Sold by: Mphasis
Deployed on AWS
A Deep Learning based solution which provides syntactically and semantically correct PHP code recommendations for an input text query.
Overview
Autocode Text To PHP Code Recommender takes a code related user text query as input and returns 3 optimal PHP 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 PHP 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 the model, with no upfront commitment. Charges depend on the instance type you pick and the running time in host-hours. Dimensions split into two groups. Batch inference processes code in bulk jobs. Real-time inference serves requests on demand. Within each group, you choose from general-purpose, compute-optimized, memory-optimized, and GPU instance families in sizes from large to 24xlarge. Larger instances add more CPU, memory, or GPU capacity. Your total cost scales with the instance size and the hours you run it.
Top-of-mind questions for buyers
One host-hour is one hour that a single chosen instance runs the model. You pay for each hour the instance is active. Billing meters running time, so partial usage still consumes the instance while it runs. Larger instance types add more CPU, memory, or GPU per host-hour.
Batch inference runs the model on grouped code as a scheduled job, then stops. Real-time inference keeps an instance running to answer requests on demand. Both charge per host-hour. Batch suits bulk processing you can start and finish. Real-time suits ongoing request handling that stays available.
Software charges accrue only while the instance runs and meters host-hours. A stopped instance stops the host-hour meter. For batch mode, charges apply during the job and end when it completes. Underlying AWS storage or resource fees may still apply separately from the software charge.
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 PHP ?
- 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.json - filename where the inference results are written to.
Resources
Link to Instructions Notebook: https://tinyurl.com/uf2bonl Link to Sample Input: https://tinyurl.com/rlo2fy4 Link to Sample Output: https://tinyurl.com/tyebsbw
- 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
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 Ruby 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.