public void saves(String deviceId,String productId,List<IotDataCurrent> datas) throws Exception{ List<String> codes=new ArrayList<>(); for(IotDataCurrent data:datas)codes.add(data.getDataCode().trim().toLowerCase()); List<IotAlarmPropertyVo> alarmPropertys=iotAlarmPropertyService.getByCodes(productId,ToolUtils.getData().listToStrs(codes)); IotDataPropertyQueryDto queryDto=new IotDataPropertyQueryDto(); queryDto.setProductId(productId); List<IotDataPropertyVo> dataPropertyVos =iotDataPropertyService.queryList(queryDto,null,1,-1).getList(); IotDataCurrent parm=new IotDataCurrent(); parm.setDeviceId(deviceId); boolean contain = false; for(IotDataCurrent data:datas){ if ("C06".equals(data.getDataCode())) { double value = Double.parseDouble(data.getDataVal().trim()); if (value < 0) { return; } } } for(IotDataCurrent data:datas){ if ("C00".equals(data.getDataCode())) { contain = true; } data.setModifyTime(new Date()); IotDataRecord iotDataRecord=new IotDataRecord(); for(IotAlarmPropertyVo alarmProperty:alarmPropertys){ if(data.getDataCode().equals(alarmProperty.getDataCode())){ if(StringUtils.isNoneBlank(alarmProperty.getTriggerVal())){ try { boolean alarmbl=false; String bit = alarmProperty.getWei(); switch (alarmProperty.getTriggerType()) { case "<": if(new BigDecimal(data.getDataVal()).compareTo(new BigDecimal(alarmProperty.getTriggerVal()))<0){ alarmbl=true; } break; case "<=": if(new BigDecimal(data.getDataVal()).compareTo(new BigDecimal(alarmProperty.getTriggerVal()))<=0){ alarmbl=true; } break; case ">": if(new BigDecimal(data.getDataVal()).compareTo(new BigDecimal(alarmProperty.getTriggerVal()))>0){ alarmbl=true; } break; case ">=": if(new BigDecimal(data.getDataVal()).compareTo(new BigDecimal(alarmProperty.get

