Tensorflow lite models github
In that blog post, they have provided codes to run it on Android and IOS devices but not for edge devices. models in C++. The model used here is ESRGAN (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks). TensorFlow examples. Now we will create two TensorFlow Lite models — Non-quantized and quantized, base on the one that we created. I’m seeing slow object detection inference times on models trained using the efficientdet_lite0. The key features of TensorFlow Lite are optimized for on-device machine learning, with a focus on latency, privacy, connectivity, size, and power consumption. Tensorflow Model Zoo Tutorial. so. Choosing a neural network and preparing the training pipeline. 2-RELEASE before p7, and 11. Firebase. layers. Github demo source code (kotlin) Training. Now that BL602 has loaded the TensorFlow Lite Model (and knows everything about the wavy curve), let’s test it! This command asks BL602 to infer the output value of the wavy curve, given the input value 0. The submissions will be a single model in TensorflowLite format Our evaluation server during the competition will use a fixed Github commit Load the TF Lite model and JSON file in Android. We are now working to extend MOT techniques and tooling beyond TensorFlow Lite to A repository that shares tuning results of trained models generated by TensorFlow / Keras. Additional Links: Tensorflow github repository. All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically Python 3. The last step is about putting TensorFlow Lite model into our mobile app. TensorFlow Lite. 4K Convert a deep learning model (a MobileNetV2 variant) from Pytorch to TensorFlow Lite. There’s also a codelab with source code on GitHub for you to run through the code yourself. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. 12. Next steps. 0 + JAVA. Setting Up Our Gradle Dependencies for the TensorFlow Lite Model. The only suspicious thing after conversion is the 'while' loop that came from nowhere. 7 ឧសភា 2020 TensorFlow Model Garden. This codelab focuses on TFLite. pb file) to a TensorFlow Lite A repository that shares tuning results of trained models generated by TensorFlow / Keras. However, they have only provided one MobileNet v1 SSD model with Tensorflow lite which is described here. com/tensorflow/examples cd examples/tensorflow_examples/lite/model_maker/pip_package pip install -e . This is the second article in a series of articles about using TensorFlow Lite on Android. TF-TRT. TensorFlow does have bindings for other programming languages . TensorFlow Lite, OpenVINO, CoreML, TensorFlow. 1 - TensorFlow installed from (source or binary): binary - TensorFlow version (or github SHA if from source): tf-nightly==2. Main; ⭐⭐⭐⭐⭐ Tflite Face Detection; Tflite Face Detection Main; ⭐⭐⭐⭐⭐ Jetson Nano Tensorflow Lite; Jetson Nano Tensorflow Lite TensorFlow Lite. I have tried one android project cloned from github which embedded a tflite model in it. Why? If your board has internet connectivity (either Ethernet or Wifi), you may want to load different models as per user needs, or maybe you host your own models and want to keep them updated so they improve the end user experience There is a TensorFlow Lite sample application that demonstrates the smart reply model on Android. Perform inference on the model in the Android app. roma. In My model I require 2 inputs to predict the specific output. Github demo source code (kotlin) **System information** - OS Platform and Distribution (e. After training, a TensorFlow Lite model will be exported which can directly provide top-K predictions among the recommendation candidates. Tensorflow Lite Android. Customize the TensorFlow model. 5-2 seconds on my MacBook Pro (single thread, no GPU). Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. Note that the model we converted upsamples a 50x50 low resolution image to a 200x200 high To host your TensorFlow Lite model on Firebase: In the ML Kit section of the Firebase console, click the Custom tab. Github demo source code (kotlin) TensorFlow Lite for microcontrollers, you use the same model, but there's a different interpreter, and the interpreter is optimized very heavily for these tiny devices. **System information** - OS Platform and Distribution (e. js They are a generalization of vectors and matrices to potentially higher dimensions. Input(shape=[256, 256, 3]) out = tf This guide will show you the steps to get TensorFlow 2 and TensorFlow Lite running on your Raspberry Pi 4 along with an object detection demo. com/bitsy-ai/tensorflow-arm-bin/releases 8 ធ្នូ 2020 Tensorflow lite models can be used on Android and IOS, git clone -b v2. Start with the official TensorFlow Docker image, like github you can pull/commit/push and implictly fork when you do this between sources. TensorFlow. Github demo source code (kotlin) tensorflow-nightly prod/tensorflow/nightly_release/macos/cpu_py36/184/20210415-010116/github/tensorflow/pip_pkg/tf_nightly-2. Inside this project, you will also learn how to build an app with custom C++ ops. In this video, I'll create a simple deep learning model using Keras and convert it to TensorFlow Lite for use on mobile, or IoT devices. tflite models that were using float values for input and output tensors (they Yourownmodel Tflite Raspberrypi ⭐ 3. 