diff --git a/v0.10.0/en/_sources/software/cli.rst.txt b/v0.10.0/en/_sources/software/cli.rst.txt index ef1fd13..bd43b6c 100644 --- a/v0.10.0/en/_sources/software/cli.rst.txt +++ b/v0.10.0/en/_sources/software/cli.rst.txt @@ -191,11 +191,12 @@ The following is an example usage of furiosa-bench without an output path option 99th percentile (us): 79874 99th percentile (us): 307568 -If an output path is specified, furiosa-bench will save a json document as the following: +If an output path is specified, furiosa-bench will save a JSON document as the following: .. code-block:: sh - $ furiosa-bench mnist-8.onnx --workload L -n 1000 -w 8 -t 2 -o mnist.json | cat mnist.json + $ furiosa-bench mnist-8.onnx --workload L -n 1000 -w 8 -t 2 -o mnist.json + $ cat mnist.json { "model_data": { diff --git a/v0.10.0/en/_sources/software/compiler.rst.txt b/v0.10.0/en/_sources/software/compiler.rst.txt index eb6b951..63f6792 100644 --- a/v0.10.0/en/_sources/software/compiler.rst.txt +++ b/v0.10.0/en/_sources/software/compiler.rst.txt @@ -76,7 +76,7 @@ The usage of ``furiosa-compiler`` is as the following: ``SOURCE`` is the file path of -`TFLite `_ or `Onnx `_. +`TFLite `_ or `ONNX `_. You have to use quantized models through :ref:`ModelQuantization` for NPU accleration. You can omit the option `-o OUTPUT`, and you can also choose to designate the output file name. diff --git a/v0.10.0/en/_sources/software/profiler.rst.txt b/v0.10.0/en/_sources/software/profiler.rst.txt index bffa211..aeb2bc5 100644 --- a/v0.10.0/en/_sources/software/profiler.rst.txt +++ b/v0.10.0/en/_sources/software/profiler.rst.txt @@ -1,7 +1,7 @@ .. _Profiling: *********************************** -Performance profiling +Performance Profiling *********************************** Low latency and high throughput performance are critical factors in many DNN applications. @@ -19,7 +19,7 @@ It is thus not enabled by default. You can create trace by using one of the foll .. _ProfilerEnabledByEnv: -Tracing Enabled by Environmet Variable +Tracing via Environment Variable ============================================================ You can enable trace generation by setting the path of the file to which the trace result will be written in ``FURIOSA_PROFILER_OUTPUT_PATH``. The advantage of this method is that the code remains unchanged. The downside is that you cannot set a specific section or category for measurement. @@ -51,7 +51,7 @@ If you enter ``chrome://tracing`` in Chrome's address bar, the trace viewer will .. _ProfilerEnabledByContext: -Tracing Enabled by Profiler Context +Tracing via Profiler Context ============================================================ You can also trace a model inference performance by using a Profiler Context in your Python code. The advantages of this method, in comparison to the tracing by environment variable, are as follows: diff --git a/v0.10.0/en/api/python/furiosa.common.html b/v0.10.0/en/api/python/furiosa.common.html index a12714b..e9359d1 100644 --- a/v0.10.0/en/api/python/furiosa.common.html +++ b/v0.10.0/en/api/python/furiosa.common.html @@ -62,7 +62,7 @@
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    • diff --git a/v0.10.0/en/searchindex.js b/v0.10.0/en/searchindex.js index 49856a6..050011e 100644 --- a/v0.10.0/en/searchindex.js +++ b/v0.10.0/en/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api/python/furiosa.common", "api/python/furiosa.common.transport", "api/python/furiosa.optimizer", "api/python/furiosa.optimizer.frontend", "api/python/furiosa.optimizer.frontend.onnx", "api/python/furiosa.optimizer.frontend.onnx.transformer", "api/python/furiosa.optimizer.frontend.onnx.transformer.experimental", "api/python/furiosa.optimizer.frontend.onnx.utils", "api/python/furiosa.optimizer.interfaces", "api/python/furiosa.quantizer", "api/python/furiosa.runtime", "api/python/furiosa.server", "api/python/furiosa.server.api", "api/python/furiosa.server.api.cli", "api/python/furiosa.server.api.grpc", "api/python/furiosa.server.api.grpc.generated", "api/python/furiosa.server.api.rest", "api/python/furiosa.server.handlers", "api/python/furiosa.server.types", 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"furiosa.runtime.tensor.Tensor.view"]], "view() (furiosa.runtime.tensor.tensorarray method)": [[10, "furiosa.runtime.tensor.TensorArray.view"]], "zeros() (in module furiosa.runtime.tensor)": [[10, "furiosa.runtime.tensor.zeros"]], "modelnotfound": [[11, "furiosa.server.errors.ModelNotFound"]], "modelservererror": [[11, "furiosa.server.errors.ModelServerError"]], "furiosa.server.errors": [[11, "module-furiosa.server.errors"]], "furiosa.server.utils": [[19, "module-furiosa.server.utils"]]}}) \ No newline at end of file diff --git a/v0.10.0/en/software/c-sdk.html b/v0.10.0/en/software/c-sdk.html index d986676..ba8b309 100644 --- a/v0.10.0/en/software/c-sdk.html +++ b/v0.10.0/en/software/c-sdk.html @@ -67,7 +67,7 @@
  • Command Line Tools
  • Compiler
  • Model Quantization
    • -
  • Performance profiling
    • +
  • Performance Profiling
  • FuriosaAI Model Zoo
  • Model Server (Serving Framework)
  • Kubernetes Support
    • diff --git a/v0.10.0/en/software/cli.html b/v0.10.0/en/software/cli.html index bf1b286..a39f902 100644 --- a/v0.10.0/en/software/cli.html +++ b/v0.10.0/en/software/cli.html @@ -76,7 +76,7 @@
  • Compiler
  • Model Quantization
    • -
  • Performance profiling
    • +
  • Performance Profiling
  • FuriosaAI Model Zoo
  • Model Server (Serving Framework)
  • Kubernetes Support
    • @@ -303,8 +303,9 @@

    furiosactl 99th percentile (us): 307568 -

    If an output path is specified, furiosa-bench will save a json document as the following:

    -
    $ furiosa-bench mnist-8.onnx --workload L -n 1000 -w 8 -t 2 -o mnist.json | cat mnist.json
+
    If an output path is specified, furiosa-bench will save a JSON document as the following:
    
+
    $ furiosa-bench mnist-8.onnx --workload L -n 1000 -w 8 -t 2 -o mnist.json
+$ cat mnist.json
 
   {
       "model_data": {
diff --git a/v0.10.0/en/software/compiler.html b/v0.10.0/en/software/compiler.html
index d983561..7b65031 100644
--- a/v0.10.0/en/software/compiler.html
+++ b/v0.10.0/en/software/compiler.html
@@ -67,7 +67,7 @@
 
 
 
  • Model Quantization
    • 
-
  • Performance profiling
    • 
+
  • Performance Profiling
    • 
 
  • FuriosaAI Model Zoo
    • 
 
  • Model Server (Serving Framework)
    • 
 
  • Kubernetes Support
    • 
@@ -183,7 +183,7 @@
 
    
 
    
 
    SOURCE is the file path of
-TFLite or Onnx.
+TFLite or ONNX.
 You have to use quantized models through Model Quantization for NPU accleration.
    
 
    You can omit the option -o OUTPUT, and you can also choose to designate the output file name.
 When omitted, the default output file name is output.enf. Here, enf stands for Executable NPU Format.
diff --git a/v0.10.0/en/software/installation.html b/v0.10.0/en/software/installation.html
index 3650109..28d2f23 100644
--- a/v0.10.0/en/software/installation.html
+++ b/v0.10.0/en/software/installation.html
@@ -74,7 +74,7 @@
 
  • Command Line Tools
    • 
 
  • Compiler
    • 
 
  • Model Quantization
    • 
-
  • Performance profiling
    • 
+
  • Performance Profiling
    • 
 
  • FuriosaAI Model Zoo
    • 
 
  • Model Server (Serving Framework)
    • 
 
  • Kubernetes Support
    • 
diff --git a/v0.10.0/en/software/intro.html b/v0.10.0/en/software/intro.html
index e7b444c..e855d8e 100644
--- a/v0.10.0/en/software/intro.html
+++ b/v0.10.0/en/software/intro.html
@@ -71,7 +71,7 @@
 
  • Command Line Tools
    • 
 
  • Compiler
    • 
 
  • Model Quantization
    • 
-
  • Performance profiling
    • 
+
  • Performance Profiling
    • 
 
  • FuriosaAI Model Zoo
    • 
 
  • Model Server (Serving Framework)
    • 
 
  • Kubernetes Support
    • 
diff --git a/v0.10.0/en/software/kubernetes_support.html b/v0.10.0/en/software/kubernetes_support.html
index 2986210..dffca85 100644
--- a/v0.10.0/en/software/kubernetes_support.html
+++ b/v0.10.0/en/software/kubernetes_support.html
@@ -62,7 +62,7 @@
 
