the
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> YOLOv8 MLOps Integration using ClearML
<strong>Watch:</strong> YOLO11 MLOps Integration using ClearML
</p>
### Advanced Features in ClearML
@ -180,7 +180,7 @@ ClearML's user-friendly interface allows easy cloning, editing, and enqueuing of
## Summary
This guide has led you through the process of integrating ClearML with Ultralytics' YOLOv8. Covering everything from initial setup to advanced model management, you've discovered how to leverage ClearML for efficient training, experiment tracking, and workflow optimization in your machine learning projects.
This guide has led you through the process of integrating ClearML with Ultralytics' YOLO11. Covering everything from initial setup to advanced model management, you've discovered how to leverage ClearML for efficient training, experiment tracking, and workflow optimization in your machine learning projects.
For further details on usage, visit [ClearML's official documentation](https://clear.ml/docs/latest/docs/integrations/yolov8/).
@ -188,9 +188,9 @@ Additionally, explore more integrations and capabilities of Ultralytics by visit
## FAQ
### What is the process for integrating Ultralytics YOLOv8 with ClearML?
### What is the process for integrating Ultralytics YOLO11 with ClearML?
Integrating Ultralytics YOLOv8 with ClearML involves a series of steps to streamline your MLOps workflow. First, install the necessary packages:
Integrating Ultralytics YOLO11 with ClearML involves a series of steps to streamline your MLOps workflow. First, install the necessary packages:
```bash
pip install ultralytics clearml
@ -202,19 +202,19 @@ Next, initialize the ClearML SDK in your environment using:
clearml-init
```
You then configure ClearML with your credentials from the [ClearML Settings page](https://app.clear.ml/settings/workspace-configuration). Detailed instructions on the entire setup process, including model selection and training configurations, can be found in our [YOLOv8 Model Training guide](../modes/train.md).
You then configure ClearML with your credentials from the [ClearML Settings page](https://app.clear.ml/settings/workspace-configuration). Detailed instructions on the entire setup process, including model selection and training configurations, can be found in our [YOLO11 Model Training guide](../modes/train.md).
### Why should I use ClearML with Ultralytics YOLOv8 for my machine learning projects?
### Why should I use ClearML with Ultralytics YOLO11 for my machine learning projects?
Using ClearML with Ultralytics YOLOv8 enhances your machine learning projects by automating experiment tracking, streamlining workflows, and enabling robust model management. ClearML offers real-time metrics tracking, resource utilization monitoring, and a user-friendly interface for comparing experiments. These features help optimize your model's performance and make the development process more efficient. Learn more about the benefits and procedures in our [MLOps Integration guide](../modes/train.md).
Using ClearML with Ultralytics YOLO11 enhances your machine learning projects by automating experiment tracking, streamlining workflows, and enabling robust model management. ClearML offers real-time metrics tracking, resource utilization monitoring, and a user-friendly interface for comparing experiments. These features help optimize your model's performance and make the development process more efficient. Learn more about the benefits and procedures in our [MLOps Integration guide](../modes/train.md).
### How do I troubleshoot common issues during YOLOv8 and ClearML integration?
### How do I troubleshoot common issues during YOLO11 and ClearML integration?
If you encounter issues during the integration of YOLOv8 with ClearML, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips. Typical problems might involve package installation errors, credential setup, or configuration issues. This guide provides step-by-step troubleshooting instructions to resolve these common issues efficiently.
If you encounter issues during the integration of YOLO11 with ClearML, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips. Typical problems might involve package installation errors, credential setup, or configuration issues. This guide provides step-by-step troubleshooting instructions to resolve these common issues efficiently.
### How do I set up the ClearML task for YOLOv8 model training?
### How do I set up the ClearML task for YOLO11 model training?
Setting up a ClearML task for YOLOv8 training involves initializing a task, selecting the model variant, loading the model, setting up training arguments, and finally, starting the model training. Here's a simplified example:
Setting up a ClearML task for YOLO11 training involves initializing a task, selecting the model variant, loading the model, setting up training arguments, and finally, starting the model training. Here's a simplified example:
```python
from clearml import Task
@ -224,11 +224,11 @@ from ultralytics import YOLO
# Step 1: Creating a ClearML Task
task = Task.init(project_name="my_project", task_name="my_yolov8_task")
# Step 2: Selecting the YOLOv8 Model
model_variant = "yolov8n"
# Step 2: Selecting the YOLO11 Model
model_variant = "yolo11n"
task.set_parameter("model_variant", model_variant)
# Step 3: Loading the YOLOv8 Model
# Step 3: Loading the YOLO11 Model
model = YOLO(f"{model_variant}.pt")
# Step 4: Setting Up Training Arguments
@ -241,6 +241,6 @@ results = model.train(**args)
Refer to our [Usage guide](#usage) for a detailed breakdown of these steps.
### Where can I view the results of my YOLOv8 training in ClearML?
### Where can I view the results of my YOLO11 training in ClearML?
After running your YOLOv8 training script with ClearML, you can view the results on the ClearML results page. The output will include a URL link to the ClearML dashboard, where you can track metrics, compare experiments, and monitor resource usage. For more details on how to view and interpret the results, check our section on [Viewing the ClearML Results Page](#viewing-the-clearml-results-page).
After running your YOLO11 training script with ClearML, you can view the results on the ClearML results page. The output will include a URL link to the ClearML dashboard, where you can track metrics, compare experiments, and monitor resource usage. For more details on how to view and interpret the results, check our section on [Viewing the ClearML Results Page](#viewing-the-clearml-results-page).