2-RELEASE before p6, and 11. Convert Tensorflow SSD models to TFLite format. Tensors. Lite. 04 …): MacOS 10. Host models on Firebase. Convert bitmap to the format supported by a machine learning model. e, it has a small binary size, it doesn't require operating system support, any standard TensorFlow Lite is a set of tools that enables on-device machine learning by helping developers run their models on mobile, embedded, and IoT devices. TensorFlow. I will be referring to various files of the GitHub repository of this project. It adheres to constraints required in these embedded environments, i. -j -t label_image Available Options to build TensorFlow Lite. 2) Tensorflow v2 TensorFlow Lite models. I’m using the TF Lite model maker example notebook for object detection with a custom dataset and am seeing inference times of 1. 4-STABLE before r369559, 13. Read the GitHub page to learn how the app works. About Tensorflow Model Zoo Tutorial. Note that the model we converted upsamples a 50x50 low resolution image to a 200x200 high The last step is about putting TensorFlow Lite model into our mobile app. (test_data) # Export to Tensorflow Lite model TensorFlow Lite For Microcontrollers is a software framework, an optimized version of TensorFlow, targeted to run tensorflow models on tiny, low-powered hardware such as microcontrollers. 4-RELEASE before p9, copy-on-write logic failed to invalidate shared memory page mappings between multiple processes allowing an unprivileged process to maintain a mapping after it is freed A repository that shares tuning results of trained models generated by TensorFlow / Keras. 17. OpenVINO. In this article, you trained and registered a TensorFlow model, and learned about options for deployment. Because of TensorFlow 2. I have fixed input size, no resize ops in the model, all ops are supported by TFLite builtins. deploy(ws, "tensorflow-web-service", [model]) The full how-to covers deployment in Azure Machine Learning in greater depth. Train a custom object detection model using TensorFlow Lite Model Maker. Could you tell whats changes do I need to do in coding for taking two inputs. ckpt/. js, TF-TRT, MediaPipe, ONNX [. 9 This guide will show you the steps to get TensorFlow 2 and TensorFlow Lite running on your Raspberry Pi 4 along with an object detection demo. 7K: Xamarin. 2 Run an Inference. Tensors are the core datastructure of TensorFlow. docker pull Start by installing the required packages, including the Model Maker package from the GitHub repo and the pycocotools library you'll use for evaluation. Before you use a custom model in a publicly-available app, be aware of the security implications. create (data) Step 4. In the previous section the development environment was set up for working with TensorFlow models and Android development. The weights from this model were ported from Tensorflow/TPU. The model architectures included come from a wide variety of sources. lite ). 8. Image classification with TensorFlow Lite Model Maker with TensorFlow 2. surroundings. This is the output of know-how for converting Tensorflow checkpoints (. articolisportivi. Here is the list of available options. Tests. Specify a name that will be used to identify your model in your Firebase project, then upload the TensorFlow Lite model file (usually ending in . Yes, dogs and cats too. 1 has few new enhancements for TensorFlow Lite models: First, it lets us import a tflite model directly from the import menu and places it in an ml folder. I will be referring to various files of the GitHub . The first option is with a PiTFT if you want to have a larger display. This is an awesome list of TensorFlow Lite models with sample apps, helpful tools and learning resources - TensorFlow Lite for Microcontrollers. lite. Github demo source code (kotlin) We would like to show you a description here but the site won’t allow us. Prerequisites. Convert your Tensorflow Object Detection model to Tensorflow Lite. 0 nature, we’ll need to convert the TensorFlow model into concrete function and then do a conversion to TensorFlow Lite (more about it here). Launching the Model Optimizer for a model with custom TensorFlow operations (refer to the TensorFlow* documentation) implemented in C++ and compiled into the shared library my_custom_op. tflite, . pb file, which have the following directory structure, in addition to the saved_model. 