data_preprocess_step, data_preprocess_outputs = data_preprocess_step(
data_ingestion_outputs['raw_data_dir'], cpu_compute_target)
# Step 3: Train Model
train_step, train_outputs = train_step(data_preprocess_outputs['train_dir'],
data_preprocess_outputs['valid_dir'],
gpu_compute_target)
# Step 4: Evaluate Model
evaluate_step, evaluate_outputs = evaluate_step(
train_outputs['model_dir'], data_preprocess_outputs['test_dir'],
gpu_compute_target)
# Step 5: Deploy Model
deploy_step, deploy_outputs = deploy_step(train_outputs['model_dir'],
evaluate_outputs['accuracy_file'],
data_preprocess_outputs['test_dir'],
cpu_compute_target)
# Submit pipeline
print('Submitting pipeline ...')
pipeline_parameters = {
'num_images': 100,
'image_dim': 200,
'num_epochs': 10,
'batch_size': 16,
'learning_rate': 0.001,
'momentum': 0.9
}
pipeline = Pipeline(workspace=workspace,
steps=[
data_ingestion_step, data_preprocess_step, train_step,
# Step 4: Train Model
train_step, train_outputs = train_step(datastore,
preprocess_outputs['train_dir'],
preprocess_outputs['valid_dir'],
build_vocab_outputs['vocab_dir'],
gpu_compute_target)
# Step 5: Evaluate Model
evaluate_step, evaluate_outputs = evaluate_step(datastore,
preprocess_outputs['test_dir'],
train_outputs['model_dir'],
gpu_compute_target)
# Step 6: Deploy Model
deploy_step, deploy_outputs = deploy_step(train_outputs['model_dir'],
evaluate_outputs['eval_dir'],
preprocess_outputs['test_dir'],
cpu_compute_target)
# Submit pipeline
print('Submitting pipeline ...')
pipeline_parameters = {
'start_date': '2015-01-01',
'end_date': '2015-01-02',
'input_col': 'Title',
'output_col': 'Abstract',
'train_proportion': 0.8,
'max_epoch': 1,
}
pipeline = Pipeline(workspace=workspace,
steps=[