tpu | 易学教程

how to use torchaudio with torch xla on google colab tpu

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问题 I'm trying to run a pytorch script which is using torchaudio on a google TPU. To do this I'm using pytorch xla following this notebook, more specifically I'm using this code cell to load the xla: !pip install torchaudio import os assert os.environ['COLAB_TPU_ADDR'], 'Make sure to select TPU from Edit > Notebook settings > Hardware accelerator' VERSION = "20200220" #@param ["20200220","nightly", "xrt==1.15.0"] !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup

How to connect to private storage bucket using the Google Colab TPU

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问题 I am using google colab pro and the provided TPU. I need to upload a pre-trained model into the TPU. TPU can load data only from a google cloud storage bucket. I created a cloud storage bucket and extracted the pre-trained model files in the bucket. Now I need to give permission to the TPU to access my private bucket, but I don't know the service account of the TPU. How do I find it? For now I just have All:R read permission to the bucket and the TPU initialized successfully but clearly this

Google EdgeTPU can't get PWM to work with Python

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问题 Here is my testing code: from periphery import PWM import time # Open PWM channel 0, pin 0 pwm = PWM(0,0) # Set frequency to 1 kHz pwm.frequency = 50 # Set duty cycle to 75% pwm.duty_cycle = 0.02 pwm.enable() print(pwm.period) print(pwm.frequency) print(pwm.enabled) # Change duty cycle to 50% pwm.duty_cycle = 0.05 pwm.close() Problem is this part: # Open PWM channel 0, pin 0 pwm = PWM(0,0) I can see output when running PWM(0,0) PWM(0,1) PWM(0,2) but I get the error messsage when trying to run

Google EdgeTPU can't get PWM to work with Python

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谷歌Coral USB Accelerator最新安装使用指南

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谷歌Coral USB加速器是一种USB设备，提供Edge TPU作为计算机的协处理器。当连接到Linux，Mac或Windows主机时，它可以加快机器学习模型的推理速度。你需要做的就是在连接USB Accelerator的计算机上下载Edge TPU运行时和TensorFlow Lite库。然后，使用示例应用程序执行图像分类。系统要求：具有以下操作系统之一的计算机： ·Linux Debian 6.0或更高版本，或其任何派生版本（例如Ubuntu 10.0+），以及x86-64或ARM64系统架构（支持Raspberry Pi，但我们仅测试了Raspberry Pi 3 Model B +和Raspberry Pi 4） ·安装了MacPorts或Homebrew的macOS 10.15 ·Windows 10 -一个可用的USB端口（为获得最佳性能，请使用USB 3.0端口） -Python 3.5、3.6或3.7 操作流程一、安装Edge TPU runtime 需要Edge TPU runtime才能与Edge TPU通信。你可以按照以下说明在主机，Linux，Mac或Windows上安装它。 1、Linux系统 1）将官方提供的Debian package添加到你的系统中： 2）安装Edge TPU runtime：使用随附的USB 3.0电缆将USB

Convert Keras model to quantized Tensorflow Lite model that can be used on Edge TPU

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问题 I have a Keras model that I want to run on the Coral Edge TPU device. To do this, it needs to be a Tensorflow Lite model with full integer quantization. I was able to convert the model to a TFLite model: model.save('keras_model.h5') converter = tf.lite.TFLiteConverter.from_keras_model_file("keras_model.h5") tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) But when I run edgetpu_compiler converted_model.tflite , I get this error: Edge TPU Compiler

How to transform keras model to tpu model

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问题 I am trying to transform my Keras model in the Google cloud console into a TPU model. Unfortunatelly I am getting an error as shown below. My minimal example is the following: import keras from keras.models import Sequential from keras.layers import Dense, Activation import tensorflow as tf import os model = Sequential() model.add(Dense(32, input_dim=784)) model.add(Dense(32)) model.add(Activation('relu')) model.compile(optimizer='rmsprop', loss='mse') tpu_model = tf.contrib.tpu.keras_to_tpu

拥抱TF2.0的时代来了

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AI = 算法 + 实现忘掉 tf 1.0吧！！！ TPU tf 加速硬件学习建议忘记1.0 Tensorflow 和Pytorch 选择一个主修 Keras 逐渐淡出 TF.kreas Pytorch + caffe2 为什么是用tensorflow GPU加速（速度快）来源： https://www.cnblogs.com/D-M-C/p/12045256.html

Session lost with Keras and TPUs in Google Colab

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问题 I have been trying to get the TPUs working for a classification project. The dataset is quite big, ~150gb, so I cannot load it all in memory. Thus I have been using Dask. Dask doesn't integrate with tf.Dataset directly so I have to create a loader inspired by parallelising tf.data.Dataset.from_generator The dataset generates correctly when replacing the .fit with: iterator = ds.make_one_shot_iterator() next_element = iterator.get_next() with tf.Session() as sess: for i in range(1): val = sess

Edge TPU Compiler: ERROR: quantized_dimension must be in range [0, 1). Was 3

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问题 I'm trying to get a Mobilenetv2 model (retrained last layers to my data) to run on the Google edge TPU Coral. I've followed this tuturial https://www.tensorflow.org/lite/performance/post_training_quantization?hl=en to do the post-training quantization. The relevant code is: ... train = tf.convert_to_tensor(np.array(train, dtype='float32')) my_ds = tf.data.Dataset.from_tensor_slices(train).batch(1) # POST TRAINING QUANTIZATION def representative_dataset_gen(): for input_value in my_ds.take(30)