Installing TensorFlow on Ubuntu
Linux x64 (AMD64/EM64T) Display Driver
| Version: | 384.69 | |
| Release Date: | 2017.8.22 | |
| Operating System: | Linux 64-bit | |
| Language: | English (US) | |
| File Size: | 77.06 MB | |
Installing TensorFlow on Windows :
tensorflow52 win10 vs2015 编译 tensorflow1.2.0-rc0(支持GPU)
https://www.tensorflow.org/install/
to install TensorFlow.
To install TensorFlow, start a terminal. Then issue the appropriate pip3 install command in that terminal. To install the CPU-only version of TensorFlow, enter the following command:
C:\> pip3 install --upgrade tensorflow
To install the GPU version of TensorFlow, enter the following command:
C:\> pip3 install --upgrade tensorflow-gpu
Installing with Anaconda
The Anaconda installation is community supported, not officially supported.
Take the following steps to install TensorFlow in an Anaconda environment:
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Follow the instructions on the Anaconda download site to download and install Anaconda.
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Create a conda environment named tensorflow by invoking the following command:
C:> conda create -n tensorflow
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Activate the conda environment by issuing the following command:
C:> activate tensorflow (tensorflow)C:> # Your prompt should change
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Issue the appropriate command to install TensorFlow inside your conda environment. To install the CPU-only version of TensorFlow, enter the following command:
(tensorflow)C:> pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.1.0-cp35-cp35m-win_amd64.whl
To install the GPU version of TensorFlow, enter the following command (on a single line):
(tensorflow)C:> pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.1.0-cp35-cp35m-win_amd64.whl
Validate your installation
Start a terminal.
If you installed through Anaconda, activate your Anaconda environment.
Invoke python from your shell as follows:
$ python
Enter the following short program inside the python interactive shell:
>>> import tensorflow as tf >>> hello = tf.constant('Hello, TensorFlow!') >>> sess = tf.Session() >>> print(sess.run(hello))If the system outputs the following, then you are ready to begin writing TensorFlow programs:
Hello, TensorFlow!
If you are new to TensorFlow, see Getting Started with TensorFlow.
If the system outputs an error message instead of a greeting, see Common installation problems.
Common installation problems
We are relying on Stack Overflow to document TensorFlow installation problems and their remedies. The following table contains links to Stack Overflow answers for some common installation problems. If you encounter an error message or other installation problem not listed in the following table, search for it on Stack Overflow. If Stack Overflow doesn't show the error message, ask a new question about it on Stack Overflow and specify the tensorflow tag.
| Stack Overflow Link | Error Message |
|---|---|
| 41007279 |
[...\stream_executor\dso_loader.cc] Couldn't open CUDA library nvcuda.dll |
| 41007279 |
[...\stream_executor\cuda\cuda_dnn.cc] Unable to load cuDNN DSO |
| 42006320 |
ImportError: Traceback (most recent call last): File "...\tensorflow\core\framework\graph_pb2.py", line 6, in from google.protobuf import descriptor as _descriptor ImportError: cannot import name 'descriptor' |
| 42011070 |
No module named "pywrap_tensorflow" |
| 42217532 |
OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
|
| 43134753 |
The TensorFlow library wasn't compiled to use SSE instructions |
