Issue 1:
conda activate base

2.0.0-alpha0

2019-05-29 19:58:31.543654: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

2019-05-29 19:58:31.557759: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1

2019-05-29 19:58:31.670769: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1009] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-05-29 19:58:31.672348: I tensorflow/compiler/xla/service/service.cc:162] XLA service 0x55c9b57a0a60 executing computations on platform CUDA. Devices:

2019-05-29 19:58:31.672364: I tensorflow/compiler/xla/service/service.cc:169] StreamExecutor device (0): Graphics Device, Compute Capability 7.5

2019-05-29 19:58:31.693201: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2904000000 Hz

2019-05-29 19:58:31.693619: I tensorflow/compiler/xla/service/service.cc:162] XLA service 0x55c9b580db30 executing computations on platform Host. Devices:

2019-05-29 19:58:31.693637: I tensorflow/compiler/xla/service/service.cc:169] StreamExecutor device (0): ,

2019-05-29 19:58:31.693791: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1467] Found device 0 with properties:

name: Graphics Device major: 7 minor: 5 memoryClockRate(GHz): 1.71

pciBusID: 0000:01:00.0

totalMemory: 5.76GiB freeMemory: 5.17GiB

2019-05-29 19:58:31.693804: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1546] Adding visible gpu devices: 0

2019-05-29 19:58:31.693844: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0

2019-05-29 19:58:31.694402: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1015] Device interconnect StreamExecutor with strength 1 edge matrix:

2019-05-29 19:58:31.694413: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1021] 0

2019-05-29 19:58:31.694419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1034] 0: N

2019-05-29 19:58:31.694516: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1149] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4990 MB memory) -> physical GPU (device: 0, name: Graphics Device, pci bus id: 0000:01:00.0, compute capability: 7.5)

Number of training examples: 60000

Number of test examples: 10000

Epoch 1/5

2019-05-29 19:58:32.786674: W ./tensorflow/core/framework/model.h:202] Encountered a stop event that was not preceded by a start event.

2019-05-29 19:58:36.114172: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0

2019-05-29 19:58:36.288348: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7

2019-05-29 19:58:36.934479: E tensorflow/stream_executor/cuda/cuda_dnn.cc:338] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

2019-05-29 19:58:36.945020: E tensorflow/stream_executor/cuda/cuda_dnn.cc:338] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

2019-05-29 19:58:36.945127: W tensorflow/core/common_runtime/base_collective_executor.cc:214] 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 conv2d/Conv2D}}]]

Issue 2:
Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):Debian

Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:

TensorFlow installed from (source or binary):binary

TensorFlow version (use command below):1.13.1,1.14.0，2.0.0a0，1.9.0

Python version:3.7,3.6

Bazel version (if compiling from source):

GCC/Compiler version (if compiling from source):

CUDA/cuDNN version:Cuda 10/7.5.0,7.4.2,7.4.1,7.4.0,Cuda 9 can't support 2060

GPU model and memory:rtx 2060 6GB

You can collect some of this information using our environment capture

script

You can also obtain the TensorFlow version with: 1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"

Describe the current behavior

Describe the expected behavior

Code to reproduce the issue

conda activate base

2.0.0-alpha0

2019-05-29 19:58:31.543654: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

2019-05-29 19:58:31.557759: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1

2019-05-29 19:58:31.670769: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1009] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2019-05-29 19:58:31.672348: I tensorflow/compiler/xla/service/service.cc:162] XLA service 0x55c9b57a0a60 executing computations on platform CUDA. Devices:

2019-05-29 19:58:31.672364: I tensorflow/compiler/xla/service/service.cc:169] StreamExecutor device (0): Graphics Device, Compute Capability 7.5

2019-05-29 19:58:31.693201: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2904000000 Hz

2019-05-29 19:58:31.693619: I tensorflow/compiler/xla/service/service.cc:162] XLA service 0x55c9b580db30 executing computations on platform Host. Devices:

2019-05-29 19:58:31.693637: I tensorflow/compiler/xla/service/service.cc:169] StreamExecutor device (0): ,

2019-05-29 19:58:31.693791: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1467] Found device 0 with properties:

name: Graphics Device major: 7 minor: 5 memoryClockRate(GHz): 1.71

pciBusID: 0000:01:00.0

totalMemory: 5.76GiB freeMemory: 5.17GiB

2019-05-29 19:58:31.693804: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1546] Adding visible gpu devices: 0

2019-05-29 19:58:31.693844: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0

2019-05-29 19:58:31.694402: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1015] Device interconnect StreamExecutor with strength 1 edge matrix:

2019-05-29 19:58:31.694413: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1021] 0

2019-05-29 19:58:31.694419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1034] 0: N

2019-05-29 19:58:31.694516: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1149] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4990 MB memory) -> physical GPU (device: 0, name: Graphics Device, pci bus id: 0000:01:00.0, compute capability: 7.5)

Number of training examples: 60000

Number of test examples: 10000

Epoch 1/5

2019-05-29 19:58:32.786674: W ./tensorflow/core/framework/model.h:202] Encountered a stop event that was not preceded by a start event.

2019-05-29 19:58:36.114172: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0

2019-05-29 19:58:36.288348: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7

2019-05-29 19:58:36.934479: E tensorflow/stream_executor/cuda/cuda_dnn.cc:338] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

2019-05-29 19:58:36.945020: E tensorflow/stream_executor/cuda/cuda_dnn.cc:338] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR

2019-05-29 19:58:36.945127: W tensorflow/core/common_runtime/base_collective_executor.cc:214] 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 conv2d/Conv2D}}]]

Other info / logs

Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.