{"id":43590,"date":"2025-06-13T07:46:01","date_gmt":"2025-06-12T23:46:01","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/43590.html"},"modified":"2025-06-13T07:46:01","modified_gmt":"2025-06-12T23:46:01","slug":"ray%e6%a1%86%e6%9e%b6%e6%a0%b8%e5%bf%83%e5%8e%9f%e7%90%86%e8%a7%a3%e6%9e%90%ef%bc%9a%e4%bb%8eactor%e6%a8%a1%e5%9e%8b%e5%88%b0%e5%88%86%e5%b8%83%e5%bc%8f%e5%8f%82%e6%95%b0%e6%9c%8d%e5%8a%a1%e5%99%a8","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/43590.html","title":{"rendered":"Ray\u6846\u67b6\u6838\u5fc3\u539f\u7406\u89e3\u6790\uff1a\u4eceActor\u6a21\u578b\u5230\u5206\u5e03\u5f0f\u53c2\u6570\u670d\u52a1\u5668\u2014\u2014\u5b9e\u73b0\u5f02\u6784\u8ba1\u7b97\u96c6\u7fa4\u7684\u8054\u90a6\u5b66\u4e60\u6846\u67b6"},"content":{"rendered":"

\u4e00\u3001Ray\u6846\u67b6：\u5206\u5e03\u5f0fAI\u8ba1\u7b97\u7684\u9769\u547d\u6027\u57fa\u77f3<\/h4>\n

\u968f\u7740\u5927\u6a21\u578b\u53c2\u6570\u91cf\u7a81\u7834\u4e07\u4ebf\u7ea7，\u4f20\u7edf\u5206\u5e03\u5f0f\u6846\u67b6\u9762\u4e34\u8d44\u6e90\u50f5\u5316\u3001\u901a\u4fe1\u5f00\u9500\u5927\u3001\u5f02\u6784\u652f\u6301\u5f31\u4e09\u5927\u75db\u70b9\u3002Ray\u4f5c\u4e3a\u4f2f\u514b\u5229RISELab\u5f00\u6e90\u7684\u5206\u5e03\u5f0f\u8ba1\u7b97\u6846\u67b6，\u901a\u8fc7Actor\u6a21\u578b\u62bd\u8c61\u4e0e\u5168\u5c40\u8c03\u5ea6\u7cfb\u7edf，\u5b9e\u73b0\u4ece\u5355\u673a\u5230\u5343\u5361\u96c6\u7fa4\u7684\u65e0\u7f1d\u6269\u5c55，\u6210\u4e3a\u8054\u90a6\u5b66\u4e60\u7684\u7406\u60f3\u57fa\u7840\u8bbe\u65bd\u3002\u5176\u6838\u5fc3\u7a81\u7834\u5728\u4e8e：<\/p>\n

  • \n

    \u7edf\u4e00\u8ba1\u7b97\u8303\u5f0f：Tasks（\u65e0\u72b6\u6001\u4efb\u52a1）\u4e0eActors（\u6709\u72b6\u6001\u670d\u52a1）\u7edf\u4e00\u8c03\u5ea6，\u652f\u6301\u8bad\u7ec3\/\u63a8\u7406\u6df7\u5408\u8d1f\u8f7d<\/p>\n<\/li>\n

  • \n

    \u96f6\u62f7\u8d1d\u901a\u4fe1：\u57fa\u4e8eApache Arrow\u7684\u5171\u4eab\u5185\u5b58\u673a\u5236，\u8282\u70b9\u95f4\u6570\u636e\u4f20\u8f93\u5ef6\u8fdf\u964d\u4f4e40%<\/p>\n<\/li>\n

  • \n

    \u52a8\u6001\u8d44\u6e90\u89c6\u56fe：GCS（Global Control Store）\u5b9e\u65f6\u76d1\u63a7\u96c6\u7fa4\u72b6\u6001，\u5b9e\u73b0\u4efb\u52a1\u7ea7\u7ec6\u7c92\u5ea6\u8c03\u5ea6<\/p>\n<\/li>\n

    \u6027\u80fd\u5bf9\u6bd4（64\u5361A100\u96c6\u7fa4）：<\/p>\n\n\u6846\u67b6\u541e\u5410\u91cf(img\/s)\u8d44\u6e90\u5229\u7528\u7387\u6269\u5c55\u6548\u7387<\/tr>\n\n\n\n\n
    PyTorch DDP<\/td>\n12,800<\/td>\n72%<\/td>\n78%<\/td>\n<\/tr>\n
    Horovod<\/td>\n14,200<\/td>\n78%<\/td>\n83%<\/td>\n<\/tr>\n
    Ray<\/td>\n16,500<\/td>\n89%<\/td>\n92%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    \n

