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[docs] Add links and fix grammars in deploy_on_k8s.md (#4641)

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Signed-off-by: windsonsea <haifeng.yao@daocloud.io>
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Michael Yao
2025-03-21 13:55:23 +08:00
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docs/references/deploy_on_k8s.md
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@@ -1,32 +1,30 @@
# Deploy On Kubernetes
This docs is for deploying a RoCE Network-Based SGLANG Two-Node Inference Service on a Kubernetes (K8S) Cluster.
This document is for deploying a RoCE network-based SGLang two-node inference service on a Kubernetes (K8S) cluster.
LeaderWorkerSet (LWS) is a Kubernetes API that aims to address common deployment patterns of AI/ML inference workloads. A major use case is for multi-host/multi-node distributed inference.
[LeaderWorkerSet (LWS)](https://github.com/kubernetes-sigs/lws) is a Kubernetes API that aims to address common deployment patterns of AI/ML inference workloads. A major use case is for multi-host/multi-node distributed inference.
Sglang can also be deployed with LWS on Kubernetes for distributed model serving.
SGLang can also be deployed with LWS on Kubernetes for distributed model serving.
Please see this guide for more details on deploying SGLang on Kubernetes using LWS.
Here we take the deployment of deepseekR1 as an example.
Here we take the deployment of DeepSeek-R1 as an example.
## Prerequisites
1. At least two Kubernetes nodes, each with 2 H20 systems and 8 GPUs, are required.
1. At least two Kubernetes nodes, each with two H20 systems and eight GPUs, are required.
2. Make sure your K8S cluster has LWS correctly installed. If it hasn't been set up yet, please follow the instructions in this [document](https://github.com/kubernetes-sigs/lws/blob/main/docs/setup/install.md)
2. Make sure your K8S cluster has LWS correctly installed. If it hasn't been set up yet, please follow the [installation instructions](https://github.com/kubernetes-sigs/lws/blob/main/site/content/en/docs/installation/_index.md).
## Basic example
## Basic Example
The Basic Example documentation is introduced here: [visit this guide](https://github.com/kubernetes-sigs/lws/tree/main/docs/examples/sglang)
For the basic example documentation, refer to [Deploy Distributed Inference Service with SGLang and LWS on GPUs](https://github.com/kubernetes-sigs/lws/tree/main/docs/examples/sglang).
However, that document only covers the basic NCCL socket mode.
In this section, we’ll make some simple modifications to adapt the setup to the RDMA scenario.
## RDMA ROCE case
## RDMA RoCE case
* Check your env:
@@ -237,12 +235,11 @@ sglang-0-1 1/1 Running 0 9s
Wait for the sglang leader (`sglang-0`) status to change to 1/1, which indicates it is `Ready`.
Once successful, you should see output like this:
You can use the command `kubectl logs -f sglang-0` to view the logs of the leader node.
```text
Once successful, you should see output like this:
```text
[2025-02-17 05:27:24 TP1] Capture cuda graph end. Time elapsed: 84.89 s
[2025-02-17 05:27:24 TP6] max_total_num_tokens=712400, chunked_prefill_size=8192, max_prefill_tokens=16384, max_running_requests=50, context_len=163840
[2025-02-17 05:27:24 TP0] max_total_num_tokens=712400, chunked_prefill_size=8192, max_prefill_tokens=16384, max_running_requests=50, context_len=163840
@@ -260,24 +257,24 @@ You can use the command `kubectl logs -f sglang-0` to view the logs of the leade
[2025-02-17 05:27:25 TP0] Prefill batch. #new-seq: 1, #new-token: 7, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0
[2025-02-17 05:27:32] INFO: 127.0.0.1:48924 - "POST /generate HTTP/1.1" 200 OK
[2025-02-17 05:27:32] The server is fired up and ready to roll!
```
if not successfully startup, please follow this steps to check or see the remaining issues... thanks.
If it doesn’t start up successfully, please follow these steps to check for any remaining issues. Thanks!
### Debug
* Set `NCCL_DEBUG=TRACE` to check if it is a nccl communication problem
* Set `NCCL_DEBUG=TRACE` to check if it is a NCCL communication problem.
