I have an N-Series Azure VM (the Data Science VM) with Tesla K80 GPU. According to the NVIDIA scanner my GPU driver is up to date.
When I run my CNTK Brainscript it says "No GPUs Found" and runs in CPU mode. What can I do to troubleshoot?
requestnodes [MPIWrapper]: using 1 out of 1 MPI nodes on a single host (1 reques
ted); we (0) are in (participating)
-------------------------------------------------------------------
Build info:
Built time: Dec 22 2016 01:43:24
Last modified date: Thu Dec 22 01:35:04 2016
Build type: Release
Build target: GPU
With 1bit-SGD: yes
With ASGD: yes
Math lib: mkl
CUDA_PATH: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8
.0
CUB_PATH: c:\src\cub-1.4.1
CUDNN_PATH: C:\local\cudnn-8.0-windows10-x64-v5.1
Build Branch: HEAD
Build SHA1: 8e8b5ff92eff4647be5d41a5a515956907567126
Built by svcphil on DPHAIM-24
Build Path: C:\jenkins\workspace\CNTK-Build-Windows\Source\CNTK\
-------------------------------------------------------------------
No GPUs found
Edit: here is the output from NVidia_smi.exe:
C:\Program Files\NVIDIA Corporation\NVSMI>.\nvidia-smi.exe
Fri Jan 13 19:00:43 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 369.30 Driver Version: 369.30 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K80 TCC | 0BD1:00:00.0 Off | Off |
| N/A 43C P8 27W / 149W | 0MiB / 12189MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla K80 TCC | 5871:00:00.0 Off | Off |
| N/A 35C P8 34W / 149W | 0MiB / 12189MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
The Windows Data Science VM bydefault does not come with the GPU drivers, CUDA etc. We do have an extension called "Deep Learning toolkit for DSVM" that adds on drivers, CUDA and GPU edition of deep learning software like CNTK, Tensorflow, MxNet.
More Info: http://aka.ms/dsvm/deeplearning
We also recently released a Ubuntu version of DSVM with builtin CUDA, GPU drivers and several more deep learning tools and can be deployed either on GPU VM or CPU only VMs on Azure.
Would it be possible for you to run the python notebooks and see if you could run them with the device being set to gpu(id)? or from activated CNTK python environment you could try setting some device.
import cntk as C
from cntk.device import set_default_device, gpu
C.device.set_default_device(C.device.gpu(0))
This might give you some clues whether it is Brainscript specific issue.
Well the python script and Brainscript work now, after installing CUDA (I installed it to run NVIDIA_SMI). I should not have assumed that the Azure Data Science image (that only works with an N Series VM) has the necessary NVIDIA libraries pre-installed. :-)
Related
I'm trying to get pytorch with cuda 10 compatibility via :
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
from(https://discuss.pytorch.org/t/pytorch-with-cuda-11-compatibility/89254)
but there is timeout error:
Proceed ([y]/n)? y
Downloading and Extracting Packages
pytorch-mutex-1.0 | 3 KB | | 0%
torchvision-0.12.0 | 8.8 MB | | 0%
ffmpeg-4.3 | 9.9 MB | | 0%
pytorch-1.11.0 | 622.9 MB | | 0%
CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/pytorch/noarch/pytorch-mutex-1.0-cuda.tar.bz2>
Elapsed: -
CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/pytorch/noarch/pytorch-mutex-1.0-cuda.tar.bz2>
Elapsed: -
So I was running WSL2 and didn't shut down for many days. A reboot fixed the issue.
Docker newbie question here, so please be nice.
I know this might be asked before but I could not find anything related to nvidia-docker.
I completed the installation instructions on the official guide.
When I wanted to test Nvidia-docker:
docker run --gpus all nvidia/cuda:10.0-base nvidia-smi
I got this error:
(base) user#adminme:~$ docker run --gpus all --rm nvidia/cuda nvidia-smi
docker: Got permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Post http://%2Fvar%2Frun%2Fdocker.sock/v1.40/containers/create: dial unix /var/run/docker.sock: connect: permission denied.
See 'docker run --help'.