19. Number classifier. ONNX. python. EfficientNet-Lite makes EfficientNet more suitable for mobile devices by introducing ReLU6 activation functions and removing squeeze-and-excitation blocks. Use your training data. Keras, easily convert a model to . , 2018) (USE) is a model that encodes text into 512-dimensional embeddings. Annotating images and serializing the dataset. GitHub Gist: instantly share code, notes, and snippets. Convert the Keras model to a TensorFlow Lite model; Convert the Keras’ tokenizer vocabulary to a JSON file. model. 0-STABLE before n245765-bec0d2c9c841, 12. model = image_classifier. 1. 2-STABLE before r369859, 11. Detect multiple objects with bounding boxes. TensorFlow Lite example apps. Similarly to the notebook, we can see input and output data shapes and their types. tflite,. 75 depth SSD models, both models trained on the Common Objects in Context (COCO) dataset, converted to TensorFlow Lite. loss, accuracy = model. xml/. I can bring this down to around 0. org. h5, . Create a assets folder and place your label file and model file in it. 0. bin, . evaluate Step 5. TensorFlow Lite models can be made even smaller and more efficient through quantization, which converts 32-bit parameter data into 8-bit representations (which is required by the Edge TPU). That set was later used Tensorflow Face Detection Github About Tensorflow Face Detection Github If you are not founding for Tensorflow Face Detection Github, simply check out our links below : A repository that shares tuning results of trained models generated by TensorFlow / Keras. CoreML. Github demo source code (kotlin) TensorFlow Lite. For the new tensorflow in colab 2. 1 0. This time I'll show you how to download models from internet. From what I've read, the process I need to follow is to convert the ONNX model to a TensorFlow model, then convert that TensorFlow model to a TensorFlow Lite model. See full list on github. The conversion process should be: Pytorch →ONNX → Tensorflow → TFLite. Step 3. js models, and PyTorch checkpoints (. TFLite is targeted Code Cell 8 (Converting the Trained neural network into a Tensorflow Lite file) saved_model_dir = you could download these files from the assets folder of my GitHub Repo → https://git. View Android example. Classify handwritten digits through the TensorFlow Lite model. 2), when model is saved using tf. How to train your own object detection models using the TensorFlow Object Detection API (2020 Update) This started as a summary of this nice tutorial, but has since then become its own thing. Model Optimizer falls back to TensorFlow to infer output shape of operations implemented in the library if a custom TensorFlow operation library is provided. Task Library is a cross-platform library that makes it easy to deploy TensorFlow Lite models with just a few lines of code in your mobile apps. 9 TensorFlow Lite. We can start using the demo app provided in the TensorFlow GitHub git clone https://github. There are two main setup paths to choose from. Model. 2 you need to increase the number of samples to get a good result, I tried with 5000 and worked. I'll up the number of samples. export (export_dir = '/tmp/') Notebook. Something very similar was done in the post Inspecting TensorFlow Lite image classification model (see TFLite-Checker Github repository for the implementation). TensorFlow Lite Model Maker 6 មេសា 2021 Note that you can't train models using Tensorflow Lite. Here we make a TensorFlow Lite model from a pre-trained model. 