  • Command Line Tools
    • 
 
  • Compiler
    • 
 
  • Model Quantization
    • 
-
  • Performance profiling
    • 
+
  • Performance Profiling
    • 
 
  • FuriosaAI Model Zoo
    • 
 
  • Model Server (Serving Framework)
    • 
 
  • Kubernetes Support
      
diff --git a/v0.10.0/en/software/profiler.html b/v0.10.0/en/software/profiler.html
index 96a4c11..c247a34 100644
--- a/v0.10.0/en/software/profiler.html
+++ b/v0.10.0/en/software/profiler.html
@@ -4,7 +4,7 @@
   
 
   
-  Performance profiling — Furiosa SDK Documentation 0.10.0 documentation
+  Performance Profiling — Furiosa SDK Documentation 0.10.0 documentation
       
       
       
@@ -62,10 +62,10 @@
 
    • Command Line Tools
      • 
 
    • Compiler
      • 
 
    • Model Quantization
      • 
-
    • Performance profiling
        
+
      • Performance Profiling
          
 
        • Trace Analysis
          • 
-
        • Tracing Enabled by Environmet Variable
          • 
-
        • Tracing Enabled by Profiler Context
            
+
          • Tracing via Environment Variable
            • 
+
          • Tracing via Profiler Context
@@ -113,7 +113,7 @@
           
            
   
              
       
              • 
-      
            • Performance profiling
              • 
+      
            • Performance Profiling
              • 
       
            • 
              View page source
       
              • 
@@ -124,7 +124,7 @@
            
              
              
   
              
-
              Performance profiling
              
+
              Performance Profiling
              
 
              Low latency and high throughput performance are critical factors in many DNN applications.
 For performance optimization, model developers and ML engineers must understand the model performance and be able to analyze bottlenecks.
 To assist developers with this process, Furiosa SDK provides a profiling tool.
              
@@ -135,8 +135,8 @@ 
              Trace Analysis
-
              Tracing Enabled by Environmet Variable
              
+
              
+
              Tracing via Environment Variable
              
 
              You can enable trace generation by setting the path of the file to which the trace result will be written in FURIOSA_PROFILER_OUTPUT_PATH. The advantage of this method is that the code remains unchanged. The downside is that you cannot set a specific section or category for measurement.
              
 
              git clone https://github.com/furiosa-ai/furiosa-sdk
 cd furiosa-sdk/examples/inferences
@@ -152,8 +152,8 @@ 
              Trace AnalysisTracing
 
              
 
              
-
              
-
              Tracing Enabled by Profiler Context
              
+
              
+
              Tracing via Profiler Context
              
 
              You can also trace a model inference performance by using a Profiler Context in your Python code. The advantages of this method, in comparison to the tracing by environment variable, are as follows:
              
 
 
-
            • Performance profiling
              • 
+
            • Performance Profiling
              • 
 
            • FuriosaAI Model Zoo
              • 
 
            • Model Server (Serving Framework)
              • 
 
            • Kubernetes Support
              • 
@@ -228,7 +228,7 @@ 
              Model Quantization APIs
           
              
 
diff --git a/v0.10.0/en/software/tutorials.html b/v0.10.0/en/software/tutorials.html
index 921491d..b908ac4 100644
--- a/v0.10.0/en/software/tutorials.html
+++ b/v0.10.0/en/software/tutorials.html
@@ -62,7 +62,7 @@
 
            • Command Line Tools
              • 
 
            • Compiler
              • 
 
            • Model Quantization
              • 
-
            • Performance profiling
              • 
+
            • Performance Profiling
              • 
 
            • FuriosaAI Model Zoo
              • 
 
            • Model Server (Serving Framework)
              • 
 
            • Kubernetes Support
              • 
diff --git a/v0.10.0/ko/_sources/releases/0.10.0.rst.txt b/v0.10.0/ko/_sources/releases/0.10.0.rst.txt
index 8622c3b..e49ff09 100644
--- a/v0.10.0/ko/_sources/releases/0.10.0.rst.txt
+++ b/v0.10.0/ko/_sources/releases/0.10.0.rst.txt
@@ -2,11 +2,12 @@
 Release Notes - 0.10.0
 *********************************************************
 
-Furiosa SDK 0.10.0은 메이저 릴리스(Major Release)로 멀티 디바이스를 지원하는 차세대 런타임 엔진,
-추론 시간을 크게 줄일 수 있는 최적화 옵션, NPU의 사용량과 연산,
-IO 비중을 OpenTelemetry를 통해 제공하는 기능 개선 등을 포함한다.
+Furiosa SDK 0.10.0은 메이저 릴리스(Major Release)로
+여러 장치를 지원하는 차세대 런타임 엔진,
+모델의 양자화 연산자를 제거하여 성능을 향샹 시키는 최적화 기능의 사용성 개선,
+NPU 사용률 등의 여러 지표를 OpenMetrics 형식으로 제공하는 기능 등을 포함한다.
 furiosa-litmus는 리포팅 목적의 정보 수집이 가능하도록 개선 되었으며,
-furiosa-compiler 명령행 도구의 파이썬 의존성 제거, 벤치마크 커맨드 도구 추가 등의 기능 개선이 이루어졌다.
+furiosa-compiler 명령행 도구의 파이썬 의존성 제거, 벤치마크 명령행 도구 추가 등의 기능 개선이 이루어졌다.
 그 외에도 다수의 기능 추가, 버그 수정, 성능 개선 등을 포함한다.
 
 .. list-table:: 컴포넌트 버전 정보
@@ -70,7 +71,8 @@ APT 패키지 관리자 설정 및 설치 방법에 대한 자세한 설명은 :
     pip install --upgrade furiosa-sdk
 
 .. warning::
-  pip를 업그레이드 할 때 아래 오류를 접한다면, ``pip install --upgrade pip`` 커맨드를 통해 pip를 먼저 업그레이드 해야 한다.
+
+  pip를 업그레이드 할 때 아래 오류를 접한다면, ``pip install --upgrade pip`` 명령을 통해 pip를 먼저 업그레이드 해야 한다.
 
   .. code-block:: sh
 
@@ -90,7 +92,8 @@ FuriosaRT는 추론을 제공하는 핵심 라이브러리로 Python API를 제
 거의 호환될 뿐 아니라 다음과 같은 새로운 기능을 포함한다.
 
 
-**새 API 도입**
+새 API 도입
+-----------------------------------
 
 FuriosaRT는 파이썬 `asyncio `_ 를
 사용한 비동기(async) API를 새로이 지원한다. 기존의 동기 API는 배치 작업에는 충분하였으나
@@ -99,7 +102,10 @@ FuriosaRT는 파이썬 `asyncio 
 새 API에 대한 상세한 설명은 `Furiosa SDK API Reference - furiosa.runtime `_
 에서 찾아 볼 수 있다.
 
-**다수의 NPU 지원 장치 설정 방법 개선**
+.. _RELEASE_0_10_0_DeviceSelector:
+
+멀티 NPU 지원 및 장치 설정 방법 개선
+---------------------------------------------
 
 FuriosaRT에서는 하나의 세션에서 다수의 장치를 지원한다. 따라서 별도 구현 없이도 다수의 장치를 이용하여 고성능 추론을 수행할 수 있다.
 또한, 개선된 장치 지정 방법을 제공한다. 기존에는 환경변수
@@ -109,7 +115,7 @@ FuriosaRT에서는 하나의 세션에서 다수의 장치를 지원한다. 따
 
 이번 릴리스 부터 ``devfs`` 이름을 연상 시키는 환경 변수 ``NPU_DEVNAME`` 대신
 ``FURIOSA_DEVICES`` 라는 새로운 환경변수가 도입되었다. 또한 이 환경 변수는
-NPU 종류, 구성, 개수를 표현하는 텍스트 설정을 받아드린다.
+NPU 종류, 구성, 개수를 표현하는 텍스트 설정을 받아 들인다.
 
 .. code-block:: sh
 
@@ -131,18 +137,66 @@ NPU 종류, 구성, 개수를 표현하는 텍스트 설정을 받아드린다.
 자세한 장치 설정 방법은 `Furiosa SDK API Reference - Device Specification `_ 에서 찾아볼 수 있다.
 
 
-서빙 프레임워크 및 모델 서버
+처리량 향상
+---------------------------------------------
+(TODO) - 데이터 추가 후 작성 완료
+
+모델 서버와 서빙 프레임워크 개선
 ================================================================
-``furiosa-server``, ``furioa-serving`` 은 각각 모델의 서빙을 제공하는 프레임워크와
-서빙 어플리케이션을 개발하기 위한 라이브러리이다. 이번 릴리스에서는 차세대 런타임인 FuriosaRT를
-사용하도록 개선되었으며 FuriosaRT가 제공하는 장점을 활용할 수 있도록 개선되었다.
+``furiosa-server``, ``furioa-serving`` 은 각각 모델의 서빙을 제공하는 웹 서버와
+서빙 애플리케이션을 개발하기 위한 라이브러리이다.
+
+이번 릴리스에서는 차세대 런타임인 FuriosaRT를 사용하도록 개선되었으며 구체적인 개선 사항은 다음과 같다.
+
+* :ref:`RELEASE_0_10_0_DeviceSelector` 이 그대로 적용되어 다수의 장치를 쉽게 설정할 수 있다.
+* FuriosaRT가 제공하는 `asyncio `_ 기반의 새 API를 사용하도록 내부가 개선되어 더 적은 리소스로 더 높은 성능을 낼 수 있다.
+* FuriosaRT의 성능 향상 혜택을 그대로 볼 수 있다 (특히 더 높은 워커 수를 이용할 수 있다).
 
