{"id":50410,"date":"2023-08-08T16:53:03","date_gmt":"2024-01-26T13:34:42","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%b0%9d%e8%af%95%e5%9c%a8databricks%e7%9a%84llm%e4%bc%98%e5%8c%96%e6%a8%a1%e5%9e%8b%e6%9c%8d%e5%8a%a1%e4%b8%ad%e9%83%a8%e7%bd%b2llama2%e6%a8%a1%e5%9e%8b%e3%80%82\/"},"modified":"2024-04-30T17:35:43","modified_gmt":"2024-04-30T09:35:43","slug":"%e5%b0%9d%e8%af%95%e5%9c%a8databricks%e7%9a%84llm%e4%bc%98%e5%8c%96%e6%a8%a1%e5%9e%8b%e6%9c%8d%e5%8a%a1%e4%b8%ad%e9%83%a8%e7%bd%b2llama2%e6%a8%a1%e5%9e%8b%e3%80%82","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%b0%9d%e8%af%95%e5%9c%a8databricks%e7%9a%84llm%e4%bc%98%e5%8c%96%e6%a8%a1%e5%9e%8b%e6%9c%8d%e5%8a%a1%e4%b8%ad%e9%83%a8%e7%bd%b2llama2%e6%a8%a1%e5%9e%8b%e3%80%82\/","title":{"rendered":"\u5c1d\u8bd5\u5728Databricks\u7684LLM\u4f18\u5316\u6a21\u578b\u670d\u52a1\u4e2d\u90e8\u7f72Llama2\u6a21\u578b"},"content":{"rendered":"

\u6211\u4f1a\u4e3a\u60a8\u8fdb\u884c\u6d4f\u89c8\u8fd9\u4e2a\u793a\u4f8b\u7b14\u8bb0\u672c\u3002<\/p>\n

 <\/p>\n

Llama2\u670d\u52a1\u7684\u4f18\u5316\u793a\u4f8b<\/h1>\n

\u901a\u8fc7\u6700\u4f18\u5316LLM\u4fdd\u5b58\uff0c\u60a8\u53ef\u4ee5\u5c06Databricks\u6a21\u578b\u670d\u52a1\u90e8\u7f72\u5230\u5177\u6709\u81ea\u52a8\u4f18\u5316\u529f\u80fd\u7684\u6700\u65b0OSS LLM\u548cGPU\uff0c\u4ee5\u63d0\u4f9b\u6539\u8fdb\u7684\u5ef6\u8fdf\u548c\u541e\u5410\u91cf\u3002\u76ee\u524d\uff0c\u6211\u4eec\u652f\u6301Llama2\u548cMosaic MPT\u7c7b\u6a21\u578b\u7684\u4f18\u5316\u3002<\/p>\n

\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u6f14\u793a\u4ee5\u4e0b\u5185\u5bb9:<\/p>\n

    \n
  1. \n
      \u4eceHugging Face transformers\u4e0b\u8f7d\u6a21\u578b<\/ol>\n<\/li>\n<\/ol>\n

       <\/p>\n

        \n
      1. \n
          \u5c06\u6a21\u578b\u8bb0\u5f55\u5728Databricks\u7684Unity Catalog\u6216Workspace Registry\u4e2d\uff0c\u4ee5\u4f18\u5316\u7684serving\u652f\u6301\u683c\u5f0f<\/ol>\n<\/li>\n<\/ol>\n

           <\/p>\n

            \u542f\u7528\u6a21\u578b\u7684\u4f18\u5316serving\u3002<\/ol>\n

            \u524d\u7f6e\u6761\u4ef6<\/h2>\n
              \n
            • \n
                \u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u3092\u5341\u5206\u306a\u30e1\u30e2\u30ea\u30fc\u3092\u6301\u3064\u30af\u30e9\u30b9\u30bf\u30fc\u306b\u30a2\u30bf\u30c3\u30c1<\/ul>\n<\/li>\n<\/ul>\n

                 <\/p>\n

                  \n
                • \n
                    MLflow\u30d0\u30fc\u30b8\u30e7\u30f32.7.0\u4ee5\u964d\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u3053\u3068<\/ul>\n<\/li>\n<\/ul>\n

                     <\/p>\n

                      7B\u4ee5\u4e0a\u306e\u30b5\u30a4\u30ba\u306e\u30e2\u30c7\u30eb\u3092\u53d6\u308a\u6271\u3046\u969b\u306b\u306f\u7279\u306bUC\u3067\u306e\u30e2\u30c7\u30eb\u7ba1\u7406\u3092\u884c\u3046\u3053\u3068<\/ul>\n

