Hyperparameter Optimization - Jupyter Notebook
Trains is now ClearML
This documentation applies to the legacy Trains versions. For the latest documentation, see ClearML.
hyperparameter_search.ipynb demonstrates integrating Trains into a Jupyter Notebook which performs automated hyperparameter optimization. This is an example of Trains automation. It creates a Trains HyperParameterOptimizer object, which is a search controller. The search controller's search strategy optimizer is OptimizerBOHB. The example maximizes total accuracy by finding an optimal batch size, base learning rate, and dropout. Trains automatically logs the optimization's top performing experiments.
The experiment whose hyperparameters are optimized is named
image_classification_CIFAR10. It is created by running another Trains example, image_classification_CIFAR10.ipynb, which must run before
hyperparameter_search.py runs, it creates an experiment named
Hyper-Parameter Optimization which is associated with the
Hyper-Parameter Search project.
The optimizer Task,
Hyper-Parameter Optimization, and the experiments appear individually in the Trains Web (UI).
Scalars for total accuracy and remaining budget by iteration, and a plot of total accuracy by iteration appear in RESULTS > SCALARS. Remaining budget indicates the percentage of total iterations for all jobs left before that total is reached.
These scalars are reported automatically by Trains from
HyperParameterOptimizer when it runs.
A plot for the optimization of total accuracy by job appears in RESULTS > SCALARS.
This is also reported automatically by Trains when
HyperParameterOptimizer hyperparameters, including the optimizer parameters appear in CONFIGURATIONS > HYPER PARAMETERS.
These hyperparameters are those in the optimizer Task, where the
HyperParameterOptimizer object is created.
optimizer = HyperParameterOptimizer( base_task_id=TEMPLATE_TASK_ID, # This is the experiment we want to optimize # here we define the hyper-parameters to optimize hyper_parameters=[ UniformIntegerParameterRange('number_of_epochs', min_value=5, max_value=15, step_size=1), UniformIntegerParameterRange('batch_size', min_value=2, max_value=12, step_size=2), UniformParameterRange('dropout', min_value=0, max_value=0.5, step_size=0.05), UniformParameterRange('base_lr', min_value=0.0005, max_value=0.01, step_size=0.0005), ], # this is the objective metric we want to maximize/minimize objective_metric_title='accuracy', objective_metric_series='total', objective_metric_sign='max', # maximize or minimize the objective metric max_number_of_concurrent_tasks=3, # number of concurrent experiments # setting optimizer - trains supports GridSearch, RandomSearch or OptimizerBOHB optimizer_class=OptimizerBOHB, # can be replaced with OptimizerBOHB execution_queue='default', # queue to schedule the experiments for execution optimization_time_limit=30., # time limit for each experiment (optional, ignored by OptimizerBOHB) pool_period_min=1, # Check the experiments every x minutes # set the maximum number of experiments for the optimization. # OptimizerBOHB sets the total number of iteration as total_max_jobs * max_iteration_per_job total_max_jobs=12, # setting OptimizerBOHB configuration (ignored by other optimizers) min_iteration_per_job=15000, # minimum number of iterations per experiment, till early stopping max_iteration_per_job=150000, # maximum number of iterations per experiment )
All console output from
Hyper-Parameter Optimization appears in RESULTS tab, LOG sub-tab.
Trains automatically logs each job, meaning each experiment that executes with a set of hyperparameters, separately. Each appears as an individual experiment in the Trains Web (UI), where the Task name is
image_classification_CIFAR10 and the hyperparameters appended.
image_classification_CIFAR10: base_lr=0.0075 batch_size=12 dropout=0.05 number_of_epochs=6
You can use Trains Web (UI) experiment comparison to visualize the following:
- Side by side hyperparameter value comparison
- Metric comparison by hyperparameter
- Scalars by specific values and series
- Debug images
Side by side hyperparameter value comparison
In the experiment comparison window, HYPER PARAMETERS tab, select Values in the list (the right of + Add Experiment), and hyperparameter differences appear with a different background color.
Metric comparison by hyperparameter
Select Parallel Coordinates in the list, click a Performance Metric, and then select the checkboxes of the hyperparameters.
Scalar values comparision
In the SCALARS tab, select Last Values, Min Values, or Max Values. Value differences appear with a different background color.
Scalar series comparision
Select Graph and the scalar series for the jobs appears, where each scalar plot shows the series for all jobs.
Debug samples comparision
In the DEBUG SAMPLES tab, debug images appear.