+모델 서버에 대한 자세한 설명은 :ref:`ModelServing` 을 참고한다.
 
 모델 양자화 도구(quantization tool) 개선
 ================================================================
-``furiosa-quantizer`` 는 이미 트레이닝 된 모델을 양자화된 모델로 변환해주는 라이브러리이다.
-(TODO)
-
+``furiosa-quantizer`` 는 트레이닝 된 모델을 양자화된 모델로 변환(post-training quantization) 해주는
+라이브러리이다. 이번 0.10.0 릴리스에는 양자화 도구의 사용성을 크게 개선 하였으며 이를 위해
+``furiosa.quantizer.quantize()`` 함수의 일부 파라메터가 변경되었다.
+
+기존 문제점
+-----------------------------------------------------------------
+``furiosa.quantizer.quantize()`` 함수는 모델 양자화 도구의 핵심 함수로
+ONNX 모델을 양자화된 모델(quantized model)로 변환하여 반환한다.
+입력 타입을 ``float32`` 대신 ``uint8`` 로 직접 사용할 수 있도록 하는 ``with_quantize``
+파라메터를 제공하는데, 이미지와 같이 원본 데이터(픽셀 값)이 ``uint8`` 로 표현되는 경우에 실행 중 양자화 과정을 생략할 수 있어
+큰 성능 향상을 기대할 수 있다.
+예를 들면, YOLOv5 Large 같은 모델의 경우 이 옵션을 사용하면 60.639 ms 에서 0.277 ms 로 실행 시간이 크게 개선된다.
+
+동일한 방법으로 출력 타입도 ``float32`` 대신 ``unt8`` 로 직접 사용할 수 있도록 하는 ``normalized_pixel_outputs``
+옵션을 제공하는데 모델의 출력이 RGB 형태로 표현되는 이미지이거나, 정수 값으로 직접 사용 가능한 경우 선택할 수 있다.
+이 옵션 역시 큰 성능 향상을 기대할 수 있다.
+
+응용에 따라 두 옵션이 사용 가능한 경우 수배에서 수백배까지도 실행 시간을 단축시킬 수 있다.
+그러나 다음과 같은 한계와 피드백이 있었다.
+
+* 모델의 출력이 이미지가 아닌 경우 파라메터 이름 ``normalized_pixel_outputs`` 가 의미를 잘 표현하지 못한다.
+* ``normalized_pixel_outputs`` 옵션은 지정된 출력 텐서의 범위를 0과 1 사이 실수로 전제하는데 다양한 응용에 적용하는데 한계가 있다.
+* ``with_quantize``, ``normalized_pixel_outputs`` 옵션 모두 ``uint8`` 타입만 지원했으며 ``int8`` 타입을 지원하지 못했다.
+
+개선 사항
+-----------------------------------------------------------------
+* ``furiosa.quantizer.quantize()`` 에서 ``with_quantize``, ``normalized_pixel_outputs`` 가 파라미터에서 제거 된다.
+* 대신 `ModelEditor `_ 추가되어 모델의 입력/출력 타입에 대한 상세한 최적화 옵션을 설정할 수 있으며 다음 기능을 제공한다.
+  * ``convert_input_type(tensor_name, TensorType)`` 메소드는 텐서 이름을 받아 해당 입력 텐서에 해당되는
+  ``quantize`` 연산자를 제거하고 입력 타입을 ``TensorType`` 으로 변경한다.
+  * ``convert_output_type(tensor_name, TensorType, tensor_range)`` 메소드는 텐서 이름을 받아 해당 출력 텐서에 해당되는
+  ``dequantize`` 연산자를 제거하고 입력 타입을 ``TensorType`` 으로 변경하고 주어진 ``tensor_range`` 범위에 맞게 스케일하도록 모델을 수정한다.
+* ``convert_{output,input}_type`` 함수는 텐서 이름을 사용하기 때문에 ``furiosa.quantizer`` 모듈 아래 ``get_pure_input_names(ModelProto)`` ``get_output_names(ModelProto)`` 같은 헬퍼 함수를 사용하여 텐서 이름을 얻어야 한다.
+
+.. note::
+
+  이번 릴리즈에 포함된 ``furiosa.quantizer.quantize()`` 에서 ``with_quantize``, ``normalized_pixel_outputs``
+  가 파라미터에서 제거 된 것은 기존 코드를 수정해야 하는 변경이다.
+
+더 자세한 내용은 `ModelEditor `_ 의
+API 레퍼런스를 참고할 수 있으며 실제 적용 예는 :ref:`Tutorial` 의 예제 코드에서 찾아볼 수 있다.
 
 컴파일러 개선
 =====================
@@ -154,21 +208,21 @@ NPU 종류, 구성, 개수를 표현하는 텍스트 설정을 받아드린다.
 
 그 외에도 컴파일러 명령행 도구는 다음 사항이 개선되었다.
 
-* ``furiosa compile`` 대신 ``furiosa-compiler`` 커맨드를 직접 사용하실 수 있다.
-* ``furiosa-compiler`` 커맨드는 Python 의존성을 필요하지 않는다.
+* ``furiosa compile`` 대신 ``furiosa-compiler`` 명령을 직접 사용할 수 있다.
+* ``furiosa-compiler`` 명령은 네이티브 실행 파일(native executable)이 되어 Python 실행 환경을 요구하지 않는다.
 * ``apt install furiosa-compiler`` 명령을 통해 컴파일러를 설치할 수 있다.
-* Python 의존성을 가지는 기존 ``furiosa compile`` 커맨드는 현재는 호환성을 위해 유지되며 향후 릴리스에서 제거 될 수 있다.
+* Python 의존성을 가지는 기존 ``furiosa compile`` 명령는 현재는 호환성을 위해 유지되며 향후 릴리스에서 제거 될 수 있다.
 
 컴파일러 명령행 도구의 자세한 사용법은 :ref:`CompilerCli` 에서 찾아볼 수 있다.
 
 성능 프로파일러(Profiler) 개선
 ================================================================
-성능 프로파일러는 추론의 실제 실행시간을 구간 별로 측정하여 성능 분석을 돕는 도구이다.
+성능 프로파일러는 추론의 실제 실행시간을 구간별로 측정하여 성능 분석을 돕는 도구이다.
 이번 릴리스에서는 :ref:`ProfilerEnabledByContext` 기능에 임시 중단/재개 기능이 추가 되었다.
 
-사용자는 프로파일링 컨텍스트 내에서 전/후처리 시간, 워밍업(warming up) 시간 등 프로파일링을 원하지 않는 구간을
-을 제외하고 프로파일링 함으로써 프로파일링에 소요되는 오버헤드 및 프로파일링 결과물의 크기를 줄일 수 있다.
-간단히 ``profile.pause()`` 와 ``profile.resume()`` 함수를 호출하여 프로파일링을 임시로 중단/재개할 수 있으며
+사용자는 프로파일링 컨텍스트 내에서 전/후처리 시간, 워밍업(warming up) 시간 등 프로파일링이 불필요한 구간을
+제외하고 프로파일링 함으로써 프로파일링에 소요되는 오버헤드 및 프로파일링 결과물의 크기를 줄일 수 있다.
+``profile.pause()`` 와 ``profile.resume()`` 함수를 호출하여 프로파일링을 임시로 중단/재개할 수 있다.
 자세한 사용법과 예제는 :ref:`TemporarilyDisablingProfiler` 에서 찾아볼 수 있다.
 
 furiosa-litmus 개선
@@ -177,18 +231,18 @@ furiosa-litmus 개선
 0.10.0 릴리스에서는 ``furiosa-litmus`` 명령행 도구에 에러 리포팅을 위한 정보 수집 기능이 추가되었다.
 또한 검사하는 단계가 3단계에서 4단계로 확장되었으며 4단계는 모델을 실제 런타임에서 실행하여 추론 성능을 측정한다.
 
-다음과 같이 ``--dump `` 옵션을 사용하면 ``furiosa-litmus``가 수집한
-각 단계별 로그와 새로 추가된 4단계에서 실행한 추론 실행의 프로파일링 결과를 ``-.zip``
-파일로 저장할 수 있다.
+``furiosa-litmus`` 실행 시 ``--dump `` 옵션을 사용하면
+각 단계별로 출력되는 로그와 컴파일 단계에서 생성하는 메타데이터, 추론 단계에서 수집하는 프로파일링 결과를 모아
+``-.zip`` 파일로 저장한다.
 
