Some model types, such as DRF, GBM, and Deep Learning, support checkpointing. A checkpoint resumes model training so that you can iterate your model. The dataset must be the same. The following model parameters must be the same when restarting a model from a checkpoint:
Must be the same as in checkpoint model | |||
---|---|---|---|
drop_na20_cols |
response_column |
activation |
|
use_all_factor_levels |
adaptive_rate |
autoencoder |
|
rho |
epsilon |
sparse |
|
sparsity_beta |
col_major |
rate |
|
rate_annealing |
rate_decay |
momentum_start |
|
momentum_ramp |
momentum_stable |
nesterov_accelerated_gradient |
|
ignore_const_cols |
max_categorical_features |
nfolds |
|
distribution |
tweedie_power |
The following parameters can be modified when restarting a model from a checkpoint:
Can be modified | |||
---|---|---|---|
seed |
checkpoint |
epochs |
|
score_interval |
train_samples_per_iteration |
target_ratio_comm_to_comp |
|
score_duty_cycle |
score_training_samples |
score_validation_samples |
|
score_validation_sampling |
classification_stop |
regression_stop |
|
quiet_mode |
max_confusion_matrix_size |
max_hit_ratio_k |
|
diagnostics |
variable_importances |
initial_weight_distribution |
|
initial_weight_scale |
force_load_balance |
replicate_training_data |
|
shuffle_training_data |
single_node_mode |
fast_mode |
|
l1 |
l2 |
max_w2 |
|
input_dropout_ratio |
hidden_dropout_ratios |
loss |
|
overwrite_with_best_model |
missing_values_handling |
average_activation |
|
reproducible |
export_weights_and_biases |
elastic_averaging |
|
elastic_averaging_moving_rate |
elastic_averaging_regularization |
mini_batch_size |
model_id
. To view the model_id
, click the Model menu then click List All Models. Note: The model type must be the same as the checkpointed model.
model_id
in the checkpoint entry field.