Closed mariembenslama closed 5 years ago
@mariembenslama since our displayInterval
is set to 100
and batchSize
is set to 64
, it means the training loss will be printed every 6400
samples. You can set the displayInterval
to 10
then it may seems ten times quickly. Of course, you can use the command watch -n 1 nvidia-smi
to checkout the using case of GPU
, it will show something like
Sun Apr 28 17:44:06 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.104 Driver Version: 410.104 CUDA Version: 10.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla P40 Off | 00000000:3B:00.0 Off | 0 |
| N/A 36C P0 128W / 250W | 1063MiB / 22919MiB | 66% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 249645 C python 1053MiB |
+-----------------------------------------------------------------------------+
If there are still problems, please let me know, thank you~
Thanks! I guess it's good now :) it jumped quickly to a good intervalle of accuracy and loss ^-^
Thank youuu~~~ Is the model (DL in general) always taking too long to grasp all the alphabets? I have about 3340 alphabets, at epoch 9/1000 Loss is 0.5456 and Accuracy is 0.004 but it's growing up. Right now I have 100000 train and 10000 test, I will train this sample, add a checkpoint, then add more data and train again.
Do you think by completing the train it'll give a good accuracy in the end?
@mariembenslama I believe you can do it! But you can add more training data to it to let it know the right decline direction. If you have more training data, after 9/1000
epoch, the loss can be lower! Good luck!
Thank you! I will do as you say 😊
Thanks a lot again 😀😀😀
Hello,
I have a RAM with 13GB, I activated cuda in the params.py file but the training and test are still slow comparing to the capacity of my machine.
It's supposed to run quickly I mean.
I'm wondering if cuda is actually working or not in reality?