Abstract: A computer readable media, a method, and a system registering a third party application providing an available communication system between a local user and a remote user identity, storing information related to the available communication system in a first database, obtaining contact information for the remote user identity from the third party application, determining a communication type for the third party application, pairing the remote user identity with a contact, and updating a graphical representation of contact information.

Abstract: A robotic cleaner includes a housing, a suction conduit with an opening, and a leading roller mounted in front of a brush roll. An inter-roller air passageway may be defined between the leading roller and the brush roll wherein the lower portion of the leading roller is exposed to a flow path to the suction conduit and an upper portion of the leading roller is outside of the flow path. Optionally, a combing unit includes a plurality of combing protrusions extending into the leading roller and having leading edges not aligned with a center of the leading roller. Optionally, a sealing strip is located along a rear side of the opening and along a portion of left and right sides of the opening. The underside may define side edge vacuum passageways extending from the sides of the housing partially between the leading roller and the sealing strip towards the opening.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for creating machine learning models. Application developers can select a machine learning template from a plurality of templates appropriate for the type of data used in their application. Templates can include multiple templates for classification of images, text, sound, motion, and tabular data. A graphical user interface allows for intuitive selection of training data, validation data, and integration of the trained model into the application. The techniques further display a numerical score for both the training accuracy and validation accuracy using the test data. The application provides a live mode that allows for execution of the machine learning model on a mobile device to allow for testing the model from data from one or more of the sensors (i.e., camera or microphone) on the mobile device.

Abstract: A computer readable media, a method, and a system registering a third party application providing an available communication system between a local user and a remote user identity, storing information related to the available communication system in a first database, obtaining contact information for the remote user identity from the third party application, determining a communication type for the third party application, pairing the remote user identity with a contact, and updating a graphical representation of contact information.

Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology provides for parsing a line of code in a project of an integrated development environment (IDE). The subject technology executes indirectly, using the interpreter, the parsed line of code. The interpreter references a translated source code document generated by a source code translation component from a machine learning (ML) document written in a particular data format. The translated source code document includes code in a chosen programming language specific to the IDE, and the code of the translated source code document is executable by the interpreter. Further the subject technology provides, by the interpreter, an output of the executed parsed line of code.

Abstract: An interactive software development environment in one embodiment can interactively provide outputs of execution or evaluation of software entered into the environment prior to compilation of the software and can automatically add one or more error handling expressions that isolate errors from effecting future software. The environment can automatically add the one or more error handling expressions for each line for software or for a set of software to wrap the set to catch and isolate errors. The execution or evaluation of software in the environment can be, for example, by read-evaluate-print-loop functionality provided by the environment.

Abstract: Methods and systems for developing software can perform both compilation operations and read-evaluate-print-loop (REPL) operations on source code. In one embodiment, an integrated development environment can include a source code editor and a compiler and can perform the following operations: receiving source code and performing REPL operations as the source code is received; receiving edits in the source code for which REPL operations have been performed and compiling, by the compiler, the edited source code; and storing results of execution of the edited source code for use in subsequent REPL operations.

Abstract: A training operation for training a machine learning model may be initiated. At a predetermined checkpoint during the training operation, checkpoint information comprising a representation of the machine learning model in a partially trained state may be generated and stored in a non-volatile storage medium. The training operation for training the machine learning model may be continued after the predetermined checkpoint.

Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for creating machine learning models. Application developers can select a machine learning template from a plurality of templates appropriate for the type of data used in their application. Templates can include multiple templates for classification of images, text, sound, motion, and tabular data. A graphical user interface allows for intuitive selection of training data, validation data, and integration of the trained model into the application. The techniques further display a numerical score for both the training accuracy and validation accuracy using the test data. The application provides a live mode that allows for execution of the machine learning model on a mobile device to allow for testing the model from data from one or more of the sensors (i.e., camera or microphone) on the mobile device.

Abstract: An interactive software development environment in one embodiment can interactively provide outputs of execution or evaluation of software entered into the environment prior to compilation of the software and can automatically add one or more error handling expressions that isolate errors from effecting future software. The environment can automatically add the one or more error handling expressions for each line for software or for a set of software to wrap the set to catch and isolate errors. The execution or evaluation of software in the environment can be, for example, by read-evaluate-print-loop functionality provided by the environment.

Abstract: The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.

Abstract: Methods and systems for developing software can perform both compilation operations and read-evaluate-print-loop (REPL) operations on source code. In one embodiment, an integrated development environment can include a source code editor and a compiler and can perform the following operations: receiving source code and performing REPL operations as the source code is received; receiving edits in the source code for which REPL operations have been performed and compiling, by the compiler, the edited source code; and storing results of execution of the edited source code for use in subsequent REPL operations.

Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology provides for parsing a line of code in a project of an integrated development environment (IDE). The subject technology executes indirectly, using the interpreter, the parsed line of code. The interpreter references a translated source code document generated by a source code translation component from a machine learning (ML) document written in a particular data format. The translated source code document includes code in a chosen programming language specific to the IDE, and the code of the translated source code document is executable by the interpreter. Further the subject technology provides, by the interpreter, an output of the executed parsed line of code.

Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.

Abstract: The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.