I am using a new M2 Samsung 980 PRO. Samsung Magician says there is a unknown NVMe driver installed. I tried to do a backup based on DISM with Heise C'T wimage. Some files are corrupt, thats why backup fails. I simply would like to install a suitable NVMe driver to get rid of corrupt files on the M2 drive. Any suggestions?
I tried to do the backup four times. Each backup failed between 11 and 16%. I did already "sfc /scannow" and "chkdsk /f /r". No errors found. But when I start again the backup, corrupt files stop the backup process...
Logfile:
"[57648] [0x80070017] ReadWriteDataInternal:(363): Datenfehler (CRC-Prüfung)
[57648] [0xc144012e]
2023-01-24 23:31:30, Error DISM DISM WIM Provider: PID=57648 P:\Program Files (x86)\Reference Assemblies\Microsoft\Framework.NETFramework\v4.8\System.ServiceModel.Discovery.dll (HRESULT=0x80070006) - CWimManager::WimProviderMsgLogCallback
[57648] [0x80070006] AddFileNodeToImage:(921): Das Handle ist ungültig."
I am trying to deploy an object detection model on Google Coral. I trained the model using the following config file, which I tried to closely match to the demo config file from the docker image described here.
I successfully trained my model, then I ran the script ./convert_checkpoint_to_edgetpu_tflite.sh seemingly successfully with the following output:
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/.venv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/models/research/object_detection/anchor_generators/multiple_grid_anchor_generator.py:183: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
2019-09-12 11:15:11.539092: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-09-12 11:15:11.707588: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x68382b0 executing computations on platform CUDA. Devices:
2019-09-12 11:15:11.707625: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-09-12 11:15:11.728473: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3298290000 Hz
2019-09-12 11:15:11.729431: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x68a1b90 executing computations on platform Host. Devices:
2019-09-12 11:15:11.729473: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
2019-09-12 11:15:11.729783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635
pciBusID: 0000:05:00.0
totalMemory: 10.73GiB freeMemory: 10.34GiB
2019-09-12 11:15:11.729823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-09-12 11:15:11.732474: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-12 11:15:11.732509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2019-09-12 11:15:11.732523: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2019-09-12 11:15:11.732730: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10057 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:05:00.0, compute capability: 7.5)
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/.venv/lib/python3.5/site-packages/tensorflow/python/tools/freeze_graph.py:127: 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.
2019-09-12 11:15:15.451695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-09-12 11:15:15.451741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-12 11:15:15.451748: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2019-09-12 11:15:15.451753: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2019-09-12 11:15:15.451857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10057 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:05:00.0, compute capability: 7.5)
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/.venv/lib/python3.5/site-packages/tensorflow/python/tools/freeze_graph.py:232: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.compat.v1.graph_util.convert_variables_to_constants
WARNING:tensorflow:From /media/wwang/WorkDir/projects/SANATA/.venv/lib/python3.5/site-packages/tensorflow/python/framework/graph_util_impl.py:245: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.compat.v1.graph_util.extract_sub_graph
2019-09-12 11:15:17.880135: I tensorflow/tools/graph_transforms/transform_graph.cc:317] Applying strip_unused_nodes
CONVERTING frozen graph to TF Lite file...
2019-09-12 11:15:19.959403: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-09-12 11:15:20.105331: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x3f28f50 executing computations on platform CUDA. Devices:
2019-09-12 11:15:20.105370: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-09-12 11:15:20.124476: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3298290000 Hz
2019-09-12 11:15:20.125267: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x3f92630 executing computations on platform Host. Devices:
2019-09-12 11:15:20.125297: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
2019-09-12 11:15:20.125542: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635
pciBusID: 0000:05:00.0
totalMemory: 10.73GiB freeMemory: 10.34GiB
2019-09-12 11:15:20.125569: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-09-12 11:15:20.127390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-12 11:15:20.127411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2019-09-12 11:15:20.127420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2019-09-12 11:15:20.127553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10057 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:05:00.0, compute capability: 7.5)
TFLite graph generated at model_exported/output_tflite_graph.tflite
Then I ran edgetpu_compiler output_tflite_graph.tflite also seemingly successfully with the following output:
Edge TPU Compiler version 2.0.258810407
INFO: Initialized TensorFlow Lite runtime.
Model compiled successfully in 383 ms.
Input model: model_exported/output_tflite_graph.tflite
Input size: 1.65MiB
Output model: output_tflite_graph_edgetpu.tflite
Output size: 2.33MiB
On-chip memory available for caching model parameters: 7.00MiB
On-chip memory used for caching model parameters: 2.11MiB
Off-chip memory used for streaming uncached model parameters: 0.00B
Number of Edge TPU subgraphs: 1
Total number of operations: 115
Operation log: output_tflite_graph_edgetpu.log
Model successfully compiled but not all operations are supported by the Edge TPU. A percentage of the model will instead run on the CPU, which is slower. If possible, consider updating your model to use only operations supported by the Edge TPU. For details, visit g.co/coral/model-reqs.
Number of operations that will run on Edge TPU: 114
Number of operations that will run on CPU: 1
See the operation log file for individual operation details.
And the following output_tflite_graph_edgetpu.log file:
Edge TPU Compiler version 2.0.258810407
Input: output_tflite_graph.tflite
Output: output_tflite_graph_edgetpu.tflite
Operator Count Status
DEPTHWISE_CONV_2D 33 Mapped to Edge TPU
RESHAPE 13 Mapped to Edge TPU
LOGISTIC 1 Mapped to Edge TPU
CUSTOM 1 Operation is working on an unsupported data type
ADD 10 Mapped to Edge TPU
CONCATENATION 2 Mapped to Edge TPU
CONV_2D 55 Mapped to Edge TPU
Finally, I put my converted output_tflite_graph_edgetpu.tflite on the Coral, and got the following error:
Traceback (most recent call last):
File "main.py", line 224, in <module>
main()
File "main.py", line 221, in main
run_app(add_render_gen_args, render_gen)
File "/home/mendel/projects/DARTS/object_detection/edge_tpu_vision/edgetpuvision/apps.py", line 75, in run_app
display=args.displaymode):
File "/home/mendel/projects/DARTS/object_detection/edge_tpu_vision/edgetpuvision/gstreamer.py", line 243, in run_gen
inference_size = render_overlay_gen.send(None) # Initialize.
File "main.py", line 154, in render_gen
engines, titles = utils.make_engines(args.model, DetectionEngine)
File "/home/mendel/projects/DARTS/object_detection/edge_tpu_vision/edgetpuvision/utils.py", line 53, in make_engines
engine = engine_class(model_path)
File "/usr/lib/python3/dist-packages/edgetpu/detection/engine.py", line 55, in __init__
super().__init__(model_path)
File "/usr/lib/python3/dist-packages/edgetpu/swig/edgetpu_cpp_wrapper.py", line 300, in __init__
this = _edgetpu_cpp_wrapper.new_BasicEngine(*args)
RuntimeError: Failed to allocate tensors.
What am I doing wrong?
Thanks!
PS: I realize this may be more suited for a git issue, but I am not sure where to post google-coral issues on git...
I had the exact same problem with the Coral board as well after following the tutorial to retrain an object detection model.
For me the issue was due to the compiler targeting a different runtime version than I had on the Coral board. On the Coral board, you can check the runtime version like this:
python3 -c "import edgetpu.basic.edgetpu_utils; print(edgetpu.basic.edgetpu_utils.GetRuntimeVersion())"
On my board the runtime version was 10 which is not the same as the default for the compiler which was 12. If you indeed have the same issue then you can compile the model targeting this version using:
edgetpu_compiler --min_runtime_version 10 your_model.tflite
Source:
https://coral.withgoogle.com/docs/edgetpu/compiler/#compiler-and-runtime-versions
Could you try with the newest compiler and check back? I know there were a few bugs in the compiler it self which is fixed. Guide is here:
https://coral.withgoogle.com/news/updates-09-2019/
Operation System: Windows Server 2016 R2
I have a RAMDisk driver can be installed with "Add Legacy Hardware Wizard" (hdwwiz.exe) successfully. From the output of devcon hwids *, the device can be seen as below.
ROOT\UNKNOWN\0000
Name: RAMDrive [ QSoft ] Enterprise (x64)
Hardware IDs:
ramdriv
However, I need complete the installation via Ansible, hence hdwwiz.exe cannot be used. It has to be done via command line without interaction.
I tried several approaches and none of them works.
Approach I : DevCon.exe (Windows Device Console)
C:\Ramdisk64_inst>devcon.exe install RAMDriv.inf ramdriv
Device node created. Install is complete when drivers are installed...
