How to Configure Algorithms

List of supported algorithms for neural architecture search

This page describes neural architecture search (NAS) algorithms that Katib supports and how to configure them.

NAS Algorithms

Katib currently supports several search algorithms for NAS:

Efficient Neural Architecture Search (ENAS)

The algorithm name in Katib is enas.

The ENAS example — enas-gpu.yaml — which attempts to show all possible operations. Due to the large search space, the example is not likely to generate a good result.

Katib supports the following algorithm settings for ENAS:

Setting Name	Type	Default value	Description
controller_hidden_size	int	64	RL controller lstm hidden size. Value must be >= 1.
controller_temperature	float	5.0	RL controller temperature for the sampling logits. Value must be > 0. Set value to "None" to disable it in the controller.
controller_tanh_const	float	2.25	RL controller tanh constant to prevent premature convergence. Value must be > 0. Set value to "None" to disable it in the controller.
controller_entropy_weight	float	1e-5	RL controller weight for entropy applying to reward. Value must be > 0. Set value to "None" to disable it in the controller.
controller_baseline_decay	float	0.999	RL controller baseline factor. Value must be > 0 and <= 1.
controller_learning_rate	float	5e-5	RL controller learning rate for Adam optimizer. Value must be > 0 and <= 1.
controller_skip_target	float	0.4	RL controller probability, which represents the prior belief of a skip connection being formed. Value must be > 0 and <= 1.
controller_skip_weight	float	0.8	RL controller weight of skip penalty loss. Value must be > 0. Set value to "None" to disable it in the controller.
controller_train_steps	int	50	Number of RL controller training steps after each candidate runs. Value must be >= 1.
controller_log_every_steps	int	10	Number of RL controller training steps before logging it. Value must be >= 1.

Differentiable Architecture Search (DARTS)

The algorithm name in Katib is darts.

The DARTS example — darts-gpu.yaml.

Katib supports the following algorithm settings for DARTS:

Setting Name	Type	Default value	Description
num_epochs	int	50	Number of epochs to train model
w_lr	float	0.025	Initial learning rate for training model weights. This learning rate annealed down to `w_lr_min` following a cosine schedule without restart.
w_lr_min	float	0.001	Minimum learning rate for training model weights.
w_momentum	float	0.9	Momentum for training training model weights.
w_weight_decay	float	3e-4	Training model weight decay.
w_grad_clip	float	5.0	Max norm value for clipping gradient norm of training model weights.
alpha_lr	float	3e-4	Initial learning rate for alphas weights.
alpha_weight_decay	float	1e-3	Alphas weight decay.
batch_size	int	128	Batch size for dataset.
num_workers	int	4	Number of subprocesses to download the dataset.
init_channels	int	16	Initial number of channels.
print_step	int	50	Number of training or validation steps before logging it.
num_nodes	int	4	Number of DARTS nodes.
stem_multiplier	int	3	Multiplier for initial channels. It is used in the first stem cell.

Next steps

Learn how NAS algorithms work in Katib.

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Last modified May 8, 2024: Katib: Reorganized Katib Docs (#3723) (9903837f)