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=[