tensorflow

Possible to virtualize NVIDIA GeForce GTX 1070 Graphics Card for Distributed Tensorflow?

会有一股神秘感。 提交于 2021-01-27 10:46:30
问题 I am running Windows 10 on Intel Core i7-8700 CPU with 16 GB RAM, 1 TB HDD and dedicated NVIDIA GeForce GTX 1070 graphics card. I plan to launch 3 Ubuntu instances hosted by my Windows 10 PC. The Ubuntus will be running Distributed Tensorflow (tensorflow-gpu) code, that will using GPU for training a Neural Network. (to mention, already I've tried the setup on Windows but failed) Q. Shall my NVIDIA GPU be virtualized among those Virtual Machines or Not? If YES, then is there any further

Possible to virtualize NVIDIA GeForce GTX 1070 Graphics Card for Distributed Tensorflow?

喜你入骨 提交于 2021-01-27 10:44:55
问题 I am running Windows 10 on Intel Core i7-8700 CPU with 16 GB RAM, 1 TB HDD and dedicated NVIDIA GeForce GTX 1070 graphics card. I plan to launch 3 Ubuntu instances hosted by my Windows 10 PC. The Ubuntus will be running Distributed Tensorflow (tensorflow-gpu) code, that will using GPU for training a Neural Network. (to mention, already I've tried the setup on Windows but failed) Q. Shall my NVIDIA GPU be virtualized among those Virtual Machines or Not? If YES, then is there any further

Tensorflow-GPU still processing on CPU

五迷三道 提交于 2021-01-27 10:01:09
问题 Tensorflow-gpu version - 1.4.0 CUDA version - 8.0 cuDNN - v6.0 output from nvidia-smi: +-----------------------------------------------------------------------------+ | NVIDIA-SMI 388.59 Driver Version: 388.59 | |-------------------------------+----------------------+----------------------+ | GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+====================

是时候学习机器学习系统设计了!斯坦福CS 329S开课,课件、笔记同步更新

。_饼干妹妹 提交于 2021-01-27 09:53:22
这是一门新的课程——在学习了算法、框架等内容后,是时候深入了解一下「机器学习系统设计」了! 机器之心报道,作者:蛋酱。 近日,斯坦福大学宣布开设一门全新课程:CS 329S《机器学习系统设计》。 课程主页: https:// stanford-cs329s.github.io / 这门课程的主讲人、计算机科学家 Chip Huyen 也在推特上认真宣传了一波(很多人应该都读过她的博客文章,因为这位小姐姐确实很有名气)。 机器学习系统设计的概念是指,为了满足特定要求,针对机器学习系统对软件体系架构、基础架构、算法和数据进行定义的过程。虽然现有的系统也可以满足大部分模型搭建的需求,但我们必须承认:首先,工具空间是不断革新的;其次,业务需求是不断变化的;最后,数据分布也是持续更替的。因此,「系统」是很容易过时的。如果不能及时更新,那么出错、崩溃都是可以预料的。这也是本门课程开设的初衷。 本门课程旨在为现实中的机器学习系统提供一个迭代框架,该框架的目标是构建一个可部署、可信赖、可扩展的系统。首先要考虑的是每个 ML 项目的利益相关者及目标,不同的目标则需要不同的设计选择,且要考虑如何权衡。 课程涵盖了 从项目界定、数据管理、模型开发、部署、基础架构、团队架构到业务分析的所有步骤 ,在每个步骤中,都会探讨不同解决方案的动机、挑战和局限性。在课程的最后一部分,将会探讨机器学习生产生态系统的未来

Low GPU utilisation when running Tensorflow

寵の児 提交于 2021-01-27 07:10:20
问题 I've been doing Deep Reinforcement Learning using Tensorflow and OpenAI gym. My problem is low GPU utilisation. Googling this issue, I understood that it's wrong to expect much GPU utilisation when training small networks ( eg. for training mnist). But my Neural Network is not so small, I think. The architecture is similar to the given in the original deepmind paper (more or less). The architecture of my network is summarized below Convolution layer 1 (filters=32, kernel_size=8x8, strides=4)

Low GPU utilisation when running Tensorflow

狂风中的少年 提交于 2021-01-27 07:09:29
问题 I've been doing Deep Reinforcement Learning using Tensorflow and OpenAI gym. My problem is low GPU utilisation. Googling this issue, I understood that it's wrong to expect much GPU utilisation when training small networks ( eg. for training mnist). But my Neural Network is not so small, I think. The architecture is similar to the given in the original deepmind paper (more or less). The architecture of my network is summarized below Convolution layer 1 (filters=32, kernel_size=8x8, strides=4)

Dataset does not fit in memory

放肆的年华 提交于 2021-01-27 07:08:45
问题 I have an MNIST like dataset that does not fit in memory, (process memory, not gpu memory). My dataset is 4GB. This is not a TFLearn issue. As far as I know model.fit requires an array for x and y . TFLearn example: model.fit(x, y, n_epoch=10, validation_set=(val_x, val_y)) I was wondering is there's a way where we can pass a "batch iterator", instead of an array. Basically for each batch I would load the necessary data from disk. This way I would not run into process memory overflow errors.

Keras creating three classes instead of two

那年仲夏 提交于 2021-01-27 06:51:07
问题 I am trying to train a model to identify images containing fire VS images that contain forests. I am training the model on a remote server using Linode. I am using Python 2.7 and Ubuntu 16.04.5. When i run the following code locally or in Jupyter notebooks it will create 2 classes, but when i want to run it on the server it creates 3 classes. The code that classifies the model: def onehot(x): return np.array(OneHotEncoder().fit_transform(x.reshape(-1,1)).todense()) model = keras.applications

Keras creating three classes instead of two

。_饼干妹妹 提交于 2021-01-27 06:46:08
问题 I am trying to train a model to identify images containing fire VS images that contain forests. I am training the model on a remote server using Linode. I am using Python 2.7 and Ubuntu 16.04.5. When i run the following code locally or in Jupyter notebooks it will create 2 classes, but when i want to run it on the server it creates 3 classes. The code that classifies the model: def onehot(x): return np.array(OneHotEncoder().fit_transform(x.reshape(-1,1)).todense()) model = keras.applications

How does the tensorflow.python.data.ops.dataset_ops.DatasetV1Adapter work?

心不动则不痛 提交于 2021-01-27 06:40:31
问题 I am trying to wrap my head around ML and AI using TensorFlow. There is an example problem on the website which discusses the processing of .CSV data. The .CVS data is said to have been taken from the titanic and essentially contains categorical and numerical features that will be used to label a passenger as dead or alive. First of all, if anyone know or has any resources or references that discusses that example in more detail than is done on the TensorFlow website, please could you kindly