How to Configure Algorithms

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)