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description: Learn how to use Ultralytics HUB for cloud for efficient and user-friendly AI model training. For easy model creation, training, evaluation and deployment, follow our detailed guide.
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keywords: Ultralytics, HUB Models, AI model training, model creation, model training, model evaluation, model deployment
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---
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# Cloud Training
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Ultralytics provides a web-based cloud training platform, enabling rapid and streamlined deployment of custom object detection models. Users benefit from a straightforward interface that facilitates the selection of their desired dataset and training method. Ultralytics further streamlines the process by offering a diverse array of pre-built options and architectural configurations.
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Read more about creating and other details of a Model at our [HUB Models page](models.md)
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## Selecting an Instance
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For details on Picking a model, and instances for it, please read [Instances guide Page](models.md)
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## Steps to train the Model
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Once the instance has been selected, training a model using ultralytics Hub is a three step process, as below: <br />
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1. Picking a Dataset - Read more about Dataset, steps to add/remove dataset from [Dataset page](datasets.md) <br />
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2. Picking a Model - Read more about Models, steps to create / share and handle a model [HUB Models page](models.md) <br />
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3. Training the Model on the chosen Dataset <br />
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Ultralytics HUB offers three training options:
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-**Ultralytics Cloud**
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-**Google Colab** - Read more about training via Google Colab [HUB Models page](models.md)
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-**Bring your own agent** - Read more about training via your own Agent [HUB Models page](models.md)
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In order to start training your model, follow the instructions presented in these steps.
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## Training via Ultralytics Cloud
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To start training your model using Ultralytics Cloud, we need to simply select the Training Duration, Available Instances, and Payment options.<br />
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**Training Duration** - The Ultralytics offers two kind of training durations <br />
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1. Training based on `Epochs` - This option lets you train your model based on number of times your Dataset needs to go through the cycle of Train, Label and Test. The exact pricing based on number of Epochs is hard to determine. Hence, if the credit gets exhausted before intended number of Epochs, the training pauses and we get a prompt to Top-up and resume Training. <br />
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2. Timed Training - The timed training features allows you to fix the time duration of the entire Training process and also determines the estimated amount before the start of Training. <br />
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When the training starts, you can click **Done** and monitor the training progress on the Model page.
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## Monitor your training
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Once the model and mode of the training has been selected, a User can monitor the training procedure on the `Train` section with the link provided in the terminal (on your agent / Google colab) or a button from Ultralytics Cloud.
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## Stopping and resuming your training
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Once the training has started a user can `Stop` the training, which will also correspondingly pause the credit usage for the user. A user can again `Resume` the training from the point as described in the below screenshot.
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## Payments and Billing options
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Ultralytics HUB offers `Pay Now` as upfront and/or use `Ultralytics HUB Account` as a wallet to top-up and fulfil the billing. A user can pick from amongst two types of Account namely `Free` and `Pro` user. <br />
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The user can navigate to the profile by clicking the Profile picture in the bottom left corner
Once your dataset ZIP is ready, navigate to the [Datasets](https://hub.ultralytics.com/datasets) page by clicking on the **Datasets** button in the sidebar.
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??? tip "Tip"
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You can also upload a dataset directly from the [Home](https://hub.ultralytics.com/home) page.
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Click on the **Upload Dataset** button on the top right of the page. This action will trigger the **Upload Dataset** dialog.
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Upload your dataset in the _Dataset .zip file_ field.
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You have the additional option to set a custom name and description for your Ultralytics HUB dataset.
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When you're happy with your dataset configuration, click **Upload**.
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After your dataset is uploaded and processed, you will be able to access it from the Datasets page.
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You can view the images in your dataset grouped by splits (Train, Validation, Test).
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??? tip "Tip"
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Each image can be enlarged for better visualization.
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Also, you can analyze your dataset by click on the **Overview** tab.
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Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) on your dataset.
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## Share Dataset
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Navigate to the Dataset page of the dataset you want to share, open the dataset actions dropdown and click on the **Share** option. This action will trigger the **Share Dataset** dialog.
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Set the general access to "Unlisted" and click **Save**.
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Now, anyone who has the direct link to your dataset can view it.
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??? tip "Tip"
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You can easily click on the dataset's link shown in the **Share Dataset** dialog to copy it.
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## Edit Dataset
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## Edit / Delete Dataset
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Navigate to the Dataset page of the dataset you want to edit, open the dataset actions dropdown and click on the **Edit** option. This action will trigger the **Update Dataset** dialog.
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You can also edit a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
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Apply the desired modifications to your dataset and then confirm the changes by clicking **Save**.
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## Delete Dataset
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Navigate to the Dataset page of the dataset you want to delete, open the dataset actions dropdown and click on the **Delete** option. This action will delete the dataset.
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You can also delete a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
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??? note "Note"
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If you change your mind, you can restore the dataset from the [Trash](https://hub.ultralytics.com/trash) page.
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The YOLO Inference API allows you to access the YOLOv8 object detection capabilities via a RESTful API. This enables you to run object detection on images without the need to install and set up the YOLOv8 environment locally.
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 Screenshot of the Inference API section in the trained model Preview tab.
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 Screenshot of the Inference API section in the trained model Preview tab.
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## API URL
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This will send the query parameters along with the file in the POST request. See the table below for a full list of available inference arguments.
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