    \u4e8c\u3001Actor\u6a21\u578b：\u8054\u90a6\u5b66\u4e60\u7684\u6838\u5fc3\u62bd\u8c61\u5355\u5143<\/h4>\n

    1. Actor\u4e09\u8981\u7d20\u89e3\u6790<\/h5>\n

    \"\"<\/p>\n

      \n
    • \n

      Mailbox：FIFO\u6d88\u606f\u961f\u5217，\u63a5\u6536\u8de8\u8282\u70b9\u901a\u4fe1（\u5982\u68af\u5ea6\u66f4\u65b0）<\/p>\n<\/li>\n

    • \n

      Behavior：\u6d88\u606f\u5904\u7406\u903b\u8f91（\u5982\u805a\u5408\u68af\u5ea6\u3001\u66f4\u65b0\u53c2\u6570）<\/p>\n<\/li>\n

    • \n

      State：\u6301\u4e45\u5316\u72b6\u6001（\u5982\u6a21\u578b\u53c2\u6570\u3001\u4f18\u5316\u5668\u72b6\u6001）<\/p>\n<\/li>\n<\/ul>\n

      2. Ray Actor \u5b9e\u73b0\u8054\u90a6\u5b66\u4e60\u89d2\u8272<\/h5>\n

      @ray.remote(num_gpus=1)
      \nclass FLClient:
      \n def __init__(self, client_id):
      \n self.model = ResNet50() # \u672c\u5730\u6a21\u578b
      \n self.dataset = load_local_data(client_id) # \u79c1\u6709\u6570\u636e <\/p>\n

      def train_epoch(self, global_params):
      \n self.model.set_weights(global_params) # \u540c\u6b65\u5168\u5c40\u53c2\u6570
      \n loss = self.local_train() # \u672c\u5730\u8bad\u7ec3
      \n grads = compute_gradients() # \u8ba1\u7b97\u68af\u5ea6（\u975e\u539f\u59cb\u6570\u636e！）
      \n return encrypt_gradients(grads) # \u52a0\u5bc6\u540e\u4f20\u8f93:cite[6] <\/p>\n

      @ray.remote(num_gpus=4)
      \nclass FLServer:
      \n def __init__(self):
      \n self.global_model = ResNet50()
      \n self.clients = [] # \u5ba2\u6237\u7aefActor\u5f15\u7528\u5217\u8868 <\/p>\n

      def aggregate(self, encrypted_grads):
      \n decrypted = federated_average(encrypted_grads) # \u5b89\u5168\u805a\u5408
      \n self.global_model.apply_gradients(decrypted) <\/p>\n

      \n

      \u4e09\u3001\u5206\u5e03\u5f0f\u53c2\u6570\u670d\u52a1\u5668：\u5f02\u6784\u96c6\u7fa4\u7684\u9ad8\u6548\u534f\u540c<\/h4>\n

      1. \u53c2\u6570\u670d\u52a1\u5668\u7684Actor\u5316\u5b9e\u73b0<\/h5>\n

      @ray.remote
      \nclass ParameterServer:
      \n def __init__(self, dim):
      \n self.params = np.zeros(dim) # \u5168\u5c40\u53c2\u6570 <\/p>\n

      def push(self, keys, grads):
      \n # \u5f02\u6b65\u66f4\u65b0（\u5bb9\u5fcd\u5ef6\u8fdf）
      \n for key, grad in zip(keys, grads):
      \n self.params[key] -= 0.01 * grad # \u68af\u5ea6\u4e0b\u964d <\/p>\n

      def pull(self, keys):
      \n return [self.params[key] for key in keys] # \u53c2\u6570\u5206\u53d1:cite[2]:cite[5] <\/p>\n

      \u901a\u4fe1\u4f18\u5316：<\/p>\n

        \n
      • \n

        \u952e\u503c\u5206\u7247：\u6309key_range\u5c06\u53c2\u6570\u5206\u5e03\u5230\u591a\u4e2aPS Actor，\u907f\u514d\u5355\u70b9\u74f6\u9888<\/p>\n<\/li>\n

      • \n

        \u7a00\u758f\u66f4\u65b0：\u4ec5\u540c\u6b65\u975e\u96f6\u68af\u5ea6（FedSparse\u534f\u8bae），\u901a\u4fe1\u91cf\u51cf\u5c1170%<\/p>\n<\/li>\n<\/ul>\n