160969. Most included models have pretrained weights. Yourownmodel Tflite Raspberrypi ⭐ 3. About DEMO. 31 មីនា 2021 Machine Learning models on constrained devices // TensorFlow Lite Clone the TensorFlow examples GitHub repository to your computer to 23 មីនា 2019 With the launch of TensorFlow Lite, embedding machine learning models in Android apps has never been easier. com/tensorflow/tensorflow export 13 មេសា 2021 A Flutter plugin for accessing TensorFlow Lite. com articolisportivi. Section Code Insert code cell below. # infer 0. Evaluate the model. Convert a TensorFlow model, Keras model, or concrete function to TensorFlow Lite. 04 x86_64 Tensorflow v1. Python is the primary language in which TensorFlow models are typically developed and trained. , 3. Github demo source code (kotlin) service = Model. We have utility functions for common cases like Scalar, 1D, 2D, 3D and 4D tensors, as well a number of functions to initialize tensors in ways useful for machine learning. TensorFlow Lite uses many techniques for this such as quantized kernels that allow smaller and faster (fixed-point math) models. x, you can train a model with tf. Contribute to tensorflow/examples development by creating an account on GitHub. -j -t benchmark_model cmake --build . Preprocess bitmap (cropping, rotating, transformation, or color enhancements). It enables on-device machine learning inference with low latency and a small binary size. You cannot train a model directly with TensorFlow Lite; instead you must convert your model from a TensorFlow file (such as a . convert import convert_jax_hlo as _convert_jax_hlo The converter will inspect the sparsity pattern of the model weights and. The weights are either: TensorFlow Lite models Convert the model to TensorFlow Lite. TensorFlow Lite on GitHub. Load the TF Lite model and JSON file in Android. In order t o test the converted models, a set of roughly 1,000 input tensors was generated, and the PyTorch model’s output was calculated for each. Training the network. TensorFlow’s Model Optimization Toolkit (MOT) has been used widely for converting/optimizing TensorFlow models to TensorFlow Lite models with smaller size, better performance and acceptable accuracy to run them on mobile and IoT devices. Before you begin Environment Ubuntu16. You can follow this tutorial to learn how to use the toolkit and In the video, you can learn the steps to build a custom object detector: Prepare the training data. 0+ (Bazel 0. We create a classifier in Python using TensorFlow and Keras. The TFLite model is converted from this implementation hosted on TF Hub. com TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded devices. it; Tensorflow Face Detection Github ⭐⭐⭐⭐⭐ Multi Label Text Classification Tensorflow; Multi Label Text Classification Tensorflow A repository that shares tuning results of trained models generated by TensorFlow / Keras. Explore optimized TF Lite models and on-device ML solutions for mobile and edge use cases. g. 11. 13/tensorflow/lite/schema/schema. pth) into quantization models for Tensorflow Lite. See these other articles to learn more about Azure Machine Learning. Model Summaries. 2-STABLE before r369551, 11. pb file) to a TensorFlow Lite Xamarin. tflite models that were using float values for input and output tensors (they A repository that shares tuning results of trained models generated by TensorFlow / Keras. 6. Build TensorFlow Lite Benchmark Tool and Label Image Example (Optional) In the tflite_build directory, cmake --build . This is a beta release of Firebase ML. 4. Explore pre-trained TensorFlow Lite models and learn how to use them in sample apps for a variety of ML applications. We will be using scripts from the TF Model Garden, so start by cloning this repo: git clone https://github. pb,saved_model,tfjs,tftrt,mlmodel,. [. Visualizer for neural network, deep learning, and machine learning models An awesome list of TensorFlow Lite models, samples, tutorials, Contribute to tensorflow/tflite-micro development by creating an account on a port of TensorFlow Lite designed to run machine learning models on DSPs, An awesome list of TensorFlow Lite models, samples, tutorials, tools and learning resources. In addition to the trained model, we provide an open-sourced toolkit in GitHub to train models with your own data. io/JLhpv. dev20191203 **Command used to run the converter or code if you’re using the Python API** ``` import pathlib inpt = tf. These embeddings can then be used as inputs to natural language processing tasks such as sentiment classification and textual similarity analysis. Github demo source code (kotlin) I created a model through tensorflow and then converted the same to tensorflow lite. Edge TPU is Google's purpose-built ASIC chip designed to run TensorFlow Lite machine learning (ML) models on small client computing devices such as 22 វិច្ឆិកា 2020 TensorFlow Lite models running on Raspberry Pi 4 boards can achieve pip install https://github. This is a tiny little Details: Nov 08, 2020 · From Tensorflow Version (2. In the book, jump to “Converting the Model for TensorFlow Lite”. Deploy the model on your mobile app using TensorFlow Lite Task Library. Introduction. TensorFlow lite provides a 5 សីហា 2020 I just posted this feature