I'm quite new in deep learning and, in order to improve my knowledge, I've been reading some books and following a video course on line.
In this videocourse I have to do an exercise with convolution neaural network.
I've builded a CNN with 10.000 images with dimension 64x64 pixels. (to recognize cats and dogs images)
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Convolution2D(32,3,3,input_shape=(64,64,3),activation='relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2,2)))
classifier.add(Convolution2D(32,3,3,activation='relu'))
classifier.add(MaxPooling2D(pool_size = (2,2)))
# Step 3 - Flattening
classifier.add(Flatten())
#step 4 - Full Connection CNN
classifier.add(Dense(output_dim = 128 ,activation='relu'))
classifier.add(Dense(output_dim = 1 ,activation='sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam' , loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
traininig_set = train_datagen.flow_from_directory(
'dataset/training_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
'dataset/test_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
classifier.fit_generator(traininig_set,
steps_per_epoch=8000,
epochs=25,
validation_data=test_set,
validation_steps=2000)
The first time I installed Anaconda I didn't install the GPU module and when I started fitting my CNN
I had to wait 1190 seconds per epoch with the CPU working at 70%.
For your information my computer is quite fast. It's an i7 6800k overclocked to 4.2ghz an MSI GTX1080 video cards and 32gb 3333Mhz.
I've tought that with this computer installing the tensorflow gpu module was almost compulsory.
I watched in some posts how to check if the tensorflow is correctly configured to use GPU
and launching:
In [1]: from tensorflow.python.client import device_lib
In [2]: print(device_lib.list_local_devices())
I have this result:
2017-10-16 10:41:25.780983: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\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.
2017-10-16 10:41:25.781067: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\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.
2017-10-16 10:41:26.635590: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.8225
pciBusID 0000:03:00.0
Total memory: 8.00GiB
Free memory: 6.61GiB
2017-10-16 10:41:26.635807: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:976] DMA: 0
2017-10-16 10:41:26.636324: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:986] 0: Y
2017-10-16 10:41:26.637179: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:03:00.0)
[name: "/cpu:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 16495731140373557390
, name: "/gpu:0"
device_type: "GPU"
memory_limit: 6740156088
locality {
bus_id: 1
}
incarnation: 6266244792178813148
physical_device_desc: "device: 0, name: GeForce GTX 1080, pci bus id: 0000:03:00.0"
]
With gpu:0, I read in the documentation that TensorFlow automatically will use GPU for computation.
Launching the fit method with this configuration I have to wait 950 sec per epoch, well better than 1190 seconds. The cpu never gets over 10% and, strangely, the GPU never gets over 10-13%.
I assume there is something wrong with my configuration because, the teacher in the course, with a MacBook notebook (I don't know the exact configuration actually) without tensorflow GPU module takes approximately 90 seconds per epoch.
I'm not a python or tensorflow expert, but it really seems there is something wrong or something else to understand.
Could someone give some advice, something to read, some tests to do to understand better where is the bottleneck?
Thank you
I don't have a GPU on windows, but I got a really good deal installing the Intel Distribution of Python with Anaconda: https://software.intel.com/en-us/articles/using-intel-distribution-for-python-with-anaconda.
For tensorflow, the best seems to be a python 3.5 environment (in the previous link, use python=3.5)
I then installed tensorflow with pip inside this environment made with anaconda. Follow installing with anaconda.
Then Keras with conda install keras. (But make sure it won't replace previous numpy and other installations, find proper installation commands not to replace these optimal packages). Maybe pip install keras could be better in case the conda version doesn't work. (Again, use the proper options not to replace your existing packages) - Don't let this keras installation replace your numpy packages or your tensorflow packages!
This gave me all processors absolutely 100% (according to windows resource monitor)
If this doesn't solve your problem, you can also try getting the numpy and scipy packages from here. Unfortunately I had no success at all with the keras and tensorflow packages from this source, but numpy is quality stuff.
With GPU, your problem may be the lack of a proper CUDA driver and the CUDNN library?
Follow this and this.
