VIRTUAL MACHINE LEARNING DEVELOPMENT ENVIRONMENT

1 . A method for machine learning application development, comprising:

at a client system:

exposing the environment executing on a remote CPU to a user via a web interface;

receiving application code developed by the user through the web interface;

in response to performance of a single action on the web interface, sending a request, comprising a set of hardware selections, to a control plane;

at a server of the control plane:

receiving the request;

initializing hardware according to the set of hardware selections;

running a static fork of the environment on the hardware, wherein the forked environment comprises packages and configurations from the environment, without reinstalling the packages;

executing the application code, developed within the environment on the CPU, using the forked environment on the hardware without changes to the application code; and

in response to satisfaction of a timeout condition, shutting down the forked environment on the hardware.

2 . The method of claim 1 , wherein the hardware preferences comprise a type of hardware.

3 . The method of claim 2 , wherein the type of hardware comprises a graphics processing unit (GPU).

4 . The method of claim 1 , further comprising automatically installing a monitoring module on the hardware, wherein metrics output by the monitoring module are streamed to the web interface in real time.

5 . The method of claim 1 , wherein the single action comprises a request to execute the application code on the hardware.

6 . A method for machine learning application development, comprising:

supporting an environment executing on a CPU;

exposing the environment to a user via a web interface, wherein the user develops application code within the environment through the web interface; and

in response to performance of a single action on the web interface, automatically:

initializing a graphics processing unit (GPU);

running a static fork of the environment on the GPU, wherein the forked environment comprises packages and configurations from the environment, without reinstalling the packages;

executing the application code, developed within the environment on the CPU, using the forked environment on the GPU without changes to the application code; and

in response to satisfaction of a timeout condition, shutting down the forked environment on the GPU.

7 . The method of claim 6 , wherein the environment is associated with a user, wherein the GPU is initialized on a cloud computing provider using credentials of the user.

8 . The method of claim 6 , wherein the GPU and CPU are each associated with a GPU device module and CPU device module, respectively, wherein each device module comprises the same set of submodules, wherein each submodule comprises device-specific logic, wherein executing the application code without changes comprises executing a submodule from the GPU device module for a device-specific call within the code.

9 . The method of claim 6 , wherein the application code continues executing when the web interface is closed.

10 . The method of claim 6 , further comprising exposing a uniform resource identifier (URI) for the application code executing on the GPU, wherein the single action comprises receiving a request at the URI.

11 . The method of claim 6 , further comprising a plurality of environments, wherein all environments are communicatively connected to a shared database.

12 . The method of claim 11 , wherein code executing in an environment of the plurality of environments uses outputs written to the database by code from another environment.

13 . The method of claim 11 , wherein the plurality of environments are organized into a pipeline, wherein code executing in preceding environments write outputs to the shared database, and code executing in succeeding environments uses the outputs read from the shared database.

14 . A method for machine learning development, comprising:

in response to a single action being performed on a runtime environment running on a first device, automatically:

initializing a second device having a different device type from the first device;

forking the runtime environment;

running the forked runtime environment on the second device;

executing code, developed on the first device, on the second device without manual changes to the code; and

writing outputs generated by the code to a shared database accessible by the runtime environment.

15 . The method of claim 14 , wherein the first device comprises a CPU and the second device comprises a GPU.

16 . The method of claim 14 , wherein the runtime environment comprises a set of packages, wherein the forked runtime environment is run without reinstalling the set of packages.

17 . The method of claim 14 , wherein executing code on the second device without manual changes comprises:

determining a computing resource module for the device type of the second device, the computing resource module comprising a set of standard submodules comprising a standard submodule identifier and device-specific logic;

executing the standard submodule from the computing resource module when the standard submodule identifier is detected in the code.

18 . The method of claim 17 , wherein the first device is associated with a first computing resource module, wherein the first computing resource module comprises the same set of standard submodules, wherein each standard submodule comprises logic specific to the first device.

19 . The method of claim 14 , further comprising automatically shutting down the second device after the forked runtime environment has idled for a threshold duration.

20 . The method of claim 19 , wherein shutting down the second device comprises snapshotting the forked runtime environment before shutting down the second device, the method further comprising:

receiving a request to execute the code on the forked runtime environment;

initializing a third device using the snapshot of the forked runtime environment; and

executing the code on the third device.