How long does it take to train the U-net baseline from the benchmark on 1 GPU?

Hi all,

I am wondering how long it does take to train the U-net baseline on a single GPU (e.g. time per epoch).

Thank you,

It is quite fast. In colab pro, it takes only 15 minutes per epoch.

Nice, thank you for the info !!

Any tips, mine is taking long time per epoch

Mine is making almost 60 mins per epoch. Can you pls confirm yours?

@Kushal_Gandhi can you tell me is it dedicated GPU or Google colab or Google Colab Pro ?

If not using the GPU, it could be long (usually long)

  • On the Google Colab Pro - with files in the session (means not loading from personal drive) takes - 5-7 mins. Mine was with benchmark code, resnet50 backbone and 10 as batch size . Also, look over a bit of discussion here - Submission Jobs failing because of CUDA out of memory, important take away is to have code change in place like @Max_Schaefer suggested.

  • On a dedicated GPU (1 with 24GB), I’m able to see again see below 5 mins with resnet50 backbone and 10 as batch size. Here lower the batch size, less memory it uses , good to keep in check if you are using a larger GPU box.

Hope this helps.

Actually I am using keras. I made simple 5-6 layer model to check if my data fetching code is working or not. On that simple 5 layer model, it is taking an hour per epoch. I believe, its issue with my data fetching code. But do not know how to solve. below is my code. Pls help me if there is other way to fetch data in keras.

I am using Microsoft Planetary computer hub for training.
I have not downloaded data. Can you pls also confirm what is size of provided data set?

class image_process(keras.utils.Sequence):
def init(self, x,y,batch_size):
self.x = x
self.y = y
self.batch_size = batch_size

def __len__(self):
    return math.ceil(len(self.x)/self.batch_size)

def __getitem__(self,idx):
    batch_x = self.x[idx*self.batch_size:(idx+1)*self.batch_size]
    batch_y = self.y[idx*self.batch_size:(idx+1)*self.batch_size]
    ch_main_x = np.array([])
    ch_main_y = np.array([])
    for i in batch_x.index:
        ch_B02 =[i,"B02_path"]).read().reshape(512,512,1)
        ch_B03 =[i,"B03_path"]).read().reshape(512,512,1)
        ch_B04 =[i,"B04_path"]).read().reshape(512,512,1)
        ch_B08 =[i,"B08_path"]).read().reshape(512,512,1)
        ch_Label =[i,"label_path"]).read().reshape(512,512,1)
        ch_x = np.concatenate((ch_B02,ch_B03,ch_B04,ch_B08), axis = -1).reshape(1,512,512,3)
        ch_main_x = np.append(ch_main_x,ch_x).reshape(-1,512,512,3)
        ch_main_y = np.append(ch_main_y,ch_Label).reshape(-1,512,512,1)
    return ch_main_x, ch_main_y

@Kushal_Gandhi apologies for the delay, I suspect the hardware is shared so that might be the problem