request on the Google Tensorflow Github site to allow Tensorflowjs models to be directly converted to TensorflowLite 27 សីហា 2018 With the advancement in deep learning in the past few years, we are able to create complex machine learning models for detecting objects. Main; ⭐⭐⭐⭐⭐ Tensorflow Get All Weights; Tensorflow Get All Weights Main; ⭐⭐⭐⭐⭐ Tensorflow Documentation Pdf Download; Tensorflow Documentation Pdf Download Conda Environment. tflite or . View Image. Interpreter 122. in the Tensorflow github that experimental support for Tensorflow Lite was added Please refer to https://github. Click Add custom model (or Add another model ). h5), Tensorflow. TensorFlow Lite models. Image classification can be a multi-step process, similar to this: Capture image or frame from device’s camera. Estimate poses for single or multiple Tensorflow Lite Android. 4-RELEASE before p10, missing message validation in libradius(3) could allow malicious clients or servers to trigger denial of service in vulnerable servers or clients respectively. 0 (Bazel 0. 0-RC5 before p1, 12. h5,. Github demo source code (kotlin) Please refer the "Create a CMake project which uses TensorFlow Lite" section. Japanese English - English - 1. com/google/flatbuffers for details # and . pb file. 0-STABLE before n245117, 12. Github demo source code (kotlin) In other ones, the root of the issue is dynamic shapes of input (resizing basically), but I don't get where it can come from in my model. The model Solutions to common problems. Netron visualization of Mnist, TensorFlow Lite model You can see there not only input and output but actually entire machine learning model, layer by layer. Github demo source code (kotlin) Tensorflow has recently released its object detection API for Tensorflow 2 which has a very large model zoo. it; Tensorflow Face Detection Github A repository that shares tuning results of trained models generated by TensorFlow / Keras. If you are search for Tensorflow Model Zoo Tutorial, simply found out our links below : TensorFlow Lite. 1 https://github. I also try to convert it to OpenVINO's IR model as much as possible. 15. 2) Tensorflow v1. These bindings have the low-level primitives that are required to build a more complete API, however, lack much of the higher-level API richness of A repository that shares tuning results of trained models generated by TensorFlow / Keras. , Linux Ubuntu 16. The model generates reply suggestions to conversational chat messages. Identify hundreds of objects, including people, activities, animals, plants, and places. A repository that shares tuning results of trained models generated by TensorFlow / Keras. To host your TensorFlow Lite model on ModelZoo curates and provides a platform for deep learning researchers to easily find code and pre-trained models for a variety of platforms and uses. Age Estimation | Gender Classification | Android Application | TensorFlow separable convolutions as the “lite” model in the README of the GitHub repo. A few days ago I showed you how to load Tensorflow Lite Tinyml models from an SD card in Arduino. Github demo source code (kotlin) The Universal Sentence Encoder ( Cer et al. (test_data) # Export to Tensorflow Lite model TensorFlow is an end-to-end open source platform for machine learning. pb), keras_model (. Gpu Bindings for Google's TensorFlow Lite GPU package (Google Play Services dependency) 2. js GraphModel converted from the USE lite ( module Benchmarking was done using both TensorFlow and TensorFlow Lite on a Raspberry Pi 3, Model B+, and on the 4GB version of the Raspberry Pi 4, Model B. com/tensorflow/tensorflow/r1. 27 កញ្ញា 2021 How to Run TensorFlow Lite Object Detection Models on the Raspberry Pi (with We'll use Anaconda's git package to download the TensorFlow Dec 02, 2020 · This gist demonstrates taking a model (a TensorFlow graph) created by a Python program and running the training loop in a C program. 0-RELEASE before p1, 12. Android Bindings for Google Play Services - Xamarin. Conda Environment. pb), saved_model (. AndroidStudio 4. Tensorflow has recently released its object detection API for Tensorflow 2 which has a very large model zoo. meta), FreezeGraph (. Currently, we support image classification, text classification and question answer tasks. 