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Training VGG13 net with RX6600 is slow #326
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Hi @thinksmert,
Can you send us the device placement logs? Just add the following snippet at the start of your script: import tensorflow as tf
tf.debugging.set_log_device_placement(True) and then, redirect the output to a file. For example: python script.py > log.txt |
Hi, |
And I wonder if the plugin will support the tensorflow-gpu? |
Hi, |
@thinksmert Thanks! Can you do the same thing with the tensorflow-gpu package (and without tensorflow-directml-plugin) on your Nvidia card? This will help us compare what is supposed to happen versus what is actually happening. |
Hi, |
Hi, For your reference. |
Hi, |
The logs are identical between DML and CUDA, so it's hard to say just from that. Can I ask where you got that VGG13 script from? Running the exact same script would help us investigate this on our end. |
Hi, |
my environment:
windows 11 64bit
python 3.9 64bit
tensorflow 2.10
tensorflow-directml-plugin 0.2.0.dev221020
AMD Radeon RX 6600 Nvidia RTX1060
Conda 22.9.0
I'm training a VGG13 net in miniConda enviroment.I have two configurations:
1.Nvidia RTX1060 + tensorflow-gpu
2.RX6600(more powerful than RTX1060) + tensorflow-cpu + tensorflow-directml-plugin
With first configuration,it is very fast, about 6s each train period.But with second configuration,it is slower than the first configuration,only about 30s each train period.
I guess the reason of second configuration is slower, is it just uses tensorflow-cpu not tensorflow-gpu?Is it right?
Is there any way can improve the trainning speed with that second configuration? Or when tensorflow-directml-plugin can support tensorflow-gpu?
Thanks
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