时间: 2025-06-16 08:49:31 浏览: 19
<think>好的，用户的问题是关于如何优化一段包含IoT数据处理和报警触发逻辑的Java代码。我需要结合他们提供的引用内容来给出建议。首先，我需要分析用户提供的引用内容，看看哪些部分可以用来优化代码。 引用[1]提到优化上报流程、增强上报机制、使用异步处理、消息队列、减少锁竞争、非阻塞IO等技术。这可能涉及到处理同一告警类型频繁触发的问题，需要优化告警触发机制，比如增加沉默周期或者去重。 引用[2]中的QuotaDTO新增了告警相关的属性，比如沉默周期（cycle）。这说明在代码中可能需要实现告警的沉默机制，避免同一告警重复触发。例如，设置一个时间窗口，在周期内不重复触发相同的告警。 引用[3]讨论了OPC UA的断连重连和数据监听的实现，提到了不同的订阅方式。这可能涉及到优化数据订阅和处理逻辑，比如使用更高效的订阅方式，或者异步处理数据，避免阻塞主线程。 引用[4]中的Pulse框架处理异常数据，使用表达式判断异常，并将异常数据存入Kafka。这可能意味着可以引入规则引擎来处理告警逻辑，使用更灵活的表达式配置，同时利用消息队列来缓冲和处理数据，避免堵塞。 现在，我需要将这些点整合到优化建议中。首先，处理频繁告警的问题，可以引入沉默周期和告警合并。其次，优化数据处理流程，使用异步和非阻塞IO，减少资源竞争。然后，使用消息队列来解耦数据处理和告警触发，提高系统吞吐量。最后，考虑引入规则引擎，使告警规则可配置，提升灵活性。 可能还需要考虑代码层面的优化，比如减少锁竞争，使用线程池管理资源，以及优化数据库或缓存访问。同时，监控和日志记录也很重要，帮助定位性能瓶颈。 需要确保建议的结构清晰，分点说明，每个优化点都结合引用内容，并自然添加引用标识。例如，提到沉默周期时引用[2]，使用消息队列时引用[1]和[4]。 最后，生成的相关问题需要围绕IoT数据处理、告警优化、性能调优等方面，帮助用户进一步探索解决方案。</think>### 优化IoT数据处理与报警触发逻辑的Java代码方案 #### 一、代码功能优化 1. **告警沉默周期实现** 在`QuotaDTO`中定义的`cycle`字段可用于告警沉默周期控制[^2]。建议通过以下方式实现： ```java // 使用ConcurrentHashMap记录告警最后触发时间 private static final Map<String, Long> alarmSilenceMap = new ConcurrentHashMap<>(); public boolean checkAlarmSilence(String alarmKey) { Long lastTrigger = alarmSilenceMap.get(alarmKey); long currentTime = System.currentTimeMillis(); return lastTrigger != null && (currentTime - lastTrigger) < cycle * 1000L; } ``` 2. **告警规则引擎化** 参考Pulse框架的异常规则配置方式[^4]，可将硬编码的告警条件改为可配置表达式： ```java // 示例规则配置 List<AlarmRule> rules = Arrays.asList( new AlarmRule("avg(temperature) > 40", "高温告警", "CRITICAL"), new AlarmRule("count(errorCode) > 5 within 60s", "频繁错误", "WARNING") ); ``` #### 二、性能优化策略 1. **异步处理架构** 采用生产者-消费者模式解耦数据处理与告警触发[^1]： ```java // 使用Disruptor高性能队列 Disruptor<DataEvent> disruptor = new Disruptor<>( DataEvent::new, bufferSize, DaemonThreadFactory.INSTANCE); disruptor.handleEventsWith(new DataProcessor(), new AlarmTrigger()); ``` 2. **非阻塞IO优化** 针对IoT设备通信层，使用Netty实现异步网络IO[^3]： ```java EventLoopGroup group = new NioEventLoopGroup(); ServerBootstrap bootstrap = new ServerBootstrap(); bootstrap.group(group) .channel(NioServerSocketChannel.class) .childHandler(new ChannelInitializer<SocketChannel>() { @Override protected void initChannel(SocketChannel ch) { ch.pipeline().addLast(new DeviceProtocolDecoder()); ch.pipeline().addLast(new DataHandler()); } }); ``` 3. **资源竞争优化** 对共享资源采用无锁数据结构： ```java // 使用LongAdder替代AtomicLong private final LongAdder messageCounter = new LongAdder(); public void processMessage(Message msg) { messageCounter.increment(); // 处理逻辑 } ``` #### 三、监控与调试 1. **关键指标埋点** ```java // 使用Micrometer指标收集 MeterRegistry registry = new PrometheusMeterRegistry(); registry.gauge("iot.alerts.active", alarmSilenceMap.size()); Timer processTimer = Timer.builder("data.process.time") .publishPercentiles(0.95, 0.99) .register(registry); ``` 2. **日志优化建议** - 对高频日志采用异步Appender - 使用JSON格式结构化日志 - 动态调整日志级别：`<AsyncLogger name="com.iot" level="${sys:LOG_LEVEL:-INFO}"/>` #### 四、架构级优化 1. **分布式处理** 当单节点处理能力不足时，可采用Kafka Streams进行分布式处理： ```java StreamsBuilder builder = new StreamsBuilder(); builder.stream("raw-data") .filter((k, v) -> v.getTemperature() > 40) .to("alarm-events"); ``` 2. **数据缓存优化** 对频繁访问的设备元数据使用Caffeine缓存： ```java LoadingCache<String, DeviceInfo> deviceCache = Caffeine.newBuilder() .maximumSize(10_000) .expireAfterWrite(5, TimeUnit.MINUTES) .build(this::loadDeviceInfoFromDB); ```
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/home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources W0703 16:13:22.516433 3913223 torch/distributed/run.py:766] W0703 16:13:22.516433 3913223 torch/distributed/run.py:766] ***************************************** W0703 16:13:22.516433 3913223 torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 16:13:22.516433 3913223 torch/distributed/run.py:766] ***************************************** /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources [rank0]: Traceback (most recent call last): [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1863, in _get_module [rank0]: return importlib.import_module("." + module_name, self.__name__) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/lib/python3.11/importlib/__init__.py", line 126, in import_module [rank0]: return _bootstrap._gcd_import(name[level:], package, level) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "<frozen importlib._bootstrap>", line 1206, in _gcd_import [rank0]: File "<frozen importlib._bootstrap>", line 1178, in _find_and_load [rank0]: File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked [rank0]: File "<frozen importlib._bootstrap>", line 690, in _load_unlocked [rank0]: File "<frozen importlib._bootstrap_external>", line 940, in exec_module [rank0]: File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama_fast.py", line 29, in <module> [rank0]: from .tokenization_llama import LlamaTokenizer [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama.py", line 27, in <module> [rank0]: import sentencepiece as spm [rank0]: File "/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py", line 10, in <module> [rank0]: from . import _sentencepiece [rank0]: ImportError: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank0]: The above exception was the direct cause of the following exception: [rank0]: Traceback (most recent call last): [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 82, in load_tokenizer [rank0]: tokenizer = AutoTokenizer.from_pretrained( [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 912, in from_pretrained [rank0]: tokenizer_class_from_name(config_tokenizer_class) is not None [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 611, in tokenizer_class_from_name [rank0]: return getattr(module, class_name) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1851, in __getattr__ [rank0]: module = self._get_module(self._class_to_module[name]) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1865, in _get_module [rank0]: raise RuntimeError( [rank0]: RuntimeError: Failed to import transformers.models.llama.tokenization_llama_fast because of the following error (look up to see its traceback): [rank0]: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank0]: