Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Autocode Design to Code
By:
Latest Version:
2.7
Deep Learning based low-code solution which generates HTML, CSS, HTML-JET code from hand drawn wire frames as well as visual designs.
Product Overview
Autocode is an automated software code development platform. It converts wire-frames and visual designs in image format to corresponding HTML, CSS, HTML-JET code. This solution has the ability to automatically learn web elements in hand drawn wire-frames and map them to corresponding code in HTML. It is a Deep Learning based rapid prototyping platform designed to help design thinking teams, software developers, testers and support teams. It can generate code from multiple input formats like wire-frames, and visual designs.
Key Data
Version
By
Type
Model Package
Highlights
Automated code generation from hand drawn as well as digital wire-frames that helps in faster creation of prototypes as well as accelerate application development. The solution can detect user interface elements like buttons, text boxes, labels, etc. in wire-frames and convert to corresponding HTML and CSS code.
Uses image processing models that capture element level details from wire-frames and generates corresponding HTML code. The Deep Learning based models have been trained using transfer learning concepts.
Autocode is a Deep Learning based automated software development platform for rapid prototyping that can help software developers, testers and support teams. Need customized Deep Learning solutions? Get in touch!
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$10.00/hr
running on ml.m5.large
Model Batch Transform$20.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.4xlarge | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large Vendor Recommended | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input
Supported content types: image/jpeg
The images needs to be in the jpeg and png format.
Guidelines:
a. Wire-frames should either be a scanned image (using Camscanner) or a digital wire-frame
b. The image should be scanned via either a phone app or scanner without any shadow or noise to work properly.
c. Try to draw wireframe objects as straight as possible
d. File size limit < 4mb.
Objects supported by Autocode- button, imagebox, text box, text area, combo box, search box, paragraph , help , logo, radio button, checkbox, table grid and mail box .
Output
Content type: application/json
Sample output:
{
"generated_webpage_html":
"<!DOCTYPE html>
<html lang=\"en\">
<head>
<title>Bootstrap Example</title>
}Invoking endpoint
AWS CLI Command
You can invoke endpoint using AWS CLI:
!aws sagemaker-runtime invoke-endpoint --endpoint-name $model_name --body fileb://$sample.jpg--content-type 'image/jpeg' --region us-east-2 output.jsonSubstitute the following parameters:
"endpoint-name"- name of the inference endpoint where the model is deployedsample.jpg- input image json serializedimage/jpeg- MIME type of the given input imageout.json- filename where the inference results are written to.
Python
Python code to process the output(more detailed example can be found in sample notebook):
f = open('output.json', mode='r',encoding='utf-8')
def prediction_wrapper(prediction):
p_json_parse = json.loads(prediction)
return p_json_parse
generated_code=prediction_wrapper(f.read())Resources
Link to Instructions Notebook: https://tinyurl.com/y3wsstzr Link to Sample Input Images: https://tinyurl.com/y3wkfn7y Link to Sample Output: https://tinyurl.com/sws7as9
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Autocode Design to Code
For any assistance reach out to us at:
AWS Infrastructure
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