                      \u6b65\u9aa41\uff1a\u4e3a\u4e86\u4f18\u5316LLM\u5b58\u50a8\uff0c\u8bb0\u5f55\u6a21\u578b\u3002<\/h1>\n
                      # \u5fc5\u8981\u306a\u4f9d\u5b58\u95a2\u4fc2\u3092\u66f4\u65b0\/\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\r\n<\/span>!<\/span>pip<\/span> install<\/span> -<\/span>U<\/span> mlflow<\/span>\r\n!<\/span>pip<\/span> install<\/span> -<\/span>U<\/span> transformers<\/span>\r\n!<\/span>pip<\/span> install<\/span> -<\/span>U<\/span> accelerate<\/span>\r\n!<\/span>pip<\/span> install<\/span> -<\/span>U<\/span> flash_attn<\/span> # DBR 13.3 ML\u3067\u30a8\u30e9\u30fc\u306b\u306a\u3063\u305f\u306e\u3067\u8ffd\u52a0\r\n<\/span>dbutils<\/span>.<\/span>library<\/span>.<\/span>restartPython<\/span>()<\/span>\r\n<\/code><\/pre>\n
                      \u8f93\u5165 Hugging Face \u7684\u4ee4\u724c\u3002\u66f4\u591a\u8be6\u60c5\u8bf7\u70b9\u51fb\u6b64\u5904\u3002<\/p>\n
                      import<\/span> huggingface_hub<\/span>\r\n# \u65e2\u306bhugging face\u306b\u30ed\u30b0\u30a4\u30f3\u3057\u3066\u3044\u308b\u969b\u306b\u306f\u30b9\u30ad\u30c3\u30d7\r\n<\/span>huggingface_hub<\/span>.<\/span>login<\/span>()<\/span>\r\n<\/code><\/pre>\n
                      from<\/span> transformers<\/span> import<\/span> AutoModelForCausalLM<\/span>,<\/span> AutoTokenizer<\/span>\r\nimport<\/span> torch<\/span>\r\n\r\nmodel<\/span> =<\/span> AutoModelForCausalLM<\/span>.<\/span>from_pretrained<\/span>(<\/span>\r\n    \"<\/span>meta-llama\/Llama-2-7b-chat-hf<\/span>\"<\/span>,<\/span> \r\n    torch_dtype<\/span>=<\/span>torch<\/span>.<\/span>bfloat16<\/span>\r\n)<\/span>\r\ntokenizer<\/span> =<\/span> AutoTokenizer<\/span>.<\/span>from_pretrained<\/span>(<\/span>\r\n    \"<\/span>meta-llama\/Llama-2-7b-chat-hf<\/span>\"<\/span>\r\n)<\/span>\r\n<\/code><\/pre>\n
                      import<\/span> mlflow<\/span>\r\nfrom<\/span> mlflow.models.signature<\/span> import<\/span> ModelSignature<\/span>\r\nfrom<\/span> mlflow.types.schema<\/span> import<\/span> ColSpec<\/span>,<\/span> Schema<\/span>\r\nimport<\/span> numpy<\/span> as<\/span> np<\/span>\r\n\r\n# \u30e2\u30c7\u30eb\u306e\u5165\u51fa\u529b\u30b9\u30ad\u30fc\u30de\u306e\u5b9a\u7fa9\r\n<\/span>input_schema<\/span> =<\/span> Schema<\/span>([<\/span>\r\n    ColSpec<\/span>(<\/span>\"<\/span>string<\/span>\"<\/span>,<\/span> \"<\/span>prompt<\/span>\"<\/span>),<\/span>\r\n    ColSpec<\/span>(<\/span>\"<\/span>double<\/span>\"<\/span>,<\/span> \"<\/span>temperature<\/span>\"<\/span>,<\/span> optional<\/span>=<\/span>True<\/span>),<\/span>\r\n    ColSpec<\/span>(<\/span>\"<\/span>integer<\/span>\"<\/span>,<\/span> \"<\/span>max_tokens<\/span>\"<\/span>,<\/span> optional<\/span>=<\/span>True<\/span>),<\/span>\r\n    ColSpec<\/span>(<\/span>\"<\/span>string<\/span>\"<\/span>,<\/span> \"<\/span>stop<\/span>\"<\/span>,<\/span> optional<\/span>=<\/span>True<\/span>),<\/span>\r\n    