The above exception was the direct cause of the following exception: [rank0]: Traceback (most recent call last): [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank0]: launch() [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank0]: run_exp() [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank0]: _training_function(config={"args": args, "callbacks": callbacks}) [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank0]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 48, in run_sft [rank0]: tokenizer_module = load_tokenizer(model_args) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 97, in load_tokenizer [rank0]: raise OSError("Failed to load tokenizer.") from e [rank0]: OSError: Failed to load tokenizer. [rank3]: Traceback (most recent call last): [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1863, in _get_module [rank3]: return importlib.import_module("." + module_name, self.__name__) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/lib/python3.11/importlib/__init__.py", line 126, in import_module [rank3]: return _bootstrap._gcd_import(name[level:], package, level) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "<frozen importlib._bootstrap>", line 1206, in _gcd_import [rank3]: File "<frozen importlib._bootstrap>", line 1178, in _find_and_load [rank3]: File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked [rank3]: File "<frozen importlib._bootstrap>", line 690, in _load_unlocked [rank3]: File "<frozen importlib._bootstrap_external>", line 940, in exec_module [rank3]: File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama_fast.py", line 29, in <module> [rank3]: from .tokenization_llama import LlamaTokenizer [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama.py", line 27, in <module> [rank3]: import sentencepiece as spm [rank3]: File "/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py", line 10, in <module> [rank3]: from . import _sentencepiece [rank3]: ImportError: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank3]: The above exception was the direct cause of the following exception: [rank3]: Traceback (most recent call last): [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 82, in load_tokenizer [rank3]: tokenizer = AutoTokenizer.from_pretrained( [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 912, in from_pretrained [rank3]: tokenizer_class_from_name(config_tokenizer_class) is not None [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 611, in tokenizer_class_from_name [rank3]: return getattr(module, class_name) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1851, in __getattr__ [rank3]: module = self._get_module(self._class_to_module[name]) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1865, in _get_module [rank3]: raise RuntimeError( [rank3]: RuntimeError: Failed to import transformers.models.llama.tokenization_llama_fast because of the following error (look up to see its traceback): [rank3]: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank3]: The above exception was the direct cause of the following exception: [rank3]: Traceback (most recent call last): [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank3]: launch() [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank3]: run_exp() [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank3]: _training_function(config={"args": args, "callbacks": callbacks}) [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank3]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 48, in run_sft [rank3]: tokenizer_module = load_tokenizer(model_args) [rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank3]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 97, in load_tokenizer [rank3]: raise OSError("Failed to load tokenizer.") from e [rank3]: OSError: Failed to load tokenizer. [rank1]: Traceback (most recent call last): [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1863, in _get_module [rank1]: return importlib.import_module("." + module_name, self.__name__) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/lib/python3.11/importlib/__init__.py", line 126, in import_module [rank1]: return _bootstrap._gcd_import(name[level:], package, level) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "<frozen importlib._bootstrap>", line 1206, in _gcd_import [rank1]: File "<frozen importlib._bootstrap>", line 1178, in _find_and_load [rank1]: File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked [rank1]: File "<frozen importlib._bootstrap>", line 690, in _load_unlocked [rank1]: File "<frozen importlib._bootstrap_external>", line 940, in exec_module [rank1]: File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama_fast.py", line 29, in <module> [rank1]: from .tokenization_llama import LlamaTokenizer [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama.py", line 27, in <module> [rank1]: import sentencepiece as spm [rank1]: File "/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py", line 10, in <module> [rank1]: from . import _sentencepiece [rank1]: ImportError: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank1]: The above exception was the direct cause of the following exception: [rank1]: Traceback (most recent call last): [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 82, in load_tokenizer [rank1]: tokenizer = AutoTokenizer.from_pretrained( [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 912, in from_pretrained [rank1]: tokenizer_class_from_name(config_tokenizer_class) is not None [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 611, in tokenizer_class_from_name [rank1]: return getattr(module, class_name) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1851, in __getattr__ [rank1]: module = self._get_module(self._class_to_module[name]) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1865, in _get_module [rank1]: raise RuntimeError( [rank1]: RuntimeError: Failed to import transformers.models.llama.tokenization_llama_fast because of the following error (look up to see its traceback): [rank1]: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank1]: