# 12. test that tensorflow links with libcudnn
workon deep learning_3.5
python
# 13. Check the Tensorflow version
python -c "import tensorflow; print(tensorflow.__version__)"
# 14. Test that Tensorflow is using the GPU
python -c "import tensorflow as tf; sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))"
##### Expected Output #########
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: GeForce GTX 1060 6GB
major: 6 minor: 1 memoryClockRate (GHz) 1.7845
pciBusID 0000:01:00.0
Total memory: 5.93GiB
Free memory: 5.66GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0
I tensorflow/core/common_runtime/direct_session.cc:257] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0
# http://stackoverflow.com/questions/43335531/how-to-use-sse4-1-instructions-without-install-tensorflow-from-source
# 15. Setup Keras
For best performance, set `image_data_format="channels_last"` in your Keras config at ~/.keras/keras.json.
# Or use env vars
export KERAS_BACKEND=tensorflow
# 16. test Keras
git clone http://github.com/rcmalli/keras-squeezenet.git
cd keras-squeezenet
python3.5 test.py
Using TensorFlow backend.
Downloading
data from
https://github.com/rcmalli/keras-squeezenet/releases/download/v1.0/squeezenet_weights_tf_dim_ordering_tf_kernels.h5
4530176/5059384
[=========================>....] - ETA: 0s2017-10-18
20:57:06.845299: W tensorflow/core/platform/cpu_feature_guard.cc:45] The
TensorFlow library wasn't compiled to use SSE4.1 instructions, but
these are available on your machine and could speed up CPU computations.
2017-10-18
20:57:06.845317: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE4.2 instructions, but
these are available on your machine and could speed up CPU computations.
2017-10-18
20:57:06.845321: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX instructions, but
these are available on your machine and could speed up CPU computations.
2017-10-18
20:57:06.845324: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX2 instructions, but
these are available on your machine and could speed up CPU computations.
2017-10-18
20:57:06.845326: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use FMA instructions, but
these are available on your machine and could speed up CPU computations.
2017-10-18
20:57:06.952926: I
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful
NUMA node read from SysFS had negative value (-1), but there must be at
least one NUMA node, so returning NUMA node zero
2017-10-18 20:57:06.953146: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1060 6GB
major: 6 minor: 1 memoryClockRate (GHz) 1.7845
pciBusID 0000:01:00.0
Total memory: 5.93GiB
Free memory: 5.79GiB
2017-10-18 20:57:06.953159: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-10-18 20:57:06.953162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-10-18
20:57:06.953167: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060 6GB,
pci bus id: 0000:01:00.0)
(deep_learning_3.5) depappas@berlin:~/keras-squeezenet$
# 17. Install TFLearn
# http://tflearn.org/installation/
# TFLearn Installation
# To install TFLearn, the easiest way is to run one of the following options.
# For the bleeding edge version:
pip install git+https://github.com/tflearn/tflearn.git
# For the latest stable version:
pip install tflearn
#You can also install from source by running this command (from source folder):
python setup.py install
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