diff --git a/mprofile_20240605162109.dat b/mprofile_20240605162109.dat deleted file mode 100644 index 5e8562a..0000000 --- a/mprofile_20240605162109.dat +++ /dev/null @@ -1,28 +0,0 @@ -CMDLINE /usr/bin/python3 train_singlegpu.py -MEM 1.109375 1717615269.6916 -MEM 14.167969 1717615269.7921 -MEM 21.085938 1717615269.8925 -MEM 26.617188 1717615269.9930 -MEM 32.312500 1717615270.0934 -MEM 38.429688 1717615270.1939 -MEM 40.683594 1717615270.2943 -MEM 46.445312 1717615270.3947 -MEM 54.089844 1717615270.4953 -MEM 58.347656 1717615270.5959 -MEM 63.308594 1717615270.6964 -MEM 65.226562 1717615270.7970 -MEM 70.898438 1717615270.8975 -MEM 76.828125 1717615270.9980 -MEM 82.242188 1717615271.0986 -MEM 87.656250 1717615271.1991 -MEM 93.328125 1717615271.2996 -MEM 98.742188 1717615271.4001 -MEM 104.671875 1717615271.5006 -MEM 135.867188 1717615271.6011 -MEM 117.457031 1717615271.7016 -MEM 125.246094 1717615271.8022 -MEM 125.503906 1717615271.9026 -MEM 125.503906 1717615272.0029 -MEM 127.074219 1717615272.1032 -MEM 127.074219 1717615272.2036 -MEM 73.593750 1717615272.3040 diff --git a/mprofile_20240605162319.dat b/mprofile_20240605162319.dat deleted file mode 100644 index b859d5e..0000000 --- a/mprofile_20240605162319.dat +++ /dev/null @@ -1,257 +0,0 @@ -CMDLINE /usr/bin/python3 train_singlegpu.py -MEM 1.171875 1717615399.9716 -MEM 18.777344 1717615400.0720 -MEM 25.433594 1717615400.1725 -MEM 30.570312 1717615400.2729 -MEM 36.207031 1717615400.3734 -MEM 40.167969 1717615400.4738 -MEM 46.121094 1717615400.5743 -MEM 56.503906 1717615400.6747 -MEM 63.429688 1717615400.7751 -MEM 70.007812 1717615400.8755 -MEM 84.187500 1717615400.9758 -MEM 98.109375 1717615401.0761 -MEM 140.132812 1717615401.1765 -MEM 125.375000 1717615401.2769 -MEM 125.632812 1717615401.3772 -MEM 126.433594 1717615401.4775 -MEM 127.203125 1717615401.5778 -MEM 127.203125 1717615401.6781 -MEM 127.316406 1717615401.7785 -MEM 127.316406 1717615401.8787 -MEM 134.515625 1717615401.9790 -MEM 134.773438 1717615402.0793 -MEM 136.054688 1717615402.1796 -MEM 140.953125 1717615402.2799 -MEM 140.953125 1717615402.3802 -MEM 146.867188 1717615402.4805 -MEM 147.125000 1717615402.5809 -MEM 153.562500 1717615402.6813 -MEM 154.335938 1717615402.7817 -MEM 154.335938 1717615402.8820 -MEM 154.335938 1717615402.9825 -MEM 154.335938 1717615403.0828 -MEM 154.335938 1717615403.1832 -MEM 154.335938 1717615403.2836 -MEM 161.039062 1717615403.3839 -MEM 161.812500 1717615403.4842 -MEM 162.328125 1717615403.5845 -MEM 169.273438 1717615403.6848 -MEM 169.531250 1717615403.7852 -MEM 176.992188 1717615403.8855 -MEM 176.992188 1717615403.9857 -MEM 184.449219 1717615404.0860 -MEM 184.449219 1717615404.1863 -MEM 186.488281 1717615404.2866 -MEM 191.898438 1717615404.3869 -MEM 192.156250 1717615404.4872 -MEM 199.632812 1717615404.5875 -MEM 