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#: ../../source/developer_guide/performance/profile_execute_duration.md:1
msgid "Profile Execute Duration"
msgstr "配置执行持续时间"

#~ msgid ""
#~ "The execution duration of each stage "
#~ "(including pre/post-processing, model forward,"
#~ " etc.) usually needs to be captured"
#~ " during a complete inference process. "
#~ "Typically, this is done by using "
#~ "`torch.xpu.synchronize()` and obtaining CPU "
#~ "timestamps, which increases the performance"
#~ " overhead of host/device synchronization."
#~ msgstr ""
#~ "在完整的推理过程中，通常需要记录每个阶段（包括前/后处理、模型前向等）的执行时长。一般通过使用 "
#~ "`torch.xpu.synchronize()` 并获取 CPU "
#~ "时间戳来实现，这会增加主机/设备同步的性能开销。"

#~ msgid ""
#~ "**To reduce the performance overhead, we"
#~ " add this feature, using the XPU "
#~ "event timestamp mechanism to observe the"
#~ " device execution time asynchronously.**"
#~ msgstr "**为了减少性能开销，我们添加了此功能，使用 XPU 事件时间戳机制异步观测设备的执行时间。**"

#~ msgid "Usage"
#~ msgstr "用法"

#~ msgid ""
#~ "Use the environment variable "
#~ "`VLLM_KUNLUN_MODEL_EXECUTE_TIME_OBSERVE` to enable "
#~ "this feature."
#~ msgstr "使用环境变量 `VLLM_KUNLUN_MODEL_EXECUTE_TIME_OBSERVE` 来启用此功能。"

#~ msgid ""
#~ "Use the non-blocking API "
#~ "`ProfileExecuteDuration().capture_async` to set "
#~ "observation points asynchronously when you "
#~ "need to observe the execution duration."
#~ msgstr ""
#~ "当你需要观察执行时长时，可以使用非阻塞 API "
#~ "`ProfileExecuteDuration().capture_async` 异步设置观察点。"

#~ msgid ""
#~ "Use the blocking API "
#~ "`ProfileExecuteDuration().pop_captured_sync` at an "
#~ "appropriate time to get and print "
#~ "the execution durations of all observed"
#~ " stages."
#~ msgstr ""
#~ "在适当的时机使用阻塞式 API "
#~ "`ProfileExecuteDuration().pop_captured_sync` 获取并打印所有已观察到阶段的执行时长。"

#~ msgid ""
#~ "**We have instrumented the key inference"
#~ " stages (including pre-processing, model"
#~ " forward pass, etc.) for execute "
#~ "duration profiling. Execute the script "
#~ "as follows:**"
#~ msgstr "**我们已经对关键的推理阶段（包括预处理、模型前向传递等）进行了执行时长分析的检测。请按如下方式执行脚本：**"

#~ msgid "Example Output"
#~ msgstr "示例输出"