 .. code-block:: sh
 
-  $ furiosa-litmus  --dump 
+  $ furiosa-litmus  --dump 
 
 수집된 정보에는 모델 자체를 포함하지 않으며 모델 네트워크의 메타데이터, 메모리 사용량,
 환경 정보(파이썬 버전, SDK, 컴파일러의 버전 및 주요 의존 라이브러리 버전) 등을 포함한다.
-`unzip` 명령을 통해 직접 압축을 풀어 내용을 확인할 수 있으며 버그 리포팅시 해당 파일을 첨부하면
-버그 리포팅에 도움이 된다.
+``unzip`` 명령으로 직접 압축을 풀어 내용을 확인할 수 있으며
+버그 리포팅 시 해당 파일을 첨부하면 진단 및 분석에 도움이 된다.
 
 
 furiosa-bench 명령행 도구 추가
@@ -196,16 +250,19 @@ furiosa-bench 명령행 도구 추가
 성능 벤치마크 명령행 도구인 ``furiosa-bench`` 가 추가되었다.
 새로 도입되는 furiosa-bench 커맨드는 다양한
 워크로드와 런타임 설정을 간단하게 제어할 수 있는 옵션을 제공한다. 지연 시간과 처리량 위주 워크로드를 선택 가능하며
-얼마나 많은 장치를 사용하는지, 얼마나 오래 수행할지 등을 옵션으로 설정할 수 있다. ONNX와 Tflite 모델을
-사용할 수 있으며 이미 컴파일러를 통해 컴파일된 ENF 포맷의 모델도 입력으로 받아 들인다.
+얼마나 많은 장치를 사용할지, 얼마나 오래 수행할지 등을 옵션으로 설정할 수 있다. ONNX와 Tflite 모델을
+사용할 수 있으며 모델을 furiosa-compiler로 컴파일한 ENF 파일도 사용할 수 있다.
 커맨드의 자세한 설명은 :ref:`FuriosaBench` 에서 찾아볼 수 있다.
 
-처리량 벤치마크 예시
+처리량(throughput) 벤치마크 예시
 
 .. code-block::
 
   $ furiosa-bench ./model.onnx --workload throughput -n 10000 --devices "warboy(1)*2" --workers 8 --batch 8
 
+
+지연시간(latency) 벤치마크 예시
+
 .. code-block::
 
   $ furiosa-bench ./model.onnx --workload latency -n 10000 --devices "warboy(2)*1"
@@ -227,33 +284,36 @@ furiosa-toolkit은 NPU 관리 및 상태 모니터링 기능을 제공하는 명
 **furiosactl의 출력 포맷 추가**
 
 이전까지는 ``list``, ``info`` 등의 서브 커맨드 실행 시 아스키를 이용한 표 형식으로 내용을 출력하고 있었다.
-이번 릴리스에는 ``--output`` 옵션을 통해 `json`, `yaml`과 같은 구조적 데이터로 출력하는 기능이 추가되었다.
-따라서, 사용자는 출력된 데이터를 파이프라인을 통해 다른 도구로 전달하거나, 스크립트 언어 등에서 쉽게 처리할 수 있다.
+이번 릴리스에는 ``--format`` 옵션을 통해 ``json``, ``yaml`` 과 같은 구조적 데이터로 출력하는 기능이 추가되었다.
+따라서 사용자는 출력 결과를 파일로 저장하거나 파이프라인을 통해 다른 도구 또는 스크립트로 전달하여 다양한 방식으로 활용할 수 있다.
 
 
 .. code-block::
 
-  $ furiosactl info --output json
-  [{"dev_name":"npu7","product_name":"warboy","device_uuid":"844972B6-8252-4A9F-8A20-142A8B9CE5DA","device_sn":"WBYB0233HH178KREO","firmware":"1.6.0, 7a3b908","temperature":"47°C","power":"0.99 W","pci_bdf":"0000:d6:00.0","pci_dev":"492:0"}]
+  $ furiosactl info --format json
+  [{"dev_name":"npu7","product_name":"warboy","device_uuid":"","device_sn":"","firmware":"1.6.0, 7a3b908","temperature":"47°C","power":"0.99 W","pci_bdf":"0000:d6:00.0","pci_dev":"492:0"}]
 
-  $ furiosactl info --output yaml
+  $ furiosactl info --format yaml
   - dev_name: npu7
     product_name: warboy
-    device_uuid: 844972B6-8252-4A9F-8A20-142A8B9CE5DA
-    device_sn: WBYB0233HH178KREO
+    device_uuid: 
+    device_sn: 
     firmware: 1.6.0, 7a3b908
     temperature: 47°C
     power: 0.98 W
     pci_bdf: 0000:d6:00.0
     pci_dev: 492:0
 
-또한 ``info`` 서브 커맨드가 NPU 클럭 속도(Clock Frequency)를 출력하도록 개선되었다.
+또한 ``info`` 서브 커맨드에 추가/개선된 사항이 있다.
+
+* NPU 클럭 속도(Clock Frequency) 항목을 추가하였다.
+* PCI 카드 전체에서 사용하는 전력(power)을 표시하도록 개선하였다.
 
 **furiosa-npu-metrics-exporter 개선**
 
 ``furiosa-npu-metrics-exporter`` 커맨드는 NPU의 성능 및 상태
 지표들을 `OpenMetrics `_
-형식으로 서빙하는 웹서버이다. 이 지표들은 Prometheus 등 OpenTelemetry의 수집 도구를 통해 수집할 수 있다.
+형식으로 서빙하는 웹서버이다. 이 지표들은 Prometheus 를 비롯한 OpenMetrics 호환 수집 도구를 통해 수집할 수 있다.
 
 0.10.0 릴리스에 포함된 버전은 NPU의 클럭 속도(Clock Frequency)와 NPU 사용률(NPU Utialization)을
 출력 지표로 포함하도록 개선되었다. NPU 사용률 기능은 아직 실험적인 기능으로 아래와 같이 ``--enable-npu-utilization``
diff --git a/v0.10.0/ko/_sources/software/cli.rst.txt b/v0.10.0/ko/_sources/software/cli.rst.txt
index 3066576..870c203 100644
--- a/v0.10.0/ko/_sources/software/cli.rst.txt
+++ b/v0.10.0/ko/_sources/software/cli.rst.txt
@@ -137,7 +137,7 @@ NPU에 존재하는 각 코어가 사용 중인지 유휴 상태인지 여부를
 furiosa-bench (벤치마크 도구)
 ####################################
 
-``furiosa-bench`` 명령은 다양한 런타임 설정을 통해 주어진 모델을 실행시켜 지연시간 및 처리량을 얻을수 있다. 
+``furiosa-bench`` 명령은 다양한 런타임 설정을 통해 주어진 모델을 실행시켜 지연시간 및 처리량을 얻을 수 있다.
 
 문법 개요
 
@@ -195,7 +195,8 @@ MODEL_PATH 는 ONNX, TFLite 혹은 furiosa-compiler를 통해 생성된 ENF를 
 
 .. code-block:: sh
 
-  $ furiosa-bench mnist-8.onnx --workload L -n 1000 -w 8 -t 2 -o mnist.json | cat mnist.json
+  $ furiosa-bench mnist-8.onnx --workload L -n 1000 -w 8 -t 2 -o mnist.json
+  $ cat mnist.json
 
     {
         "model_data": {
diff --git a/v0.10.0/ko/_sources/software/compiler.rst.txt b/v0.10.0/ko/_sources/software/compiler.rst.txt
index 8c99d6b..ed3f684 100644
--- a/v0.10.0/ko/_sources/software/compiler.rst.txt
+++ b/v0.10.0/ko/_sources/software/compiler.rst.txt
@@ -74,7 +74,7 @@ FuriosaAI의 컴파일러는 `TFLite `_ 와 `On
             Print version
 
 ``SOURCE`` 는
-`TFLite `_ 나 `Onnx `_ 파일의 경로이며
+`TFLite `_ 나 `ONNX `_ 파일의 경로이며
 NPU 가속을 위해서는 :ref:`ModelQuantization` 의 결과로 생성된 모델을 사용해야 한다.
 
 ``-o OUTPUT`` 은 생략 가능한 옵션이며 지정한다면 출력되는 파일 이름을 지정할 수 있다.
@@ -204,7 +204,7 @@ scheme 으로 시작하는 URL을 설정하면 Redis 클러스터를 캐쉬 스
   # When you want to specify a Redis cluster over SSL as the cache storage
   export FC_CACHE_STORE_URL=rediss://:@127.0.0.1:25945
 
-캐쉬는 기본으로 30일의 유효시간을 가지고 있으며 환경변수 ``FC_CACHE_LIFETIME`` 를 통해 초 단위 설정을 통해
+캐쉬는 기본으로 30일의 유효기간을 가지고 있으며 환경변수 ``FC_CACHE_LIFETIME`` 를 통해 초 단위 설정을 통해
 오버라이드 가능하다.
 
