training_repo = 'http://deepdetect.com/dd/datasets/mnist_csv/mnist_test.csv' # service creation model = {'repository':model_repo} parameters_input = {'connector':'csv'} parameters_mllib = {} parameters_output = {} dd.put_service(sname,model,description,mllib, parameters_input,parameters_mllib,parameters_output,'unsupervised') # training train_data = [training_repo] parameters_input = {'id':'','separator':',','label':'label'} parameters_mllib = {'iterations':500} parameters_output = {} predout = dd.post_train(sname,train_data,parameters_input,parameters_mllib,parameters_output,async=True) time.sleep(1) train_status = '' while True: train_status = dd.get_train(sname,job=1,timeout=3) if train_status['head']['status'] == 'running': print train_status['body']['measure'] else: print train_status predout = train_status break predictions = predout['body']['predictions'] N = len(predictions) points = np.empty((N,2),dtype=np.float)
parameters_input_service = {'connector':'txt'} if template == "mlp": parameters_mllib_service = {'template':template,'nclasses':nclasses,'layers':layers,'activation':activation} elif template == "lregression": parameters_mllib_service = {'template':template,'nclasses':nclasses,'activation':activation} parameters_output_service = {'measure':['mcll','f1']} dd.put_service(service_name,model,description,mllib,parameters_input_service,parameters_mllib_service,parameters_output_service) #Start training the service iterations = int(service['iterations']) solver_type = service['solver_type'] base_lr = float(service['base_lr']) parameters_input_training = {'shuffle':True,'test_split':test_split,'min_count':min_count,'min_word_length':min_word_length,'count':False} parameters_mllib_training = {'gpu':True,'solver':{'iterations':iterations,'test_interval':test_interval,'base_lr':base_lr,'solver_type':solver_type},'net':{'batch_size':batch_size}} parameters_output_training = {'measure':['mcll','f1','cmdiag','cmfull']} train_data = [root_repository+'dataset/'] training_service = dd.post_train(service_name.lower(),train_data,parameters_input_training,parameters_mllib_training,parameters_output_training,async=True) job_number = training_service['head']['job'] #Get training data while the service is running sleep(20) status_code = 200 count_job_data = 1 while status_code == 200: job_data = dd.get_train(service_name.lower(),job=job_number, measure_hist=True) status_code = job_data['status']['code'] if not 'accp' in job_data['body']['measure']: sleep(20) continue if job_data['head']['status'] == 'running': log_file.write("job running time "+str(job_data['head']['time'])+"\n") log_file.write("Iteration number "+str(job_data['body']['measure']['iteration'])+"\n") log_file.flush()