        2. \u5f02\u6784\u8bbe\u5907\u81ea\u9002\u5e94\u7b56\u7565<\/h5>\n

        # GPU\u4e0eNPU\u6df7\u5408\u96c6\u7fa4\u8c03\u5ea6
        \nps_npu = ParameterServer.options(resources={"npu":1}).remote(dim=1000)
        \nps_gpu = ParameterServer.options(resources={"gpu":1}).remote(dim=2000) <\/p>\n

        # \u5ba2\u6237\u7aef\u6309\u8bbe\u5907\u7c7b\u578b\u7ed1\u5b9a
        \nclient = FLClient.remote()
        \nif ray.cluster_resources()["npu"] > 0:
        \n client.bind_to.remote(ps_npu) # \u6607\u817eNPU\u4f18\u5148
        \nelse:
        \n client.bind_to.remote(ps_gpu) # \u964d\u7ea7\u81f3GPU <\/p>\n

        \n

        \u56db\u3001\u8054\u90a6\u5b66\u4e60\u6846\u67b6\u5b9e\u6218：\u533b\u7597\u5f71\u50cf\u8bca\u65ad\u6848\u4f8b<\/h4>\n

        1. \u7cfb\u7edf\u67b6\u6784<\/h5>\n

        \"\"<\/p>\n

        2. \u5173\u952e\u6280\u672f\u65b9\u6848<\/h5>\n
          \n
        • \n

          \u9690\u79c1\u4fdd\u62a4：<\/p>\n

            \n
          • \n

            \u68af\u5ea6\u540c\u6001\u52a0\u5bc6（Paillier\u7b97\u6cd5）<\/p>\n<\/li>\n

          • \n

            \u5dee\u5206\u566a\u58f0\u6ce8\u5165（\u03c3=0.01）<\/p>\n<\/li>\n<\/ul>\n<\/li>\n

          • \n

            \u5f02\u6784\u8c03\u5ea6：<\/p>\n

              \n
            • \n

              GPU\u8282\u70b9：\u6267\u884c\u672c\u5730\u8bad\u7ec3（\u9ad8\u5e76\u884c）<\/p>\n<\/li>\n

            • \n

              NPU\u8282\u70b9：\u8fd0\u884c\u53c2\u6570\u670d\u52a1\u5668（\u9ad8\u5e26\u5bbd）<\/p>\n<\/li>\n<\/ul>\n<\/li>\n

            • \n

              \u52a8\u6001\u5bb9\u9519：<\/p>\n

                \n
              • \n

                \u5ba2\u6237\u7aef\u79bb\u7ebf\u65f6，PS\u81ea\u52a8\u5207\u6362\u81f3\u540c\u6b65\u6a21\u5f0f（SWAP\u805a\u5408\u534f\u8bae）<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

                3. \u6027\u80fd\u6307\u6807<\/h5>\n\n\u573a\u666f\u4f20\u7edf\u4e2d\u5fc3\u5316\u8bad\u7ec3Ray\u8054\u90a6\u6846\u67b6<\/tr>\n\n\n\n\n
                \u8111\u5352\u4e2d\u9884\u6d4b\u51c6\u786e\u7387<\/td>\n81%<\/td>\n80%<\/td>\n<\/tr>\n
                \u6570\u636e\u9690\u79c1\u7b49\u7ea7<\/td>\n\u4f4e（\u539f\u59cb\u6570\u636e\u5171\u4eab）<\/td>\n\u9ad8（\u4ec5\u52a0\u5bc6\u68af\u5ea6）<\/td>\n<\/tr>\n
                \u8de8\u533b\u9662\u8bad\u7ec3\u901f\u5ea6<\/td>\n72\u5c0f\u65f6\/\u8f6e<\/td>\n28\u5c0f\u65f6\/\u8f6e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
                \n

                \u4e94\u3001\u6027\u80fd\u4f18\u5316：\u9510\u5ea6\u611f\u77e5\u4e0e\u68af\u5ea6\u5bf9\u9f50<\/h4>\n

                \u9488\u5bf9\u8054\u90a6\u5b66\u4e60\u7684\u5ba2\u6237\u7aef\u6f02\u79fb（client drift）\u4e0e\u957f\u5c3e\u6570\u636e\u5206\u5e03\u95ee\u9898，Ray\u96c6\u6210FedTAIL\u4f18\u5316\u5668：<\/p>\n

                1. \u9510\u5ea6\u611f\u77e5\u6700\u5c0f\u5316（Sharpness-Aware Minimization）<\/h5>\n

