# ML Samples with Docker for Windows Container ### Windows base image for containers install > https://hub.docker.com/_/microsoft-windows Upgrade os版本到Microsoft Windows [Version 10.0.17763.1339] = 2020年7月14日-KB4558998 （作業系統組建17763.1339） 更新完後執行docker前必須先到該路徑執行dockerd.exe 接著docker 中pull下來 ``` docker pull mcr.microsoft.com/windows:[version] ``` :::info 測試是使用10.0.17763.1339-amd64或是1809兩個版本都ok 以下皆以1809為範例操作 :::  --- ### DirectX Container Sample > https://github.com/MicrosoftDocs/Virtualization-Documentation/tree/master/windows-container-samples/directx :::info 這邊是用[WinMLRunner(Windows-Machine-Learning Tool)](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Tools/WinMLRunner)去跑tinyyolov2的一個sample 主要為了示範GPU加速容器化及運行DirectX workload ::: 用notepad++ create a file called "Dockerfile" Dockerfile中寫入以下 ``` > Dockerfile FROM mcr.microsoft.com/windows:1809 WORKDIR C:/App # Download and extract the ONNX model to be used for evaluation. RUN curl.exe -o tiny_yolov2.tar.gz https://onnxzoo.blob.core.windows.net/models/opset_7/tiny_yolov2/tiny_yolov2.tar.gz && \ tar.exe -xf tiny_yolov2.tar.gz && \ del tiny_yolov2.tar.gz # Download and extract cli tool for evaluation .onnx model with WinML. RUN curl.exe -L -o WinMLRunner_x64_Release.zip https://github.com/microsoft/Windows-Machine-Learning/releases/download/1.2.1.1/WinMLRunner.v1.2.1.1.zip && \ tar.exe -xf C:/App/WinMLRunner_x64_Release.zip && \ del WinMLRunner_x64_Release.zip # Run the model evaluation when container starts. ENTRYPOINT ["C:/App/WinMLRunner v1.2.1.1/x64/WinMLRunner.exe", "-model", "C:/App/tiny_yolov2/model.onnx", "-terse", "-iterations", "100", "-perf"] ``` 接著回到cmd cd到剛檔案 將該dockerfile build起來 ``` docker build . -t winml-runner ``` build完如果沒出錯 就可run ``` docker run --isolation process --device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599 winml-runner ``` 這支sample跑一個ML Model 100次 一開始用CPU優化，接著跑GPU 最後產出一個report --- ### Windows Container Sample - Python > https://github.com/MicrosoftDocs/Virtualization-Documentation/tree/master/windows-container-samples/python :::info 這個sample主要是在windows container裡搭建python3.7.3環境 然後簡單的print一個hello world出來 創建一個新的資料夾 接著新增一個Dockerfile ::: ``` > Dockerfile # This dockerfile utilizes components licensed by their respective owners/authors. # Prior to utilizing this file or resulting images please review the respective licenses at: https://docs.python.org/3/license.html FROM mcr.microsoft.com/windows:1809 LABEL Description="Python" Vendor="Python Software Foundation" Version="3.7.3" RUN powershell.exe -Command \ $ErrorActionPreference = 'Stop'; \ [Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12; \ wget https://www.python.org/ftp/python/3.7.3/python-3.7.3.exe -OutFile c:\python-3.7.3.exe ; \ Start-Process c:\python-3.7.3.exe -ArgumentList '/quiet InstallAllUsers=1 PrependPath=1' -Wait ; \ Remove-Item c:\python-3.7.3.exe -Force RUN echo print("Hello World!") > c:\hello.py CMD ["py", "c:/hello.py"] ``` 跟上一個範例一樣 先build ``` docker build -t 'name' ``` ``` docker run -it 'name' ``` >Install Python via command line/powershell without UI (quietly/slient install python) 透過cmd以無UI的方式安裝Python 選擇版本:https://www.python.org/ftp/python/ ``` > powershell Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12 wget https://www.python.org/ftp/python/[version].exe -OutFile c:\[version].exe Start-Process c:\[version].exe -ArgumentList '/quiet InstallAllUsers=1 PrependPath=1' ``` --- ### Tensorflow Directml Sample > https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-tensorflow-windows ::: warning *這邊注意的是tensorflow只支援64 bits Python 3.5 - 3.7 以及tensorflow需要msvcp140.dll這個元件 解決方式是安裝Microsoft Visual C++ 2015 Redistributable Update 3 範例中用的python檔 放在dockerfile同一個directory中 ::: ``` > Dockerfile FROM mcr.microsoft.com/windows:1809 # assign work directory WORKDIR /python # move all files to work directory including test.py COPY . /python # Silent Install Microsoft Visual C++ 2015 Redistributable Update 3 RUN powershell.exe -Command \ wget https://download.microsoft.com/download/9/3/F/93FCF1E7-E6A4-478B-96E7-D4B285925B00/vc_redist.x64.exe -OutFile vc_redist.x64.exe ; \ Start-Process vc_redist.x64.exe -ArgumentList '/q /norestart' -Wait Remove-Item vc_redist.x64.exe -Force # Silent Install Python 3.6.1 64bits RUN powershell.exe -Command \ $ErrorActionPreference = 'Stop'; \ [Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12; \ wget https://www.python.org/ftp/python/3.6.1/python-3.6.1rcl-amd64.exe -OutFile python-3.6.1rcl-amd64.exe ; \ Start-Process python-3.6.1rcl-amd64.exe -ArgumentList '/quiet InstallAllUsers=1 PrependPath=1' -Wait ; \ Remove-Item python-3.6.1rcl-amd64.exe -Force RUN pip install tensorflow-directml # -u to insure python print is working CMD ["py", "-u", "test.py"] ``` test.py中的內容只是用來測試tensorflow是否成功安裝 ```python= import tensorflow.compat.v1 as tf tf.enable_eager_execution(tf.ConifProto(log_device_placement=True)) print(tf.add([1.0, 2.0], [3.0, 4.0])) ``` ``` docker build -t tensorflow-directml . ``` ``` docker run -it tensorflow-directml ``` result: ```python 2020-07-23 20:06:09.756930: I tensorflow/core/common_runtime/dml/dml_device_factory.cc:45] DirectML device enumeration: found 1 compatible adapters. 2020-07-23 20:06:09.917532: I tensorflow/core/common_runtime/dml/dml_device_factory.cc:32] DirectML: creating device on adapter 0 (Microsoft Basic Render Driver) 2020-07-23 20:06:09.433379: I tensorflow/stream_executor/platform/default/dso_loader.cc:60] Successfully opened dynamic library DirectMLba106a7c621ea741d2159d8708ee581c11918380.dll 2020-07-23 20:06:09.558039: I tensorflow/core/common_runtime/eager/execute.cc:571] Executing op Add in device /job:localhost/replica:0/task:0/device:DML:0 tf.Tensor([4. 6.], shape=(2,), dtype=float32) ``` 已經包好push到Docker hub https://hub.docker.com/r/msxlol/tensorflow-directml-sample *How to Use* ``` docker run msxlol/tensorflow-directml ``` --- ### GPU Enabled Images Examples >https://www.tensorflow.org/install/docker ``` docker run --gpus all -it --rm tensorflow/tensorflow:latest-gpu \ python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))" ``` result: