feat: do not remove the swarm master in master node

src/communication.py

@@ -1,8 +1,13 @@
+from os.path import isdir
 import socket
+import os
 import json
+import threading
 import docker
 import time
 
+from docker.models.containers import Image
+
 class ClusterCommunicationModule():
     def __init__(self, host, port, node_manager):
         # TCP server
@@ -47,6 +52,8 @@ class ClusterCommunicationModule():
                 if self.node_manager.docker_client.swarm.attrs == {}:
                     print("Build new docker swarm...")
                     self.node_manager.docker_client.swarm.init(advertise_addr=self.host, listen_addr=f"{self.host}:2377", force_new_cluster=True)
+                    print("Create new overlay network")
+                    self.node_manager.docker_client.networks.create(name='train-net', driver='overlay', attachable=True)
 
                 # send docker swarm token to the worker
                 token = self.node_manager.docker_client.swarm.attrs['JoinTokens']['Worker']
@@ -69,7 +76,7 @@ class ClusterCommunicationModule():
             # join docker swarm cluster
             token = self.client_sock.recv(1024).decode().split('  ')[-1]
             print("Receive Docker Swarm Join_Token=", token)
-            status = self.node_manager.docker_client.swarm.join(remote_addrs=[f'{addr[0]}:2377'], join_token=token)
+            status = self.node_manager.docker_client.swarm.join(remote_addrs=[f'{addr[0]}:2377'], join_token=token, advertise_addr=f'{addr[0]}:2377')
 
             if not status:
                 print("Some Errors!")
@@ -109,6 +116,16 @@ class ClusterCommunicationModule():
         elif command == '[START_LISTEN_TASK]':
             self.client_sock.send('[START_LISTEN_TASK_CHECK]  {}'.encode())
             print("The master has started listening for new task from Sepolia testnet...")
+        elif command == '[RUN_CONTAINER]':
+            args = json.loads(args)
+            image = args['image']
+            train = args['train']
+
+            print(f"[RUN_CONTAINER] {image}")
+            self.run_container(image, train, args)
+
+            print(f"[RUN_CONTAINER SUCCESS] {image}")
+            self.client_sock.send('[RUN_CONTAINER_SUCCESS]  {}'.encode())
 
         return True
 
@@ -163,10 +180,11 @@ class ClusterCommunicationModule():
     def exit(self):
         if self.node_manager.status == 'master':
             for conn in self.worker_conns:
-                conn.send('[STOP]  {}'.encode())
-                check, args = conn.recv(1024).decode().split('  ')
-                print(f'{args} has stopped.')
-            self.node_manager.docker_client.swarm.leave(force=True)
+                try:
+                    conn.send('[STOP]  {}'.encode())
+                    check, args = conn.recv(1024).decode().split('  ')
+                except:
+                    print(f'{args} has stopped.')
 
         if self.node_manager.status == 'worker':
             self.node_manager.docker_client.swarm.leave()
@@ -176,6 +194,113 @@ class ClusterCommunicationModule():
         for conn in self.worker_conns:
             conn.close()
 
+    def run_container(self, image_name, train, train_args={}):
+        '''
+        train_args
+            - index
+            - node_num
+        '''
+        if not os.path.isdir('./dataset_dir'):
+            os.mkdir('./dataset_dir')
+            print("Create ./dataset_dir dir.")
+        if not os.path.isdir('./output'):
+            os.mkdir('./output')
+            print("Create ./output dir.")
+
+        if not train:
+            container = self.node_manager.docker_client.containers.run(
+                image_name,
+                volumes={'dataset_dir': {'bind': '/dataset', 'mode': 'rw'}},
+                detach=True
+            )
+        else:
+            container = self.node_manager.docker_client.containers.run(
+                image_name,
+                volumes={
+                    'dataset_dir': {'bind': '/dataset', 'mode': 'rw'},
+                    'output': {'bind': '/output', 'mode': 'rw'},
+                },
+                network='train-net',
+                runtime='nvidia',
+                device_requests=[
+                    docker.types.DeviceRequest(count=-1, capabilities=[['gpu']])
+                ],
+                name=f'train-{train_args["index"]}',
+                environment={
+                    'GPU_NUM': self.node_manager.GPU_num,
+                    'NODE_NUM': train_args['node_num'],
+                    'NODE_RANK': train_args['index'],
+                    'MASTER_IP': 'train-0' if self.node_manager.status == 'worker' else '127.0.0.1',
+                    'MASTER_PORT': 21046,
+                },
+                detach=True
+            )
+
+        print(container.short_id)
+        for line in container.logs(stream=True):
+            print(line.strip().decode())
+
+        result = container.wait()
+        status_code = result['StatusCode']
+        if status_code != 0:
+            print(f'[ERROR] some error occur in the docker container, error_code={status_code}')
+        container.remove()
+
+    def scatter_container(self, image_name, train):
+        def master_run(image_name):
+            print("[Master] run")
+            train_args = {
+                'index': 0,
+                'node_num': len(self.worker_conns)+1
+            }
+            self.run_container(image_name, train, train_args)
+            print("[Master] finished")
+
+        def send_and_wait(conn_index, conn, image_name):
+            try:
+                # build command
+                data = {
+                    'image': image_name,
+                    'train': train,
+                    'index': conn_index+1,
+                    'node_num': len(self.worker_conns)+1
+                }
+                command = '[RUN_CONTAINER]  {}'.format(json.dumps(data))
+
+                # send
+                conn.send(command.encode())
+                print(f"[WORKER {conn_index}] Send command {command}")
+
+                # wait
+                data, args = conn.recv(1024).decode().split('  ')
+                print(f"[WORKER {conn_index}] finished")
+
+            except:
+                print("[WARN] connection {conn_index} disconnected.")
+            
+        # build threads
+        threads = []
+        master_t = threading.Thread(target=master_run, args=(image_name, ))
+        threads.append(master_t)
+
+        for index, conn in enumerate(self.worker_conns):
+            t = threading.Thread(target=send_and_wait, args=(index, conn, image_name))
+            threads.append(t)
+
+        # start threads & wait
+        for thread in threads:
+            thread.start()
+        for thread in threads:
+            thread.join()
+
+        # all finished
+        print("\n[INFO] All workers finished.")
+
+
+        '''
+        conn.send('[RUN_CONTAINER]  '.encode())
+        data, args = conn.recv(1024).decode().split('  ')
+        '''
 