199.632812 1717615404.6878 -MEM 207.109375 1717615404.7881 -MEM 207.109375 1717615404.8884 -MEM 213.296875 1717615404.9887 -MEM 214.761719 1717615405.0890 -MEM 214.957031 1717615405.1894 -MEM 222.171875 1717615405.2897 -MEM 222.429688 1717615405.3900 -MEM 229.648438 1717615405.4903 -MEM 229.906250 1717615405.5906 -MEM 236.093750 1717615405.6910 -MEM 237.382812 1717615405.7913 -MEM 237.898438 1717615405.8916 -MEM 244.859375 1717615405.9919 -MEM 245.117188 1717615406.0922 -MEM 252.335938 1717615406.1925 -MEM 252.593750 1717615406.2928 -MEM 258.781250 1717615406.3931 -MEM 260.070312 1717615406.4934 -MEM 260.585938 1717615406.5937 -MEM 267.546875 1717615406.6940 -MEM 267.804688 1717615406.7944 -MEM 275.023438 1717615406.8947 -MEM 275.281250 1717615406.9949 -MEM 281.726562 1717615407.0953 -MEM 282.757812 1717615407.1956 -MEM 283.273438 1717615407.2959 -MEM 290.234375 1717615407.3962 -MEM 290.234375 1717615407.4965 -MEM 297.710938 1717615407.5969 -MEM 297.710938 1717615407.6972 -MEM 298.226562 1717615407.7976 -MEM 305.187500 1717615407.8979 -MEM 305.445312 1717615407.9983 -MEM 305.445312 1717615408.0987 -MEM 312.921875 1717615408.1990 -MEM 312.921875 1717615408.2993 -MEM 319.109375 1717615408.3996 -MEM 320.398438 1717615408.4999 -MEM 320.656250 1717615408.6002 -MEM 327.875000 1717615408.7005 -MEM 328.132812 1717615408.8008 -MEM 335.609375 1717615408.9011 -MEM 335.609375 1717615409.0014 -MEM 341.796875 1717615409.1018 -MEM 343.085938 1717615409.2021 -MEM 343.601562 1717615409.3024 -MEM 350.562500 1717615409.4026 -MEM 350.820312 1717615409.5029 -MEM 358.039062 1717615409.6032 -MEM 358.296875 1717615409.7035 -MEM 365.000000 1717615409.8039 -MEM 365.773438 1717615409.9042 -MEM 366.289062 1717615410.0044 -MEM 373.250000 1717615410.1047 -MEM 373.507812 1717615410.2051 -MEM 380.726562 1717615410.3055 -MEM 380.984375 1717615410.4057 -MEM 388.460938 1717615410.5060 -MEM 388.460938 1717615410.6064 -MEM 390.523438 1717615410.7066 -MEM 395.937500 1717615410.8069 -MEM 396.195312 1717615410.9072 -MEM 403.414062 1717615411.0075 -MEM 403.671875 1717615411.1078 -MEM 411.148438 1717615411.2081 -MEM 411.148438 1717615411.3084 -MEM 417.335938 1717615411.4088 -MEM 418.625000 1717615411.5091 -MEM 418.882812 1717615411.6094 -MEM 426.101562 1717615411.7097 -MEM 426.359375 1717615411.8100 -MEM 433.578125 1717615411.9103 -MEM 433.835938 1717615412.0106 -MEM 440.023438 1717615412.1110 -MEM 441.312500 1717615412.2113 -MEM 441.828125 1717615412.3116 -MEM 448.789062 1717615412.4119 -MEM 449.046875 1717615412.5122 -MEM 456.265625 1717615412.6125 -MEM 456.523438 1717615412.7127 -MEM 462.968750 1717615412.8131 -MEM 464.000000 1717615412.9133 -MEM 464.515625 1717615413.0136 -MEM 471.476562 1717615413.1139 -MEM 471.734375 1717615413.2142 -MEM 478.949219 1717615413.3145 -MEM 479.207031 