tensorflow: [Windows] Couldn't open CUDA library cupti64_80.dll

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This issue is not applicable for me as CuDNN is getting loaded. I checked the CuDNN folder manually, there is no such dll.

Environment info

Operating System: Windows 10 Pro

Installed version of CUDA and cuDNN: (please attach the output of ls -l /path/to/cuda/lib/libcud*):

D:\TensorFlow>nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Sat_Sep__3_19:05:48_CDT_2016
Cuda compilation tools, release 8.0, V8.0.44

cudnn-8.0-windows10-x64-v5.1.zip

If installed from binary pip package, provide:

1. A link to the pip package you installed: Sorry, I cant remember which package pip fetched. I just ran this command.

D:\TensorFlow>pip3.5 install --upgrade tensorflow-gpu
Requirement already up-to-date: tensorflow-gpu in c:\users\windows\appdata\local\programs\python\python35\lib\site-packages
Requirement already up-to-date: wheel>=0.26 in c:\users\windows\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: six>=1.10.0 in c:\users\windows\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: numpy>=1.11.0 in c:\users\windows\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: protobuf==3.1.0 in c:\users\windows\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: setuptools in c:\users\windows\appdata\local\programs\python\python35\lib\site-packages (from protobuf==3.1.0->tensorflow-gpu)

1. The output from python -c "import tensorflow; print(tensorflow.__version__)".

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cublas64_80.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cudnn64_5.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cufft64_80.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library nvcuda.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library curand64_80.dll locally
0.12.0-rc1

If possible, provide a minimal reproducible example (We usually don’t have time to read hundreds of lines of your code)

import tensorflow as tf
import numpy as np
import random

vLength = 8

nodeCount = [vLength,8,8,vLength]

SUMMARY_DIR = 'D:/Summary/'

BATCH_SIZE = 10
TRAIN_SIZE = 100

def model(x_t):    
    layerCount = len(nodeCount)
    layer = [None for _ in range(layerCount)]
    weights = [None for _ in range(layerCount - 1)]
    biases = [None for _ in range(layerCount - 1)]
    layer[0] = x_t
    for i in range(layerCount-1):
        weights[i] = tf.Variable(tf.random_normal([nodeCount[i],nodeCount[i+1]]))
        biases[i] = tf.Variable(tf.random_normal([nodeCount[i+1]]))
          
        layer[i+1] = tf.add(tf.matmul(layer[i],weights[i]),biases[i])
        if(i != layerCount-2):
            layer[i+1] = tf.nn.tanh(layer[i+1])
        else:
            layer[i+1] = tf.nn.softmax(layer[i+1])

    return layer[i+1]

def getNextBatch():
    v = [0.0 for _ in range(vLength)]
    v[0] = 1.0

    r = [None for _ in range(BATCH_SIZE)]
    for i in range(BATCH_SIZE):
        random.shuffle(v)
        r[i] = v.copy()
    return r,r
    
m = None
def trainNN():
    x_t = tf.placeholder(tf.float32,[None,vLength],'input')
    y_t = tf.placeholder(tf.float32, [None, vLength],'actual')
    
    m = model(x_t)    

    with tf.name_scope('Cost_Function'):
        cost = tf.reduce_mean(-tf.reduce_sum(y_t * tf.log(m)))
        
    with tf.name_scope('Learning_Rate'):
        learning_rate = tf.Variable(0.5,dtype=tf.float32)

    with tf.name_scope('Optimizer'):
        optimizer = tf.train.AdamOptimizer().minimize(cost)

    with tf.name_scope('testing'):
        correct = tf.equal(tf.argmax(y_t,1),tf.argmax(m,1))
        accuracy = tf.reduce_mean(tf.cast(correct,'float'))
        tf.summary.scalar('accuracy',accuracy)

    epochs = 10

    with tf.Session() as sess:
        merged = tf.summary.merge_all()
        
        sess.run(tf.global_variables_initializer())
        
        for epoch in range(epochs):            
            tw = tf.summary.FileWriter(SUMMARY_DIR+'/epoch'+str(epoch),sess.graph)
            epochLoss = 0
            c = 0
            for i in range(TRAIN_SIZE):
                bx,by = getNextBatch()    
                fd = {x_t:bx,y_t:by}                
                run_metadata = tf.RunMetadata()
                run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
                summary,c,_ = sess.run([merged,cost,optimizer],
                              feed_dict=fd,
                              options=run_options,
                              run_metadata=run_metadata)
                if(i%BATCH_SIZE == 0):
                    tw.add_summary(summary,i)
                    tw.add_run_metadata(run_metadata,'step%d'%i)
                
                epochLoss += c       
            
            tx,ty = getNextBatch()
            print('Epoch ',epoch,'/',epochs,':',epochLoss/TRAIN_SIZE,' ',accuracy.eval({x_t:tx,y_t:ty}))
       
        tw.close()            

trainNN()

What other attempted solutions have you tried?

I googled the said dll file. No, results.

Could PTI stand for Parameter Tuning Interface? I found this link, but I am hesitant to installed anything that is not prescribed officially.

Logs or other output that would be helpful

(If logs are large, please upload as attachment or provide link).

D:\TensorFlow>D:\TensorFlow\identity_dnn_bare.py
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cublas64_80.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cudnn64_5.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cufft64_80.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library nvcuda.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library curand64_80.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:885] Found device 0 with properties:
name: GeForce 930MX
major: 5 minor: 0 memoryClockRate (GHz) 1.0195
pciBusID 0000:01:00.0
Total memory: 2.00GiB
Free memory: 1.66GiB
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:906] DMA: 0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:916] 0:   Y
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 930MX, pci bus id: 0000:01:00.0)
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:119] Couldn't open CUDA library cupti64_80.dll
F c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\platform\default\gpu\cupti_wrapper.cc:59] Check failed: ::tensorflow::Status::OK() == (::tensorflow::Env::Default()->GetSymbolFromLibrary( GetDsoHandle(), kName, &f)) (OK vs. Not found: cuptiActivityRegisterCallbacks not found)could not find cuptiActivityRegisterCallbacksin libcupti DSO