按照生产模式配置两个节点并在两方都执行一段lr任务。执行日志提示
No avaiable node types can fulfill resource request {'bob':1.0,'CPU':1.0} .Add suitable node types to this cluster to resolve this issue
 No avaiable node types can fulfill resource request {'CPU':1.0,'alice':1.0} .Add suitable node types to this cluster to resolve this issue

两个节点 alice:192.168.1.1:9010  和bob: 192.168.1.2:9010

1、启动每个节点ray
ray start --head --node-ip-address="192.168.1.1" --port="9010" --include-dashboard=False --disable-usage-stats
ray start --head --node-ip-address="192.168.1.2" --port="9010" --include-dashboard=False --disable-usage-stats

2、sf.init初始化sf集群
alice:
cluster_config ={
    'parties': {
        'alice': {
            'address': '192.168.1.1:9011',
            'listen_addr': '0.0.0.0:9011'
        },
        'bob': {
            'address': '192.168.1.2:9011',
            'listen_addr': '0.0.0.0:9011'
        },
    },
    'self_party': 'alice'
}

sf.init(address='192.168.1.1:9010', cluster_config=cluster_config)

bob
cluster_config ={
    'parties': {
        'alice': {
            'address': '192.168.1.1:9011',
            'listen_addr': '0.0.0.0:9011'
        },
        'bob': {
            'address': '192.168.1.2:9011',
            'listen_addr': '0.0.0.0:9011'
        },
    },
    'self_party': 'bob'
}

sf.init(address='192.168.1.2:9010', cluster_config=cluster_config)


3、分别在alice和bob执行算法

spu_cluster_config= {
    'runtime_config': {
	'protocol': spu.spu_pb2.SEMI2K,
	'field': 'FM128',
	'fxp_fraction_bits": "32"
    },
    'nodes': [
        {
           'id':'alice',
	    'party': 'alice',
           'address': '192.168.1.1:9012'
        },
	 {
           'id':'bob',
	    'party': 'bob',
           'address': '192.168.1.2:9012'
        },
    ]
}

spu_link_desc = {
	'connect_retry_interval_ms': 30000,
	'recv_timeout_ms': 30*60*1000
}

sf.shutdown()
sf.init(address='192.168.1.1:9010', cluster_config=cluster_config,num_cpus=128)
#如果bob，则换成sf.init(address='192.168.1.2:9010', cluster_config=cluster_config,num_cpus=128)
alice=sf.PYU('alice')
bob=sf.PYU('bob')
spud=sf.SPU(cluster_def=spu_cluster_config,link_desc=spu_link_desc)

def create_conv_model():
    def create_model():
        from tensorflow import keras
        from tensorflow.keras import layers
        # Create model
        model = tf.keras.Sequential(
            [
            tf.keras.layers.Dense(1,input_shape=( v_data.shape[1],),activation=tf.nn.sigmoid)
            ])
        model.compile(
            optimizer='adam',
            loss='binary_crossentropy',  # 逻辑回归使用 binary_crossentropy 作为损失函数
            metrics=[tf.keras.metrics.AUC()])
        return model

    return create_model

def get_data(path,train):
    from sklearn.preprocessing import StandardScaler
    
    import numpy as np
    data = pd.read_csv(path, encoding='utf-8')
    if train:
        print('正在get_data函数中处理训练数据....')
        print('data  columns:',data.columns)
        
        data = data.iloc[:,0:-1]
        #scaler = StandardScaler()
        #x = scaler.fit_transform(data)
        
        return data
    else:
        print('正在get_data函数中处理label数据....')
        data = data.iloc[:,-1:]
        y = np.array(data)
        return y
print('加载v_data中.....')
v_data = FedNdarray(
    partitions={
    alice: alice(get_data)(path=r'/root/dataset/检测/augmented_sensor_45_data1.csv',train = True),
    bob: bob(get_data)(path=r'/root/dataset/检测/augmented_sensor_45_data2.csv',train = True),
    },
    partition_way=PartitionWay.HORIZONTAL,
    )
print('加载v_data完毕.....')

print('加载label_data 中.....')
label_data = FedNdarray(
        partitions={
        alice: alice(get_data)(path=r'/root/dataset/检测/augmented_sensor_45_data1.csv',train = False),
        bob: bob(get_data)(path=r'/root/dataset/检测/augmented_sensor_45_data2.csv',train = False),
        },
        partition_way=PartitionWay.HORIZONTAL,
)

print('加载label_data 完毕.....')
device_list = [alice,bob]
secure_aggregator = SecureAggregator(alice, device_list)
spu_aggregator = SPUAggregator(spud)
fed_model = FLModel(server=alice,
device_list=device_list,
model=model,
aggregator=secure_aggregator,
strategy="fed_avg_w",
backend = "tensorflow")
print('开始训练中.....')
history = fed_model.fit(v_data,
label_data,
epochs=1,
sampler_method="batch",
batch_size=1023,
aggregate_freq=1000)

#问题
按照上述步骤执行，日志会提示：  No avaiable node types can fulfill resource request {'bob':1.0,'CPU':1.0} .Add suitable node types to this cluster to resolve this issue
 No avaiable node types can fulfill resource request {'CPU':1.0,'alice':1.0} .Add suitable node types to this cluster to resolve this issue