I know that there are similar question on stackoverflow, but those answers are useless in my case.
I have:
Tensorflow 2.1.0
CUDA 10.1
CUDNN 7.6.5
Nvidia GeForce 920M (DRIVER UPGRADED TO LAST VERSION)
I tried some code snipped like:
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=config)
And others similar code BUT NOTHING.
Full error: Attempting to fetch value instead of handling error Internal: failed to get device att
ribute 13 for device 0: CUDA_ERROR_UNKNOWN: unknown error
Related
I am new with Deep learning. I have a A100 GPU installed with CUDA 11.6. I installed using Conda tensor flow-1.15 and tensorflow gpu - 1.15, cudatoolkit 10.0, python 3.7 but the code I am trying to run from github has given a note as below and it shows errors which I am finding difficult to interpret where I went wrong.The error is displayed as
failed to run cuBLAS routine: CUBLAS_STATUS_EXECUTION_FAILED 2022-06-30 09:37:12.049400: I tensorflow/stream_executor/stream.cc:4925] [stream=0x55d668879990,impl=0x55d668878ac0] did not memcpy device-to-host; source: 0x7f2fe2d0d400 2022-06-30 09:37:12.056385: W tensorflow/core/framework/op_kernel.cc:1651] OP_REQUIRES failed at iterator_ops.cc:867 : Cancelled: Operation was cancelled
tensorflow.python.framework.errors_impl.InternalError: 2 root error(s) found. (0) Internal: Blas GEMM launch failed : a.shape=(25, 25), b.shape=(25, 102400), m=25, n=102400, k=25 [[{{node Hyperprior/HyperAnalysis/layer_Hyperprior_1/MatMul}}]] (1) Internal: Blas GEMM launch failed : a.shape=(25, 25), b.shape=(25, 102400), m=25, n=102400, k=25 [[{{node Hyperprior/HyperAnalysis/layer_Hyperprior_1/MatMul}}]] [[Hyperprior/truediv_3/_3633]]
NOTE: At the moment, we only support CUDA 10.0, Python 3.6-3.7, TensorFlow 1.15, and Tensorflow Compression 1.3. TensorFlow must be installed via pip, not conda. Unfortunately, newer versions of Tensorflow or Python will not work due to various constraints in the dependencies and in the TF binary API.
I converted a tensorflow saved model to ONNX format using tf2onnx :
python3 -m tf2onnx.convert --saved-model saved_model/ --output onnx/model.onnx --opset 11
The conversion worked fine and I can run inference with the ONNX model using CPU.
I installed onnxruntime-gpu to run inference with GPU and encountered an error :
RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Relu node. Name:'FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/conv1/Relu' Status Message: /onnxruntime_src/onnxruntime/core/providers/cuda/cuda_call.cc:97 bool onnxruntime::CudaCall(ERRTYPE, const char*, const char*, ERRTYPE, const char*) [with ERRTYPE = cudaError; bool THRW = true] /onnxruntime_src/onnxruntime/core/providers/cuda/cuda_call.cc:91 bool onnxruntime::CudaCall(ERRTYPE, const char*, const char*, ERRTYPE, const char*) [with ERRTYPE = cudaError; bool THRW = true] CUDA failure 2: out of memory ; GPU=0 ; hostname=coincoin; expr=cudaMalloc((void**)&p, size);
Stacktrace:
Stacktrace:
I am the only one using the GPU which is a Titan RTX (24GB of RAM). The model runs fine on GPU using its tensorflow saved model version, with 10GB of the GPU's RAM.
Versions are :
tensorflow 1.14.0
CUDA 10.0
CuDNN 7.6.5
onnx 1.6.0
onnxruntime 1.1.0
tf2onnx 1.9.2
python 3.6
Ubuntu 18.04
according to your information and Versions, maybe two solutions to solve the problem:
the information is out of memory, please check the gpu memory is available yet, if not available, set the gpu memory:
config = tf.ConfigProto()
config.gpu_options.visible_device_list = "0"
config.gpu_options.per_process_gpu_memory_fraction = 0.1
set_session(tf.Session(config=config))
downgrade onnxruntime-gpu, according to your Versions, CUDA 10.0 matchs with onnxruntime-gpu==1.0.0, not 1.1.0, which needs cuda 11
https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.0
I am trying to run tensorflow with CPU support.
tensorflow:
Version: 1.14.0
Keras:
Version: 2.3.1
When I try to run the following piece of code :
def run_test_harness(trainX,trainY,testX,testY):
datagen=ImageDataGenerator(rescale=1.0/255.0)
train_it = datagen.flow(trainX, trainY, batch_size=1)
test_it = datagen.flow(testX, testY, batch_size=1)
model=define_model()
history = model.fit_generator(train_it, steps_per_epoch=len(train_it),
validation_data=test_it, validation_steps=len(test_it), epochs=1, verbose=0)
I get the following error as shown in image:
Image shows the error
I tried to configure bazel for the same but it was of no use. It would be helpful if someone could direct me to resources or help with the problem. Thank you
EDIT : (Warning messages)
WARNING:tensorflow:From /home/neha/valiance/kerascpu/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
WARNING:tensorflow:From /home/neha/valiance/kerascpu/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
2020-10-22 12:41:36.023849: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-10-22 12:41:36.326420: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2299965000 Hz
2020-10-22 12:41:36.327496: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5502350 executing computations on platform Host. Devices:
2020-10-22 12:41:36.327602: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2020-10-22 12:41:36.679930: 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.
2020-10-22 12:41:36.890241: W tensorflow/core/framework/allocator.cc:107] Allocation of 3406823424 exceeds 10% of system memory.
^Z
[1]+ Stopped python3 model.py
You should try running your code on google colab. I think there aren't enough resources available on your PC for the task you are trying to run even though you are using a batch_size of 1.
I was following one of the online tutorials but I was getting this error:
Traceback (most recent call last):
File “ssd_object_detection.py”, line 20, in
detections = net.forward()
cv2.error: OpenCV(4.3.0) /home/blah/opencv/modules/dnn/src/layers/…/cuda4dnn/primitives/…/csl/cudnn/convolution.hpp:461: error: (-217:Gpu API call) CUDNN_STATUS_EXECUTION_FAILED in function ‘convolve_with_bias_activation’
It's a python script and I use Opencv dnn module with a pre-trained model
This is my configuration:
Jetson Nano device
Ubuntu 18.04
/usr/local/cuda/bin/nvcc --version
nvcc: NVIDIA ® Cuda compiler driver
Copyright © 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_21:14:42_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
– NVIDIA CUDA: YES (ver 10.2, CUFFT CUBLAS FAST_MATH)
– NVIDIA GPU arch: 53
– NVIDIA PTX archs:
– cuDNN: YES (ver 8.0)
– NVIDIA CUDA: YES (ver 10.2, CUFFT CUBLAS FAST_MATH)
– NVIDIA GPU arch: 53
– cuDNN: YES (ver 8.0)
opencv 4.3.0 built from source with OPENCV_DNN_CUDA=ON, CUDNN_VERSION=‘8.0’, WITH_CUDA=ON, WITH_CUDNN=ON, and many other settings enabled
Python 3.7.7
This is a snippet of the code I am trying to run (it completes successfully if I don’t use the GPU). It fails at the line detections = net.forward()
CLASSES = [“background”, “aeroplane”]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
net = cv2.dnn.readNetFromCaffe(args[“prototxt”], args[“model”])
print("[INFO] setting preferable backend and target to CUDA…")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
print("[INFO] accessing video stream…")
vs = cv2.VideoCapture(args[“input”] if args[“input”] else 0)
writer = None
fps = FPS().start()
while True:
(grabbed, frame) = vs.read()
frame = imutils.resize(frame, width=400)
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 0.007843, (300, 300), 127.5)
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
....
I am trying to understand and debug my code. I try to predict with a CNN model developed under tf2.0/tf.keras on GPU, but get those error messages.
could someone help me to fix it?