 .. code-block:: sh
diff --git a/v0.10.0/ko/objects.inv b/v0.10.0/ko/objects.inv
index b3f0baa..f7ae193 100644
Binary files a/v0.10.0/ko/objects.inv and b/v0.10.0/ko/objects.inv differ
diff --git a/v0.10.0/ko/releases/0.10.0.html b/v0.10.0/ko/releases/0.10.0.html
index bc48823..2b176e9 100644
--- a/v0.10.0/ko/releases/0.10.0.html
+++ b/v0.10.0/ko/releases/0.10.0.html
@@ -77,9 +77,9 @@
 
            • 설치 또는 업그레이드 방법
              • 
 
            • 주요 변경 사항
                
 
              • 차세대 런타임 엔진(FuriosaRT) 도입 및 런타임 개선
                • 
-
              • 서빙 프레임워크 및 모델 서버
                • 
+
              • 모델 서버와 서빙 프레임워크 개선
                • 
 
              • 모델 양자화 도구(quantization tool) 개선
                • 
-
              • 컴파일러 개선
                • 
+
              • 컴파일러 개선
                • 
 
              • 성능 프로파일러(Profiler) 개선
                • 
 
              • furiosa-litmus 개선
                • 
 
              • furiosa-bench 명령행 도구 추가
                • 
@@ -135,14 +135,15 @@
              
   
                
 
                Release Notes - 0.10.0
                
-
                Furiosa SDK 0.10.0은 메이저 릴리스(Major Release)로 멀티 디바이스를 지원하는 차세대 런타임 엔진,
-추론 시간을 크게 줄일 수 있는 최적화 옵션, NPU의 사용량과 연산,
-IO 비중을 OpenTelemetry를 통해 제공하는 기능 개선 등을 포함한다.
+
                Furiosa SDK 0.10.0은 메이저 릴리스(Major Release)로
+여러 장치를 지원하는 차세대 런타임 엔진,
+모델의 양자화 연산자를 제거하여 성능을 향샹 시키는 최적화 기능의 사용성 개선,
+NPU 사용률 등의 여러 지표를 OpenMetrics 형식으로 제공하는 기능 등을 포함한다.
 furiosa-litmus는 리포팅 목적의 정보 수집이 가능하도록 개선 되었으며,
-furiosa-compiler 명령행 도구의 파이썬 의존성 제거, 벤치마크 커맨드 도구 추가 등의 기능 개선이 이루어졌다.
+furiosa-compiler 명령행 도구의 파이썬 의존성 제거, 벤치마크 명령행 도구 추가 등의 기능 개선이 이루어졌다.
 그 외에도 다수의 기능 추가, 버그 수정, 성능 개선 등을 포함한다.
                
-
-
+
                컴포넌트 버전 정보
                
+@@ -213,7 +214,7 @@ 
                설치 또는 업그레이드 방법
 
                
 
                경고
                
-
                pip를 업그레이드 할 때 아래 오류를 접한다면, pip install --upgrade pip 커맨드를 통해 pip를 먼저 업그레이드 해야 한다.
                
+
                pip를 업그레이드 할 때 아래 오류를 접한다면, pip install --upgrade pip 명령을 통해 pip를 먼저 업그레이드 해야 한다.
                
 
                ERROR: Could not find a version that satisfies the requirement furiosa-quantizer-impl==0.10.* (from furiosa-quantizer==0.10.*->furiosa-sdk) (from versions: none)
 ERROR: No matching distribution found for furiosa-quantizer-impl==0.10.* (from furiosa-quantizer==0.10.*->furiosa-sdk)
 
                
@@ -229,14 +230,17 @@ 
                차세대 런타임 엔진(FuriosaRT) 도입 및 런타임 개선furiosa-bench 와 furiosa-litmus
 같은 명령행 도구, 그리고 서빙 프레임워크 등에서 사용된다. FuriosaRT는 기존 런타임과
 거의 호환될 뿐 아니라 다음과 같은 새로운 기능을 포함한다.
                
-
                새 API 도입
                
+
                
+
                새 API 도입
                
 
                FuriosaRT는 파이썬 asyncio 를
 사용한 비동기(async) API를 새로이 지원한다. 기존의 동기 API는 배치 작업에는 충분하였으나
 개별적 요청에 따라 추론을 해야 서빙 시나리오에서는 부족함이 있었다. 새로운 런타임에서는 비동기 API가
 기본적으로 지원되며, 기존 기능을 유지하면서도 더 간결하게 사용할 수 있도록 많은 부분이 다듬어졌다.
 새 API에 대한 상세한 설명은 Furiosa SDK API Reference - furiosa.runtime
 에서 찾아 볼 수 있다.
                
-
                다수의 NPU 지원 장치 설정 방법 개선
                
+
                
+
                
+
                멀티 NPU 지원 및 장치 설정 방법 개선
                
 
                FuriosaRT에서는 하나의 세션에서 다수의 장치를 지원한다. 따라서 별도 구현 없이도 다수의 장치를 이용하여 고성능 추론을 수행할 수 있다.
 또한, 개선된 장치 지정 방법을 제공한다. 기존에는 환경변수
 NPU_DEVNAME 또는 session.create(.., device=”..”) 와 같은 API를 이용해서 devfs 의
@@ -244,7 +248,7 @@ 
                차세대 런타임 엔진(FuriosaRT) 도입 및 런타임 개선devfs 이름을 연상 시키는 환경 변수 NPU_DEVNAME 대신
 FURIOSA_DEVICES 라는 새로운 환경변수가 도입되었다. 또한 이 환경 변수는
-NPU 종류, 구성, 개수를 표현하는 텍스트 설정을 받아드린다.
                
+NPU 종류, 구성, 개수를 표현하는 텍스트 설정을 받아 들인다.
                
 
                export FURIOSA_DEVICES="warboy(2)*8"
 
                
 
                
@@ -259,38 +263,88 @@ 
                차세대 런타임 엔진(FuriosaRT) 도입 및 런타임 개선Furiosa SDK API Reference - Device Specification 에서 찾아볼 수 있다.
                
 
                
 
                
-
                서빙 프레임워크 및 모델 서버
                
-
                furiosa-server, furioa-serving 은 각각 모델의 서빙을 제공하는 프레임워크와
-서빙 어플리케이션을 개발하기 위한 라이브러리이다. 이번 릴리스에서는 차세대 런타임인 FuriosaRT를
-사용하도록 개선되었으며 FuriosaRT가 제공하는 장점을 활용할 수 있도록 개선되었다.
                
+
                처리량 향상
                
+
                (TODO) - 데이터 추가 후 작성 완료
                
+
                
+
+
                
+
                모델 서버와 서빙 프레임워크 개선
                
+
                furiosa-server, furioa-serving 은 각각 모델의 서빙을 제공하는 웹 서버와
+서빙 애플리케이션을 개발하기 위한 라이브러리이다.
                
+
                이번 릴리스에서는 차세대 런타임인 FuriosaRT를 사용하도록 개선되었으며 구체적인 개선 사항은 다음과 같다.
                
+
                  
+
                • 멀티 NPU 지원 및 장치 설정 방법 개선 이 그대로 적용되어 다수의 장치를 쉽게 설정할 수 있다.
                  • 
+
                • FuriosaRT가 제공하는 asyncio 기반의 새 API를 사용하도록 내부가 개선되어 더 적은 리소스로 더 높은 성능을 낼 수 있다.
                  • 
+
                • FuriosaRT의 성능 향상 혜택을 그대로 볼 수 있다 (특히 더 높은 워커 수를 이용할 수 있다).
                  • 
+
                
+
                모델 서버에 대한 자세한 설명은 모델 서버 (서빙 프레임워크) 을 참고한다.
                
 
                
 
                
 
                모델 양자화 도구(quantization tool) 개선
                
-
                furiosa-quantizer 는 이미 트레이닝 된 모델을 양자화된 모델로 변환해주는 라이브러리이다.
-(TODO)
                
+
                furiosa-quantizer 는 트레이닝 된 모델을 양자화된 모델로 변환(post-training quantization) 해주는
+라이브러리이다. 이번 0.10.0 릴리스에는 양자화 도구의 사용성을 크게 개선 하였으며 이를 위해
+furiosa.quantizer.quantize() 함수의 일부 파라메터가 변경되었다.
                
+
                
+
                기존 문제점
                
+
                furiosa.quantizer.quantize() 함수는 모델 양자화 도구의 핵심 함수로
+ONNX 모델을 양자화된 모델(quantized model)로 변환하여 반환한다.
+입력 타입을 float32 대신 uint8 로 직접 사용할 수 있도록 하는 with_quantize
+파라메터를 제공하는데, 이미지와 같이 원본 데이터(픽셀 값)이 uint8 로 표현되는 경우에 실행 중 양자화 과정을 생략할 수 있어
+큰 성능 향상을 기대할 수 있다.
+예를 들면, YOLOv5 Large 같은 모델의 경우 이 옵션을 사용하면 60.639 ms 에서 0.277 ms 로 실행 시간이 크게 개선된다.
                
+
                동일한 방법으로 출력 타입도 float32 대신 unt8 로 직접 사용할 수 있도록 하는 normalized_pixel_outputs
+옵션을 제공하는데 모델의 출력이 RGB 형태로 표현되는 이미지이거나, 정수 값으로 직접 사용 가능한 경우 선택할 수 있다.
+이 옵션 역시 큰 성능 향상을 기대할 수 있다.
                
+
                응용에 따라 두 옵션이 사용 가능한 경우 수배에서 수백배까지도 실행 시간을 단축시킬 수 있다.
+그러나 다음과 같은 한계와 피드백이 있었다.
                
+
                  
+
                • 모델의 출력이 이미지가 아닌 경우 파라메터 이름 normalized_pixel_outputs 가 의미를 잘 표현하지 못한다.
                  • 
+
                • normalized_pixel_outputs 옵션은 지정된 출력 텐서의 범위를 0과 1 사이 실수로 전제하는데 다양한 응용에 적용하는데 한계가 있다.
                  • 
+
                • with_quantize, normalized_pixel_outputs 옵션 모두 uint8 타입만 지원했으며 int8 타입을 지원하지 못했다.
                  • 
+
                
 
                
-
                
-
                컴파일러 개선
                
+
                
+
                개선 사항
                
+
                  
+
                • furiosa.quantizer.quantize() 에서 with_quantize, normalized_pixel_outputs 가 파라미터에서 제거 된다.
                  • 
+
                • 대신 ModelEditor 추가되어 모델의 입력/출력 타입에 대한 상세한 최적화 옵션을 설정할 수 있으며 다음 기능을 제공한다.
+* convert_input_type(tensor_name, TensorType) 메소드는 텐서 이름을 받아 해당 입력 텐서에 해당되는
+quantize 연산자를 제거하고 입력 타입을 TensorType 으로 변경한다.
+* convert_output_type(tensor_name, TensorType, tensor_range) 메소드는 텐서 이름을 받아 해당 출력 텐서에 해당되는
+dequantize 연산자를 제거하고 입력 타입을 TensorType 으로 변경하고 주어진 tensor_range 범위에 맞게 스케일하도록 모델을 수정한다.
                  • 
+
                • convert_{output,input}_type 함수는 텐서 이름을 사용하기 때문에 furiosa.quantizer 모듈 아래 get_pure_input_names(ModelProto) get_output_names(ModelProto) 같은 헬퍼 함수를 사용하여 텐서 이름을 얻어야 한다.
                  • 
+
                
+
                
+
                참고
                
+
                이번 릴리즈에 포함된 furiosa.quantizer.quantize() 에서 with_quantize, normalized_pixel_outputs
+가 파라미터에서 제거 된 것은 기존 코드를 수정해야 하는 변경이다.
                
+
                
+
                더 자세한 내용은 ModelEditor 의
+API 레퍼런스를 참고할 수 있으며 실제 적용 예는 튜토리얼 및 코드 예제 의 예제 코드에서 찾아볼 수 있다.
                
+
                
+
                
+
                
+
                컴파일러 개선
                
 
                0.10.0의 컴파일러는 Dequantize 연산자의 NPU 가속을 지원한다. 따라서 Dequantize 연산을 잘 활용할 수 있는
 모델은 성능이 개선될 수 있다. Dequantize 연산자 가속과 관련된 다른 최적화 관련 옵션은 최적화 가이드에서 찾아볼 수 있다.
                
 
                또한, 컴파일러 결과 캐시 기능이 충분히 안정화 되었다고 판단되어 결과 캐쉬 유효기간이 기존 2일에서 30일로 변경된다.
 컴파일러 캐쉬 (Compiler Cache) 에서 자세한 설명을 찾아볼 수 있다.
                
 
                그 외에도 컴파일러 명령행 도구는 다음 사항이 개선되었다.
                
 
                  
-
                • furiosa compile 대신 furiosa-compiler 커맨드를 직접 사용하실 수 있다.
                  • 
-
                • furiosa-compiler 커맨드는 Python 의존성을 필요하지 않는다.
                  • 
+
                • furiosa compile 대신 furiosa-compiler 명령을 직접 사용할 수 있다.
                  • 
+
                • furiosa-compiler 명령은 네이티브 실행 파일(native executable)이 되어 Python 실행 환경을 요구하지 않는다.
                  • 
 
                • apt install furiosa-compiler 명령을 통해 컴파일러를 설치할 수 있다.
                  • 
-
                • Python 의존성을 가지는 기존 furiosa compile 커맨드는 현재는 호환성을 위해 유지되며 향후 릴리스에서 제거 될 수 있다.
                  • 
+
                • Python 의존성을 가지는 기존 furiosa compile 명령는 현재는 호환성을 위해 유지되며 향후 릴리스에서 제거 될 수 있다.
                  • 
 
                
 
                컴파일러 명령행 도구의 자세한 사용법은 furiosa-compiler 에서 찾아볼 수 있다.
                
 
                
 
                
 
                성능 프로파일러(Profiler) 개선
                
-
                성능 프로파일러는 추론의 실제 실행시간을 구간 별로 측정하여 성능 분석을 돕는 도구이다.
+
                성능 프로파일러는 추론의 실제 실행시간을 구간별로 측정하여 성능 분석을 돕는 도구이다.
 이번 릴리스에서는 프로파일러 컨텍스트를 이용한 트레이스 기능에 임시 중단/재개 기능이 추가 되었다.
                
-
                사용자는 프로파일링 컨텍스트 내에서 전/후처리 시간, 워밍업(warming up) 시간 등 프로파일링을 원하지 않는 구간을
-을 제외하고 프로파일링 함으로써 프로파일링에 소요되는 오버헤드 및 프로파일링 결과물의 크기를 줄일 수 있다.
-간단히 profile.pause() 와 profile.resume() 함수를 호출하여 프로파일링을 임시로 중단/재개할 수 있으며
+
                사용자는 프로파일링 컨텍스트 내에서 전/후처리 시간, 워밍업(warming up) 시간 등 프로파일링이 불필요한 구간을
+제외하고 프로파일링 함으로써 프로파일링에 소요되는 오버헤드 및 프로파일링 결과물의 크기를 줄일 수 있다.
+profile.pause() 와 profile.resume() 함수를 호출하여 프로파일링을 임시로 중단/재개할 수 있다.
 자세한 사용법과 예제는 프로파일러 컨텍스트의 일시적 중단/재개 에서 찾아볼 수 있다.
                
 
                
 
                
@@ -298,29 +352,30 @@ 
                furiosa-litmus 개선furiosa-litmus 는 모델에 대한 NPU와 Furiosa SDK의 호환성을 검사하는 도구이다.
 0.10.0 릴리스에서는 furiosa-litmus 명령행 도구에 에러 리포팅을 위한 정보 수집 기능이 추가되었다.
 또한 검사하는 단계가 3단계에서 4단계로 확장되었으며 4단계는 모델을 실제 런타임에서 실행하여 추론 성능을 측정한다.
                
-
                다음과 같이 --dump <OUTPUT_SUFFIX> 옵션을 사용하면 furiosa-litmus``가 수집한
-각 단계별 로그와 새로 추가된 4단계에서 실행한 추론 실행의 프로파일링 결과를 ``<OUTPUT_SUFFIX>-<unix_epoch>.zip
-파일로 저장할 수 있다.
                
-
                $ furiosa-litmus <MODEL_PATH> --dump <OUTPUT_SUFFIX>
+
                furiosa-litmus 실행 시 --dump <OUTPUT_PREFIX> 옵션을 사용하면
+각 단계별로 출력되는 로그와 컴파일 단계에서 생성하는 메타데이터, 추론 단계에서 수집하는 프로파일링 결과를 모아
+<OUTPUT_PREFIX>-<unix_epoch>.zip 파일로 저장한다.
                
+
                $ furiosa-litmus <MODEL_PATH> --dump <OUTPUT_PREFIX>
 
                
 
                
 
                수집된 정보에는 모델 자체를 포함하지 않으며 모델 네트워크의 메타데이터, 메모리 사용량,
 환경 정보(파이썬 버전, SDK, 컴파일러의 버전 및 주요 의존 라이브러리 버전) 등을 포함한다.
-unzip 명령을 통해 직접 압축을 풀어 내용을 확인할 수 있으며 버그 리포팅시 해당 파일을 첨부하면
-버그 리포팅에 도움이 된다.
                
+unzip 명령으로 직접 압축을 풀어 내용을 확인할 수 있으며
+버그 리포팅 시 해당 파일을 첨부하면 진단 및 분석에 도움이 된다.
                
 
                
 
                
 
                furiosa-bench 명령행 도구 추가
                
 
                성능 벤치마크 명령행 도구인 furiosa-bench 가 추가되었다.
 새로 도입되는 furiosa-bench 커맨드는 다양한
 워크로드와 런타임 설정을 간단하게 제어할 수 있는 옵션을 제공한다. 지연 시간과 처리량 위주 워크로드를 선택 가능하며
-얼마나 많은 장치를 사용하는지, 얼마나 오래 수행할지 등을 옵션으로 설정할 수 있다. ONNX와 Tflite 모델을
-사용할 수 있으며 이미 컴파일러를 통해 컴파일된 ENF 포맷의 모델도 입력으로 받아 들인다.
+얼마나 많은 장치를 사용할지, 얼마나 오래 수행할지 등을 옵션으로 설정할 수 있다. ONNX와 Tflite 모델을
+사용할 수 있으며 모델을 furiosa-compiler로 컴파일한 ENF 파일도 사용할 수 있다.
 커맨드의 자세한 설명은 furiosa-bench (벤치마크 도구) 에서 찾아볼 수 있다.
                