Updating drivers for ramdriv from C:\Ramdisk64_inst\RAMDriv.inf.
devcon.exe failed.
C:\Ramdisk64_inst>devcon.exe install RAMDriv.inf ROOT\UNKNOWN\0000
Device node created. Install is complete when drivers are installed...
Updating drivers for ROOT\UNKNOWN\0000 from C:\Ramdisk64_inst\RAMDriv.inf.
devcon.exe failed.
Here is the log from C:\Windows\INF\setupapi.dev.log
>>> [Device Install (UpdateDriverForPlugAndPlayDevices) - ramdriv]
>>> Section start 2018/12/20 07:10:35.670
cmd: C:\Ramdisk64_inst\devcon.exe install C:\Ramdisk64_inst\RAMDriv.inf ramdriv
ndv: INF path: C:\Ramdisk64_inst\RAMDriv.inf
ndv: Install flags: 0x00000001
! ndv: Unable to find any matching devices.
<<< Section end 2018/12/20 07:10:35.717
<<< [Exit status: FAILURE(0xe000020b)]
>>> [Device Install (UpdateDriverForPlugAndPlayDevices) - ROOT\UNKNOWN\0000]
>>> Section start 2018/12/20 07:11:50.687
cmd: devcon.exe install RAMDriv.inf ROOT\UNKNOWN\0000
ndv: INF path: C:\Ramdisk64_inst\RAMDriv.inf
ndv: Install flags: 0x00000001
! ndv: Unable to find any matching devices.
<<< Section end 2018/12/20 07:11:50.734
<<< [Exit status: FAILURE(0xe000020b)]
Approach 2 : DPInst.exe (Driver Package Installer)
C:\Ramdisk64_inst>dpinst.exe /PATH C:\Ramdisk64_inst /Q /C /LM
INFO: Option set: dumping log info to console.
INFO: Current working directory: 'C:\Ramdisk64_inst'
INFO: Running on path 'C:\Ramdisk64_inst'
INFO: No valid 'dpinst.xml' file provided.
INFO: Install option set: Running in quiet mode. Suppressing Wizard and OS popups.
INFO: Install option set: legacy mode on.
INFO: Found driver package: 'C:\Ramdisk64_inst\RAMDriv.inf'.
INFO: Preinstalling 'c:\ramdisk64_inst\ramdriv.inf' ...
INFO: ENTER: DriverPackagePreinstallW
INFO: Driver package is already preinstalled 'c:\ramdisk64_inst\ramdriv.inf'.
SUCCESS:c:\ramdisk64_inst\ramdriv.inf is preinstalled.
INFO: RETURN: DriverPackagePreinstallW (0xB7)
INFO: ENTER: DriverPackageGetPathW
INFO: RETURN: DriverPackageGetPathW (0x0)
INFO: ENTER: DriverPackageInstallW
WARNING:DRIVER_PACKAGE_LEGACY_MODE flag set but not supported on Plug and Play driver on VISTA. Flag will be ignored.
INFO: Installing INF file 'c:\ramdisk64_inst\ramdriv.inf' (Plug and Play).
INFO: Looking for Model Section [DiskDevice.NTamd64]...
INFO: No matching devices found in INF "C:\Windows\System32\DriverStore\FileRepository\ramdriv.inf_amd64_fcc99ac0622d865b\ramdriv.inf" on the Machine.
INFO: No drivers installed. No devices found that match driver(s) contained in 'C:\Windows\System32\DriverStore\FileRepository\ramdriv.inf_amd64_fcc99ac0622d865b\ramdriv.inf'.
INFO: RETURN: DriverPackageInstallW (0xE000020B)
INFO: No matching device was found for 'c:\ramdisk64_inst\ramdriv.inf'. Driver will be installed when plugged in.
INFO: Returning with code 0x100
Approach 3 : rundll32 calls SetupAPI
C:\Ramdisk64_inst>rundll32.exe setupapi.dll,InstallHinfSection DiskInstall 128 C:\Ramdisk64_inst\RAMDriv.inf
It ends without any error, but the driver is not installed.
Approach 4 : PnPUtil
C:\Ramdisk64_inst>pnputil.exe /add-driver C:\Ramdisk64_inst\RAMDriv.inf /install /subdirs /restart
Microsoft PnP Utility
Adding driver package: RAMDriv.inf
Driver package added successfully.
