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| fedscale.edge.execution | ||
| ======================= | ||
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| `This directory <https://github.com/SymbioticLab/FedScale/tree/master/fedscale/edge/mnn>`__ contains minimum files modified from `MNN Android | ||
| Demo <https://github.com/alibaba/MNN/tree/master/project/android/demo>`__. | ||
| The training and testing will be conducted by MNN C++ backend, while the | ||
| task execution and communication with server will be managed by Java. | ||
| The sample has been tested upon image classification with a simple | ||
| linear model and a small subset of | ||
| `ImageNet-MINI <https://www.kaggle.com/datasets/ifigotin/imagenetmini-1000>`__. | ||
| This documentation contains a step-by-step tutorial on how to download, | ||
| build and config this app on your own device, and modify this app for | ||
| your own implementation and deployment. | ||
|
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| Download and build sample android app | ||
| ------------------------------------- | ||
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| 1. Download and unzip `sample dataset | ||
| (TrainTest.zip) <https://drive.google.com/file/d/1nfi3SVzjaE0LPxwj_5DNdqi6rK7BU8kb/view?usp=sharing>`__ | ||
| to ``assets/`` directory. Remove ``TrainTest.zip`` after unzip to | ||
| save space on your mobile device. After unzip, you should see 3 files | ||
| and 2 directories under ``assets/``: | ||
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| 1. ``TrainSet``: Training set directory, contains 316 images. | ||
| 2. ``TestSet``: Testing set directory, contains 34 images. | ||
| 3. ``conf.json``: Configuration file for mobile app. | ||
| 4. ``train_labels.txt``: Training label file with format | ||
| ``<filename> <label>``, where ``<filename>`` is the path after | ||
| ``TrainSet/``. | ||
| 5. ``test_labels.txt``: Testing label file with the same format as | ||
| ``train_labels.txt``. | ||
|
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||
| 2. Run ``sh install.sh``. If you are on Windows, please run this command | ||
| in WSL. This step will clone MNN, replace `MNN Android | ||
| Demo <https://github.com/alibaba/MNN/tree/master/project/android/demo>`__ | ||
| with FedScale Android Executor under ``MNN/project/android/demo``. | ||
| **Note**: you may want to move MNN directory to another directory, | ||
| otherwise the full file path might be too long for cmake to build. | ||
| 3. Install `Android Studio <https://developer.android.com/studio>`__ and | ||
| open project ``MNN/project/android/demo``. Download necessary SDKs, | ||
| NDKs and CMake when prompted. My version: | ||
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| - SDK: API 28 | ||
| - NDK: 20.0.5594570 | ||
| - Android Gradle Plugin Version: 3.5.3 | ||
| - Gradle Version: 5.4.1 | ||
| - Source Compatibility: Java 8 | ||
| - Target Compatibility: Java 8 | ||
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| 4. Make project. Android Studio will compile and build the app for you. | ||
|
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| Test this app with default setting | ||
| ---------------------------------- | ||
|
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| 1. ssh to your own server and run | ||
|
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| :: | ||
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| cd fedscale/cloud/aggregation/android | ||
| python3 android_aggregator.py --experiment_mode=mobile --num_participants=1 --model=linear | ||
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| 2. Change aggregator IP address inside ``assets/conf.json`` and click | ||
| ``Run`` inside Android Studio. | ||
|
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| Customize your own app | ||
| ---------------------- | ||
|
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| 1. If you want to use your own dataset, please put your data under | ||
| ``assets/TrainSet`` and ``assets/TestSet``, make sure that your label | ||
| has the same format as my label file. | ||
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| 1. If you want to change the file/dir name under ``assets``, please | ||
| make sure to change the corresponding config in ``assets`` | ||
| attribute inside ``assets/conf.json``. | ||
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| 2. If you want to use your own model for **image classification**, | ||
| please either change ``channel``, ``width`` and ``height`` inside | ||
| ``assets/conf.json`` to your own input and change ``num_classes`` to | ||
| your own classes, or override these attributes when sending | ||
| ``CLIENT_TRAIN`` request. | ||
| 3. If you want to use your own model for tasks other than image | ||
| classification, you may need to write your own JNI C++ trainer and | ||
| tester. Please refer to `MNN <https://github.com/alibaba/MNN>`__ for | ||
| further development guide. You may also need to change ``channel``, | ||
| ``width`` and ``height`` inside ``assets/conf.json`` to your own | ||
| input and change or remove ``num_classes``. | ||
| 4. Please note that MNN does not support DropOut and will SegFault | ||
| during training. MNN may have other potential bugs. |
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