The above exception was the direct cause of the following exception: [rank1]: Traceback (most recent call last): [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank1]: launch() [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank1]: run_exp() [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank1]: _training_function(config={"args": args, "callbacks": callbacks}) [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank1]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 48, in run_sft [rank1]: tokenizer_module = load_tokenizer(model_args) [rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank1]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 97, in load_tokenizer [rank1]: raise OSError("Failed to load tokenizer.") from e [rank1]: OSError: Failed to load tokenizer. [rank2]: Traceback (most recent call last): [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1863, in _get_module [rank2]: return importlib.import_module("." + module_name, self.__name__) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/lib/python3.11/importlib/__init__.py", line 126, in import_module [rank2]: return _bootstrap._gcd_import(name[level:], package, level) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "<frozen importlib._bootstrap>", line 1206, in _gcd_import [rank2]: File "<frozen importlib._bootstrap>", line 1178, in _find_and_load [rank2]: File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked [rank2]: File "<frozen importlib._bootstrap>", line 690, in _load_unlocked [rank2]: File "<frozen importlib._bootstrap_external>", line 940, in exec_module [rank2]: File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama_fast.py", line 29, in <module> [rank2]: from .tokenization_llama import LlamaTokenizer [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/llama/tokenization_llama.py", line 27, in <module> [rank2]: import sentencepiece as spm [rank2]: File "/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py", line 10, in <module> [rank2]: from . import _sentencepiece [rank2]: ImportError: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank2]: The above exception was the direct cause of the following exception: [rank2]: Traceback (most recent call last): [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 82, in load_tokenizer [rank2]: tokenizer = AutoTokenizer.from_pretrained( [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 912, in from_pretrained [rank2]: tokenizer_class_from_name(config_tokenizer_class) is not None [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/models/auto/tokenization_auto.py", line 611, in tokenizer_class_from_name [rank2]: return getattr(module, class_name) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1851, in __getattr__ [rank2]: module = self._get_module(self._class_to_module[name]) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/usr/local/lib/python3.11/dist-packages/transformers/utils/import_utils.py", line 1865, in _get_module [rank2]: raise RuntimeError( [rank2]: RuntimeError: Failed to import transformers.models.llama.tokenization_llama_fast because of the following error (look up to see its traceback): [rank2]: cannot import name '_sentencepiece' from partially initialized module 'sentencepiece' (most likely due to a circular import) (/usr/local/lib/python3.11/dist-packages/sentencepiece/__init__.py) [rank2]: The above exception was the direct cause of the following exception: [rank2]: Traceback (most recent call last): [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank2]: launch() [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank2]: run_exp() [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank2]: _training_function(config={"args": args, "callbacks": callbacks}) [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank2]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 48, in run_sft [rank2]: tokenizer_module = load_tokenizer(model_args) [rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank2]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/model/loader.py", line 97, in load_tokenizer [rank2]: raise OSError("Failed to load tokenizer.") from e [rank2]: OSError: Failed to load tokenizer. [rank0]:[W703 16:13:30.861219244 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) W0703 16:13:31.449512 3913223 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3913282 closing signal SIGTERM W0703 16:13:31.450263 3913223 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3913283 closing signal SIGTERM W0703 16:13:31.450724 3913223 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3913284 closing signal SIGTERM E0703 16:13:31.765744 3913223 torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 0 (pid: 3913281) of binary: /usr/bin/python3.11 Traceback (most recent call last): File "/usr/local/bin/torchrun", line 8, in <module> sys.exit(main()) ^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 892, in main run(args) File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 883, in run elastic_launch( File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 139, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 270, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ /home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2025-07-03_16:13:31 host : wiseatc-Super-Server rank : 0 (local_rank: 0) exitcode : 1 (pid: 3913281) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ Traceback (most recent call last): File "/home/wiseatc/.local/bin/llamafactory-cli", line 8, in <module> sys.exit(main()) ^^^^^^ File "/home/wiseatc/LLaMA-Factory/src/llamafactory/cli.py", line 130, in main process = subprocess.run( ^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/subprocess.py", line 569, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['torchrun', '--nnodes', '1', '--node_rank', '0', '--nproc_per_node', '4', '--master_addr', '127.0.0.1', '--master_port', '38589', '/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py', 'saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-07-03-16-00-01/training_args.yaml']' returned non-zero exit status 1.