ColSpec<\/span>(<\/span>\"<\/span>integer<\/span>\"<\/span>,<\/span> \"<\/span>candidate_count<\/span>\"<\/span>,<\/span> optional<\/span>=<\/span>True<\/span>)<\/span>\r\n])<\/span>\r\n\r\noutput_schema<\/span> =<\/span> Schema<\/span>([<\/span>\r\n    ColSpec<\/span>(<\/span>'<\/span>string<\/span>'<\/span>,<\/span> '<\/span>predictions<\/span>'<\/span>)<\/span>\r\n])<\/span>\r\n\r\nsignature<\/span> =<\/span> ModelSignature<\/span>(<\/span>inputs<\/span>=<\/span>input_schema<\/span>,<\/span> outputs<\/span>=<\/span>output_schema<\/span>)<\/span>\r\n\r\n# \u5165\u529b\u30b5\u30f3\u30d7\u30eb\u306e\u5b9a\u7fa9\r\n<\/span>input_example<\/span> =<\/span> {<\/span>\r\n    \"<\/span>prompt<\/span>\"<\/span>:<\/span> np<\/span>.<\/span>array<\/span>([<\/span>\r\n        \"<\/span>Below is an instruction that describes a task. <\/span>\"<\/span>\r\n        \"<\/span>Write a response that appropriately completes the request.<\/span>\\n\\n<\/span>\"<\/span>\r\n        \"<\/span>### Instruction:<\/span>\\n<\/span>\"<\/span>\r\n        \"<\/span>What is Apache Spark?<\/span>\\n\\n<\/span>\"<\/span>\r\n        \"<\/span>### Response:<\/span>\\n<\/span>\"<\/span>\r\n    ])<\/span>\r\n}<\/span>\r\n<\/code><\/pre>\n
                      \u8981\u542f\u7528\u6700\u4f18\u5316\u670d\u52a1\uff0c\u60a8\u9700\u8981\u5728\u8bb0\u5f55\u6a21\u578b\u65f6\u8c03\u7528mlflow.transformers.log_model\uff0c\u5e76\u5305\u542b\u4ee5\u4e0b\u65b9\u5f0f\u7684\u5b57\u5178\u4f5c\u4e3a\u9644\u52a0\u7684\u5143\u6570\u636e:<\/p>\n
                      metadata = {\"task\": \"llm\/v1\/completions\"}\r\n<\/code><\/pre>\n
                      \u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u6307\u5b9a\u4e86\u7528\u4e8e\u6a21\u578b\u670d\u52a1\u7aef\u70b9\u7684API\u7b7e\u540d\u3002<\/p>\n
                      import<\/span> mlflow<\/span>\r\n\r\n# UC\u3067\u306e\u30e2\u30c7\u30eb\u7ba1\u7406\u3092\u884c\u308f\u306a\u3044\u5834\u5408\u306b\u306f\u4ee5\u4e0b\u306e\u884c\u3092\u30b3\u30e1\u30f3\u30c8\u30a2\u30a6\u30c8\u3057\u307e\u3059\r\n# 3\u30ec\u30d9\u30eb\u306e\u540d\u524d\u7a7a\u9593\u3067\u306f\u306a\u304f\u3001\u30b7\u30f3\u30d7\u30eb\u306b\u30e2\u30c7\u30eb\u540d\u3092\u6307\u5b9a\u3057\u307e\u3059\r\n<\/span>mlflow<\/span>.<\/span>set_registry_uri<\/span>(<\/span>'<\/span>databricks-uc<\/span>'<\/span>)<\/span>\r\nCATALOG<\/span> =<\/span> \"<\/span>quickstart_catalog_taka<\/span>\"<\/span>\r\nSCHEMA<\/span> =<\/span> \"<\/span>llm-catalog<\/span>\"<\/span>\r\nregistered_model_name<\/span> =<\/span> f<\/span>\"<\/span>{<\/span>CATALOG<\/span>}<\/span>.<\/span>{<\/span>SCHEMA<\/span>}<\/span>.llama2-7b<\/span>\"<\/span>\r\n\r\n# \u65b0\u898fMLflow\u30e9\u30f3\u3092\u30b9\u30bf\u30fc\u30c8\r\n<\/span>with<\/span> mlflow<\/span>.<\/span>start_run<\/span>():<\/span>\r\n    components<\/span> =<\/span> {<\/span>\r\n        \"<\/span>model<\/span>\"<\/span>:<\/span> model<\/span>,<\/span>\r\n        \"<\/span>tokenizer<\/span>\"<\/span>:<\/span> tokenizer<\/span>,<\/span>\r\n    }<\/span>\r\n    mlflow<\/span>.