A CI/CD assist device accepts configuration data and test data collectively from a terminal (a vendor terminal) of a provider that provides a network service to a customer. The configuration data specifies a functional unit required to provide the network service, and the test data specifies test content for the network service or the functional unit. Each of an NOS, an NOS, and an NOS automatically builds the functional unit specified by the configuration data that is accepted by the CI/CD assist device. Each of a test device, a test device, and a test device automatically conducts a test on the network service or the functional unit based on the test data. The network service and the functional unit are built in each environment.

A distillation system extracts knowledge from a large pre-trained sequence-to-sequence neural transformer model into a smaller bi-encoder. The pre-trained sequence-to-sequence neural transformer model is trained to translate data from a first domain into a second domain on a large corpus. A teacher model is generated from the pre-trained model by fine-tuning the pre-trained neural transformer model on a smaller translation task with true translation pairs. The fine-tuned model is then used to generate augmented data values which are used with the true translation pairs to train the bi-encoder. The bi-encoder is used for perform cross-domain searches.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing integrated circuit architectures or compiler designs using an optimization engine. The optimization engine includes an auto-encoder and one or more regressors. Once trained, the optimization engine can encode initial, discrete input values of a set of input characteristics into a continuous domain and use continuous optimization techniques to identify final input values of the set of input characteristics that optimize one or more output characteristics.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Source code routines are generated for storage management in a storage code development management tool. A script that includes the source code routines is generated. The storage code development management tool receives indications based on an execution of an object code generated via execution of the script. The storage code development management tool modifies the source code routines based on the received indications.

A solution to the problems caused by manual computer programming is provided. A Pseudo-code Design Platform (PDP) reduces the amount of technical skill, length of time, and cost required to create and maintain computer applications. The Pseudo-code Compiler performs as a true Integrated Design Environment. All of the software development steps are performed automatically 100% of the time, within the Pseudo-code Design Platform, just in the opposite order of the conventional process.

A method, apparatus, electronic device, storage medium and program product of code management are provided. In response to a request for building an executable file, corresponding developed code is obtained from a code library. The developed code is compiled into intermediate code to determine security of the intermediate code. In response to determining that the intermediate code is secure, an executable file is generated based on the intermediate code.

A model designer creates models for machine learning applications while focusing on reducing the carbon footprint of the machine learning application. The model designer can automatically extract features of a machine learning application from requirements documents and automatically generate source code to implement that machine learning application. The model designer then uses computing statistics of previous models and machine learning applications to determine hardware limitations or restrictions to be placed on machine learning application or model. The designer then adds or adjusts the source code to enforce these hardware limitations and restrictions.

A model designer creates models for machine learning applications while focusing on reducing the carbon footprint of the machine learning application. The model designer can automatically extract features of a machine learning application from requirements documents and automatically generate source code to implement that machine learning application. The model designer then uses computing statistics of previous models and machine learning applications to determine hardware limitations or restrictions to be placed on machine learning application or model. The designer then adds or adjusts the source code to enforce these hardware limitations and restrictions.