<think>我们遇到了一个错误日志，显示在尝试加载LlamaTokenizer时出现了循环导入的问题，具体是无法从sentencepiece模块中导入_sentencepiece。错误发生在使用transformers库的AutoTokenizer.from_pretrained方法时...

/**************************************************************************** ** ** This file is part of the LibreCAD project, a 2D CAD program ** ** Copyright (C) 2010 R. van Twisk ([email protected]) ** Copyright (C) 2001-2003 RibbonSoft. All rights reserved. ** ** ** This file may be distributed and/or modified under the terms of the ** GNU General Public License version 2 as published by the Free Software ** Foundation and appearing in the file gpl-2.0.txt included in the ** packaging of this file. ** ** This program is distributed in the hope that it will be useful, ** but WITHOUT ANY WARRANTY; without even the implied warranty of ** MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ** GNU General Public License for more details. ** ** You should have received a copy of the GNU General Public License ** along with this program; if not, write to the Free Software ** Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA ** ** This copyright notice MUST APPEAR in all copies of the script! ** **********************************************************************/ #ifndef QC_APPLICATIONWINDOW_H #define QC_APPLICATIONWINDOW_H #undef QT_NO_WORKSPACE #include <qworkspace.h> #include "qc_mdiwindow.h" #include "qg_mainwindowinterface.h" #ifdef RS_SCRIPTING #include "qs_scripter.h" #include <qsproject.h> #endif class QG_LibraryWidget; class QG_CadToolBar; class QC_DialogFactory; class QG_LayerWidget; class QG_BlockWidget; class QG_CommandWidget; class QG_CoordinateWidget; class QG_MouseWidget; class QG_SelectionWidget; class QG_RecentFiles; class QG_PenToolBar; class QHelpEngine; class QC_PluginInterface; /** * Main application window. Hold together document, view and controls. * * @author Andrew Mustun */ class QC_ApplicationWindow: public QMainWindow, public QG_MainWindowInterface { Q_OBJECT public: QC_ApplicationWindow(); ~QC_ApplicationWindow(); void initActions(); void initMenuBar(); void initToolBar(); void initStatusBar(); void initSettings(); void restoreDocks(); void storeSettings(); void updateRecentFilesMenu(); void initMDI(); void initView(); bool queryExit(bool force); /** Catch hotkey for giving focus to command line. */ virtual void keyPressEvent(QKeyEvent* e); virtual void keyReleaseEvent(QKeyEvent* e); public slots: virtual void show(); void finishSplashScreen(); void slotFocus(); void slotBack(); void slotKillAllActions(); //void slotNext(); void slotEnter(); void slotFocusCommandLine(); void slotError(const QString& msg); void slotWindowActivated(QWidget* w); void slotWindowsMenuAboutToShow(); void slotWindowsMenuActivated(int); void slotTileHorizontal(); void slotTileVertical(); void slotPenChanged(RS_Pen p); /** generates a new document for a graphic. */ QC_MDIWindow* slotFileNew(RS_Document* doc=NULL); /** opens a document */ void slotFileOpen(); /** * opens a recent file document * @param id File Menu id of the file */ void slotFileOpenRecent(int id); /** * opens the given file. */ void slotFileOpen(const QString& fileName, RS2::FormatType type); /** saves a document */ void slotFileSave(); /** saves a document under a different filename*/ void slotFileSaveAs(); /** auto-save document */ void slotFileAutoSave(); /** exports the document as bitmap */ void slotFileExport(); bool slotFileExport(const QString& name, const QString& format, QSize size, bool black, bool bw=false); /** closes the current file */ void slotFileClose(); /** closing the current file */ void slotFileClosing(); /** prints the current file */ void slotFilePrint(); /** shows print preview of the current file */ void slotFilePrintPreview(bool on); /** exits the application */ void slotFileQuit(); /** toggle the grid */ void slotViewGrid(bool toggle); /** toggle the draft mode */ void slotViewDraft(bool toggle); /** toggle the statusbar */ void slotViewStatusBar(bool toggle); // void slotBlocksEdit(); void slotOptionsGeneral(); void slotScriptOpenIDE(); void slotScriptRun(); void slotRunStartScript(); void slotRunScript(); void slotRunScript(const QString& name); void slotInsertBlock(); void slotInsertBlock(const QString& name); /** shows an about dlg*/ void slotHelpAbout(); void slotHelpManual(); /** dumps entities to file */ void slotTestDumpEntities(RS_EntityContainer* d=NULL); /** dumps undo info to stdout */ void slotTestDumpUndo(); /** updates all inserts */ void slotTestUpdateInserts(); /** draws some random lines */ void slotTestDrawFreehand(); /** inserts a test block */ void slotTestInsertBlock(); /** inserts a test ellipse */ void slotTestInsertEllipse(); /** inserts a test text */ void slotTestInsertText(); /** inserts a test image */ void slotTestInsertImage(); /** unicode table */ void slotTestUnicode(); /** math experimental */ void slotTestMath01(); /** resizes window to 640x480 for screen shots */ void slotTestResize640(); /** resizes window to 640x480 for screen shots */ void slotTestResize800(); /** resizes window to 640x480 for screen shots */ void slotTestResize1024(); signals: void gridChanged(bool on); void draftChanged(bool on); void printPreviewChanged(bool on); void windowsChanged(bool windowsLeft); public: /** * @return Pointer to application window. */ static QC_ApplicationWindow* getAppWindow() { return appWindow; } /** * @return Pointer to workspace. */ QWorkspace* getWorkspace() { return workspace; } /** * @return Pointer to the currently active MDI Window or NULL if no * MDI Window is active. */ QC_MDIWindow* getMDIWindow() { if (workspace!=NULL) { return (QC_MDIWindow*)workspace->activeWindow(); } else { return NULL; } } /** * Implementation from RS_MainWindowInterface (and QS_ScripterHostInterface). * * @return Pointer to the graphic view of the currently active document * window or NULL if no window is available. */ virtual RS_GraphicView* getGraphicView() { QC_MDIWindow* m = getMDIWindow(); if (m!=NULL) { return m->getGraphicView(); } return NULL; } /** * Implementation from RS_MainWindowInterface (and QS_ScripterHostInterface). * * @return Pointer to the graphic document of the currently active document * window or NULL if no window is available. */ virtual RS_Document* getDocument() { QC_MDIWindow* m = getMDIWindow(); if (m!=NULL) { return m->getDocument(); } return NULL; } /** * Creates a new document. Implementation from RS_MainWindowInterface. */ virtual void createNewDocument( const QString& fileName = QString::null, RS_Document* doc=NULL) { slotFileNew(doc); if (fileName!=QString::null && getDocument()!=NULL) { getDocument()->setFilename(fileName); } } /** * Implementation from QG_MainWindowInterface. * * @return Pointer to this. */ virtual QMainWindow* getMainWindow() { return this; } /** * @return Pointer to action handler. Implementation from QG_MainWindowInterface. */ virtual QG_ActionHandler* getActionHandler() { return actionHandler; } //virtual QToolBar* createToolBar(const QString& name); //virtual void addToolBarButton(QToolBar* tb); /** * @return Pointer to the qsa object. */ #ifdef RS_SCRIPTING QSProject* getQSAProject() { if (scripter!=NULL) { return scripter->getQSAProject(); } else { return NULL; } } #endif void redrawAll(); void updateGrids(); /** * Implementation from QG_MainWindowInterface. */ virtual void setFocus2() { setFocus(); } /** Block list widget */ QG_BlockWidget* getBlockWidget(void) { return blockWidget; } protected: void closeEvent(QCloseEvent*); virtual void mouseReleaseEvent(QMouseEvent* e); private: /** Pointer to the application window (this). */ static QC_ApplicationWindow* appWindow; QTimer *autosaveTimer; /** Workspace for MDI */ QWorkspace* workspace; /** Dialog factory */ QC_DialogFactory* dialogFactory; /** Layer list widget */ QG_LayerWidget* layerWidget; /** Block list widget */ QG_BlockWidget* blockWidget; /** Library browser widget */ QG_LibraryWidget* libraryWidget; /** Layer list dock widget */ QDockWidget* layerDockWindow; /** Block list dock widget */ QDockWidget* blockDockWindow; /** Library list dock widget */ QDockWidget* libraryDockWindow; /** Command line */ QG_CommandWidget* commandWidget; QDockWidget* commandDockWindow; /** Coordinate widget */ QG_CoordinateWidget* coordinateWidget; /** Mouse widget */ QG_MouseWidget* mouseWidget; /** Selection Status */ QG_SelectionWidget* selectionWidget; /** Option widget for individual tool options */ QToolBar* optionWidget; /** Recent files list */ QG_RecentFiles* recentFiles; /** Action handler. */ QG_ActionHandler* actionHandler; #ifdef RS_SCRIPTING /** Scripting interface. */ QS_Scripter* scripter; #endif QMenu* fileMenu; QMenu* windowsMenu; QMenu* scriptMenu; QMenu* helpMenu; QMenu* testMenu; /** the main toolbars */ QToolBar* fileToolBar; QToolBar* editToolBar; QToolBar* zoomToolBar; // Toolbar for selecting the current pen QG_PenToolBar* penToolBar; // Toolbar for CAD tools QG_CadToolBar* cadToolBar; QHelpEngine* helpEngine; QDockWidget *helpWindow; QAction* scriptOpenIDE; QAction* scriptRun; QAction* helpAboutApp; QAction* helpManual; QAction *testDumpEntities; QAction *testDumpUndo; QAction *testUpdateInserts; QAction *testDrawFreehand; QAction *testInsertBlock; QAction *testInsertText; QAction *testInsertImage; QAction *testUnicode; QAction *testInsertEllipse; QAction *testMath01; QAction *testResize640; QAction *testResize800; QAction *testResize1024; //Plugin support private: void loadPlugins(); QMenu *findMenu(const QString &searchMenu, const QObjectList thisMenuList, const QString& currentEntry); QList<QC_PluginInterface*> loadedPlugins; public slots: void execPlug(); }; #endif 给出完整的代码注释

