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New HUB Cloud Training docs page (#8174)
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Priytosh Tripathi
GitHub
parent
79e0d59eeb
commit
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.github/workflows/ci.yaml
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.github/workflows/ci.yaml
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@ -6,8 +6,13 @@ name: Ultralytics CI
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on:
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push:
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branches: [main]
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paths-ignore:
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- "docs/**"
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- "mkdocs.yml"
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pull_request:
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branches: [main]
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paths-ignore:
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- "docs/**"
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schedule:
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- cron: "0 0 * * *" # runs at 00:00 UTC every day
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workflow_dispatch:
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.github/workflows/docker.yaml
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@ -6,6 +6,8 @@ name: Publish Docker Images
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on:
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push:
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branches: [main]
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paths-ignore:
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- "docs/**"
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workflow_dispatch:
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inputs:
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Dockerfile:
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73
docs/en/hub/cloud-training.md
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docs/en/hub/cloud-training.md
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---
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comments: true
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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
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Click on the Billing tab to know about your current plan and option to upgrade it.
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User is prompted about different available plans, and can pick from the available plans as stated below.
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The user will then Navigate to the Payment page, fill in the details and payment is done.
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@ -57,17 +57,11 @@ check_dataset('path/to/coco8.zip')
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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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@ -75,31 +69,31 @@ You have the additional option to set a custom name and description for your Ult
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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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@ -115,7 +109,7 @@ Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) on y
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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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??? tip "Tip"
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@ -125,7 +119,7 @@ Navigate to the Dataset page of the dataset you want to share, open the dataset
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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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@ -133,38 +127,20 @@ Now, anyone who has the direct link to your dataset can view it.
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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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??? tip "Tip"
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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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??? tip "Tip"
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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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@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, Inference API, object detection, RESTful API, Pyt
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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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@ -88,7 +88,7 @@ In this example, the `data` dictionary contains the query arguments `size`, `con
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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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| Inference Argument | Default | Type | Notes |
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|--------------------|---------|---------|------------------------------------------------|
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| ------------------ | ------- | ------- | ---------------------------------------------- |
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| `size` | `640` | `int` | valid range is `32` - `1280` pixels |
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| `confidence` | `0.25` | `float` | valid range is `0.01` - `1.0` |
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| `iou` | `0.45` | `float` | valid range is `0.0` - `0.95` |
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@ -10,111 +10,54 @@ keywords: Ultralytics, HUB Models, AI model training, model creation, model trai
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The process is user-friendly and efficient, involving a simple three-step creation and accelerated training powered by Ultralytics YOLOv8. During training, real-time updates on model metrics are available so that you can monitor each step of the progress. Once training is completed, you can preview your model and easily deploy it to real-world applications. Therefore, Ultralytics HUB offers a comprehensive yet straightforward system for model creation, training, evaluation, and deployment.
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The entire process of training a Model is detailed on our [Cloud Training Page](cloudtraining.md)
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## Train Model
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Navigate to the [Models](https://hub.ultralytics.com/models) page by clicking on the **Models** button in the sidebar.
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Training the Model using HUB is a 4 step process <br />
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**Execute the pre-requisites script** - Run the already mention scripts to prepare the virtual Environment.<br />
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**Provide the API and start Training** - Once the model has been prepared, we can provide the API key as provided in the previous model (by simple copying and pasting the code block) and executing it.<br />
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**Check the results and Metrics** - Upon successful code execution, a link is presented that directs the user to the Metrics Page. This page provides comprehensive details regarding the trained model, including model specifications, box loss, class loss, object loss, dataset information, and image distributions. Additionally, the deploy tab offers access to the trained model's documentation and license details.<br />
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**Test your model** - Ultralytics HUB offers testing the model using custom Image, device camera or even links to test it using your `iPhone` or `Android` device.<br />
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??? tip "Tip"
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You can also train a model directly from the [Home](https://hub.ultralytics.com/home) page.
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Click on the **Train Model** button on the top right of the page. This action will trigger the **Train Model** dialog.
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The **Train Model** dialog has three simple steps, explained below.
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### 1. Dataset
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In this step, you have to select the dataset you want to train your model on. After you selected a dataset, click **Continue**.
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??? tip "Tip"
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You can skip this step if you train a model directly from the Dataset page.
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### 2. Model
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In this step, you have to choose the project in which you want to create your model, the name of your model and your model's architecture.
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??? note "Note"
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Ultralytics HUB will try to pre-select the project.
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If you opened the **Train Model** dialog as described above, Ultralytics HUB will pre-select the last project you used.
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If you opened the **Train Model** dialog from the Project page, Ultralytics HUB will pre-select the project you were inside of.