-
                처리량 벤치마크 예시
                
+
                처리량(throughput) 벤치마크 예시
                
 
                $ furiosa-bench ./model.onnx --workload throughput -n 10000 --devices "warboy(1)*2" --workers 8 --batch 8
 
                
 
                
+
                지연시간(latency) 벤치마크 예시
                
 
                $ furiosa-bench ./model.onnx --workload latency -n 10000 --devices "warboy(2)*1"
 
                
 
                
@@ -335,16 +390,16 @@ 
                furiosa-toolkit 개선list, info 등의 서브 커맨드 실행 시 아스키를 이용한 표 형식으로 내용을 출력하고 있었다.
-이번 릴리스에는 --output 옵션을 통해 json, `yaml`과 같은 구조적 데이터로 출력하는 기능이 추가되었다.
-따라서, 사용자는 출력된 데이터를 파이프라인을 통해 다른 도구로 전달하거나, 스크립트 언어 등에서 쉽게 처리할 수 있다.
                
-
                $ furiosactl info --output json
-[{"dev_name":"npu7","product_name":"warboy","device_uuid":"844972B6-8252-4A9F-8A20-142A8B9CE5DA","device_sn":"WBYB0233HH178KREO","firmware":"1.6.0, 7a3b908","temperature":"47°C","power":"0.99 W","pci_bdf":"0000:d6:00.0","pci_dev":"492:0"}]
+이번 릴리스에는 --format 옵션을 통해 json, yaml 과 같은 구조적 데이터로 출력하는 기능이 추가되었다.
+따라서 사용자는 출력 결과를 파일로 저장하거나 파이프라인을 통해 다른 도구 또는 스크립트로 전달하여 다양한 방식으로 활용할 수 있다.
                
+
                $ furiosactl info --format json
+[{"dev_name":"npu7","product_name":"warboy","device_uuid":"<device_uuid>","device_sn":"<device_sn>","firmware":"1.6.0, 7a3b908","temperature":"47°C","power":"0.99 W","pci_bdf":"0000:d6:00.0","pci_dev":"492:0"}]
 
-$ furiosactl info --output yaml
+$ furiosactl info --format yaml
 - dev_name: npu7
   product_name: warboy
-  device_uuid: 844972B6-8252-4A9F-8A20-142A8B9CE5DA
-  device_sn: WBYB0233HH178KREO
+  device_uuid: <device_uuid>
+  device_sn: <device_sn>
   firmware: 1.6.0, 7a3b908
   temperature: 47°C
   power: 0.98 W
@@ -352,11 +407,15 @@ 
                furiosa-toolkit 개선info 서브 커맨드가 NPU 클럭 속도(Clock Frequency)를 출력하도록 개선되었다.
                
+
                또한 info 서브 커맨드에 추가/개선된 사항이 있다.
                
+
                  
+
                • NPU 클럭 속도(Clock Frequency) 항목을 추가하였다.
                  • 
+
                • PCI 카드 전체에서 사용하는 전력(power)을 표시하도록 개선하였다.
                  • 
+
                
 
                furiosa-npu-metrics-exporter 개선
                
 
                furiosa-npu-metrics-exporter 커맨드는 NPU의 성능 및 상태
 지표들을 OpenMetrics
-형식으로 서빙하는 웹서버이다. 이 지표들은 Prometheus 등 OpenTelemetry의 수집 도구를 통해 수집할 수 있다.
                
+형식으로 서빙하는 웹서버이다. 이 지표들은 Prometheus 를 비롯한 OpenMetrics 호환 수집 도구를 통해 수집할 수 있다.
                
 
                0.10.0 릴리스에 포함된 버전은 NPU의 클럭 속도(Clock Frequency)와 NPU 사용률(NPU Utialization)을
 출력 지표로 포함하도록 개선되었다. NPU 사용률 기능은 아직 실험적인 기능으로 아래와 같이 --enable-npu-utilization
 옵션을 통해 활성화해야 한다.
                