<think>我们被要求为QC_ApplicationWindow类的头文件添加详细注释。但用户并没有直接提供该类的头文件代码，而是提供了一些引用片段。不过，根据之前的对话，我们知道QC_ApplicationWindow是LibreCAD的主窗口类。...

Traceback (most recent call last): File "F:\PythonProject\build\工程数学建模\尝试\尝试1.py", line 7, in <module> df['市'].to_excel(file,index=False,columns=['aaaaa']) ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "F:\PythonProject\.venv\Lib\site-packages\pandas\util\_decorators.py", line 333, in wrapper return func(*args, **kwargs) File "F:\PythonProject\.venv\Lib\site-packages\pandas\core\generic.py", line 2406, in to_excel formatter = ExcelFormatter( df, ...<7 lines>... inf_rep=inf_rep, ) File "F:\PythonProject\.venv\Lib\site-packages\pandas\io\formats\excel.py", line 567, in __init__ raise KeyError("passes columns are not ALL present dataframe") KeyError: 'passes columns are not ALL present dataframe'

Safely saves a DataFrame to an Excel file after validating column names. Parameters: df (pd.DataFrame): The input DataFrame. filename (str): Path of output Excel file. required_columns (list): ...

/home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources W0703 16:30:36.069853 3914856 torch/distributed/run.py:766] W0703 16:30:36.069853 3914856 torch/distributed/run.py:766] ***************************************** W0703 16:30:36.069853 3914856 torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 16:30:36.069853 3914856 torch/distributed/run.py:766] ***************************************** /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,321 >> loading file tokenizer.model [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file tokenizer.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file added_tokens.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file special_tokens_map.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file tokenizer_config.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,322 >> loading file chat_template.jinja /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources /home/wiseatc/.local/lib/python3.11/site-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources [INFO|tokenization_utils_base.py:2313] 2025-07-03 16:30:43,904 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. [INFO|configuration_utils.py:697] 2025-07-03 16:30:43,913 >> loading configuration file /mnt/data1/models/1.5B/config.json [INFO|configuration_utils.py:771] 2025-07-03 16:30:43,919 >> Model config Qwen2Config { "_name_or_path": "/mnt/data1/models/1.5B", "architectures": [ "Qwen2ForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 151643, "eos_token_id": 151643, "hidden_act": "silu", "hidden_size": 1536, "initializer_range": 0.02, "intermediate_size": 8960, "max_position_embeddings": 131072, "max_window_layers": 21, "model_type": "qwen2", "num_attention_heads": 12, "num_hidden_layers": 28, "num_key_value_heads": 2, "rms_norm_eps": 1e-06, "rope_scaling": null, "rope_theta": 10000, "sliding_window": 4096, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.49.0", "use_cache": true, "use_mrope": false, "use_sliding_window": false, "vocab_size": 151936 } [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file tokenizer.model [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file tokenizer.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file added_tokens.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file special_tokens_map.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file tokenizer_config.json [INFO|tokenization_utils_base.py:2048] 2025-07-03 16:30:43,920 >> loading file chat_template.jinja [INFO|tokenization_utils_base.py:2313] 2025-07-03 16:30:44,493 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. /usr/local/lib/python3.11/dist-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via init_process_group or barrier . Using the current device set by the user. warnings.warn( # warn only once [rank1]:[W703 16:30:45.102845887 ProcessGroupNCCL.cpp:4718] [PG ID 0 PG GUID 0 Rank 1] using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. /usr/local/lib/python3.11/dist-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via init_process_group or barrier . Using the current device set by the user. warnings.warn( # warn only once [rank2]:[W703 16:30:45.126706430 ProcessGroupNCCL.cpp:4718] [PG ID 0 PG GUID 0 Rank 2] using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. /usr/local/lib/python3.11/dist-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via init_process_group or barrier . Using the current device set by the user. warnings.warn( # warn only once [rank3]:[W703 16:30:45.136836682 ProcessGroupNCCL.cpp:4718] [PG ID 0 PG GUID 0 Rank 3] using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. Setting num_proc from 16 back to 1 for the train split to disable multiprocessing as it only contains one shard. Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 120 examples [00:00, 6525.39 examples/s] Converting format of dataset (num_proc=16): 0%| | 0/120 [00:00<?, ? examples/s] Converting format of dataset (num_proc=16): 0%| | 0/120 [00:00<?, ? examples/s] Converting format of dataset (num_proc=16): 0%| | 0/120 [00:00<?, ? examples/s] /usr/local/lib/python3.11/dist-packages/torch/distributed/distributed_c10d.py:4631: UserWarning: No device id is provided via init_process_group or barrier . Using the current device set by the user. warnings.warn( # warn only once [rank0]:[W703 16:31:05.679961201 ProcessGroupNCCL.cpp:4718] [PG ID 0 PG GUID 0 Rank 0] using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device. [rank0]: multiprocess.pool.RemoteTraceback: [rank0]: """ [rank0]: Traceback (most recent call last): [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker [rank0]: result = (True, func(*args, **kwds)) [rank0]: ^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 688, in _write_generator_to_queue [rank0]: for i, result in enumerate(func(**kwargs)): [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3501, in _map_single [rank0]: for i, example in iter_outputs(shard_iterable): [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3475, in iter_outputs [rank0]: yield i, apply_function(example, i, offset=offset) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3398, in apply_function [rank0]: processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/converter.py", line 94, in __call__ [rank0]: if self.dataset_attr.prompt and example[self.dataset_attr.prompt]: [rank0]: ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 278, in __getitem__ [rank0]: value = self.data[key] [rank0]: ~~~~~~~~~^^^^^ [rank0]: KeyError: 'instruction' [rank0]: """ [rank0]: The above exception was the direct cause of the following exception: [rank0]: Traceback (most recent call last): [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 23, in <module> [rank0]: launch() [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py", line 19, in launch [rank0]: run_exp() [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 110, in run_exp [rank0]: _training_function(config={"args": args, "callbacks": callbacks}) [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/tuner.py", line 72, in _training_function [rank0]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/train/sft/workflow.py", line 51, in run_sft [rank0]: dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/loader.py", line 304, in get_dataset [rank0]: dataset = _get_merged_dataset(data_args.dataset, model_args, data_args, training_args, stage) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/loader.py", line 182, in _get_merged_dataset [rank0]: datasets[dataset_name] = _load_single_dataset(dataset_attr, model_args, data_args, training_args) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/loader.py", line 162, in _load_single_dataset [rank0]: return align_dataset(dataset, dataset_attr, data_args, training_args) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/LLaMA-Factory/src/llamafactory/data/converter.py", line 279, in align_dataset [rank0]: return dataset.map( [rank0]: ^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 557, in wrapper [rank0]: out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3171, in map [rank0]: for rank, done, content in iflatmap_unordered( [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 728, in iflatmap_unordered [rank0]: [async_result.get(timeout=0.05) for async_result in async_results] [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 728, in [rank0]: [async_result.get(timeout=0.05) for async_result in async_results] [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [rank0]: File "/home/wiseatc/.local/lib/python3.11/site-packages/multiprocess/pool.py", line 774, in get [rank0]: raise self._value [rank0]: KeyError: 'instruction' [rank0]:[W703 16:31:06.912491219 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) W0703 16:31:07.960560 3914856 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3914916 closing signal SIGTERM W0703 16:31:07.961188 3914856 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3914917 closing signal SIGTERM W0703 16:31:07.961536 3914856 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3914918 closing signal SIGTERM E0703 16:31:08.371267 3914856 torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 0 (pid: 3914915) of binary: /usr/bin/python3.11 Traceback (most recent call last): File "/usr/local/bin/torchrun", line 8, in <module> sys.exit(main()) ^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 892, in main run(args) File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 883, in run elastic_launch( File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 139, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 270, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ /home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2025-07-03_16:31:07 host : wiseatc-Super-Server rank : 0 (local_rank: 0) exitcode : 1 (pid: 3914915) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ Traceback (most recent call last): File "/home/wiseatc/.local/bin/llamafactory-cli", line 8, in <module> sys.exit(main()) ^^^^^^ File "/home/wiseatc/LLaMA-Factory/src/llamafactory/cli.py", line 130, in main process = subprocess.run( ^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/subprocess.py", line 569, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['torchrun', '--nnodes', '1', '--node_rank', '0', '--nproc_per_node', '4', '--master_addr', '127.0.0.1', '--master_port', '41919', '/home/wiseatc/LLaMA-Factory/src/llamafactory/launcher.py', 'saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-07-03-16-29-46/training_args.yaml']' returned non-zero exit status 1.

