88 lines
8.5 KiB
Markdown
88 lines
8.5 KiB
Markdown
# Main Tab
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This chapter will provide a detailed overview of the convenient cloud-based resource management approach offered by this solution.
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## Upload Model
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To use extra models for inference, you could upload model through steps below in two ways, and follow steps in [txt2img](txt2img-guide.md) or [img2img](img2img-guide.md)to inference with extra models as need.
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Method One:
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1. Within Stable Diffusion WebUI, navigate to solution main tab **Amazon SageMaker**, find session **Cloud Models Management**.
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2. Select the **from WebUI** tab, which means to upload the model from the models file path where the WebUI service is deployed.
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3. Enter the model path where the WebUI service is deployed under corresponding model text box.
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> **Note**: You can upload multiple kinds of models by entering multiple local model paths in text box.
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4. Click **Upload Models to Cloud** to start uploading process.
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5.Message will appear on left right once uploading completes.
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Method Two:
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1. Within Stable Diffusion WebUI, navigate to solution main tab **Amazon SageMaker** main tab, find session **Cloud Models Management**.
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2. Select the **from Laptop** tab, which means to upload the model from the local path to access the WebUI.
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3. Select the type of model to upload, currently supports six types: SD Checkpoints, Textual Inversion, LoRA model, ControlNet model, Hypernetwork, VAE
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3. Select the model file to be uploaded locally.
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> **Note**: You can select models multiple, but subject to browser restrictions, it is best to select no more than 10 files, and the total size should not exceed 8g.
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4. Click **Upload Models to Cloud** to start uploading process.
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5. The upload will be uploaded in pieces asynchronously based on the file size and quantity. After each piece is uploaded, you will see a prompt under the **Choose File** button
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Method Three:
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1. Within Stable Diffusion WebUI, navigate to the **Amazon SageMaker** main tab and find session **Cloud Models Management**.
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2. Select the **from URL** tab. This option allows you to upload models to S3 from URLs where the models are downloaded.
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3. Choose the type of model you want to upload. Currently, six types are supported: SD Checkpoints, Textual Inversion, LoRA model, ControlNet model, Hypernetwork, VAE.
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4. In the **URL list (Comma-separated in English)** input box, enter the URL list of model downloads, separated by commas.
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5. In the **Models Description (Optional)** input box, provide a JSON-formatted description (optional).
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> **Note**: You can select multiple model files, but it's advisable not to exceed 5 files in your selection, with a total size not exceeding 12 GB, as constrained by Lambda memory and concurrent thread limits.
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6. Click **Upload Models to Cloud** to start the model upload process.
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7. You will see a prompt in the **Label** section below indicating the progress of the upload request.
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## Amazon SageMaker Endpoint Management
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### Deploy new endpoint
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1. Navigate to the **Amazon SageMaker** main tab of the solution. In the **Cloud Assets Management** module, locate the **Deploy New SageMaker Endpoint** section.
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2. The default deployment type for the solution is ml.g5.2xlarge, with 1 instance. The endpoint autoscaling feature is enabled by default. Simply click the **Deploy** button to initiate the deployment of the Sagemaker endpoint.
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3. If users wish to specify the endpoint name, instance type, and maximum instance count for the endpoint's instances, they can check the **Advanced Endpoint Configuration** checkbox. This will display additional parameters for user input. The following table lists the names and descriptions of these parameters:
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| Parameter Name | Description |
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| Endpoint Name (Optional) | If you need to specify a name for the Sagemaker endpoint, enter it in this input box. If not modified, the default endpoint name will be esd-type-XXXXX. |
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| Endpoint Type | Select the inference type Async/Real time for the deployed Endpoint |
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| Instance Type | Select the instance type for the deployed endpoint from the dropdown list. |
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| Max Instance Number | Choose the maximum number of instances for the deployed endpoint from the dropdown list. If Autoscaling is selected, Sagemaker will elastically scale between 0 and the Max Instance Number based on average CPU usage. |
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| Enable Autoscaling | If this checkbox is selected, Async inference will scale elastically between 0 and Max Instance Numbers based on the average backlog of each instance, while Real-time inference will scale elastically between 1 and Max Instance Numbers based on the average number of calls per instance. |
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| Min Instance Number | If Enable Autoscaling is true, This value will be the minimum number of Endpoint instances |
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4. After selecting the default endpoint configuration or setting up the advanced endpoint configuration, click **Deploy**. You'll see a message indicating **Endpoint deployment started** under **Label**.
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5. You can navigate to tab **txt2img**, session **Amazon SageMaker Inference**, refresh and select drop down list **Select Cloud SageMaker Endpoint** to check all the deployment status of endpoints.
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> **Note:** The format of the drop down list is：endpoint name+ deployment status (including Creating/Failed/InService)+deployment completing time.
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6. It will take around 3 minutes for endpoint deployment status changing to *InService*, which indicates that the endpoint has been successfully deployed.
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### Delete deployed endpoints
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1. Refresh and select endpoint(s) under the dropdown list of **Select Cloud SageMaker Endpoint**.
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2. Click **Delete**, message *Endpoint delete completed* will appear on the left side, which indicates that the selected endpoint(s) has been successfully deleted.
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# AWS Dataset Management
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## Create Dataset
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In functions such as model fine-tuning, it is necessary to provide a file of images for fine-tuning work. This functional module helps users quickly upload images to the cloud.
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1. Navigate to main tab **Amazon SageMaker**, section **AWS Dataset Management**，sub-tab **Create**.
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2. Click **Click to Upload a File**, in the local file browser that pops up, confirm to select all the images required for one model fine-tuning.
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3. Enter file name in **Dataset Name**, enter file description in **Dataset Description**, click **Create Dataset**.
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4. The message **Complete Dataset XXXX creation** will be displayed on the right side once the process completes.
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## Explore Dataset
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After the dataset has been successfully uploaded, this feature module allows users to quickly obtain the corresponding cloud-based address of the dataset. Users can copy this address and paste it into the location where the collection of images needs to be uploaded.
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1. Navigate to **Amazon SageMaker**，**AWS Dataset Management** session，**Browse** tab, refresh the list **Dataset From Cloud** and select desired dataset.
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2. Field **dataset s3 location** will display the corresponding S3 path on the cloud. User can copy to use as need.
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