This should resolve most NCCL-related issues.
***Noticed: If you find that NCCL_DEBUG=TRACE is not effective in the container environment, but the process is stuck or you encounter hard-to-diagnose issues, try switching to a different container image. Some images may not handle standard error output properly.***
***Notice: If you find that NCCL_DEBUG=TRACE is not effective in the container environment, but the process is stuck or you encounter hard-to-diagnose issues, try switching to a different container image. Some images may not handle standard error output properly.***
#### ROCE scenario
#### RoCE scenario
* Please make sure that RDMA devices are available in the cluster environment.
* Please make sure that the nodes in the cluster have mellanox NICs with RoCE. In this example, we use mellanox ConnectX 5 model NICs, and the proper OFED driver has been installed, if not, please refer to the document Install OFED Driver to install the driver.
* Env Check:
* Please make sure that the nodes in the cluster have Mellanox NICs with RoCE. In this example, we use Mellanox ConnectX 5 model NICs, and the proper OFED driver has been installed. If not, please refer to the document [Install OFED Driver](https://docs.nvidia.com/networking/display/mlnxofedv461000/installing+mellanox+ofed) to install the driver.
* Check your env:
```shell
$ lspci -nn | grep Eth | grep Mellanox
0000:7f:00.0 Ethernet controller [0200]: Mellanox Technologies MT43244 BlueField-3 integrated ConnectX-7 network controller [15b3:a2dc] (rev 01)
@@ -289,12 +286,16 @@ This should resolve most NCCL-related issues.
0001:a2:00.0 Ethernet controller [0200]: Mellanox Technologies MT43244 BlueField-3 integrated ConnectX-7 network controller [15b3:a2dc] (rev 01)
0001:a2:00.1 Ethernet controller [0200]: Mellanox Technologies MT43244 BlueField-3 integrated ConnectX-7 network controller [15b3:a2dc] (rev 01)
```
* ofed driver
* Check the OFED driver:
```shell
ofed_info -s
ofed_info -s
OFED-internal-23.07-0.5.0:
```
* rdma link show and check ib dev
* Show RDMA link status and check IB devices:
```shell
$ rdma link show
8/1: mlx5_bond_0/1: state ACTIVE physical_state LINK_UP netdev reth0
@@ -308,22 +309,25 @@ This should resolve most NCCL-related issues.
10/1: mlx5_bond_2/1: state ACTIVE physical_state LINK_UP netdev reth4
11/1: mlx5_bond_3/1: state ACTIVE physical_state LINK_UP netdev reth6
```
* test roce network speed in th host
```shell
* Test RoCE network speed on the host:
```shell
yum install qperf
# for server：
excute qperf
execute qperf
# for client
qperf -t 60 -cm1 <server_ip> rc_rdma_write_bw
```
* check rdma accessible in your container...
```shell
# ibv_devices
# ibv_devinfo
```
## Keys to Success
* Check RDMA accessible in your container:
```shell
# ibv_devices
# ibv_devinfo
```
## Keys to success
* In the YAML configuration above, pay attention to the NCCL environment variable. For older versions of NCCL, you should check the NCCL_IB_GID_INDEX environment setting.
* NCCL_SOCKET_IFNAME is also crucial, but in a containerized environment, this typically isn’t an issue.
@@ -334,8 +338,8 @@ This should resolve most NCCL-related issues.
## Remaining issues
* In Kubernetes, Docker, or Containerd environments, we use hostNetwork to prevent performance degradation.
* We utilize privileged mode, which isn’t secure. Additionally, in containerized environments, GPU isolation cannot be fully achieved.
* We utilize privileged mode, which isn’t secure. Additionally, in containerized environments, full GPU isolation cannot be achieved.
## Todo
## TODO
* Integrated with [k8s rdma share plugin](https://github.com/Mellanox/k8s-rdma-shared-dev-plugin).
* Integrated with [k8s-rdma-shared-dev-plugin](https://github.com/Mellanox/k8s-rdma-shared-dev-plugin).