<\/span>transformers<\/span>.<\/span>log_model<\/span>(<\/span>\r\n        transformers_model<\/span>=<\/span>components<\/span>,<\/span>\r\n        task<\/span> =<\/span> \"<\/span>text-generation<\/span>\"<\/span>,<\/span>\r\n        artifact_path<\/span>=<\/span>\"<\/span>model<\/span>\"<\/span>,<\/span>\r\n        registered_model_name<\/span>=<\/span>registered_model_name<\/span>,<\/span>\r\n        signature<\/span>=<\/span>signature<\/span>,<\/span>\r\n        input_example<\/span>=<\/span>input_example<\/span>,<\/span>\r\n        metadata<\/span>=<\/span>{<\/span>\"<\/span>task<\/span>\"<\/span>:<\/span> \"<\/span>llm\/v1\/completions<\/span>\"<\/span>}<\/span>\r\n    )<\/span>\r\n<\/code><\/pre>\n
                      \"Screenshot<\/div>\n
                      \"Screenshot<\/div>\n
                      \u6b65\u9aa42\uff1a\u8bbe\u7f6e\u548c\u521b\u5efa\u6a21\u578b\u4fdd\u5b58\u7684GPU\u7aef\u70b9\u3002<\/h1>\n
                      \u8981\u66f4\u6539\u7ec8\u7aef\u8282\u70b9\u540d\uff0c\u8bf7\u4fee\u6539\u4ee5\u4e0b\u5355\u5143\u683c\u3002\u8c03\u7528\u7ec8\u7aef\u8282\u70b9API\u4e4b\u540e\uff0c\u8bb0\u5f55\u7684Llama2\u6a21\u578b\u5c06\u81ea\u52a8\u90e8\u7f72\u5230\u4f18\u5316\u7684LLM\u670d\u52a1\u4e2d\u3002workload_type\u9700\u8981\u6307\u5b9a\u73af\u5883\u4e2d\u53ef\u7528\u89c4\u683c\u7684GPU\u3002<\/p>\n
                      # MLflow\u30a8\u30f3\u30c9\u30dd\u30a4\u30f3\u30c8\u540d\u306e\u8a2d\u5b9a\r\n<\/span>endpoint_name<\/span> =<\/span> \"<\/span>taka-llama2-7b<\/span>\"<\/span>\r\n\r\n# \u767b\u9332\u3055\u308c\u305fMLflow\u30e2\u30c7\u30eb\u540d\r\n<\/span>model_name<\/span> =<\/span> registered_model_name<\/span>\r\n\r\n# MLflow\u30e2\u30c7\u30eb\u306e\u6700\u65b0\u30d0\u30fc\u30b8\u30e7\u30f3\u306e\u53d6\u5f97\r\n<\/span>model_version<\/span> =<\/span> 1<\/span>\r\n\r\n# \u8a08\u7b97\u8cc7\u6e90\u30bf\u30a4\u30d7\u306e\u6307\u5b9a (CPU, GPU_SMALL, GPU_MEDIUM, etc.)\r\n<\/span>workload_type<\/span> =<\/span> \"<\/span>GPU_MEDIUM<\/span>\"<\/span>\r\n\r\n# \u8a08\u7b97\u8cc7\u6e90\u306e\u30b9\u30b1\u30fc\u30eb\u30a2\u30a6\u30c8\u30b5\u30a4\u30ba\u306e\u6307\u5b9a (Small, Medium, Large, etc.)\r\n<\/span>workload_size<\/span> =<\/span> \"<\/span>Small<\/span>\"<\/span> \r\n\r\n# \u30bc\u30ed\u3078\u306e\u30b9\u30b1\u30fc\u30eb\u306e\u6307\u5b9a (CPU\u30a8\u30f3\u30c9\u30dd\u30a4\u30f3\u30c8\u3067\u306e\u307f\u30b5\u30dd\u30fc\u30c8)\r\n<\/span>scale_to_zero<\/span> =<\/span> False<\/span> \r\n\r\n# \u73fe\u5728\u306e\u30ce\u30fc\u30c8\u30d6\u30c3\u30af\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u304b\u3089API\u30a8\u30f3\u30c9\u30dd\u30a4\u30f3\u30c8\u3068\u30c8\u30fc\u30af\u30f3\u3092\u53d6\u5f97\r\n<\/span>API_ROOT<\/span> =<\/span> dbutils<\/span>.<\/span>notebook<\/span>.<\/span>entry_point<\/span>.<\/span>getDbutils<\/span>().<\/span>notebook<\/span>().<\/span>getContext<\/span>().<\/span>apiUrl<\/span>().<\/span>get<\/span>()<\/span> \r\nAPI_TOKEN<\/span> =<\/span> dbutils<\/span>.<\/span>notebook<\/span>.<\/span>entry_point<\/span>.<\/span>getDbutils<\/span>().<\/span>notebook<\/span>().<\/span>getContext<\/span>().<\/span>apiToken<\/span>().<\/span>get<\/span>()<\/span>\r\n<\/code><\/pre>\n
