MACHINE LEARNING HYPER TUNING
An information handling system may include at least one processor; and a non-transitory memory coupled to the at least one processor. The information handling system may be configured to: communicatively couple to a cloud platform for execution of a machine learning task; and cause the cloud platform to execute a hyper server that is configured to: determine a plurality of sets of possible values for hyperparameters of the machine learning task; for each of the plurality of sets of possible values, dispatch a model comprising the set to a hyper client configured to execute a model training process based on the set; receive, for each set, statistics relating to the model training process for the set; and determine, based on the received statistics, a particular set that is preferred.
1 . An information handling system comprising:
at least one processor; and
a non-transitory memory coupled to the at least one processor;
wherein the information handling system is configured to:
communicatively couple to a cloud platform for execution of a machine learning task; and
cause the cloud platform to execute a hyper server that is configured to:
determine a plurality of sets of possible values for hyperparameters of the machine learning task;
for each of the plurality of sets of possible values, dispatch a model comprising the set to a hyper client configured to execute a model training process based on the set;
receive, for each set, statistics relating to the model training process for the set; and
determine, based on the received statistics, a particular set that is preferred.
2 . The information handling system of claim 1 , wherein the machine learning task is a deep learning task.
3 . The information handling system of claim 1 , wherein the model comprises a neural network.
4 . The information handling system of claim 3 , wherein the hyperparameters include a learning rate, a momentum, a regularization, a dropout probability, a batch normalization, and a number of hidden units.
5 . The information handling system of claim 1 , wherein the statistics include a generalization error.
6 . The information handling system of claim 5 , wherein the particular set that is preferred is associated with a smallest generalization error.
7 . A method comprising:
an information handling system communicatively coupling to a cloud platform for execution of a machine learning task; and
the information handling system causing the cloud platform to execute a hyper server that is configured to:
determine a plurality of sets of possible values for hyperparameters of the machine learning task;
for each of the plurality of sets of possible values, dispatch a model comprising the set to a hyper client configured to execute a model training process based on the set;
receive, for each set, statistics relating to the model training process for the set; and
determine, based on the received statistics, a particular set that is preferred.
8 . The method of claim 7 , wherein the machine learning task is a deep learning task.
9 . The method of claim 7 , wherein the model comprises a neural network.
10 . The method of claim 9 , wherein the hyperparameters include a learning rate, a momentum, a regularization, a dropout probability, a batch normalization, and a number of hidden units.
11 . The method of claim 7 , wherein the statistics include a generalization error.
12 . The method of claim 11 , wherein the particular set that is preferred is associated with a smallest generalization error.
13 . An article of manufacture comprising a non-transitory, computer-readable medium having computer-executable code thereon that is executable by an information handling system for:
communicatively coupling to a cloud platform for execution of a machine learning task; and
causing the cloud platform to execute a hyper server that is configured to:
determine a plurality of sets of possible values for hyperparameters of the machine learning task;
for each of the plurality of sets of possible values, dispatch a model comprising the set to a hyper client configured to execute a model training process based on the set;
receive, for each set, statistics relating to the model training process for the set; and
determine, based on the received statistics, a particular set that is preferred.
14 . The article of claim 13 , wherein the machine learning task is a deep learning task.
15 . The article of claim 13 , wherein the model comprises a neural network.
16 . The article of claim 15 , wherein the hyperparameters include a learning rate, a momentum, a regularization, a dropout probability, a batch normalization, and a number of hidden units.
17 . The article of claim 13 , wherein the statistics include a generalization error.
18 . The article of claim 17 , wherein the particular set that is preferred is associated with a smallest generalization error.