After a while, I had to reinstall the OS, so I rebuilt the environment. It is a memorandum at that time.
The items are as follows.
The version is pretty important. If you use an unsupported version, you may need to set it separately or it may not work.
·reference
It seems that Tensorflow is compatible with those with Compute Capability of 3.5 or higher.
The correspondence table can be found here [https://developer.nvidia.com/cuda-gpus#compute).
The grabber of this PC is NVIDIA GeForce GTX 1060, so it's 6.1.
If you can confirm it, keep the driver up to date. Driver page: https://www.nvidia.co.jp/Download/index.aspx?lang=jp
Download page for CUDA v10.0: https://developer.nvidia.com/cuda-10.0-download-archive When selecting the installer, select "High speed (recommended)" and install by default.
By the way, at the time of writing (2019/11), v10.2 is the latest. However, when I insert v10.2, Tensorflow seems to refer to the path of v10.0, and I get the following error later.
> python -c "import tensorflow as tf; print(tf.__version__)"
2019-11-28 19:22:20.012829: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cudart64_100.dll not found
2.0.0
There seems to be a workaround, but I haven't tried it with priority on operation for the time being. Workaround: Additional procedure when you want to use with CUDA 10.1
CuDNN download page: https://developer.nvidia.com/rdp/cudnn-download It's free, but membership registration is required. After logging in, download the following version corresponding to CUDA.
Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0
CuDNN is a zip file, so unzip it and poke it into the CUDA folder. By default, the CUDA folder is:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\
I've included Anaconda because I wanted to separate the environment for CPU and GPU. DL page: https://www.anaconda.com/distribution/
The installed Anaconda is as follows. (You don't have to worry about the version)
Anaconda 2019.10 for Windows Installer
Python 3.7 version
Add environment variables when installing. (The default is unchecked)
Check ʻAdd Anaconda to my PATH environment variable in ʻAdvanced Options.
Since I have added environment variables, it may be better to restart after installation.
Start Anaconda Navigator.
Program list → Anaconda3(64-bit) → Anaconda Navigater (Anaconda3)
Once launched, select Environments from the menu on the left and press Create. Here, we will build the environment for actual operation.
--Set value --Name: Feel free (here, py37-gpu) --Select python 3.7 for Package. (About half a year ago, Tensorflow didn't work in 3.7, but it seems to have been supported.)
Press Create to create the environment.
When the environment is created, click (py37-gpu), then click ▶ → select ʻOpen Terminal`. Then Terminal will start.
If you want to add a package with pip etc. in the future, follow this procedure. (There seems to be an easier way ...)
・ When using CPU
> pip install tensorflow
・ When using GPU
> pip install tensorflow-gpu
Enter the following command in Terminal. (One liner)
> python -c "import tensorflow as tf; print(tf.__version__)"
・ CPU version display result
>python -c "import tensorflow as tf; print(tf.__version__)"
2.0.0
-GPU version display result (dll must be Successfully)
> python -c "import tensorflow as tf; print(tf.__version__)"
2019-11-28 19:59:12.696817: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
2.0.0
Enter the following command in Terminal.
> python
>>> from tensorflow.python.client import device_lib
>>> device_lib.list_local_devices()
This is the execution result.
(py37-gpu) C:\Users\poco> python
Python 3.7.5 (default, Oct 31 2019, 15:18:51) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>>
>>>
>>> from tensorflow.python.client import device_lib
2019-11-28 21:33:00.714942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
>>>
>>>
>>> device_lib.list_local_devices()
2019-11-28 21:33:05.777537: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-11-28 21:33:05.790386: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2019-11-28 21:33:05.813090: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1060 3GB major: 6 minor: 1 memoryClockRate(GHz): 1.7085
pciBusID: 0000:01:00.0
2019-11-28 21:33:05.818802: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-11-28 21:33:05.823869: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-28 21:33:06.275014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-28 21:33:06.279606: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2019-11-28 21:33:06.282091: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2019-11-28 21:33:06.285366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:0 with 2108 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 3GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 15688037898080382701
, name: "/device:GPU:0"
device_type: "GPU" #If the GPU is displayed, k
memory_limit: 2210712780
locality {
bus_id: 1
links {
}
}
incarnation: 506641733629436041
physical_device_desc: "device: 0, name: GeForce GTX 1060 3GB, pci bus id: 0000:01:00.0, compute capability: 6.1"
]
I'm using VSCode as my IDE. DL page: https://code.visualstudio.com/
Extentions
After launching VSCode, you can add it by searching for each name from Extensions on the left menu.
| name | Remarks |
|---|---|
| Japanese Language Pack for Visual Studio Code | I'm Japanese |
| Python | For python |
Other than this, please do as you like.
There are many ways to do this, so this is just an example.
Create a working folder. In VSCode, specify a working folder with "File-> Open Folder".
Next, create an appropriate python file under the working folder. Describe hello world as a test file.
hello.py
import tensorflow as tf
msg = tf.constant('TensorFlow 2.0 Hello World')
tf.print(msg)
Then open this file in VSCode.
Execute "Menu-> Debug-> Add Configuration-> Python File".
Then launch.json will open, so edit it.
However, I need to check the python path before editing, so check it with Anaconda.
Open Anaconda's Terminal and run the conda info -e command.
(py37-gpu) C:\Users\poco> conda info -e
# conda environments:
#
base C:\Users\poco\Anaconda3
py37-cpu C:\Users\poco\Anaconda3\envs\py37-cpu
py37-gpu * C:\Users\poco\Anaconda3\envs\py37-gpu
I want to run python in the environment of the path specified here, so
After checking, add " pythonPath " to launch.json as follows.
launch.json
{
//You can use IntelliSense to learn the available attributes.
//Hover and display the description of existing attributes.
//Check the following for more information: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python: Current File",
"type": "python",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal",
// ↓ python.Add and add exe
"pythonPath": "C:\\Users\\poco\\Anaconda3\\envs\\py37-gpu\\python.exe"
}
]
}
After adding it, save it and press F5 or "Debug-> Start Debugging" to see if hello world can be executed.
ChainerRL
This time I'll use ChainerRL instead of Keras-RL. The library of reinforcement learning seemed to be more complete here.
Drop the required packages from Anaconda's Terminal. It is a package written before ([Reinforcement learning] OpenAI Gym x Keras-rl will implement reinforcement learning algorithm (preparation)). ..
# ChainerRL
> pip install chainerrl
> pip install cupy-cuda100 //For GPU, 100 is the version, so please match it with the version of CUDE.
# gym
> pip install gym
> pip install --no-index -f https://github.com/Kojoley/atari-py/releases atari_py
#Image system
> pip install matplotlib
> pip install pillow
> pip install opencv-python
#statistics
> pip install pandas
For the time being, the environment construction has been completed. Next time I would like to do a tutorial on ChainerRL.