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In case you don't have a project created yet, you can set the name of your project in this step and it will be created together with your model.
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!!! Info "Info"
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You can read more about the available [YOLOv8](https://docs.ultralytics.com/models/yolov8) (and [YOLOv5](https://docs.ultralytics.com/models/yolov5)) architectures in our documentation.
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When you're happy with your model configuration, click **Continue**.
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??? note "Note"
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By default, your model will use a pre-trained model (trained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset) to reduce training time.
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You can change this behaviour by opening the **Advanced Options** accordion.
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### 3. Train
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In this step, you will start training you model.
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Ultralytics HUB offers three training options:
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- Ultralytics Cloud **(COMING SOON)**
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- Google Colab
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- Bring your own agent
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In order to start training your model, follow the instructions presented in this step.
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??? note "Note"
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When you are on this step, before the training starts, you can change the default training configuration by opening the **Advanced Options** accordion.
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??? note "Note"
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When you are on this step, you have the option to close the **Train Model** dialog and start training your model from the Model page later.
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To start training your model using Google Colab, simply follow the instructions shown above or on the Google Colab notebook.
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<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
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</a>
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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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??? note "Note"
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In case the training stops and a checkpoint was saved, you can resume training your model from the Model page.
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You can change this behavior by opening the **Advanced Options** accordion.
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## Preview Model
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@ -122,27 +65,46 @@ Ultralytics HUB offers a variety of ways to preview your trained model.
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You can preview your model if you click on the **Preview** tab and upload an image in the **Test** card.
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You can also use our Ultralytics Cloud API to effortlessly [run inference](inference-api.md) with your custom model.
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Furthermore, you can preview your model in real-time directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by [downloading](https://ultralytics.com/app_install) our [Ultralytics HUB Mobile Application](app/index.md).
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## Train the model
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Ultralytics HUB offers three training options:
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- **Ultralytics Cloud** - Read more about training via Ultralytics Cloud [Cloud Training Page](cloudtraining.md)
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- **Google Colab**
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- **Bring your own agent**
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## Training the Model on Google Colab
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To start training your model using Google Colab, simply follow the instructions shown above or on the Google Colab notebook.
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<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
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</a>
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## Bring your own Agent
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A user can create API end point through Ultralytics HUB and use their own agent to train the Model locally. Simply follow the steps on the section and then we can see the details of the training by a link generated on the terminal on the Agent. <br />
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The link takes us to the Metrics Information and Deployment completion page to know more about the model and deploy / share it.
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## Deploy Model
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You can export your model to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle and many others.
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??? tip "Tip"
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You can customize the export options of each format if you open the export actions dropdown and click on the **Advanced** option.
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## Share Model
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@ -158,17 +120,11 @@ You can export your model to 13 different formats, including ONNX, OpenVINO, Cor
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Navigate to the Model page of the model you want to share, open the model actions dropdown and click on the **Share** option. This action will trigger the **Share Model** dialog.
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??? tip "Tip"
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You can also share a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
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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 model can view it.
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@ -178,36 +134,7 @@ Now, anyone who has the direct link to your model can view it.
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## Edit Model
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## Edit and Delete Model
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Navigate to the Model page of the model you want to edit, open the model actions dropdown and click on the **Edit** option. This action will trigger the **Update Model** dialog.
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??? tip "Tip"
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You can also edit a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
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Apply the desired modifications to your model and then confirm the changes by clicking **Save**.
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## Delete Model
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Navigate to the Model page of the model you want to delete, open the model actions dropdown and click on the **Delete** option. This action will delete the model.
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??? tip "Tip"
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You can also delete a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
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??? note "Note"
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If you change your mind, you can restore the model from the [Trash](https://hub.ultralytics.com/trash) page.
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Navigate to the Model page of the model you want to edit, open the model actions dropdown and click on the **Edit** option. This action will trigger the **Update Model** dialog. Navigate to the Model page of the model you want to delete, open the model actions dropdown and click on the **Delete** option. This action will delete the model.
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@ -6,9 +6,7 @@ keywords: Ultralytics HUB, Quickstart, YOLOv5, YOLOv8, model training, quick dep
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# Quickstart Guide for Ultralytics HUB
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🚧 **Under Construction** 🚧
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|
||||
Thank you for visiting the Quickstart guide for [Ultralytics HUB](https://hub.ultralytics.com/)! We're currently hard at work building out this page to provide you with step-by-step instructions on how to get up and running with HUB in no time.