diff --git a/v0.10.0/ko/searchindex.js b/v0.10.0/ko/searchindex.js
index a6b6d73..716a56f 100644
--- a/v0.10.0/ko/searchindex.js
+++ b/v0.10.0/ko/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["customer-support/bugs", "index", "npu/intro", "npu/supported_operators", "releases/0.10.0", "releases/0.5.0", "releases/0.6.0", "releases/0.7.0", "releases/0.8.0", "releases/0.9.0", "software/c-sdk", "software/cli", "software/compiler", "software/installation", "software/intro", "software/kubernetes_support", "software/profiler", "software/python-sdk", "software/quantization", "software/references", "software/serving", "software/tutorials", "software/vm_support"], "filenames": ["customer-support/bugs.rst", "index.rst", "npu/intro.rst", "npu/supported_operators.rst", "releases/0.10.0.rst", "releases/0.5.0.rst", "releases/0.6.0.rst", "releases/0.7.0.rst", "releases/0.8.0.rst", "releases/0.9.0.rst", "software/c-sdk.rst", "software/cli.rst", "software/compiler.rst", "software/installation.rst", "software/intro.rst", "software/kubernetes_support.rst", "software/profiler.rst", "software/python-sdk.rst", "software/quantization.rst", "software/references.rst", "software/serving.rst", "software/tutorials.rst", "software/vm_support.rst"], "titles": ["\ubc84\uadf8 \uc2e0\uace0", "FuriosaAI NPU \ubc0f SDK  \ubb38\uc11c", "FuriosaAI NPU", "NPU \uac00\uc18d \uc9c0\uc6d0 \uc5f0\uc0b0\uc790 \ubaa9\ub85d", "Release Notes - 0.10.0", "Release Notes - 0.5.0", "Release Notes - 0.6.0", "Release Notes - 0.7.0", "Release Notes - 0.8.0", "Release Notes - 0.9.0", "C SDK \uc124\uce58 \ubc0f \uc0ac\uc6a9 \uac00\uc774\ub4dc", "\uba85\ub839\ud589 \ub3c4\uad6c", "\ucef4\ud30c\uc77c\ub7ec", "\ub4dc\ub77c\uc774\ubc84, \ud38c\uc6e8\uc5b4, \ub7f0\ud0c0\uc784 \uc124\uce58 \uac00\uc774\ub4dc", "FuriosaAI \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uc2a4\ud0dd \uc18c\uac1c", "Kubernetes \uc9c0\uc6d0", "\uc131\ub2a5 \ud504\ub85c\ud30c\uc77c\ub9c1", "Python SDK \uc124\uce58 \ubc0f \uc0ac\uc6a9 \uac00\uc774\ub4dc", "\ubaa8\ub378 \uc591\uc790\ud654", "\ub808\ud37c\ub7f0\uc2a4 \ubb38\uc11c", "\ubaa8\ub378 \uc11c\ubc84 (\uc11c\ube59 \ud504\ub808\uc784\uc6cc\ud06c)", "\ud29c\ud1a0\ub9ac\uc5bc \ubc0f \ucf54\ub4dc \uc608\uc81c", "\uac00\uc0c1 \uba38\uc2e0 \ud658\uacbd \uc9c0\uc6d0"], "terms": {"\uc0ac\uc6a9": [0, 1, 4, 5, 6, 7, 8, 9, 11, 12, 14, 18, 22], "\uc911": [0, 5, 9, 11, 14, 15], "\ud574\uacb0\ub418\uc9c0": 0, "\uc54a\ub294": [0, 3, 4, 7, 14, 16, 17], "\ubb38\uc81c\ub97c": [0, 14, 16, 17], "\uacaa\uc744": 0, "\uacbd\uc6b0": [0, 2, 3, 4, 6, 7, 10, 12, 13, 14, 15, 17, 20, 22], "furiosaai": [0, 3, 5, 6, 8, 10, 11, 12, 13, 15, 16, 18, 20, 22], "\uace0\uac1d\uc9c0\uc6d0": [0, 1, 11], "\uc13c\ud130": [0, 1, 11], "\uc5d0\uc11c": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 17], "\uc2e0\uace0\ub97c": [0, 5], "\ud560": [0, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 20], "\uc218": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22], "\uc788\ub2e4": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22], "\uc2e0\uace0\uc5d0\ub294": 0, "\uae30\ubcf8\uc801\uc73c\ub85c": [0, 4, 7, 11, 15, 16], "\ub2e4\uc74c": [0, 4, 5, 8, 9, 12, 13, 15, 17, 20], "\uc815\ubcf4\uac00": [0, 9, 16], "\ud3ec\ud568\ub418\uc5b4\uc57c": 0, "\ud55c\ub2e4": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 22], "\ubb38\uc81c": [0, 5], "\uc7ac\ud604": 0, "\ubc29\ubc95": [0, 16, 17], "\ubc1c\uc0dd": 0, "\ub2f9\uc2dc": 0, "\ub85c\uadf8": [0, 11], "\ub610\ub294": [0, 2, 7, 10, 12, 13, 15, 16, 17, 20, 22], "\uc2a4\ud06c\ub9b0\uc0f7": 0, "sdk": [0, 3, 4, 5, 8, 9, 11, 12, 15, 16, 18, 19, 20, 21], "\ubc84\uc804": [0, 3, 5, 10, 11, 12, 15, 17, 20], "\uc815\ubcf4": [0, 11], "\ubaa8\ub378": [0, 1, 5, 6, 7, 12, 16, 17, 21], "\ucef4\ud30c\uc77c": [0, 5, 7, 9, 11, 12, 14, 17, 20, 21], "\uc2e4\ud328": 0, "\uc2dc": [0, 4, 7, 9, 13, 15, 17], "\ucef4\ud30c\uc77c\ub7ec": [0, 1, 2, 8, 17], "furiosa": [0, 1, 10, 14, 15, 16, 17, 18, 20, 22], "sdk\ub294": [0, 2, 10, 11, 14, 15, 16, 17, 18, 20], "\uc5d0\ub7ec\uac00": [0, 5], "\ubc1c\uc0dd\ud558\ub294": [0, 17], "\uc544\ub798\uc640": [0, 4, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 20], 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NPU \ubaa8\ub2c8\ud130\ub9c1": [[15, "id4"]], "npu-metrics-exporter \uc218\uc9d1 \ud56d\ubaa9 \ubaa9\ub85d": [[15, "id10"]], "\uc131\ub2a5 \ud504\ub85c\ud30c\uc77c\ub9c1": [[16, "profiling"]], "\ud2b8\ub808\uc774\uc2a4 \ubd84\uc11d (Trace Analysis)": [[16, "trace-analysis"]], "\ud658\uacbd \ubcc0\uc218\ub97c \ud1b5\ud55c \ud2b8\ub808\uc774\uc2a4": [[16, "profilerenabledbyenv"]], "\ud504\ub85c\ud30c\uc77c\ub7ec \ucee8\ud14d\uc2a4\ud2b8\ub97c \uc774\uc6a9\ud55c \ud2b8\ub808\uc774\uc2a4": [[16, "profilerenabledbycontext"]], "\ud504\ub85c\ud30c\uc77c\ub7ec \ucee8\ud14d\uc2a4\ud2b8\uc758 \uc77c\uc2dc\uc801 \uc911\ub2e8/\uc7ac\uac1c": [[16, "temporarilydisablingprofiler"]], "Pandas DataFrame\uc744 \uc774\uc6a9\ud55c \ud2b8\ub808\uc774\uc2a4 \ubd84\uc11d": [[16, "pandas-dataframe"]], "Python SDK \uc124\uce58 \ubc0f \uc0ac\uc6a9 \uac00\uc774\ub4dc": [[17, "python-sdk"]], "\uc124\uce58 \uc0ac\uc804 \uc694\uad6c \uc0ac\ud56d": [[17, "id1"]], "Python \uc2e4\ud589 \ud658\uacbd \uad6c\uc131": [[17, "python"]], "Conda\ub97c \uc774\uc6a9\ud55c Python \ud658\uacbd \uad6c\uc131": [[17, "conda-python"]], "\ub3c5\ub9bd\ub41c Python \uc2e4\ud589 \ud658\uacbd \uc0dd\uc131 \ubc0f \ud65c\uc131\ud654": [[17, "id2"]], "Linux \ud328\ud0a4\uc9c0\ub97c \uc774\uc6a9\ud55c Python \ud658\uacbd \uad6c\uc131": [[17, "linux-python"]], "Python SDK \ud328\ud0a4\uc9c0 \uc124\uce58": [[17, "installpippackages"]], "\ucd94\uac00 \ud328\ud0a4\uc9c0 \ubaa9\ub85d": [[17, "pythonextrapackages"]], "Legacy Runtime/API": [[17, "legacy-runtime-api"]], "FuriosaAI Models": [[17, "furiosaai-models"]], "Model Server": [[17, "model-server"]], "Litmus": [[17, "litmus"]], "\ubaa8\ub378 \uc591\uc790\ud654": [[18, "modelquantization"]], "\ub3d9\uc791 \ubc29\uc2dd": [[18, "id2"]], "\uc591\uc790\ud654 \ubaa8\ub378\uc758 \uc815\ud655\ub3c4": [[18, "id3"]], "Quantization Accuracy": [[18, "id4"]], "\ubaa8\ub378 \uc591\uc790\ud654 APIs": [[18, "apis"]], "\ubaa8\ub378 \uc11c\ubc84 (\uc11c\ube59 \ud504\ub808\uc784\uc6cc\ud06c)": [[20, "modelserving"]], "\ubaa8\ub378 \uc11c\ubc84 \uc124\uce58": [[20, "id2"]], "\ubaa8\ub378 \uc11c\ubc84 \uc2e4\ud589": [[20, "id3"]], "\ubaa8\ub378 \uc124\uc815\uc744 \uc774\uc6a9\ud55c \ubaa8\ub378 \uc11c\ubc84 \uc2e4\ud589": [[20, "id4"]], "\uc5d4\ub4dc\ud3ec\uc778\ud2b8(Endpoint) \uc815\ubcf4": [[20, "endpoint"]], "Endpoints of KServe Predict Protocol Version 2": [[20, "id5"]], "\ud29c\ud1a0\ub9ac\uc5bc \ubc0f \ucf54\ub4dc \uc608\uc81c": [[21, "tutorial"]], "\ud29c\ud1a0\ub9ac\uc5bc": [[21, "id2"]], "\ucf54\ub4dc \uc608\uc81c": [[21, "id4"]], "\uac00\uc0c1 \uba38\uc2e0 \ud658\uacbd \uc9c0\uc6d0": [[22, "vmsupport"]], "\uac00\uc0c1 \uba38\uc2e0\uc5d0 NPU \uc5f0\uacb0 (PCI pass-through)": [[22, "npu-pci-pass-through"]], "\uc608\uc2dc \ud658\uacbd": [[22, "id2"]], "\uc120\ud589\uc870\uac74": [[22, "id3"]], "\uc791\uc5c5 \uc808\ucc28": [[22, "id4"]]}, "indexentries": {}})
\ No newline at end of file
diff --git a/v0.10.0/ko/software/cli.html b/v0.10.0/ko/software/cli.html
index d539752..78d4fcc 100644
--- a/v0.10.0/ko/software/cli.html
+++ b/v0.10.0/ko/software/cli.html
@@ -263,7 +263,7 @@ 
                furiosactl
 
                
 
                furiosa-bench (벤치마크 도구)
                
-
                furiosa-bench 명령은 다양한 런타임 설정을 통해 주어진 모델을 실행시켜 지연시간 및 처리량을 얻을수 있다.
                
+
                furiosa-bench 명령은 다양한 런타임 설정을 통해 주어진 모델을 실행시켜 지연시간 및 처리량을 얻을 수 있다.
                
 
                문법 개요
                
 
                $ furiosa-bench --help
 USAGE:
@@ -311,7 +311,8 @@ 
                furiosactl
                
 
                
 
                output 인자에 파일 이름을 지정하면 벤치마크 실행 결과를 아래와 같이 json 형식으로 파일에 기록한다.
                
-
                $ furiosa-bench mnist-8.onnx --workload L -n 1000 -w 8 -t 2 -o mnist.json | cat mnist.json
+
                $ furiosa-bench mnist-8.onnx --workload L -n 1000 -w 8 -t 2 -o mnist.json
+$ cat mnist.json
 
   {
       "model_data": {
diff --git a/v0.10.0/ko/software/compiler.html b/v0.10.0/ko/software/compiler.html
index e803f80..ddf3fa6 100644
--- a/v0.10.0/ko/software/compiler.html
+++ b/v0.10.0/ko/software/compiler.html
@@ -189,7 +189,7 @@
 
                
 
                
 
                SOURCE 는
-TFLite 나 Onnx 파일의 경로이며
+TFLite 나 ONNX 파일의 경로이며
 NPU 가속을 위해서는 모델 양자화 의 결과로 생성된 모델을 사용해야 한다.
                
 
                -o OUTPUT 은 생략 가능한 옵션이며 지정한다면 출력되는 파일 이름을 지정할 수 있다.
 생략했을 때 기본 출력 파일 이름은 output.enf 이다. ENF는 Executable NPU Format의 확장자이다.
@@ -313,7 +313,7 @@ 
                ENF 파일의 활용export FC_CACHE_STORE_URL=rediss://:<PASSWORD>@127.0.0.1:25945
 
                
 
                
-
                캐쉬는 기본으로 30일의 유효시간을 가지고 있으며 환경변수 FC_CACHE_LIFETIME 를 통해 초 단위 설정을 통해
+
                캐쉬는 기본으로 30일의 유효기간을 가지고 있으며 환경변수 FC_CACHE_LIFETIME 를 통해 초 단위 설정을 통해
 오버라이드 가능하다.
                
 
                # 2 hours cache lifetime
 export FC_CACHE_LIFETIME=7200

                컴포넌트 버전 정보