1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
|
# 在Node1启动ray服务
(.venv) tanwubin@gx10-home-master:~/projects/vllm$ ray start --head --node-ip-address=169.254.206.234 --port=6379
Enable usage stats collection? This prompt will auto-proceed in 10 seconds to avoid blocking cluster startup. Confirm [Y/n]:
Usage stats collection is enabled. To disable this, add `--disable-usage-stats` to the command that starts the cluster, or run the following command: `ray disable-usage-stats` before starting the cluster. See https://docs.ray.io/en/master/cluster/usage-stats.html for more details.
Local node IP: 169.254.206.234
--------------------
Ray runtime started.
--------------------
Next steps
Note: The following commands are intended for use on
the head node or within the cluster network.
# 这里也给出了添加集群算力节点的示例
To add another node to this Ray cluster, run
ray start --address='169.254.206.234:6379'
To connect to this Ray cluster:
import ray
ray.init(_node_ip_address='169.254.206.234')
To submit a Ray job using the Ray Jobs CLI:
RAY_API_SERVER_ADDRESS='http://127.0.0.1:8265' ray job submit --working-dir . -- python my_script.py
See https://docs.ray.io/en/latest/cluster/running-applications/job-submission/index.html
for more information on submitting Ray jobs to the Ray cluster.
To terminate the Ray runtime, run
ray stop
To view the status of the cluster, use
ray status
To monitor and debug Ray, view the dashboard at
127.0.0.1:8265
If connection to the dashboard fails, check your firewall settings and network configuration.
(.venv) tanwubin@gx10-home-master:~/projects/vllm$
(.venv) tanwubin@gx10-home-master:~/projects/vllm$ ray status
======== Autoscaler status: 2026-07-12 10:35:44.735219 ========
Node status
---------------------------------------------------------------
Active:
1 node_75b11172ec44444928f76aa6d18fc4894e025ec71cc7e223e13a9cce
Pending:
(no pending nodes)
Recent failures:
(no failures)
Resources
---------------------------------------------------------------
Total Usage:
0.0/20.0 CPU
0.0/1.0 GPU
0B/79.53GiB memory
0B/34.08GiB object_store_memory
From request_resources:
(none)
Pending Demands:
(no resource demands)
(.venv) tanwubin@gx10-home-master:~/projects/vllm$ ray list nodes
======== List: 2026-07-12 10:39:14.882201 ========
Stats:
------------------------------
Total: 1
Table:
------------------------------
NODE_ID NODE_IP IS_HEAD_NODE STATE STATE_MESSAGE NODE_NAME RESOURCES_TOTAL LABELS
0 75b11172ec44444928f76aa6d18fc4894e025ec71cc7e223e13a9cce 169.254.206.234 True ALIVE 169.254.206.234 CPU: 20.0 ray.io/accelerator-type: GB10
GPU: 1.0 ray.io/node-id: 75b11172ec44444928f76aa6d18fc4894e025ec71cc7e223e13a9cce
accelerator_type:GB10: 1.0
memory: 79.530 GiB
node:169.254.206.234: 1.0
node:__internal_head__: 1.0
object_store_memory: 34.084 GiB
# 在Node2启动ray,加入集群
(vllm) tanwubin@gx10-work:~/projects/vllm$ ray start --address='169.254.206.234:6379' --node-ip-address=169.254.118.128
Local node IP: 169.254.118.128
--------------------
Ray runtime started.
--------------------
To terminate the Ray runtime, run
ray stop
(vllm) tanwubin@gx10-work:~/projects/vllm$
# 加入集群后
(.venv) tanwubin@gx10-home-master:~/projects/vllm$ ray list nodes
======== List: 2026-07-12 10:41:24.434768 ========
Stats:
------------------------------
Total: 2
Table:
------------------------------
NODE_ID NODE_IP IS_HEAD_NODE STATE STATE_MESSAGE NODE_NAME RESOURCES_TOTAL LABELS
0 100e10685f9991b162336d38327d3e8d26b6f4bb43a3b5557cdcc27a 169.254.118.128 False ALIVE 169.254.118.128 CPU: 20.0 ray.io/accelerator-type: GB10
GPU: 1.0 ray.io/node-id: 100e10685f9991b162336d38327d3e8d26b6f4bb43a3b5557cdcc27a
accelerator_type:GB10: 1.0
memory: 82.508 GiB
node:169.254.118.128: 1.0
object_store_memory: 35.361 GiB
1 75b11172ec44444928f76aa6d18fc4894e025ec71cc7e223e13a9cce 169.254.206.234 True ALIVE 169.254.206.234 CPU: 20.0 ray.io/accelerator-type: GB10
GPU: 1.0 ray.io/node-id: 75b11172ec44444928f76aa6d18fc4894e025ec71cc7e223e13a9cce
accelerator_type:GB10: 1.0
memory: 79.530 GiB
node:169.254.206.234: 1.0
node:__internal_head__: 1.0
object_store_memory: 34.084 GiB
(.venv) tanwubin@gx10-home-master:~/projects/vllm$
(.venv) tanwubin@gx10-home-master:~/projects/vllm$ ray status
======== Autoscaler status: 2026-07-12 10:42:10.745052 ========
Node status
---------------------------------------------------------------
Active:
# 从这里可以看到集群有2个node了
1 node_75b11172ec44444928f76aa6d18fc4894e025ec71cc7e223e13a9cce
1 node_100e10685f9991b162336d38327d3e8d26b6f4bb43a3b5557cdcc27a
Pending:
(no pending nodes)
Recent failures:
(no failures)
Resources
---------------------------------------------------------------
# 从这里可以看到CPU/GPU/memory资源都聚合成集群的资源了
Total Usage:
0.0/40.0 CPU
0.0/2.0 GPU
0B/162.04GiB memory
0B/69.45GiB object_store_memory
From request_resources:
(none)
Pending Demands:
(no resource demands)
(.venv) tanwubin@gx10-home-master:~/projects/vllm$
# 本地的dashboard
View the dashboard at http://127.0.0.1:8265
|