%global _empty_manifest_terminate_build 0 Name: python-tflite-model-maker-nightly Version: 0.4.3.dev202304200506 Release: 1 Summary: TFLite Model Maker: a model customization library for on-device applications. License: Apache 2.0 URL: http://github.com/tensorflow/examples Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ea/e7/42e0f8e0a101472399a881a5f3fedaa178784145e9ac982fd336768cb614/tflite-model-maker-nightly-0.4.3.dev202304200506.tar.gz BuildArch: noarch Requires: python3-tf-models-official Requires: python3-numpy Requires: python3-pillow Requires: python3-sentencepiece Requires: python3-tensorflow-datasets Requires: python3-fire Requires: python3-flatbuffers Requires: python3-absl-py Requires: python3-urllib3 Requires: python3-tflite-support-nightly Requires: python3-tensorflow Requires: python3-numba Requires: python3-librosa Requires: python3-lxml Requires: python3-PyYAML Requires: python3-matplotlib Requires: python3-six Requires: python3-tfa-nightly Requires: python3-neural-structured-learning Requires: python3-tensorflow-model-optimization Requires: python3-Cython Requires: python3-scann Requires: python3-tensorflowjs Requires: python3-tensorflow-hub Requires: python3-tensorflow-hub %description # TFLite Model Maker ## Overview The TFLite Model Maker library simplifies the process of adapting and converting a TensorFlow neural-network model to particular input data when deploying this model for on-device ML applications. ## Requirements * Refer to [requirements.txt](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/requirements.txt) for dependent libraries that're needed to use the library and run the demo code. * Note that you might also need to install `sndfile` for Audio tasks. On Debian/Ubuntu, you can do so by `sudo apt-get install libsndfile1` ## Installation There are two ways to install Model Maker. * Install a prebuilt pip package: [`tflite-model-maker`](https://pypi.org/project/tflite-model-maker/). ```shell pip install tflite-model-maker ``` If you want to install nightly version [`tflite-model-maker-nightly`](https://pypi.org/project/tflite-model-maker-nightly/), please follow the command: ```shell pip install tflite-model-maker-nightly ``` * Clone the source code from GitHub and install. ```shell git clone https://github.com/tensorflow/examples cd examples/tensorflow_examples/lite/model_maker/pip_package pip install -e . ``` TensorFlow Lite Model Maker depends on TensorFlow [pip package](https://www.tensorflow.org/install/pip). For GPU support, please refer to TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or [installation guide](https://www.tensorflow.org/install). ## End-to-End Example For instance, it could have an end-to-end image classification example that utilizes this library with just 4 lines of code, each of which representing one step of the overall process. For more detail, you could refer to [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb). * Step 1. Import the required modules. ```python from tflite_model_maker import image_classifier from tflite_model_maker.image_classifier import DataLoader ``` * Step 2. Load input data specific to an on-device ML app. ```python data = DataLoader.from_folder('flower_photos/') ``` * Step 3. Customize the TensorFlow model. ```python model = image_classifier.create(data) ``` * Step 4. Evaluate the model. ```python loss, accuracy = model.evaluate() ``` * Step 5. Export to Tensorflow Lite model and label file in `export_dir`. ```python model.export(export_dir='/tmp/') ``` ## Notebook Currently, we support image classification, text classification and question answer tasks. Meanwhile, we provide demo code for each of them in demo folder. * [Overview for TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/guide/model_maker) * [Python API Reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker) * [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb) * [Colab for text classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb) * [Colab for BERT question answer](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb) * [Colab for object detection](https://www.tensorflow.org/lite/tutorials/model_maker_object_detection) %package -n python3-tflite-model-maker-nightly Summary: TFLite Model Maker: a model customization library for on-device applications. Provides: python-tflite-model-maker-nightly BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-tflite-model-maker-nightly # TFLite Model Maker ## Overview The TFLite Model Maker library simplifies the process of adapting and converting a TensorFlow neural-network model to particular input data when deploying this model for on-device ML applications. ## Requirements * Refer to [requirements.txt](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/requirements.txt) for dependent libraries that're needed to use the library and run the demo code. * Note that you might also need to install `sndfile` for Audio tasks. On Debian/Ubuntu, you can do so by `sudo apt-get install libsndfile1` ## Installation There are two ways to install Model Maker. * Install a prebuilt pip package: [`tflite-model-maker`](https://pypi.org/project/tflite-model-maker/). ```shell pip install tflite-model-maker ``` If you want to install nightly version [`tflite-model-maker-nightly`](https://pypi.org/project/tflite-model-maker-nightly/), please follow the command: ```shell pip install tflite-model-maker-nightly ``` * Clone