<think>我们遇到了一个新的错误：KeyError:'instruction'这个错误发生在数据预处理阶段，具体是在尝试访问数据集中的'instruction'字段时，发现该字段不存在。根据错误堆栈，问题出现在LLaMA-Factory的数据转换步骤...

Saves a udp client to the NVM so it can be automatically added on the next boot. No need for the user to manually add it again. * Only one UDP client can be saved to the NVM. * @param new_db_udp_client The client to add to NVM. Must have IP and port set. * @param clear_client Set to true to remove the current client from NVM. In that case the new_db_udp_client param will be ignored. */ void save_udp_client_to_nvm(struct db_udp_client_t *new_db_udp_client, bool clear_client) { char ip[INET_ADDRSTRLEN]; uint16_t port; if (!clear_client) { // convert addr to string char client_str[INET_ADDRSTRLEN + 6]; inet_ntop(AF_INET, &(new_db_udp_client->udp_client.sin_addr), ip, INET_ADDRSTRLEN); port = ntohs(new_db_udp_client->udp_client.sin_port); snprintf(client_str, sizeof(client_str), "%s:%d", ip, port); ESP_LOGI(TAG, "Saving UDP client %s to NVS %s", client_str, NVS_NAMESPACE); } else { // clear client from NVM by setting string to empty "" and port to 0 ip[0] = '\0'; port = 0; ESP_LOGI(TAG, "Clearing UDP client from NVM"); } nvs_handle my_handle; ESP_ERROR_CHECK(nvs_open(NVS_NAMESPACE, NVS_READWRITE, &my_handle)); ESP_ERROR_CHECK(nvs_set_str(my_handle, "udp_client_ip", ip)); ESP_ERROR_CHECK(nvs_set_u16(my_handle, "udp_client_port", port)); ESP_ERROR_CHECK(nvs_commit(my_handle)); nvs_close(my_handle); }分析这段代码

<think>嗯，我现在需要分析用户提供的这段C语言代码。用户的问题是关于如何将一个UDP客户端保存到非易失性存储器（NVM）中，以便在下次启动时自动添加，而无需用户手动操作。同时，代码还允许清除已保存的客户端。...

[INFO|<string>:438] 2025-03-04 19:33:39,759 >> Training completed. Do not forget to share your model on huggingface.co/models =) swanlab: Step 210 on key train/epoch already exists, ignored. swanlab: Step 210 on key train/num_input_tokens_seen already exists, ignored. {'train_runtime': 222.6408, 'train_samples_per_second': 7.546, 'train_steps_per_second': 0.943, 'train_loss': 3.434720888591948, 'epoch': 30.0, 'num_input_tokens_seen': 665264} 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 210/210 [03:39<00:00, 1.04s/it] [INFO|trainer.py:3942] 2025-03-04 19:33:39,764 >> Saving model checkpoint to saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-03-04-19-22-19 [INFO|configuration_utils.py:697] 2025-03-04 19:33:39,782 >> loading configuration file /root/autodl-tmp/ai/models/DeepSeek-R1-Distill-Qwen-1.5B/config.json [INFO|configuration_utils.py:771] 2025-03-04 19:33:39,783 >> Model config Qwen2Config { "architectures": [ "Qwen2ForCausalLM" ], "attention_dropout": 0.0, "bos_token_id": 151643, "eos_token_id": 151643, "hidden_act": "silu", "hidden_size": 1536, "initializer_range": 0.02, "intermediate_size": 8960, "max_position_embeddings": 131072, "max_window_layers": 21, "model_type": "qwen2", "num_attention_heads": 12, "num_hidden_layers": 28, "num_key_value_heads": 2, "rms_norm_eps": 1e-06, "rope_scaling": null, "rope_theta": 10000, "sliding_window": 4096, "tie_word_embeddings": false, "torch_dtype": "bfloat16", "transformers_version": "4.49.0", "use_cache": true, "use_mrope": false, "use_sliding_window": false, "vocab_size": 151936 } ***** train metrics ***** epoch = 30.0 num_input_tokens_seen = 665264 total_flos = 5773005GF train_loss = 3.4347 train_runtime = 0:03:42.64 train_samples_per_second = 7.546 train_steps_per_second = 0.943 Figure saved at: saves/DeepSeek-R1-1.5B-Distill/lora/train_2025-03-04-19-22-19/training_loss.png [WARNING|2025-03-04 19:33:40] llamafactory.extras.ploting:162 >> No metric eval_loss to plot. [WARNING|2025-03-04 19:33:40] llamafactory.extras.ploting:162 >> No metric eval_accuracy to plot. [INFO|modelcard.py:449] 2025-03-04 19:33:40,019 >> Dropping the following result as it does not have all the necessary fields: {'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}} swanlab: Experiment dragon-6 has completed swanlab: 🌟 Run swanlab watch /root/autodl-tmp/ai/LLaMA-Factory/swanlog to view SwanLab Experiment Dashboard locally swanlab: 🏠 View project at https://swanlab.cn/@chrisfang/llamafactory-test swanlab: 🚀 View run at https://swanlab.cn/@chrisfang/llamafactory-test/runs/l0n927vfjxvq6iclvs3a8 优化空间

<think>好的，我现在需要帮助用户分析他们提供的训练日志，并给出优化建议。首先，我要仔细阅读日志内容，理解每个部分的含义。 从日志开头可以看到训练完成的信息，还有一些关于SwanLab的提示，可能用户用了这个...

利用以上改善代码的思路帮我改善下面的代码 from maix import camera, display, image, nn, app import time, os # 初始化模型 detector = nn.YOLOv5(model="/root/models/mymodel/model_127448.mud", dual_buff=True) ocr = nn.PP_OCR("/root/models/pp_ocr.mud") # OCR模型初始化 SAVE_DIR = "/root/models/mymodel/" os.makedirs(SAVE_DIR, exist_ok=True) # 硬件初始化（关键修改点1） cam = camera.Camera( detector.input_width(), detector.input_height(), format=image.FORMAT_BGR888 # 强制指定摄像头输出格式 ) disp = display.Display() # 状态变量 last_save_time = 0 save_count = 0 # 调试信息打印（关键修改点2） print(f"[系统诊断] 摄像头格式: {cam.format}") print(f"[系统诊断] 检测器输入格式: {detector.input_format}") print(f"[系统诊断] OCR输入格式: {ocr.input_format}") def ocr_processing(img_path): """ 增强版OCR处理函数 """ try: # 加载并转换图像格式（关键修改点3） img = image.load(img_path) if img.format != ocr.input_format: print(f"[格式转换] 原始格式: {img.format} -> 目标格式: {ocr.input_format}") img = img.convert(format=ocr.input_format) # OCR识别处理 objs = ocr.detect(img) text = ''.join([obj.char_str() for obj in objs]) print(f"[识别结果] {text}") # 可视化增强处理 for obj in objs: points = obj.box.to_list() img.draw_keypoints(points, image.COLOR_RED, 4, -1, 1) img.draw_string(obj.box.x4, obj.box.y4, obj.char_str(), image.COLOR_RED) disp.show(img) return text except Exception as e: print(f"[OCR异常] {str(e)}") return "" while not app.need_exit(): try: img = cam.read() objs = detector.detect(img, conf_th=0.5, iou_th=0.45) # 物体检测处理（关键修改点4） if len(objs) > 0 and (time.time() - last_save_time) >= 3: filename = f"{SAVE_DIR}detect_{save_count}_{int(time.time())}.jpg" # 创建保存用图像副本 save_img = img.copy().convert(format=image.FORMAT_BGR888) # 确保保存格式一致 if save_img.save(filename, quality=85): print(f"[图像保存] {filename}") last_save_time = time.time() save_count += 1 ocr_result = ocr_processing(filename) print(f"[识别日志] {ocr_result}") # 显示处理增强 for obj in objs: img.draw_rect(obj.x, obj.y, obj.w, obj.h, color=image.COLOR_RED) msg = f'{detector.labels[obj.class_id]}: {obj.score:.2f}' img.draw_string(obj.x, obj.y+2, msg, scale=0.5, color=image.COLOR_RED) disp.show(img) except Exception as e: print(f"[运行异常] {str(e)}") break

<think>嗯，用户让我帮忙改善他们的代码，基于他们提到的几个关键修改点。首先，我需要仔细看一下他们提供的代码和改善思路。他们的代码是关于使用Maix库进行物体检测和OCR识别的，可能是在嵌入式设备上运行的，比如...

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