I found this answer here, but it felt a bit different for my case. I am very new to docker and still learning. let me know what you think?
here is some information about my remote Linux machine:
(base) user#adminme:~$ lspci | grep -i nvidia
02:00.0 VGA compatible controller: NVIDIA Corporation GP104 [GeForce GTX 1080] (rev a1)
02:00.1 Audio device: NVIDIA Corporation GP104 High Definition Audio Controller (rev a1)
nvidia-smi command:
(base) user#adminme:~$ nvidia-smi
Sun May 31 01:12:25 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 1080 Off | 00000000:02:00.0 Off | N/A |
| 0% 33C P8 9W / 215W | 17MiB / 8116MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 2545 G /usr/lib/xorg/Xorg 15MiB |
+-----------------------------------------------------------------------------+
docker-version :
(base) user#adminme:~$ docker --version
Docker version 19.03.10, build 9424aeaee9
The quick fix would be to run the container using sudo:
sudo docker run --gpus all nvidia/cuda:10.0-base nvidia-smi
If you want to run docker as non-root user then you need to add it to the docker group.
Create the docker group if it does not exist
sudo groupadd docker
Add your user to the docker group.
sudo usermod -aG docker $USER
Run the following command or Logout and login again and run (that doesn't work you may need to reboot your machine first)
newgrp docker
Check if docker can be run without root
docker run --gpus all nvidia/cuda:10.0-base nvidia-smi
Ref:- https://docs.docker.com/engine/install/linux-postinstall/
In addition to what nischay goyal answered, sometimes after adding the user to the docker group you have to do
su - ${USER}
in order to log out and log back.
Running the latest docker with:
docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu-jupyter jupyter notebook --notebook-dir=/tf --ip 0.0.0.0 --no-browser --allow-root --NotebookApp.allow_origin='https://colab.research.google.com'
code:
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
gives me:
2020-07-27 19:44:03.826149: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-07-27 19:44:03.826179: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: UNKNOWN ERROR (-1)
2020-07-27 19:44:03.826201: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:163] no NVIDIA GPU device is present: /dev/nvidia0 does not exist
I'm on Pop_OS 20.04, have tried installing the CUDA drivers from the Pop repository as well as from NVidia. No dice. Any help appreciated.
Running
docker run --gpus all nvidia/cuda:10.0-base nvidia-smi
gives me:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.05 Driver Version: 450.51.05 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce RTX 2080 On | 00000000:09:00.0 On | N/A |
| 0% 52C P5 15W / 225W | 513MiB / 7959MiB | 17% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
As per the docs here and here, you have to add a "gpus" argument when creating a the docker container to have gpu support.
So you should start your container something like this. The "--gpus all" makes all the gpus available on the host to be visible to the container.
docker run -it --gpus all -p 8888:8888 tensorflow/tensorflow:latest-gpu-jupyter jupyter notebook --notebook-dir=/tf --ip 0.0.0.0 --no-browser --allow-root --NotebookApp.allow_origin='https://colab.research.google.com'
Also you can try running nvidia-smi on the tensorflow image to quickly check if gpu is accessible on the container.
docker run -it --rm --gpus all tensorflow/tensorflow:latest-gpu-jupyter nvidia-smi
Would return this in my case.
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.100 Driver Version: 440.100 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 1070 Off | 00000000:07:00.0 On | N/A |
| 0% 45C P8 8W / 166W | 387MiB / 8116MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
As you can see, I'm running an older nvidia driver(440.100), so I cannot confirm that this would solve your problem. I'm also on Pop_OS 20.04 and didn't install anything other than docker along with dependencies and nvidia-container-toolkit.
Also I would highly suggest avoiding the latest tag when creating containers as it might cause you to unknowingly upgrade to a newer image. Go with version numbered images.
For example tensorflow/tensorflow:2.3.0-gpu-jupyter.
Tensorflow is only using the CPU and wont use the GPU. I assume its because it expects Cuda 10.0 and it finds 10.2.
I had installed 10.2 but have purged it and installed 10.0.
Im running Ubuntu 19.10, AMD Ryzen 2700 Cpu, RTX 2080 S.
I have installed the 440 Nvidia driver, It says cuda version 10.2 when i check with nvidia-smi and nvcc -version.
From pip3: tensorflow-gpu 1.14.0
tensorflow-datasets 2.0.0
tensorflow-estimator 1.14.0
tensorflow-metadata 0.21.1
From Nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.44 Driver Version: 440.44 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 208... Off | 00000000:08:00.0 On | N/A |
| 0% 48C P8 13W / 250W | 369MiB / 7979MiB | 3% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1110 G /usr/lib/xorg/Xorg 18MiB |
| 0 1611 G /usr/lib/xorg/Xorg 73MiB |
| 0 1816 G /usr/bin/gnome-shell 108MiB |
| 0 2655 C python3 115MiB |
+-----------------------------------------------------------------------------+
from nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
But when i check the version.txt i get 10.0.130
cat /usr/local/cuda/version.txt
CUDA Version 10.0.130
I check the devices with :
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
result:
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 4810338588393992961
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 7271419476897292826
physical_device_desc: "device: XLA_CPU device"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 4332706623198547606
physical_device_desc: "device: XLA_GPU device"
]
How do i register the 10.0.130
Is that the reason why it wont run on GPU? Its super slow on the 8 Core CPU. Any advice?
