Sold by: Mphasis
Deployed on AWS
A Deep Learning based solution which provides syntactically and semantically correct Ruby code recommendations for an input text query.
Overview
Autocode Text To Ruby Code Recommender takes a code related user text query as input and returns 3 optimal Ruby code recommendations from Github that will be syntactically and semantically correct. Considering the ever increasing number of programming languages and the frameworks that are built around them, it is very difficult to be technically fluent in all of them. Another challenge is the amount of code development time and effort spent on looking up efficient solutions to solve a problem. This solution helps in addressing these practical problems faced by the developer community.
Highlights
- This solution helps accelerate the application development cycle by providing developers with targeted code recommendations.
- The system uses a similarity-based distance measure to find the most correct and efficient Ruby code sample for the user query. The query should be coherent and focused on a single topic.
- 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!
Details
Sold by
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5d.large Inference (Real-Time) Recommended | Model inference on the ml.m5d.large instance type, real-time mode | $10.00 |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $20.00 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $20.00 |
Dimensions summary
You pay by the hour for the compute instance that runs this deep learning code recommender. Pricing splits into two usage modes. Batch inference processes grouped data in scheduled runs. Real-time inference serves live requests as they arrive. Within each mode, you choose from many AWS machine learning instance types. These range from general-purpose and compute-optimized instances to memory-optimized and GPU-backed instances. Larger instances with more processing power carry higher hourly rates. Your total cost depends on the instance you select and the number of host hours you run.
Top-of-mind questions for buyers
One host hour is one hour that a chosen machine learning instance runs the model. You pay for each hour an instance stays active. The rate matches the specific instance type you select. Running several instances at once multiplies the host hours you accrue.
Batch inference bills host hours only while a scheduled job processes grouped data, then stops. Real-time inference bills host hours the entire time an endpoint stays running to serve live requests. Batch suits periodic jobs. Real-time suits continuous request handling that must stay available.
Charges accrue per host hour while the instance runs. A real-time endpoint keeps billing as long as it stays active, even between requests. To stop software charges, shut the instance down. Batch jobs bill only during the run and stop when the job finishes.
www.mphasis.com
Helpful?
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input
Supported content types: text/plain As such there is no character limit on the query. The query should be coherent and focused on a single topic. The system may have problem in capturing context across multiple sentences so it is advised to stick to a single sentence query
Sample Input queries :
- Create confusion matrix ?
- How to input a csv file in Ruby ?
- Convert a date string into yyyymmdd format
Output
Content type: text/csv Sample Output:
Result Function Name URL Result 1 Create <https://github.com/cloudfoundry/>.. Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
!aws sagemaker-runtime invoke-endpoint --endpoint-name $model_name --body fileb://$input.json--content-type 'text/plain' --region us-east-2 output.csvSubstitute the following parameters:
- "endpoint-name" - name of the inference endpoint where the model is deployed
- input.json - input json with query
- application/json - MIME type of the given input
- out.csv - filename where the inference results are written to.
Resources
Link to Instructions Notebook: https://tinyurl.com/vn44wu2 Link to Sample Input: https://tinyurl.com/yc24sm57 Link to Sample Output: https://tinyurl.com/vhq993v
- Input MIME type
- application/json, text/plain, text/csv
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
The Mphasis AI for Software Development service enables enterprises to build customized no-code/low-code solutions to accelerate the development and deployment of software. We leverage our patented AI/ML platforms and frameworks to engage with clients across multiple use cases such as intelligent code recommendation, rapid prototyping, etc. We help enterprises target impactful AI/ML interventions that can drive business benefits. Our Assessments, Workshops, and Implementations identify the most relevant use cases in software engineering and outline the potential benefits such as efficiency, cost, and innovation.
Deep Learning based low-code solution which generates HTML, CSS, HTML-JET code from hand drawn wire frames as well as visual designs.
A Deep Learning based solution which provides syntactically and semantically correct python code recommendations for an input text query.
A Deep Learning based model which provides syntactically and semantically correct Go language code recommendations for an input text query.
A Deep Learning based solution which provides syntactically and semantically correct Java code recommendations for an input text query.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.