 class ServiceExplorationModule():
     def __init__(self, host, port, node_manager):

src/node_manager.py

@@ -85,6 +85,7 @@ class NodeManager():
         print(f"And we have load your wallet private key {WALLET_KEY} (address={self.wallet.address})")
         print()
         if self.w3.is_connected():
+            '''
             print("[INFO] Connected Successfully.")
             print()
             
@@ -116,8 +117,16 @@ class NodeManager():
             print("\n[INFO] You Receive a new task:")
             print(f"      - Download Image: {data_image}")
             print(f"      - Training Image: {train_image}")
+            '''
+
+            data_image = "snsd0805/cifar100-dataset:v1"
+            train_image = "snsd0805/cifar100-train:v3"
 
             # Start Downloading
+            # self.cluster_communication_module.scatter_container(data_image, train=False)
+
+            # start training
+            self.cluster_communication_module.scatter_container(train_image, train=True)
 
 
         else:

test.py

@@ -1,34 +1,40 @@
-from web3 import Web3
-from src.scheduler import Scheduler
+import docker
+import os
 
-SCHEDULER_ADDR = "0x544eAe853EA3774A8857573C6423E6Db95b79258"
-SCHEDULER_ABI_FILE = "../gpu-contract/abi/Scheduler.abi"
 
-PROVIDER1_KEY = "0xac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80"
-PROVIDER2_KEY = "0x59c6995e998f97a5a0044966f0945389dc9e86dae88c7a8412f4603b6b78690d"
-PROVIDER3_KEY = "0x5de4111afa1a4b94908f83103eb1f1706367c2e68ca870fc3fb9a804cdab365a"
+if not os.path.isdir('./dataset_dir'):
+    os.mkdir('./dataset_dir')
 
-CLIENT_KEY = "0x7c852118294e51e653712a81e05800f419141751be58f605c371e15141b007a6"
+docker_client = docker.from_env()
+container = docker_client.containers.run(
+    'snsd0805/cifar100-train:v3',
+    volumes={
+        'dataset_dir': {'bind': '/dataset', 'mode': 'rw'},
+        'output': {'bind': '/output', 'mode': 'rw'},
+    },
+    network='train-net',
+    runtime='nvidia',
+    device_requests=[
+        docker.types.DeviceRequest(count=-1, capabilities=[['gpu']])
+    ],
+    name='train-0',
+    environment={
+        'GPU_NUM': 1,
+        'NODE_NUM': 1,
+        'NODE_RANK': 0,
+        'MASTER_IP': 'train-0',
+        'MASTER_PORT': 21046,
+    },
+    detach=True
+)
 
-w3 = Web3(Web3.HTTPProvider('http://127.0.0.1:8545'))
 
-if __name__ == '__main__':
-    if w3.is_connected():
-        scheduler = Scheduler(w3, SCHEDULER_ADDR, SCHEDULER_ABI_FILE)
+print(container.short_id)
+for line in container.logs(stream=True):
+    print(line.strip().decode())
 
-        provider1 = w3.eth.account.from_key(PROVIDER1_KEY)
-        provider2 = w3.eth.account.from_key(PROVIDER2_KEY)
-        provider3 = w3.eth.account.from_key(PROVIDER3_KEY)
-        client = w3.eth.account.from_key(CLIENT_KEY)
+result = container.wait()
+status_code = result['StatusCode']
+print(status_code, type(status_code))
 
-        print(scheduler.getClusters())
-        scheduler.registerCluster(provider1, 1, 4)
-        scheduler.registerCluster(provider2, 2, 2)
-        scheduler.registerCluster(provider3, 3, 1)
-        scheduler.registerTaskWithConditions(client, "https://data.com", "http://train.tw", 3, 1)
-        print(scheduler.getClusters())
-        print(scheduler.getTasks())
-
-    else:
-        print("cannot connected to the chain")