|
||||
HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to easily upload their data and train new models quickly. It offers a range of pre-trained models and templates to choose from, making it easy for users to get started with training their own models. Once a model is trained, it can be easily deployed and used for real-time object detection, instance segmentation and classification tasks.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
@ -21,32 +19,39 @@ Thank you for visiting the Quickstart guide for [Ultralytics HUB](https://hub.ul
|
||||
<strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
|
||||
</p>
|
||||
|
||||
In the meantime, here's a brief overview of what you can expect from Ultralytics HUB:
|
||||
## Creating an Account
|
||||
|
||||
## What is Ultralytics HUB?
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) offers multiple and easy account creation options to get started with. A user can user can register and sign-in using `Google`, `Apple` or `Github` account or a `work email` address(preferably for corporate users).
|
||||
|
||||
Ultralytics HUB is your one-stop solution for training and deploying YOLOv5 and YOLOv8 models. It's designed with user experience in mind, featuring a drag-and-drop interface to make uploading data and training new models a breeze. Whether you're a beginner or an experienced machine learning practitioner, HUB has a range of pre-trained models and templates to accelerate your projects.
|
||||
|
||||
|
||||
## Key Features
|
||||
## The Dashboard and details
|
||||
|
||||
- **User-Friendly Interface**: Simply drag and drop your data to start training.
|
||||
- **Pre-Trained Models**: Choose from a selection of pre-trained models to kick-start your projects.
|
||||
- **Real-Time Object Detection**: Deploy trained models easily for real-time object detection, instance segmentation, and classification tasks.
|
||||
After completing the registration and login process on the HUB, users are directed to the HUB dashboard. This dashboard provides a comprehensive overview, with the Welcome tutorial prominently displayed. Additionally, the left pane conveniently offers links for tasks such as Uploading Datasets, Creating Projects, Training Models, Integrating Third-party Applications, Accessing Support, and Managing Trash.
|
||||
|
||||
## Coming Soon
|
||||
|
||||
|
||||
- Detailed Steps to Start Your First Project
|
||||
- Guide on Preparing and Uploading Datasets
|
||||
- Tutorial on Model Training and Exporting
|
||||
- Integration Options and How-To's
|
||||
- And much more!
|
||||
## Select the Model
|
||||
|
||||
## Need Help Now?
|
||||
Once we have decided on a Dataset, it's time to train the model. We first pick the Project name and Model name (or leave it to default, if they are not label specific), then pick an Architecture. Ultralytics provide a wide range of YOLOv8, YOLOv5 and YOLOv5u6 Architectures. You can also pick from previously trained or custom model.
|
||||
The latter option allows us to fine tune option likes Pre-trained, Epochs, Image Size, Caching Strategy, Type of Device, Number of GPUs, Batch Size, AMP status and Freeze option. Read more about Models [HUB Models page](models.md).
|
||||
|
||||
While we're polishing this page, feel free to:
|
||||
## Train the Model
|
||||
|
||||
- Browse through other [HUB Docs](https://docs.ultralytics.com/hub/) for detailed guides and tutorials.
|
||||
- Raise an issue on our [GitHub](https://github.com/ultralytics/hub/) for technical support.
|
||||
- Join our [Discord Community](https://ultralytics.com/discord/) for live discussions and community support.
|
||||
Once we reach the Model Training page, we are offered three-way option to train our model. We can either use Google Colab to simply follow the steps and use the API key provided at the page, or follow the steps to actually train the model locally. The third way is our upcoming Ultralytics Cloud , which enables you to directly train your model over cloud even more efficiently. Read more about Training the model at [Cloud Training Page](cloudtraining.md)
|
||||
|
||||
Stay tuned! We'll be back soon with more detailed information to help you get the most out of Ultralytics HUB. Thank you for your patience and interest!
|
||||
## Integrating the Model
|
||||
|
||||
`Ultralytics Hub` supports integrating the model with other third-party applications or to connect HUB from an external agent. Currently we support `Roboflow`, with very simple one click API Integration. Read more about Integrating the model at [Integration Page](integrations.md)
|
||||
|
||||
## Stuck? We got you!
|
||||
|
||||
We at Ultralytics we have a strong faith in user feedbacks and complaints. You can `Report a bug`, `Request a Feature` and/or `Ask question`.
|
||||
|
||||
|
||||
|
||||
## Data restoration and deletion
|
||||
|
||||
Your Dataset in your account can be restored and/or deleted permanently from the `Trash` section in the left column.
|
||||
|
||||
|
||||
|
||||
@ -358,6 +358,7 @@ nav:
|
||||
- Datasets: hub/datasets.md
|
||||
- Projects: hub/projects.md
|
||||
- Models: hub/models.md
|
||||
- Cloud Training: hub/cloud-training.md
|
||||
- Integrations: hub/integrations.md
|
||||
- Inference API: hub/inference-api.md
|
||||
- On-Premise:
|
||||
|
||||
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Reference in New Issue
Block a user