here is my environmental configuration
enviroments:
python 3.6.8
tensorflow-gpu 2.0.0-rc0
nvidia 418.x
CUDA 10.0
cuDNN 7.6+**
and the log file,
2019-09-28 13:10:59.833892: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2019-09-28 13:11:00.228025: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-09-28 13:11:00.957534: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2019-09-28 13:11:00.963310: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2019-09-28 13:11:00.963416: W tensorflow/core/common_runtime/base_collective_executor.cc:216] BaseCollectiveExecutor::StartAbort Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node mobilenetv2_1.00_192/Conv1/Conv2D}}]]
mobilenetv2_1.00_192/block_15_expand_BN/cond/then/_630/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0=====>GPU Available: True
=====> 4 Physical GPUs, 1 Logical GPUs
mobilenetv2_1.00_192/block_15_expand_BN/cond/then/_630/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_15_depthwise_BN/cond/then/_644/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_15_depthwise_BN/cond/then/_644/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_15_project_BN/cond/then/_658/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_15_project_BN/cond/then/_658/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_expand_BN/cond/then/_672/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_expand_BN/cond/then/_672/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_depthwise_BN/cond/then/_686/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_depthwise_BN/cond/then/_686/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_project_BN/cond/then/_700/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/block_16_project_BN/cond/then/_700/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/Conv_1_bn/cond/then/_714/Const: (Const): /job:localhost/replica:0/task:0/device:GPU:0
mobilenetv2_1.00_192/Conv_1_bn/cond/then/_714/Const_1: (Const): /job:localhost/replica:0/task:0/device:GPU:0
Traceback (most recent call last):
File "NSFW_Server.py", line 162, in <module>
model.predict(initial_tensor)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py", line 915, in predict
use_multiprocessing=use_multiprocessing)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_arrays.py", line 722, in predict
callbacks=callbacks)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_arrays.py", line 393, in model_iteration
batch_outs = f(ins_batch)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py", line 3625, in __call__
outputs = self._graph_fn(*converted_inputs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py", line 1081, in __call__
return self._call_impl(args, kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py", line 1121, in _call_impl
return self._call_flat(args, self.captured_inputs, cancellation_manager)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py", line 1224, in _call_flat
ctx, args, cancellation_manager=cancellation_manager)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py", line 511, in call
ctx=ctx)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[node mobilenetv2_1.00_192/Conv1/Conv2D (defined at /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_keras_scratch_graph_10727]
Function call stack:
keras_scratch_graph
The code
if __name__ == "__main__":
print("=====>GPU Available: ", tf.test.is_gpu_available())
tf.debugging.set_log_device_placement(True)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print("=====>", len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
paras_path = "./paras/{}".format(int(2011))
model = tf.keras.experimental.load_from_saved_model(paras_path)
initial_tensor = np.zeros((1, INPUT_SHAPE, INPUT_SHAPE, 3))
model.predict(initial_tensor)
You have to check that you have the right version of CUDA + CUDNN + TensorFlow (also ensure that you have all installed).
A couple of examples of running configurations are presented below(UPDATE FOR LATEST VERSIONS OF TENSORFLOW)
Cuda 11.3.1 + CuDNN 8.2.1.32 + TensorFlow 2.7.0
Cuda 11.0 + CuDNN 8.0.4 + TensorFlow 2.4.0
Cuda 10.1 + CuDNN 7.6.5 (normally > 7.6) + TensorFlow 2.2.0/TensorFlow 2.3.0 (TF >= 2.1 requires CUDA >=10.1)
Cuda 10.1 + CuDNN 7.6.5 (normally > 7.6) + TensorFlow 2.1.0 (TF >= 2.1 requires CUDA >=
10.1)
Cuda 10.0 + CuDNN 7.6.3 + / TensorFlow 1.13/1.14 / TensorFlow 2.0.
Cuda 9.0 + CuDNN 7.0.5 + TensorFlow 1.10
Usually this error appears when you have an incompatible version of TensorFlow/CuDNN installed. In my case, this appeared when I tried using an older TensorFlow with a newer version of CuDNN.
**If for some reason you get an error message like(and nothing happens afterwards) :
Relying on the driver to perform ptx compilation
Solution : Install the latest nvidia driver
[SEEMS TO BE SOLVED IN TF >= 2.5.0] (see below):
Only for Windows Users : Some late combintations of CUDA, CUDNN and TF may not work, due to a bug (a .dll extension named improperly). To handle that specific case, please consult this link: Tensorflow GPU Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
For those who are facing issues regarding the above error(For Windows platform), I sorted it just by installing CuDNN version compatible with the CUDA already installed in the system.
This suitable version can be downloaded from the website Download CuDNN from Developer's portal. You might need Nvidia account for it. This will be easily created by providing mail id and filling a questionnaire.
To check the CUDA version, run NVCC --version.
Once the suitable version is downloaded, extract the folder from the zip file.
Go to the bin folder of the extracted folder. copy the cudnn64:7.dll and paste it in the CUDA's bin folder. In my case, the location where Cuda is installed is C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin.
This would most probably solve the problem.
My system details:
Windows 10
CUDA 10.0
TensorFlow 2.0
GPU- Nvidia GTX 1060
I also found this blog Installing TensorFlow with CUDA and GPU support on Windows 10. very useful.
Check the instructions on this TensorFlow GPU instruction page for your OS. It resolved issue for me on Ubuntu 16.04.6 LTS and Tensorflow 2.0