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#: ../../source/tutorials/multi_node.md:1
msgid "Multi-Node-DP (DeepSeek)"
msgstr "多节点分布式处理（DeepSeek）"

#: ../../source/tutorials/multi_node.md:3
msgid "Getting Start"
msgstr "快速开始"

#~ msgid ""
#~ "vLLM-Kunlun now supports Data Parallel"
#~ " (DP) deployment, enabling model weights"
#~ " to be replicated across multiple "
#~ "XPUs or instances, each processing "
#~ "independent batches of requests. This is"
#~ " particularly useful for scaling throughput"
#~ " across devices while maintaining high "
#~ "resource utilization."
#~ msgstr ""
#~ "vLLM-Kunlun 现在支持数据并行（DP）部署，可以在多个 XPU "
#~ "或实例之间复制模型权重，每个实例处理独立的请求批次。这对于在保证高资源利用率的同时，实现跨设备的吞吐量扩展特别有用。"

#~ msgid ""
#~ "Each DP rank is deployed as a "
#~ "separate “core engine” process which "
#~ "communicates with front-end process(es) "
#~ "via ZMQ sockets. Data Parallel can "
#~ "be combined with Tensor Parallel, in "
#~ "which case each DP engine owns a"
#~ " number of per-XPU worker processes"
#~ " equal to the TP size."
#~ msgstr ""
#~ "每个 DP 进程作为一个单独的“核心引擎”进程部署，并通过 ZMQ "
#~ "套接字与前端进程通信。数据并行可以与张量并行结合使用，此时每个 DP 引擎拥有数量等于 TP "
#~ "大小的每 XPU 工作进程。"

#~ msgid ""
#~ "For Mixture-of-Experts (MoE) models "
#~ "— especially advanced architectures like "
#~ "DeepSeek that utilize Multi-head Latent"
#~ " Attention (MLA) — a hybrid "
#~ "parallelism approach is recommended:     - "
#~ "Use **Data Parallelism (DP)** for "
#~ "attention layers, which are replicated "
#~ "across devices and handle separate "
#~ "batches.     - Use **Expert or Tensor"
#~ " Parallelism (EP/TP)** for expert layers,"
#~ " which are sharded across devices to"
#~ " distribute the computation."
#~ msgstr ""
#~ "对于混合专家（Mixture-of-Experts, MoE）模型——尤其是像 "
#~ "DeepSeek 这样采用多头潜在注意力（Multi-head Latent "
#~ "Attention, MLA）的高级架构——推荐使用混合并行策略：\n"
#~ "    - 对于注意力层，使用 **数据并行（Data Parallelism, DP）**，这些层会在各设备间复刻，并处理不同的批次。\n"
#~ "    - 对于专家层，使用 **专家并行或张量并行（Expert or "
#~ "Tensor Parallelism, EP/TP）**，这些层会在设备间分片，从而分担计算。"

#~ msgid ""
#~ "This division enables attention layers "
#~ "to be replicated across Data Parallel"
#~ " (DP) ranks, enabling them to process"
#~ " different batches independently. Meanwhile, "
#~ "expert layers are partitioned (sharded) "
#~ "across devices using Expert or Tensor"
#~ " Parallelism(DP*TP), maximizing hardware "
#~ "utilization and efficiency."
#~ msgstr "这种划分使得注意力层能够在数据并行（DP）组内复制，从而能够独立处理不同的批次。同时，专家层通过专家或张量并行（DP*TP）在设备间进行分区（切片），最大化硬件利用率和效率。"

#~ msgid ""
#~ "In these cases the data parallel "
#~ "ranks are not completely independent, "
#~ "forward passes must be aligned and "
#~ "expert layers across all ranks are "
#~ "required to synchronize during every "
#~ "forward pass, even if there are "
#~ "fewer requests to be processed than "
#~ "DP ranks."
#~ msgstr ""
#~ "在这些情况下，数据并行的各个 rank 不是完全独立的，前向传播必须对齐，并且所有 rank "
#~ "上的专家层在每次前向传播时都需要同步，即使待处理的请求数量少于 DP rank 的数量。"