                      import<\/span> requests<\/span>\r\nimport<\/span> json<\/span>\r\n\r\ndata<\/span> =<\/span> {<\/span>\r\n    \"<\/span>name<\/span>\"<\/span>:<\/span> endpoint_name<\/span>,<\/span>\r\n    \"<\/span>config<\/span>\"<\/span>:<\/span> {<\/span>\r\n        \"<\/span>served_models<\/span>\"<\/span>:<\/span> [<\/span>\r\n            {<\/span>\r\n                \"<\/span>model_name<\/span>\"<\/span>:<\/span> model_name<\/span>,<\/span>\r\n                \"<\/span>model_version<\/span>\"<\/span>:<\/span> model_version<\/span>,<\/span>\r\n                \"<\/span>workload_size<\/span>\"<\/span>:<\/span> workload_size<\/span>,<\/span>\r\n                \"<\/span>scale_to_zero_enabled<\/span>\"<\/span>:<\/span> scale_to_zero<\/span>,<\/span>\r\n                \"<\/span>workload_type<\/span>\"<\/span>:<\/span> workload_type<\/span>,<\/span>\r\n            }<\/span>\r\n        ]<\/span>\r\n    },<\/span>\r\n}<\/span>\r\n\r\nheaders<\/span> =<\/span> {<\/span>\"<\/span>Context-Type<\/span>\"<\/span>:<\/span> \"<\/span>text\/json<\/span>\"<\/span>,<\/span> \"<\/span>Authorization<\/span>\"<\/span>:<\/span> f<\/span>\"<\/span>Bearer <\/span>{<\/span>API_TOKEN<\/span>}<\/span>\"<\/span>}<\/span>\r\n\r\nresponse<\/span> =<\/span> requests<\/span>.<\/span>post<\/span>(<\/span>url<\/span>=<\/span>f<\/span>\"<\/span>{<\/span>API_ROOT<\/span>}<\/span>\/api\/2.0\/serving-endpoints<\/span>\"<\/span>,<\/span> json<\/span>=<\/span>data<\/span>,<\/span> headers<\/span>=<\/span>headers<\/span>)<\/span>\r\n\r\nprint<\/span>(<\/span>json<\/span>.<\/span>dumps<\/span>(<\/span>response<\/span>.<\/span>json<\/span>(),<\/span> indent<\/span>=<\/span>4<\/span>))<\/span>\r\n<\/code><\/pre>\n
                      \u53c2\u8003\u7aef\u70b9<\/h1>\n
                      \"Screenshot<\/div>\n
                      \u6b65\u9aa43\uff1a\u67e5\u8be2\u7ec8\u8282\u70b9<\/h1>\n
                      \u5f53\u51c6\u5907\u597d\u7ec8\u7aef\u70b9\u540e\uff0c\u901a\u8fc7\u8fdb\u884cAPI\u8bf7\u6c42\u53ef\u4ee5\u8fdb\u884c\u67e5\u8be2\u3002\u8fd9\u53d6\u51b3\u4e8e\u6a21\u578b\u7684\u5927\u5c0f\u548c\u590d\u6742\u6027\uff0c\u4f46\u51c6\u5907\u65f6\u95f4\u9700\u898130\u5206\u949f\u4ee5\u4e0a\u3002<\/p>\n
                      GPU Medium\u7684\u60c5\u51b5\u4e0b\uff0c\u5927\u7ea630\u5206\u949f\u5c31\u4f1a\u51c6\u5907\u597d\u3002<\/div>\n
