I have a laptop that has an RTX 2060 GPU and I am using Keras and TF 2 to train an LSTM on it. I am also monitoring the gpu use by nvidia-smi and I noticed that the jupyter notebook and TF are using maximum 35% and usually the gpu is being used between 10-25%.
With current conditions, it took more than 7 hours to train this model, I want to know if I am doing something wrong or it is a limitation of Keras and TF?
My nvidia-smi output:
Sun Nov 3 00:07:37 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.26 Driver Version: 430.26 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2060 Off | 00000000:01:00.0 On | N/A |
| N/A 51C P3 22W / N/A | 834MiB / 5931MiB | 24% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1032 G /usr/lib/xorg/Xorg 330MiB |
| 0 1251 G /usr/bin/gnome-shell 333MiB |
| 0 1758 G ...equest-channel-token=622209288718607755 121MiB |
| 0 5086 G ...uest-channel-token=12207632792533837012 47MiB |
+-----------------------------------------------------------------------------+
My LSTM:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dropout
regressor = Sequential()
regressor.add(LSTM(units = 180, return_sequences = True, input_shape = (X_train.shape[1], 3)))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 180, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 180, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 180, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 180, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 180))
regressor.add(Dropout(0.2))
regressor.add(Dense(units = 1))
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
regressor.fit(X_train, y_train, epochs = 10, batch_size = 32, callbacks=[cp_callback])
TensorFlow automatically takes care of optimizing GPU resource allocation via CUDA & cuDNN, assuming latter's properly installed. The usage statistics you're seeing are mainly that of memory/compute resource 'activity', not necessarily utility (execution); see this answer. That your utility is "only" 25% is a good thing - otherwise, if you substantially increased your model size (which isn't large as-is), you'd OOM.
To increase usage, increase batch size, model size, or whatever would increase the parallelism of computations; note that making the model deeper would increase GPU's memory utility, but far less so its compute-utility.
Also, consider using CuDNNLSTM instead of LSTM, which can run 10x faster and use less GPU memory (courtesy of algorithmic artisanship), but more compute-utility. Lastly, inserting Conv1D as the first layer with strides > 1 will significantly increase train speed by reducing input size, without necessarily harming performance (it can in fact improve it).
Update: overclocking the GPU is an option, but I'd advise against it as it can wear out the GPU in the long run (and all DL is "long run"). There's also "over-volting" and other hardware tweaks, but all should be used for some short applications. What'll make the greatest difference is your input data pipeline.