Machine learning model representation and execution
Aspects of the subject disclosure may include, for example, a device, including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving user specified metadata for execution tasks associated with a machine learning (ML) model; receiving artifacts specifying program code for implementing the ML model; creating a file system structure for a container to hold the ML model; receiving environment variables for operation of the ML model; and building the container including a model image for the ML model. Other embodiments are disclosed.
1. A device, comprising:
a processing system including a processor; and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
receiving user specified metadata for execution tasks associated with a machine learning (ML) model;
receiving artifacts specifying program code for implementing the ML model;
creating a file system structure for a container to hold the ML model;
recording environment variables in the container for operating the ML model;
and building the container including an ML model image for the ML model.
2. The device of claim 1 , wherein the user specified metadata comprises a shell command for training the ML model.
3. The device of claim 2 , wherein the user specified metadata comprises a second shell command for executing the ML model.
4. The device of claim 1 , wherein the user specified metadata comprises instructions to a package manager.
5. The device of claim 1 , wherein the user specified metadata comprises hardware capabilities for executing the ML model.
6. The device of claim 5 , wherein the hardware capabilities for executing the ML model comprise a number and type of central processing units, a number and type of graphics processing units, an amount of memory, an amount of volatile memory, an amount of storage, or a combination thereof.
7. The device of claim 1 , wherein the artifacts include binary library files needed to implement the ML model.
8. The device of claim 1 , wherein the artifacts include an enumeration of values required by the ML model.
9. The device of claim 1 , wherein the environment variables specify paths in the file system structure, and wherein the file system structure comprises a path to a model artifacts folder, a data volume, and intermediate volume, a predictions volume, or a combination thereof.
10. The device of claim 9 , wherein the environment variables include a first path to a first file containing data for training the ML model, a second path to a second file containing data for testing the ML model, a third path to an intermediate volume to provide non-volatile storage for the ML model, a fourth path to a third file comprising output results, or a combination thereof.
11. The device of claim 10 , wherein the container has network connectivity during the building of the ML model image, and wherein the container does not have network connectivity during all run phases of the ML model image.
12. The device of claim 1 , wherein the building comprises layering the ML model onto a base image.
13. The device of claim 12 , wherein the building further comprises copying the artifacts into the container.
14. The device of claim 13 , wherein the building further comprises installing dependencies of the ML model into the container using a package manager.
15. The device of claim 13 , wherein the processing system comprises a plurality of processors operating in a distributed computing environment.
16. A non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
creating a file system structure for a container to hold a machine learning (ML) model;
recording environment variables in the container for operation of the ML model;
layering a model image for the ML model on a base image in the container;
copying user artifacts into the container; and
installing dependencies of the ML model into the container using a package manager.
17. The machine-readable medium of claim 16 , wherein the user artifacts comprise program code for the ML model, binary files needed to execute the program code for the ML model, or a combination thereof.
18. The machine-readable medium of claim 17 , wherein the processing system comprises a plurality of processors operating in a distributed computing environment.
19. A method, comprising:
layering, by a processing system including a processor, a base image over a generic container to create an image container;
adding, by the processing system, user-specified packages to the image container, wherein the user-specified packages are installed by a package manager;
recording, by the processing system, environment variables in the image container, wherein the environment variable are needed for operation of a machine learning (ML) model; and
layering, by the processing system, program code for the ML model into the image container.
20. The method of claim 19 , wherein the base image comprises the package manager, hardware specifications, a programming language, ML toolkits, and a repository manager.