Unfortunatelly these things vary a lot from computer to computer. I followed strictly the instructions in these sites, and in tensorflow site, for a linux machine, and the results were astonishing.
On top of Daniel's answer (check CUDA & cuDNN) - it is never a good idea to have both tensorflow and tensorflow-gpu packages installed side by side; most probably, you are using the tensorflow (i.e. the CPU) one.
To avoid this, you should uninstall both packages, and then re-install tensorflow-gpu, i.e.:
pip uninstall tensorflow tensorflow-gpu
pip install tensorflow-gpu
See also accepted answer (and comment) here, on a similar issue.
Related
I've been trying to make Tensorflow 2.8.0 work with my Windows GPU (GeForce GTX 1650 Ti), and even though it detects my GPU, any model that I make will be stuck at Epoch 1 indefinitely when I try to use the fit method till the kernel (I've tried on jupyter notebook and spyder) hangs and restarts.
Based on Tensorflow's website, I've downloaded the respective cuDNN and CUDA versions, for which I've further verified (together with tensorflow's detection of my GPU) by running the various commands:
CUDA (Supposed to be 11.2)
(on command line)
nvcc --version
Build cuda_11.2.r11.2/compiler.29373293_0
(In python)
import tensorflow.python.platform.build_info as build
print(build.build_info['cuda_version'])
Output: '64_112'
cuDNN (Supposed to be 8.1)
import tensorflow.python.platform.build_info as build
print(build.build_info['cuda_version'])
Output: '64_8' # Looks like v8 but I've actually installed v8.1 (cuDNN v8.1.1 (Feburary 26th, 2021), for CUDA 11.0,11.1 and 11.2) so I think it's fine?
GPU Checks
tf.config.list_physical_devices('GPU')
Output: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
tf.test.is_gpu_available()
Output: True
tf.test.gpu_device_name()
Output: This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Created device /device:GPU:0 with 2153 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1650 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
When I then try to fit any sort of model, it just fails following what I described above. What is surprising is that even though it can't load code such as that described in Tensorflow's CNN Tutorial, the only time it ever works is if I run the chunk of code from this stackoverflow question. This chunk of code looks almost the same as every other chunk that failed.
Can someone help me with this issue? I've been desperately testing TensorFlow with every chunk of code that I came across for the past couple of hours, and the only time where it does not get stuck at Epoch 1 is with the link above.
**(I've also tried running only on my CPU via os.environ['CUDA_VISIBLE_DEVICES'] = '-1' and everything seems to work fine)
Update (Solution)
It seems like the suggestions from this post helped - I've copied the following files from the zipped cudnn bin sub folder (cudnn-11.2-windows-x64-v8.1.1.33\cuda\bin) into my cuda bin folder (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin)
cudnn_adv_infer64_8.dll
cudnn_adv_train64_8.dll
cudnn_cnn_infer64_8.dll
cudnn_cnn_train64_8.dll
cudnn_ops_infer64_8.dll
cudnn_ops_train64_8.dll
It seems like I initially misinterpreted the copy all cudnn*.dll files as only copying over the cudnn64_8.dll file, rather than copying every other file listed above.
I want to train a sequential tensorflow (version 2.3.0) model on a single NVIDIA graphic card (RTX 2080 super). I am using the following code snippet to build and train the model. However, everytime I am running this code I do not see any GPU utilization. Any suggestion how to modify my code so I can run it on 1 GPU?
strategy = tf.distribute.OneDeviceStrategy(device="/GPU:0")
with strategy.scope():
num_classes=len(pd.unique(cats.No))
model = BuildModel((image_height, image_width, 3), num_classes)
model.summary()
model=train_model(model,valid_generator,train_generator,EPOCHS,BATCH_SIZE)
run the code below to see if tensorflow detects your GPU.
import tensorflow as tf
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
print(tf.__version__)
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
tf.test.is_gpu_available()
!python --version
I'm trying to run a simple LSTM model with following code
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.LSTM(32,
input_shape=x_train_single.shape[-2:]))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer=tf.keras.optimizers.RMSprop(), loss='mae')
single_step_history = model.fit(train_data_single, epochs=EPOCHS,
steps_per_epoch=EVALUATION_INTERVAL)
The error happened when it trying to fit the model
tensorflow.python.framework.errors_impl.UnknownError: [_Derived_] Fail to find the dnn implementation.