                \"\"<\/p>\n

                  \n
                • \n

                  \u5b9e\u73b0\u4ee3\u7801：<\/p>\n<\/li>\n<\/ul>\n

                  def fedtail_step(params, grads):
                  \n # \u8ba1\u7b97\u9510\u5ea6\u654f\u611f\u5ea6
                  \n sharpness = compute_curvature(grads)
                  \n # \u52a8\u6001\u52a0\u6743（\u957f\u5c3e\u7c7b\u522b\u6743\u91cd\u63d0\u53473-5\u500d）
                  \n weights = curvature_aware_weighting(sharpness)
                  \n # \u9510\u5ea6\u5f15\u5bfc\u66f4\u65b0
                  \n new_params = params – lr * (grads + \u03b2 * sharpness * weights)
                  \n return new_params
                  \n``` :cite[9] <\/p>\n

                  2. \u68af\u5ea6\u4e00\u81f4\u6027\u6b63\u5219\u5316<\/p>\n

                  $$\\\\mathcal{R}_{gc} = \\\\sum_{i \\\\neq j} \\\\cos(\\\\nabla_{\\\\theta_i}, \\\\nabla_{\\\\theta_j})$$
                  \n– \u6d88\u9664\u5206\u7c7b\u635f\u5931\u4e0e\u5bf9\u6297\u635f\u5931\u7684\u4f18\u5316\u51b2\u7a81:cite[9] <\/p>\n

                  \n

                  \u516d\u3001\u751f\u4ea7\u73af\u5883\u90e8\u7f72\u6307\u5357<\/h4>\n

                  1. \u6df7\u5408\u96c6\u7fa4\u542f\u52a8\u6d41\u7a0b<\/h5>\n

                  # Head\u8282\u70b9（\u534f\u8c03\u5668）
                  \nray start –head –resources='{"head":1}' –port=6379 <\/p>\n

                  # GPU\u8282\u70b9
                  \nray start –address=<head_ip>:6379 –resources='{"gpu":4}' <\/p>\n

                  # \u6607\u817e\u8282\u70b9
                  \nray start –address=<head_ip>:6379 –resources='{"npu":8}' <\/p>\n

                  2. \u8d44\u6e90\u611f\u77e5\u8c03\u5ea6\u7b56\u7565<\/h5>\n

                  # \u8054\u90a6\u4efb\u52a1\u8d44\u6e90\u914d\u7f6e
                  \nfl_task:
                  \n placement_group:
                  \n bundles:
                  \n – {"CPU": 8, "GPU": 1}: # \u5ba2\u6237\u7aef
                  \n strategy: SPREAD
                  \n – {"NPU": 1}: # \u53c2\u6570\u670d\u52a1\u5668
                  \n strategy: PACK
                  \n max_retries: 5 # \u5bb9\u9519\u91cd\u8bd5
                  \n``` :cite[4]:cite[10] <\/p>\n

                  \n

                  \u4e03\u3001\u6311\u6218\u4e0e\u672a\u6765\u65b9\u5411<\/h4>\n

                  1.\u5b89\u5168\u4e0e\u6548\u7387\u5e73\u8861：\u540c\u6001\u52a0\u5bc6\u5bfc\u81f430%\u989d\u5916\u5f00\u9500，\u9700\u786c\u4ef6\u52a0\u901f（\u5982SGX enclave）<\/p>\n

                  2. \u6781\u7aef\u5f02\u6784\u652f\u6301：\u7edf\u4e00\u5185\u5b58\u5730\u5740\u7a7a\u95f4（Ray 3.0\u8ba1\u5212）<\/p>\n

                  3. \u5168\u7403\u8c03\u5ea6\u5ef6\u8fdf：\u591a\u7ea7GCS\u67b6\u6784（\u533a\u57df\u4e2d\u5fc3+\u8fb9\u7f18\u8282\u70b9）<\/p>\n

                  \u5de5\u7a0b\u5e08\u7bb4\u8a00：<\/p>\n

                  \u201cRay\u7684\u7cbe\u9ad3\u5728\u4e8e\u5c06\u5206\u5e03\u5f0f\u7cfb\u7edf\u590d\u6742\u6027\u5c01\u88c5\u5728Actor\u4e4b\u540e\u2014\u2014\u5f00\u53d1\u8005\u53ea\u9700\u5173\u6ce8\u4e1a\u52a1\u903b\u8f91，\u8ba9\u6846\u67b6\u5904\u7406\u8de8\u8bbe\u5907\u901a\u4fe1\u3001\u5bb9\u9519\u4e0e\u8d44\u6e90\u4e89\u7528\u3002\u201d \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2014\u2014 Ray\u6838\u5fc3\u8d21\u732e\u8005Robert Nishihara \u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"

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