AUTOMATIC GRAPHICS PROCESSING UNIT SELECTION BASED ON KNOWN CONFIGURATION STATES

1 . A non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by a processor cause the processor to perform acts comprising:

executing, on a first node of a computing cluster, a first instance of a computing process that is configured to use a first graphics processing unit (GPU) in a first GPU configuration;

detecting a loss of functionality that affects the computing process; and

responsive to detecting the loss of functionality, automatically determining that a second instance of the computing process can execute on a second node by:

determining that the second instance of the computing process can execute using a second GPU in a second GPU configuration on the second node; and

configuring the second instance of the computing process to use the second GPU in the second GPU configuration on the second node,

wherein the second GPU configuration is different from the first GPU configuration; and

wherein the second GPU configuration is based on at least one known configuration state.

2 . The non-transitory computer readable medium of claim 1 , further comprising instructions which, when stored in memory and executed by the processor cause the processor to perform further acts of determining that the computing process can run using a second GPU configuration comprises retrieving a recommendation from a machine learned model.

3 . The non-transitory computer readable medium of claim 2 , wherein the recommendation from the machine learned model involves an upward compatibility from the first GPU configuration to the second GPU configuration.

4 . The non-transitory computer readable medium of claim 2 , wherein the recommendation from the machine learned model involves a downward compatibility from the first GPU configuration to the second GPU configuration.

5 . The non-transitory computer readable medium of claim 1 , further comprising instructions which, when stored in memory and executed by the processor cause the processor to perform further acts of determining that the computing process can run using a second GPU configuration comprises retrieving at least one prediction from a machine learned model.

6 . The non-transitory computer readable medium of claim 5 , wherein the prediction from the machine learned model involves a memory size pertaining to the second GPU configuration.

7 . The non-transitory computer readable medium of claim 1 , further comprising instructions which, when stored in memory and executed by the processor cause the processor to perform further acts of probing a cloud-based infrastructure for available GPU configurations.

8 . The non-transitory computer readable medium of claim 1 , further comprising instructions which, when stored in memory and executed by the processor cause the processor to perform further acts of presenting, on a computer display screen, a graphical user interface (GUI) that depicts at least a portion of the second GPU configuration in the GUI.

9 . A method comprising:

executing, on a first node of a computing cluster, a first instance of a computing process that is configured to use a first graphics processing unit (GPU) in a first GPU configuration;

detecting a loss of functionality that affects the computing process; and

responsive to detecting the loss of functionality, automatically determining that a second instance of the computing process can execute on a second node by:

determining that the second instance of the computing process can execute using a second GPU in a second GPU configuration on the second node; and

configuring the second instance of the computing process to use the second GPU in the second GPU configuration on the second node,

wherein the second GPU configuration is different from the first GPU configuration; and

wherein the second GPU configuration is based on at least one known configuration state.

10 . The method of claim 9 , further comprising determining that the computing process can run using a second GPU configuration comprises retrieving a recommendation from a machine learned model.

11 . The method of claim 10 , wherein the recommendation from the machine learned model involves an upward compatibility from the first GPU configuration to the second GPU configuration.

12 . The method of claim 10 , wherein the recommendation from the machine learned model involves a downward compatibility from the first GPU configuration to the second GPU configuration.

13 . The method of claim 9 , further comprising determining that the computing process can run using a second GPU configuration comprises retrieving at least one prediction from a machine learned model.

14 . The method of claim 13 , wherein the prediction from the machine learned model involves a memory size pertaining to the second GPU configuration.

15 . The method of claim 9 , further comprising probing a cloud-based infrastructure for available GPU configurations.

16 . The method of claim 9 , further comprising presenting, on a computer display screen, a graphical user interface (GUI) that depicts at least a portion of the second GPU configuration in the GUI.

17 . A system comprising:

a storage medium having stored thereon a sequence of instructions; and

a processor that executes the sequence of instructions to cause the processor to perform acts comprising,

executing, on a first node of a computing cluster, a first instance of a computing process that is configured to use a first graphics processing unit (GPU) in a first GPU configuration;

detecting a loss of functionality that affects the computing process; and

responsive to detecting the loss of functionality, automatically determining that a second instance of the computing process can execute on a second node by:

determining that the second instance of the computing process can execute using a second GPU in a second GPU configuration on the second node; and

configuring the second instance of the computing process to use the second GPU in the second GPU configuration on the second node,

wherein the second GPU configuration is different from the first GPU configuration; and

wherein the second GPU configuration is based on at least one known configuration state.

18 . The system of claim 17 , further comprising instructions which, when stored in memory and executed by the processor cause the processor to perform further acts of determining that the computing process can run using a second GPU configuration comprises retrieving a recommendation from a machine learned model.

19 . The system of claim 18 , wherein the recommendation from the machine learned model involves an upward compatibility from the first GPU configuration to the second GPU configuration.

20 . The system of claim 18 , wherein the recommendation from the machine learned model involves a downward compatibility from the first GPU configuration to the second GPU configuration.

21 . The system of claim 17 , further comprising instructions which, when stored in memory and executed by the processor cause the processor to perform further acts of determining that the computing process can run using a second GPU configuration comprises retrieving at least one prediction from a machine learned model.

22 . The system of claim 21 , wherein the prediction from the machine learned model involves a memory size pertaining to the second GPU configuration.

23 . The system of claim 17 , further comprising instructions which, when stored in memory and executed by the processor cause the processor to perform further acts of probing a cloud-based infrastructure for available GPU configurations.

24 . The system of claim 17 , further comprising instructions which, when stored in memory and executed by the processor cause the processor to perform further acts of presenting, on a computer display screen, a graphical user interface (GUI) that depicts at least a portion of the second GPU configuration in the GUI.