Published Name: oem7.inf
Driver package installed on matching devices.
Total driver packages: 1
Added driver packages: 1
It succeeded, but in fact driver is not installed.
None of them works. DpInst.exe says No matching devices found in INF, DevCon.exe says Unable to find any matching devices. It seems the same reason.
However the driver can be installed with hdwwiz.exe manually, does anyone know what is the secret inside hdwwiz.exe?
Legacy Drivers can´t be installed with pnputil and have to use LaunchINFSectionEx-Call
I tested the following and it works with several drivers from Windows 2000 up to Windows 10, 2012R2, 2016, 2019.
rundll32.exe advpack.dll,LaunchINFSectionEx ykmd.inf,Yubico64_Install.NT,,4,N
Pay attention to use the correct section
The correct section of the inf-File must be used, when there is no [DefaultInstall]-Section. This lacks in most answers. Look it up in your drivers inf-File and use the correct section (in my example "Yubico64_Install.NT"). Using the wrong section wont prompt an error. Im my example I use Quiet mode, no UI (4) and Never reboot (N) to install the driver automated via GPO. All options are documented in detail here:
https://learn.microsoft.com/en-us/previous-versions/windows/internet-explorer/ie-developer/platform-apis/aa768006(v%3Dvs.85)
I am trying to install the Intel SGX driver at Intel I3 6100 based CPU and I am not able to launch the enclave.
I am suspecting that it is happening due to my processor (i.e. Intel core i3). Same steps I perform at my another machine that came with I7 and it is working as expected.
Thing I tried at my end is :
First I tried to install the driver 1.7 and getting the device error.
========================================================
[root#local_host POC1]# ./sgx_linux_x64_driver_51b2884.bin
Unpacking Intel SGX Driver ... done.
Verifying the integrity of the install package ... done.
Installing Intel SGX Driver ...
/tmp/sgx-driver-d9jY01 ~/POC1
install -d /opt/intel/sgxdriver/package
install -d /opt/intel/sgxdriver/scripts
install package/* /opt/intel/sgxdriver/package
install scripts/* /opt/intel/sgxdriver/scripts
~/POC1
/opt/intel/sgxdriver/package ~/POC1
make -C /lib/modules/3.10.0-327.36.1.el7.x86_64/build SUBDIRS=/opt/intel/sgxdriver/package modules
make[1]: Entering directory `/usr/src/kernels/3.10.0-327.el7.x86_64'
CC [M] /opt/intel/sgxdriver/package/isgx_main.o
CC [M] /opt/intel/sgxdriver/package/isgx_page_cache.o
CC [M] /opt/intel/sgxdriver/package/isgx_ioctl.o
CC [M] /opt/intel/sgxdriver/package/isgx_vma.o
CC [M] /opt/intel/sgxdriver/package/isgx_util.o
LD [M] /opt/intel/sgxdriver/package/isgx.o
Building modules, stage 2.
MODPOST 1 modules
CC /opt/intel/sgxdriver/package/isgx.mod.o
LD [M] /opt/intel/sgxdriver/package/isgx.ko
make[1]: Leaving directory `/usr/src/kernels/3.10.0-327.el7.x86_64'
modprobe: ERROR: could not insert 'isgx': No such device
===========================================================
Second I tried to install the SGX 2.1.0 driver and driver install successfully but I am unable to launch enclave.
============================================================
./app
Info: Please make sure SGX module is enabled in the BIOS, and install SGX driver afterwards.
Error: Invalid SGX device.
Enter a character before exit ...
============================================================
Note : I verified at BIOS and SGX is enabled there.
I have installed ROS indigo, openni2, and plugged Orbbec Astra sensor in.
When I try to execute:
roslaunch openni2_launch openni2.launch
I get the warning that no device is connected:
No matching device found.... waiting for devices.
Reason: std::string openni2_wrapper::OpenNI2Driver::resolveDeviceURI(const string&)
# /tmp/buildd/ros-indigo-openni2-camera-0.2.3-0trusty-20150327-
0611/src/openni2_driver.cpp # 623 :
Invalid device number 1, there are 0 devices connected.
How to view simple pointcloud in rviz using Orbbec Astra camera?
Install these two packages: ros_astra_camera and ros_astra_launch
Follow instruction from the ros_astra_camera README.