#~ msgid ""
#~ "For MoE models, when any requests "
#~ "are in progress in any rank, we"
#~ " must ensure that empty “dummy” "
#~ "forward passes are performed in all "
#~ "ranks which don’t currently have any "
#~ "requests scheduled. This is handled via"
#~ " a separate DP `Coordinator` process "
#~ "which communicates with all of the "
#~ "ranks, and a collective operation "
#~ "performed every N steps to determine "
#~ "when all ranks become idle and can"
#~ " be paused. When TP is used in"
#~ " conjunction with DP, expert layers "
#~ "form an EP or TP group of "
#~ "size (DP x TP)."
#~ msgstr ""
#~ "对于 MoE 模型，当任何一个 rank 有请求正在进行时，必须确保所有当前没有请求的"
#~ " rank 都执行空的“虚拟”前向传播。这是通过一个单独的 DP `Coordinator`"
#~ " 协调器进程来实现的，该进程与所有 rank 通信，并且每隔 N "
#~ "步执行一次集体操作，以判断所有 rank 是否都处于空闲状态并可以暂停。当 TP 与 "
#~ "DP 结合使用时，专家层会组成一个规模为（DP x TP）的 EP 或 "
#~ "TP 组。"

#~ msgid "Verify Multi-Node Communication Environment"
#~ msgstr "验证多节点通信环境"

#~ msgid "Physical Layer Requirements:"
#~ msgstr "物理层要求："

#~ msgid ""
#~ "The physical machines must be located"
#~ " on the same WLAN, with network "
#~ "connectivity."
#~ msgstr "物理机器必须位于同一个 WLAN 中，并且具有网络连接。"

#~ msgid ""
#~ "All XPUs are connected with optical "
#~ "modules, and the connection status must"
#~ " be normal."
#~ msgstr "所有 XPU 都通过光模块连接，且连接状态必须正常。"

#~ msgid "Verification Process:"
#~ msgstr "验证流程："

#~ msgid ""
#~ "Execute the following commands on each"
#~ " node in sequence. The results must"
#~ " all be `success` and the status "
#~ "must be `UP`:"
#~ msgstr "在每个节点上依次执行以下命令。所有结果必须为 `success` 且状态必须为 `UP`："

#~ msgid "XPU Interconnect Verification:"
#~ msgstr "XPU 互连验证："

#~ msgid "1. Get XPU IP Addresses"
#~ msgstr "1. 获取 XPU IP 地址"

#~ msgid "2. Cross-Node PING Test"
#~ msgstr "2. 跨节点PING测试"

#~ msgid "Run with docker"
#~ msgstr "用 docker 运行"

#~ msgid ""
#~ "Assume you have two Atlas 800 "
#~ "A2(64G*8) nodes, and want to deploy "
#~ "the `deepseek-v3-w8a8` quantitative model "
#~ "across multi-node."
#~ msgstr "假设你有两台 Atlas 800 A2（64G*8）节点，并且想要在多节点上部署 `deepseek-v3-w8a8` 量化模型。"

#~ msgid ""
#~ "Before launch the inference server, "
#~ "ensure some environment variables are "
#~ "set for multi node communication"
#~ msgstr "在启动推理服务器之前，确保已经为多节点通信设置了一些环境变量。"

#~ msgid "Run the following scripts on two nodes respectively"
#~ msgstr "分别在两台节点上运行以下脚本"

#~ msgid "**node0**"
#~ msgstr "**节点0**"

#~ msgid "**node1**"
#~ msgstr "**节点1**"

#~ msgid ""
#~ "The Deployment view looks like:  ![alt"
#~ " text](../assets/multi_node_dp.png)"
#~ msgstr "部署视图如下所示：![替代文本](../assets/multi_node_dp.png)"

#~ msgid "alt text"
#~ msgstr "替代文本"

#~ msgid ""
#~ "Once your server is started, you "
#~ "can query the model with input "
#~ "prompts:"
#~ msgstr "一旦你的服务器启动，你可以通过输入提示词来查询模型："

#~ msgid "Run benchmarks"
#~ msgstr "运行基准测试"

#~ msgid ""
#~ "For details please refer to "
#~ "[benchmark](https://github.com/vllm-project/vllm-"
#~ "kunlun/tree/main/benchmarks)"
#~ msgstr ""
#~ "详细信息请参阅 [benchmark](https://github.com/vllm-project"
#~ "/vllm-kunlun/tree/main/benchmarks)"