Here is the log:
2020-02-13 14:11:31.411277: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-02-13 14:11:31.440150: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3193485000 Hz
2020-02-13 14:11:31.441076: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5625b689c790 executing computations on platform Host. Devices:
2020-02-13 14:11:31.441123: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2020-02-13 14:11:31.443001: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2020-02-13 14:11:31.472935: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-13 14:11:31.473407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce RTX 2080 SUPER major: 7 minor: 5 memoryClockRate(GHz): 1.845
pciBusID: 0000:08:00.0
2020-02-13 14:11:31.474361: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-02-13 14:11:31.487124: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-02-13 14:11:31.496148: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-02-13 14:11:31.498873: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-02-13 14:11:31.514842: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-02-13 14:11:31.525992: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-02-13 14:11:31.526168: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/lib64
2020-02-13 14:11:31.526183: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1663] Cannot dlopen some GPU libraries. Skipping registering GPU devices...
2020-02-13 14:11:31.618627: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-02-13 14:11:31.618655: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0
2020-02-13 14:11:31.618662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N
2020-02-13 14:11:31.620367: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-13 14:11:31.621395: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5625b732d5f0 executing computations on platform CUDA. Devices:
2020-02-13 14:11:31.621407: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 13330791690361361129
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 11872341970779952422
physical_device_desc: "device: XLA_CPU device"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 15007819717683015571
physical_device_desc: "device: XLA_GPU device"
]
WARNING:tensorflow:From pokeGAN.py:172: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.
WARNING:tensorflow:From pokeGAN.py:174: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
WARNING:tensorflow:From pokeGAN.py:77: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.
2020-02-13 14:11:33.799163: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-13 14:11:33.799597: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce RTX 2080 SUPER major: 7 minor: 5 memoryClockRate(GHz): 1.845
pciBusID: 0000:08:00.0
2020-02-13 14:11:33.799646: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-02-13 14:11:33.799658: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2020-02-13 14:11:33.799669: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2020-02-13 14:11:33.799684: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2020-02-13 14:11:33.799695: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2020-02-13 14:11:33.799706: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2020-02-13 14:11:33.799777: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/lib64
2020-02-13 14:11:33.799786: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1663] Cannot dlopen some GPU libraries. Skipping registering GPU devices...
2020-02-13 14:11:33.800016: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-02-13 14:11:33.800028: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]
WARNING:tensorflow:From pokeGAN.py:203: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.
2020-02-13 14:11:34.197990: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
WARNING:tensorflow:From /home/node/.local/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
WARNING:tensorflow:From pokeGAN.py:211: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
total training sample num:91
batch size: 64, batch num per epoch: 1, epoch num: 5000
start training...
Judging from your logs it looks like tensorflow finds the correct cuda version but the cudnn library is missing.
2020-02-13 14:11:31.474361: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2020-02-13 14:11:31.526168: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/lib64
Have you installed the correct version of cudnn? As you can see here
tensorflow 1.14 also requires cudnn 7.4
The only thing that worked for me to solve this issue was to completely remove CUDA and reinstall it again.
The text file has following information:
System Information
------------------
Time of this report: 5/22/2014, 14:20:52
Machine name: CONFERENCE13
Operating System: Windows 7 Professional 64-bit (6.1, Build 7601) Service Pac
k 1 (7601.win7sp1_gdr.140303-2144)
Language: English (Regional Setting: English)
System Manufacturer: Mario, Inc.
System Model: Mario Virtual Platform
BIOS: PhoenixBIOS 4.0 Release 6.0
Processor: Intel(R) Xeon(R) CPU E5-2680 0 # 2.70GHz (4 CPUs), ~2.7GHz
Memory: 2048MB RAM
Available OS Memory: 2048MB RAM
Page File: 1302MB used, 2792MB available
Windows Dir: C:\Windows
DirectX Version: DirectX 11
DX Setup Parameters: Not found
User DPI Setting: Using System DPI
System DPI Setting: 96 DPI (100 percent)
DWM DPI Scaling: Disabled
DxDiag Version: 6.01.7601.17514 32bit Unicode
------------
DxDiag Notes
------------
Display Tab 1: No problems found.
Sound Tab 1: No problems found.
Input Tab: No problems found.
