tensorflow

tensorflow gpu is only running on CPU

由 ⅰ亾dé卋堺 提交于 2020-12-07 07:17:20
问题 I installed Anaconda-Navigatoron Windows 10 and all necessary Nvidia/Cuda packages, created a new environment called tensorflow-gpu-env, updated PATH information, etc. When I run a model (build by using tensorflow.keras ), I see that CPU utilization increases significantly, GPU utilization is 0%, and the model just does not train. I run a couple of tests to make sure how things look: print(tf.test.is_built_with_cuda()) True The above output ('True') looks correct. Another try: from tensorflow

tensorflow gpu is only running on CPU

由 雨燕双飞 提交于 2020-12-07 07:17:13
问题 I installed Anaconda-Navigatoron Windows 10 and all necessary Nvidia/Cuda packages, created a new environment called tensorflow-gpu-env, updated PATH information, etc. When I run a model (build by using tensorflow.keras ), I see that CPU utilization increases significantly, GPU utilization is 0%, and the model just does not train. I run a couple of tests to make sure how things look: print(tf.test.is_built_with_cuda()) True The above output ('True') looks correct. Another try: from tensorflow

In google colab, is there a way to check what TPU verison is running?

由 孤街醉人 提交于 2020-12-06 19:22:22
问题 colab offers free TPUs. It's easy to see how many cores are given, but I was wondering if its possible to see how much memory per core? 回答1: As far as I know we don't have an Tensorflow op or similar for accessing memory info, though in XRT we do. In the meantime, would something like the following snippet work? import os from tensorflow.python.profiler import profiler_client tpu_profile_service_address = os.environ['COLAB_TPU_ADDR'].replace('8470', '8466') print(profiler_client.monitor(tpu

Load and run test a .trt model

由 一世执手 提交于 2020-12-06 18:54:08
问题 I need to run my model in NVIDIA JETSON T2, So I converted my working yoloV3 model into tensorRT(.trt format)( https://towardsdatascience.com/have-you-optimized-your-deep-learning-model-before-deployment-cdc3aa7f413d )This link mentioned helped me to convert the Yolo model into .trt .But after converting the model to .trt model I needed to test if it works fine (i.e) If the detection is good enough. I couldn't find any sample code for loading and testing .trt model. If anybody can help me ,

Load and run test a .trt model

由 删除回忆录丶 提交于 2020-12-06 18:54:05
问题 I need to run my model in NVIDIA JETSON T2, So I converted my working yoloV3 model into tensorRT(.trt format)( https://towardsdatascience.com/have-you-optimized-your-deep-learning-model-before-deployment-cdc3aa7f413d )This link mentioned helped me to convert the Yolo model into .trt .But after converting the model to .trt model I needed to test if it works fine (i.e) If the detection is good enough. I couldn't find any sample code for loading and testing .trt model. If anybody can help me ,

Deployment Error (Reason) : Please make sure all images included in the model for the production variant AllTraffic exist

由 ⅰ亾dé卋堺 提交于 2020-12-06 15:59:24
问题 I am Able to train my modelusing Sagemaker TensorFlow container. Below is the code model_dir = '/opt/ml/model' train_instance_type = 'ml.c4.xlarge' hyperparameters = {'epochs': 10, 'batch_size': 256, 'learning_rate': 0.001} script_mode_estimator = TensorFlow( entry_point='model.py', train_instance_type=train_instance_type, train_instance_count=1, model_dir=model_dir, hyperparameters=hyperparameters, role=sagemaker.get_execution_role(), base_job_name='tf-fashion-mnist', framework_version='1.12

Tensorflow 2.0 InvalidArgumentError: assertion failed: [Condition x == y did not hold element-wise:]

由 我们两清 提交于 2020-12-06 12:20:24
问题 i am training a mnist CNN. When i ran my code the problem is coming . I tried other answers but they do not work. I am a new to TensorFlow so can someone explain me this error. Here is my code. i am using Pycharm 2020.2. and Python 3.6 in anaconda. There is no help i could find. import tensorflow as tf from tensorflow.keras.models import Sequential mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = tf.keras.utils.normalize(x_train, axis=1) x

深度学习“四大名著”发布！Python、TensorFlow、机器学习、深度学习四件套（附免费下载）

由 情到浓时终转凉″ 提交于 2020-12-06 05:46:20
Python 程序员深度学习的“四大名著”： 这四本书着实很不错！我们都知道现在机器学习、深度学习的资料太多了，面对海量资源，往往陷入到“无从下手”的困惑出境。而且并非所有的书籍都是优质资源，浪费大量的时间是得不偿失的。给大家推荐这几本好书并做简单介绍。 获得方式： 1.扫码关注 “涛哥聊python” 公众号 2.后台回复关键词： 4books 注： 此处建议复制，不然容易打错 ▲长按扫描关注，回复 4books 即可获取 1. 《Deep Learning with Python》 推荐指数：★★★★☆ 本书自出版以来收到众多好评，因为是 Keras 作者写的书，所以全书基本围绕着 Keras 讲深度学习的各种实现，从 CNN，RNN 到 GAN 等，偏入门，但也承载着很多作者对深度学习整体性的思考。这是一本偏实战的书，教你使用 Keras 快速实现深度学习经典项目。看完这本书，基本能对 Keras 和深度学习实战有比较初步的掌握了。 本书源码 GitHub 地址： https://github.com/fchollet/deep-learning-with-python-notebooks 2. 《Python Machine Learning》 推荐指数：★★★☆☆ 本书使用了 Scikit-Learn 和 TensorFlow，分别讲解机器学习和深度学习

Is Tensorflow Federated-Learning only for simulating federated learning on one machine?

由 我只是一个虾纸丫 提交于 2020-12-06 04:17:22
问题 I read multiple guides on https://www.tensorflow.org/federated/federated_learning e.g. the image classification or text generation example. From what I have read I can not see how to use tensorflow federated-learning (tff) for a real world application: datasets on multiple hardware clients. It all looks like its meant only for simulating federated learning. I want to use tff on multiple machines and not simulate it on only one. I would appreciate it when someone knows if it's even possible

Is Tensorflow Federated-Learning only for simulating federated learning on one machine?

由 て烟熏妆下的殇ゞ 提交于 2020-12-06 04:16:08
问题 I read multiple guides on https://www.tensorflow.org/federated/federated_learning e.g. the image classification or text generation example. From what I have read I can not see how to use tensorflow federated-learning (tff) for a real world application: datasets on multiple hardware clients. It all looks like its meant only for simulating federated learning. I want to use tff on multiple machines and not simulate it on only one. I would appreciate it when someone knows if it's even possible
订阅 tensorflow