Abstract: The present disclosure relates to methods and systems for accelerating the development and distribution of data science workloads, including a consistent, portable and pre-configured data science workspace for development of data science applications allowing for the creation of a standardized, modular and reusable library of data science code product that can be maintained, extended and reused in a clear and repeatable manner. The code may be submitted to a build and deployment process that ensures consistency across multiple environments in terms of the application code and the operating system environment. Runtime execution may be managed through the authoring of definitions which detail aspects of how the workload should operate within a certain environment.

Abstract: The present disclosure relates to methods and systems for accelerating the development and distribution of data science workloads, including a consistent, portable and pre-configured data science workspace for development of data science applications allowing for the creation of a standardized, modular and reusable library of data science code product that can be maintained, extended and reused in a clear and repeatable manner. The code may be submitted to a build and deployment process that ensures consistency across multiple environments in terms of the application code and the operating system environment. Runtime execution may be managed through the authoring of definitions which detail aspects of how the workload should operate within a certain environment.

Abstract: The present disclosure relates to methods and systems for accelerating the development and distribution of data science workloads, including a consistent, portable and pre-configured data science workspace for development of data science containers allowing for the creation of a standardized, modular and reusable library of data science containers that can be maintained, extended and reused in a clear and repeatable manner. The containers may be submitted to a build and deployment process that ensures consistency across multiple environments in terms of the application code and the operating system environment. Runtime execution may be managed through the authoring of definitions which detail aspects of how the workload should operate within a certain environment.

Abstract: The present disclosure relates to methods and systems for accelerating the development and distribution of data science workloads, including a consistent, portable and pre-configured data science workspace for development of data science containers allowing for the creation of a standardized, modular and reusable library of data science containers that can be maintained, extended and reused in a clear and repeatable manner. The containers may be submitted to a build and deployment process that ensures consistency across multiple environments in terms of the application code and the operating system environment. Runtime execution may be managed through the authoring of definitions which detail aspects of how the workload should operate within a certain environment.

Abstract: Disclosed is a method and an apparatus for measuring the growth of leaf disks. The method comprises the following steps: a) Calibrating the capture system, b) capturing at least 2 images of a leaf disk, c) processing the image data, comprising i) segmenting the leaf disks by threshold segmentation, ii) multiple morphological erosion steps, iii) edge cleaning to remove the edge of the storage vessel, d) 3D reconstruction: generating a disparity map by means of a stereo algorithm, calculating a 3D surface model based on the previously determined calibration parameters from the disparity map, e) smoothing the previously obtained surface model, f) determining the growth rates from the time series of the areas. The apparatus comprises at least one camera, a lighting unit, a translation table in the X/Y plane for the camera and (infrared) lighting, storage vessels for leaf disks, and electronic evaluation and control units.

Abstract: Disclosed is a method and an apparatus for measuring the growth of leaf disks. The method comprises the following steps: a) Calibrating the capture system, b) capturing at least 2 images of a leaf disk, c) processing the image data, comprising i) segmenting the leaf disks by threshold segmentation, ii) multiple morphological erosion steps, iii) edge cleaning to remove the edge of the storage vessel, d) 3D reconstruction: generating a disparity map by means of a stereo algorithm, calculating a 3D surface model based on the previously determined calibration parameters from the disparity map, e) smoothing the previously obtained surface model, f) determining the growth rates from the time series of the areas. The apparatus comprises at least one camera, a lighting unit, a translation table in the X/Y plane for the camera and (infrared) lighting, storage vessels for leaf disks, and electronic evaluation and control units.