the source code from GitHub and install. ```shell git clone https://github.com/tensorflow/examples cd examples/tensorflow_examples/lite/model_maker/pip_package pip install -e . ``` TensorFlow Lite Model Maker depends on TensorFlow [pip package](https://www.tensorflow.org/install/pip). For GPU support, please refer to TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or [installation guide](https://www.tensorflow.org/install). ## End-to-End Example For instance, it could have an end-to-end image classification example that utilizes this library with just 4 lines of code, each of which representing one step of the overall process. For more detail, you could refer to [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb). * Step 1. Import the required modules. ```python from tflite_model_maker import image_classifier from tflite_model_maker.image_classifier import DataLoader ``` * Step 2. Load input data specific to an on-device ML app. ```python data = DataLoader.from_folder('flower_photos/') ``` * Step 3. Customize the TensorFlow model. ```python model = image_classifier.create(data) ``` * Step 4. Evaluate the model. ```python loss, accuracy = model.evaluate() ``` * Step 5. Export to Tensorflow Lite model and label file in `export_dir`. ```python model.export(export_dir='/tmp/') ``` ## Notebook Currently, we support image classification, text classification and question answer tasks. Meanwhile, we provide demo code for each of them in demo folder. * [Overview for TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/guide/model_maker) * [Python API Reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker) * [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb) * [Colab for text classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb) * [Colab for BERT question answer](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb) * [Colab for object detection](https://www.tensorflow.org/lite/tutorials/model_maker_object_detection) %package help Summary: Development documents and examples for tflite-model-maker-nightly Provides: python3-tflite-model-maker-nightly-doc %description help # TFLite Model Maker ## Overview The TFLite Model Maker library simplifies the process of adapting and converting a TensorFlow neural-network model to particular input data when deploying this model for on-device ML applications. ## Requirements * Refer to [requirements.txt](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/requirements.txt) for dependent libraries that're needed to use the library and run the demo code. * Note that you might also need to install `sndfile` for Audio tasks. On Debian/Ubuntu, you can do so by `sudo apt-get install libsndfile1` ## Installation There are two ways to install Model Maker. * Install a prebuilt pip package: [`tflite-model-maker`](https://pypi.org/project/tflite-model-maker/). ```shell pip install tflite-model-maker ``` If you want to install nightly version [`tflite-model-maker-nightly`](https://pypi.org/project/tflite-model-maker-nightly/), please follow the command: ```shell pip install tflite-model-maker-nightly ``` * Clone the source code from GitHub and install. ```shell git clone https://github.com/tensorflow/examples cd examples/tensorflow_examples/lite/model_maker/pip_package pip install -e . ``` TensorFlow Lite Model Maker depends on TensorFlow [pip package](https://www.tensorflow.org/install/pip). For GPU support, please refer to TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or [installation guide](https://www.tensorflow.org/install). ## End-to-End Example For instance, it could have an end-to-end image classification example that utilizes this library with just 4 lines of code, each of which representing one step of the overall process. For more detail, you could refer to [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb). * Step 1. Import the required modules. ```python from tflite_model_maker import image_classifier from tflite_model_maker.image_classifier import DataLoader ``` * Step 2. Load input data specific to an on-device ML app. ```python data = DataLoader.from_folder('flower_photos/') ``` * Step 3. Customize the TensorFlow model. ```python model = image_classifier.create(data) ``` * Step 4. Evaluate the model. ```python loss, accuracy = model.evaluate() ``` * Step 5. Export to Tensorflow Lite model and label file in `export_dir`. ```python model.export(export_dir='/tmp/') ``` ## Notebook Currently, we support image classification, text classification and question answer tasks. Meanwhile, we provide demo code for each of them in demo folder. * [Overview for TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/guide/model_maker) * [Python API Reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker) * [Colab for image classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_image_classification.ipynb) * [Colab for text classification](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb) * [Colab for BERT question answer](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb) * [Colab for object detection](https://www.tensorflow.org/lite/tutorials/model_maker_object_detection) %prep %autosetup -n tflite-model-maker-nightly-0.4.3.dev202304200506 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-tflite-model-maker-nightly -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Sun Apr 23 2023 Python_Bot - 0.4.3.dev202304200506-1 - Package Spec generated