--------------------
DirectX Debug Levels
--------------------
Direct3D: 0/4 (retail)
DirectDraw: 0/4 (retail)
DirectInput: 0/5 (retail)
DirectMusic: 0/5 (retail)
DirectPlay: 0/9 (retail)
DirectSound: 0/5 (retail)
DirectShow: 0/6 (retail)
---------------
Display Devices
---------------
Card name: Mario SVGA 3D
Manufacturer: Mario, Inc.
Chip type: Mario Virtual SVGA 3D Graphics Adapter
DAC type: n/a
Device Key: Enum\PCI\VEN_15AD&DEV_0405&SUBSYS_040515AD&REV_00
Display Memory: 223 MB
Dedicated Memory: 35 MB
Shared Memory: 188 MB
Current Mode: 1555 x 794 (32 bit) (60Hz)
Monitor Name: Generic Non-PnP Monitor
Monitor Model: unknown
Monitor Id:
Native Mode: unknown
Output Type: HD15
Driver Name: vm3dum64.dll,vm3dum,vm3dgl64.dll,vm3dgl
Driver File Version: 7.14.0001.2032 (English)
Driver Version: 7.14.1.2032
DDI Version: unknown
Driver Model: WDDM 1.0
Driver Attributes: Final Retail
Driver Date/Size: 2/11/2014 03:15:04, 258264 bytes
WHQL Logo'd: n/aWHQL Date Stamp: n/a
Device Identifier: {D7B71B4D-4745-11CF-ED71-0424A1C2CA35}
Vendor ID: 0x15AD
Device ID: 0x0405
SubSys ID: 0x040515AD
Revision ID: 0x0000
Driver Strong Name: oem13.inf:VMware.NTamd64.6.0:VM3D_AMD64:7.14.1.2032:pci\ven_15ad&dev_0405&subsys_040515ad&rev_00
Rank Of Driver: 00F60000
Video Accel:
Deinterlace Caps: n/a
D3D9 Overlay: n/a
DXVA-HD: n/a
DDraw Status: Not Available
D3D Status: Not Available
AGP Status: Not Available
-------------
Sound Devices
-------------
Description: Speakers (Mario Virtual Audio (DevTap))
Default Sound Playback: Yes
Default Voice Playback: Yes
Hardware ID: PNPB009
Manufacturer ID: 1
Product ID: 100
Type: WDM
Driver Name: vmwvaudio.sys
Driver Version: 6.00.0000.3800 (English)
Driver Attributes: Final Retail
WHQL Logo'd: n/a
Date and Size: 11/13/2013 21:22:16, 46672 bytes
Other Files:
Driver Provider: VMware
HW Accel Level: Basic
Cap Flags: 0x0
Min/Max Sample Rate: 0, 0
Static/Strm HW Mix Bufs: 0, 0
Static/Strm HW 3D Bufs: 0, 0
HW Memory: 0
Voice Management: No
EAX(tm) 2.0 Listen/Src: No, No
I3DL2(tm) Listen/Src: No, No
Sensaura(tm) ZoomFX(tm): No**
I am trying to pull card name and driver file version from the display devices and then compare with certain list as follow:
Windows XP Windows Vista Windows 7 Windows 8 Windows 8.1 Windows Server 2008 R2
View 3.1.3 build 252693
VMware SVGA II
Version: 11.6.0.35
Dated: 4/21/2010
VMware SVGA 3D
Version: 17.14.1.42
Dated: 4/21/2010
Not Supported Not Supported Not Supported Not Supported
View 4.0.2 build 294291
VMware SVGA II
Version: 11.6.0.35
Dated: 4/21/2010
Tried awk but is giving me some error, new to awk and bash need some help thank you.
awk 'BEGIN{
FS="="; OFS=" - "; DispalyDevices=""
}
function display(){
print displaydevices,cardname,driverfileversion
}
/DisplayDevices/{
if(cardname!="") display();
cardname=""; driverfileversion=""; display=$0;
gsub("Display.*PLAY"; "Display",display)
}
/cardname/{cardname=$2}
/driverfileversion/{driverfileversion=$2}
END{display}' dx_diag.txt | cat > dx_outputfile.txt
`**
The error is:
awk: syntax error at source line 1
Within this context:
BEGIN{FS="="; OFS=" - "; DispalyDevices=""}function display(){print displaydevices,cardname,driverfileversion}/DisplayDevices/{if(cardname!="") display(); cardname=""; driverfileversion=""; display=$0; >>> gsub("Display.*PLAY"; <<<
awk: illegal statement at source line 1
awk: illegal statement at source line 1**
change
gsub("Display.*PLAY"; "Display",display)
to
gsub("Display.*PLAY", "Display",display)
#-------------------^--- comma, not semi-colon
IHTH