[[{{node CudnnRNN}}]]
[[sequential/lstm/StatefulPartitionedCall]] [Op:__inference_distributed_function_3107]
There's another error like this
2020-02-22 19:08:06.478567: W tensorflow/core/kernels/data/cache_dataset_ops.cc:820] The calling
iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the
dataset, the partially cached contents of the dataset will be discarded. This can happen if you have
an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use
`dataset.take(k).cache().repeat()` instead.
I tried all methods on this question which doesn't work for me
my envrionment is
tensorflow-gpu 2.0
CUDA v10
CuDNN 7.6.5
Solution
OK.. I found that I didn't have the latest Nvidia driver, so I upgraded, and works
Answering here for the benefit of the community even if the user has provided the solution.
Upgrading Nvidia driver to the latest has resolved the issue.
You can update NVIDIA manually from here here by selecting the product details and OS, you’re going to have to download the most recent drivers from their website. You’ll then have to run the installer and overwrite the old driver.
Try below
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], enable=True)
I am training RNNs, which I built using tf.keras.layers.GRU layers. They are taking a long time to train (>2 hours), so I am going to deploy them to the GPU for training. I am wondering a few things about training on GPU:
What is the difference between tf.keras.layers.CuDNNGRU and tf.keras.layers.GRU (and also tf.keras.layers.LSTM vs. tf.keras.layers.CuDNNLSTM)? I understand from this post that CuDNNGRU layers train faster than GRU layers, but
Do the 2 layers converge to different results with the same seed?
Do the 2 layers perform the same during inference?
Do CuDNN layers require a GPU during inference?
Can GRU layers run inference on a GPU?
Are CuDNN layers easily deployable? I am currently using coremlconverter to convert my keras model to CoreML for deployment.
Is there an equivalent CuDNN layer for tf.keras.layers.SimpleRNN (i.e. tf.keras.layers.CuDNNSimpleRNN)? I am not committed to a specific architecture yet, and so I believe I would need the tf.keras.layers.CuDNNSimpleRNN layer if I decide on SimpleRNNs and the CuDNN layer has some functionality that I need.
With CuDNN layers, do I need to have tensorflow-gpu installed? Or do they still get deployed to the GPU as long as I have the relevant drivers installed?
if you are using a cuda compatible gpu, it makes absolutely sense to use CuDNN layers. They have a different implementation that tries to overcome computation parallelization issues inherent in the RNN architecture. They usually perform a bit worst though but are 3x-6x faster https://twitter.com/fchollet/status/918170264608817152?lang=en
Do the 2 layers converge to different results with the same seed?
yes
Do the 2 layers perform the same during inference?
You should have a comparable performance but not exactly the same
Do CuDNN layers require a GPU during inference?
Yes but you can convert to a CuDNN compatible GRU/LSTM
Can GRU layers run inference on a GPU?
Yes
With CuDNN layers, do I need to have tensorflow-gpu installed? Or do they still get deployed to the GPU as long as I have the relevant drivers installed?
Yes and you need a cuda compatible gpu
I am working with Keras with TensorFlow back end.
I writing search script for tuning my CuDDNNLSTM hyper parameters .
After creating ~10 different CuDDNNLSTM networks I received the error:
tensorflow\stream_executer\cuda\cuda_driver.cc:1108 could not synchronize on CUDA context: CUDA_ERROR_LAUNCH_FAILED during the search process.
in : tensorflow\python\client\session.py in _run,_do_run,_do_call
OS: WIN10 64x
Python: 3.6.5
Keras version : 2.1.6
Tensorflow/GPU: 1.10.0
CUDA:9.0
cuddn:7.3
GPU: GeForce GTX 1080 Ti
May someone encounter in that problem ?