add k8s deployment solution

paddle_fl/examples/k8s_deployment/trainer/fl_trainer.py

-from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
-from paddle_fl.core.master.fl_job import FLRunTimeJob
-import numpy as np
-import sys
-import logging
-import time
-logging.basicConfig(filename="test.log", filemode="w", format="%(asctime)s %(name)s:%(levelname)s:%(message)s", datefmt="%d-%M-%Y %H:%M:%S", level=logging.DEBUG)
-
-
-def reader():
-    for i in range(1000):
-        data_dict = {}
-        for i in range(3):
-            data_dict[str(i)] = np.random.rand(1, 5).astype('float32')
-        data_dict["label"] = np.random.randint(2, size=(1, 1)).astype('int64')
-        yield data_dict
-
-trainer_id = int(sys.argv[1]) # trainer id for each guest
-job_path = "fl_job_config"
-job = FLRunTimeJob()
-job.load_trainer_job(job_path, trainer_id)
-#job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
-job._scheduler_ep = os.environ['FL_SCHEDULER_SERVICE_HOST'] + ":" + os.environ['FL_SCHEDULER_SERVICE_PORT_FL_SCHEDULER']
-trainer = FLTrainerFactory().create_fl_trainer(job)
-trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
-trainer.start()
-print(trainer._scheduler_ep, trainer._current_ep)
-output_folder = "fl_model"
-epoch_id = 0
-while not trainer.stop():
-    print("batch %d start train" % (epoch_id))
-    train_step = 0
-    for data in reader():
-        trainer.run(feed=data, fetch=[])
-        train_step += 1
-        if train_step == trainer._step:
-            break
-    epoch_id += 1
-    if epoch_id % 5 == 0:
-        trainer.save_inference_program(output_folder)
-    

paddle_fl/examples/k8s_deployment/trainer/run_trainer.sh

-#Download config file 
-wget
-
-#Download image
-sudo docker pull [paddle-fl image]
-
-#Build docker 
-sudo docker run --name paddlefl -it -v $PWD:/root [paddle-fl image] /bin/bash
-
-sudo docker cp /path/to/config paddlefl:/path/to/config/file/at/container
-
-#Run program
-
-python -u fl_trainer.py > trainer.log &