4-STABLE before r369866, 13. When you deploy your model with Firebase, Firebase ML only downloads the model when it's needed and automatically updates your users with the latest version. x. View source on GitHub Public APIs for TFLite Model Maker, a transfer learning library to train custom TFLite models. Input(shape=[256, 256, 3]) out = tf Main; ⭐⭐⭐⭐⭐ Yolo Lite Demo; Yolo Lite Demo Main; ⭐⭐⭐⭐⭐ Tensorflow Get All Weights; Tensorflow Get All Weights Main; ⭐⭐⭐⭐⭐ Ssd Mobilenet V2 Coco; Ssd Mobilenet V2 Coco None: Remote: Low: Not required: None: None: Partial: In FreeBSD 13. Export to Tensorflow Lite model and label file in export_dir. TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on DSPs, microcontrollers and other devices with limited memory. Github demo source code (kotlin) from tensorflow. TensorFlow Lite models are ML models that are optimized to run on mobile devices. The code that I am consider as my base in Hello word project that is: github. And we are going to use TensorFlow Lite to run inference on the pretrained model. TensorFlow Lite is T How to train your own object detection models using the TensorFlow Object Detection API (2020 Update) This started as a summary of this nice tutorial, but has since then become its own thing. @bonadio good to know, thank you! I thought it was a limitation with the model itself. . Multiple examples showing how to stream images from a camera and run classification or detection models with the TensorFlow Lite API. If you use custom TensorFlow Lite models, Firebase ML can help you ensure your users are always using the best-available version of your custom model. com For the new tensorflow in colab 2. Tensors / Creation. onnx]. 1 …. models. What is TensorFlow lite (TFLite)? To meet all the criteria of the models mentioned above, Google provides an on-device inference engine called TensorFlow-lite (TFLite). save_model, the model will be saved in a folder and not just as a . Android Studio 4. It allows you to run machine learning models on edge devices with low latency, which eliminates the need for a server. In the post, we implemented *. tflite and deploy it; or you can download a pretrained TensorFlow Lite model from the model zoo. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below. This module is a TensorFlow. Step 6. fbs # Generate All the code that we will use is available in TensorFlow's GitHub repository. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to exploring the emerging intersection of mobile app The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2. 2) Tensorflow v2 I'd like to use models from here in a TensorFlow Lite (Android) application and I'm running into problems figuring out how to get the models converted. Updated: June, 2021. TensorFlow Lite is an open source deep learning framework for device-side inference , deploying machine learning models on mobile devices and IoT devices. Inferencing was carried out with the MobileNet v2 SSD and MobileNet v1 0. MediaPipe. Testing TensorFlow Lite models on Android, especially on the emulator, isn’t trivial. With TensorFlow 2. TFLM at tensorflow. 7. by Gilbert Tanner on Jan 27, 2020 · 6 min read TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. TensorFlow Lite platforms also support Core ML models as well as iOS platforms. x, 3. Ctrl+M B View source on GitHub: Download notebook [ ] 3. In this article, I'd like to share with you the quantization workflow I've been working on for six months. Environment Ubuntu16. 18 កុម្ភៈ 2021 trained tensorflow models in android platform. Android Tensorflow Lite Example ⭐ 703. keras. To get a TensorFlow Lite model: Use a pre-built model, such as one of the official TensorFlow Lite models. ML. For this section, you will need to See full list on github. The model returned from this transfer learning process was then converted into a TensorFlow Lite model, which was implemented in an Android Studio project to predict the class labels of new images. Just go to File > New > Other > TensorFlow Lite Model. Model Maker is a Python library that makes it easy to train TensorFlow Lite models using your own data with just a few lines of code, no machine learning expertise required. 75s with num_threads set to 4 but this seems to be much greater than the 37ms TensorFlow Lite. 6. An "AI-on-device" project walks with you through all necessary steps, from collecting your own data, creating and training your own Tensorflow model, generating your own Tensorflow-lite model, developing both Python and C++ programs to recognize images on Raspberry Pi 3. How it works. BL602 responds with the inferred output value 0. Before you begin TensorFlow Lite. js. dev20210415-cp36-cp36m-macosx_10_14 Main; ⭐⭐⭐⭐⭐ Tensorflow Autoencoder Anomaly Detection; Tensorflow Autoencoder Anomaly Detection Main; ⭐⭐⭐⭐⭐ Tensorflow Documentation Pdf Download; Tensorflow Documentation Pdf Download None: Local: Low: Not required: Partial: None: None: In FreeBSD 13.