                      data<\/span> =<\/span> {<\/span>\r\n    \"<\/span>inputs<\/span>\"<\/span>:<\/span> {<\/span>\r\n        \"<\/span>prompt<\/span>\"<\/span>:<\/span> [<\/span>\r\n            \"<\/span>Below is an instruction that describes a task. Write a response that appropriately completes the request.<\/span>\\n\\n<\/span>### Instruction:<\/span>\\n<\/span>What is Apache Spark?<\/span>\\n\\n<\/span>### Response:<\/span>\\n<\/span>\"<\/span>\r\n        ]<\/span>\r\n    },<\/span>\r\n    \"<\/span>params<\/span>\"<\/span>:<\/span> {<\/span>\r\n        \"<\/span>max_tokens<\/span>\"<\/span>:<\/span> 100<\/span>,<\/span> \r\n        \"<\/span>temperature<\/span>\"<\/span>:<\/span> 0.0<\/span>\r\n    }<\/span>\r\n}<\/span>\r\n\r\nheaders<\/span> =<\/span> {<\/span>\r\n    \"<\/span>Context-Type<\/span>\"<\/span>:<\/span> \"<\/span>text\/json<\/span>\"<\/span>,<\/span>\r\n    \"<\/span>Authorization<\/span>\"<\/span>:<\/span> f<\/span>\"<\/span>Bearer <\/span>{<\/span>API_TOKEN<\/span>}<\/span>\"<\/span>\r\n}<\/span>\r\n\r\nresponse<\/span> =<\/span> requests<\/span>.<\/span>post<\/span>(<\/span>\r\n    url<\/span>=<\/span>f<\/span>\"<\/span>{<\/span>API_ROOT<\/span>}<\/span>\/serving-endpoints\/<\/span>{<\/span>endpoint_name<\/span>}<\/span>\/invocations<\/span>\"<\/span>,<\/span>\r\n    json<\/span>=<\/span>data<\/span>,<\/span>\r\n    headers<\/span>=<\/span>headers<\/span>\r\n)<\/span>\r\n\r\nprint<\/span>(<\/span>json<\/span>.<\/span>dumps<\/span>(<\/span>response<\/span>.<\/span>json<\/span>(),<\/span> indent<\/span>=<\/span>4<\/span>))<\/span>\r\n<\/code><\/pre>\n
                      \u67e5\u8be2\u6210\u529f\uff01<\/p>\n
                      {<\/span>\r\n    \"predictions\"<\/span>:<\/span> [<\/span>\r\n        {<\/span>\r\n            \"candidates\"<\/span>:<\/span> [<\/span>\r\n                {<\/span>\r\n                    \"text\"<\/span>:<\/span> \"Apache Spark is an open-source data processing engine that can handle large-scale data processing tasks. It was developed at the University of California, Berkeley and is now maintained by Apache Software Foundation. Spark provides high-level APIs in Java, Python, Scala, and R, and supports a wide range of data sources, including Hadoop Distributed File System (HDFS), Cassandra, HBase, and Hive. Spark's core features include in-memory computing, fault\"<\/span>,<\/span>\r\n                    \"metadata\"<\/span>:<\/span> {<\/span>\r\n                        \"finish_reason\"<\/span>:<\/span> \"length\"<\/span>\r\n                    }<\/span>\r\n                }<\/span>\r\n            ],<\/span>\r\n            \"metadata\"<\/span>:<\/span> {<\/span>\r\n                \"input_tokens\"<\/span>:<\/span> 41<\/span>,<\/span>\r\n                \"output_tokens\"<\/span>:<\/span> 100<\/span>,<\/span>\r\n                \"total_tokens\"<\/span>:<\/span> 141<\/span>\r\n            }<\/span>\r\n        }<\/span>\r\n    ]<\/span>\r\n}<\/span>\r\n<\/code><\/pre>\n
                      Databricks\u5feb\u901f\u5165\u95e8\u6307\u5357<\/h3>\n
                      Databricks\u5feb\u901f\u5165\u95e8\u6307\u5357<\/p>\n
                      \u6570\u636e\u8109\u51b2\u514d\u8d39\u8bd5\u7528<\/h3>\n
                      Databricks\u514d\u8d39\u8bd5\u7528<\/p>\n","protected":false},"excerpt":{"rendered":"
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