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Hyperparameter Optimization

The performance of your final model is influenced by the training data, the kind of model, and the learning algorithm. The model as well as your learning algorithms have parameters of their own, like the number of layers in a neural network or the learning rate in gradient descent. In order to find the best model, it makes sense to find a good combination of these parameters. This is the task of hyperparameter optimization.

With RiseML, you can easily perform a grid search over different hyperparameter configurations. This is enabled by the parameters section in the job configuration. Here is an example:

project: ai-toaster
    cpus: 2
    mem: 4096
    gpus: 2
      - 0.0001
      - 0.001
      - 1e-6
      - 1e-7
      - 20
      - 30
  concurrency: 2
    name: nvidia/cuda:8.0-cudnn7-runtime
  run: >-
    python --num-layers {{num-layers}}
                          --learning-rate {{learning-rate}}
                          --training-data /data/ai-toaster

The parameters section defines the individual parameters and their possible values. Here, a total of 2 (lr) * 2 (lr-decay) * 2 (epochs) = 8 combinations are defined. During execution, the actual value of each parameter is passed to the command instead of {{parameter-name}}.

Executing riseml train will start a hyperparameter experiment with a sub-experiment for each combination:

$ riseml train
Syncing project (1.3 MB, 7 files)...done
144             | [2017-08-30T09:59:06Z] --> PENDING
144.1           | [2017-08-30T09:59:06Z] --> PENDING
144.2           | [2017-08-30T09:59:06Z] --> CREATED

By default, only a single sub-experiment will be run in parallel. You can control this via the concurrency parameter, so in the example above 2 sub-experiments will be run in parallel. Each sub-experiment will have its own ID, e.g., 144.1 and 144.2 as in the example above.

The riseml status command will show you what combinations have been run already:

ID: 144
Type: Set
Project: ai-toaster

144.1  STARTING  8 second(s)   epochs=20, lr=0.0001, lr-decay=1e-6
144.2  STARTING  8 second(s)   epochs=20, lr=0.0001, lr-decay=1e-7
144.3  CREATED   8 second(s)   epochs=20, lr=0.001, lr-decay=1e-6
144.4  CREATED   8 second(s)   epochs=20, lr=0.001, lr-decay=1e-7
144.5  CREATED   8 second(s)   epochs=30, lr=0.0001, lr-decay=1e-6
144.6  CREATED   8 second(s)   epochs=30, lr=0.0001, lr-decay=1e-7
144.7  CREATED   8 second(s)   epochs=30, lr=0.001, lr-decay=1e-6
144.8  CREATED   8 second(s)   epochs=30, lr=0.001, lr-decay=1e-7

Hyperparameter Optimization Output

In order to find the best hyperparameter combination, each sub-experiment needs to output a measure of its performance. Often this is something like the accuracy or loss on a development set. With Tensorflow, such statistics can be recorded and compared via TensorBoard. Each experiment's parameters are also written to the output directory as part of the configuration in a file called riseml-configuration.yml:

  name: nvidia/cuda:8.0-cudnn7-runtime
  epochs: 20
  lr: 0.0001
  lr-decay: 1e-7
project: ai-toaster
  cpus: 2
  gpus: 2
  mem: 4096
revision: 4efd8d995e0c1635296ec3c404276c8d4ff2d87c
  run: >-
    python --num-layers {{num-layers}}
                          --learning-rate {{learning-rate}}
                          --training-data /data/ai-toaster

This allows you to write performance measures to the output directory and link them to the parameters that were used to produce them.

If you have enabled the TensorBoard integration, TensorBoard provides real-time statistics for all experiments below the parent directory (e.g., 144above). This allows you to compare different runs in real-time:

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