1717615413.4148 -MEM 486.683594 1717615413.5151 -MEM 486.683594 1717615413.6154 -MEM 487.457031 1717615413.7157 -MEM 494.160156 1717615413.8160 -MEM 494.417969 1717615413.9163 -MEM 501.636719 1717615414.0166 -MEM 501.894531 1717615414.1169 -MEM 509.113281 1717615414.2172 -MEM 509.371094 1717615414.3175 -MEM 509.886719 1717615414.4178 -MEM 516.847656 1717615414.5181 -MEM 516.847656 1717615414.6184 -MEM 524.324219 1717615414.7187 -MEM 524.582031 1717615414.8190 -MEM 531.027344 1717615414.9193 -MEM 532.058594 1717615415.0196 -MEM 532.574219 1717615415.1199 -MEM 539.535156 1717615415.2202 -MEM 539.535156 1717615415.3205 -MEM 547.011719 1717615415.4209 -MEM 547.269531 1717615415.5211 -MEM 554.488281 1717615415.6214 -MEM 554.746094 1717615415.7217 -MEM 556.808594 1717615415.8220 -MEM 562.222656 1717615415.9223 -MEM 562.222656 1717615416.0226 -MEM 569.699219 1717615416.1229 -MEM 569.957031 1717615416.2232 -MEM 577.175781 1717615416.3235 -MEM 577.433594 1717615416.4238 -MEM 582.332031 1717615416.5241 -MEM 584.910156 1717615416.6244 -MEM 584.910156 1717615416.7248 -MEM 592.386719 1717615416.8251 -MEM 592.386719 1717615416.9253 -MEM 599.863281 1717615417.0257 -MEM 600.121094 1717615417.1259 -MEM 603.472656 1717615417.2262 -MEM 606.523438 1717615417.3266 -MEM 606.523438 1717615417.4269 -MEM 606.523438 1717615417.5272 -MEM 606.523438 1717615417.6275 -MEM 606.523438 1717615417.7278 -MEM 606.523438 1717615417.8281 -MEM 606.523438 1717615417.9284 -MEM 606.523438 1717615418.0287 -MEM 606.523438 1717615418.1290 -MEM 606.523438 1717615418.2293 -MEM 606.523438 1717615418.3296 -MEM 606.523438 1717615418.4299 -MEM 606.523438 1717615418.5301 -MEM 606.523438 1717615418.6305 -MEM 606.523438 1717615418.7308 -MEM 606.523438 1717615418.8311 -MEM 606.523438 1717615418.9314 -MEM 606.523438 1717615419.0317 -MEM 606.523438 1717615419.1320 -MEM 606.523438 1717615419.2323 -MEM 606.523438 1717615419.3327 -MEM 606.523438 1717615419.4330 -MEM 606.523438 1717615419.5333 -MEM 606.523438 1717615419.6336 -MEM 606.523438 1717615419.7340 -MEM 606.523438 1717615419.8345 -MEM 606.523438 1717615419.9348 -MEM 606.523438 1717615420.0351 -MEM 606.523438 1717615420.1354 -MEM 606.523438 1717615420.2357 -MEM 606.523438 1717615420.3360 -MEM 606.523438 1717615420.4363 -MEM 606.523438 1717615420.5366 -MEM 606.523438 1717615420.6370 -MEM 606.523438 1717615420.7373 -MEM 606.523438 1717615420.8377 -MEM 606.523438 1717615420.9381 -MEM 606.523438 1717615421.0384 -MEM 606.523438 1717615421.1387 -MEM 606.523438 1717615421.2390 -MEM 606.523438 1717615421.3393 -MEM 606.523438 1717615421.4396 -MEM 606.523438 1717615421.5399 -MEM 606.523438 1717615421.6402 -MEM 606.523438 1717615421.7405 -MEM 606.523438 1717615421.8408 -MEM 606.523438 1717615421.9411 -MEM 606.523438 1717615422.0415 -MEM 606.523438 1717615422.1418 -MEM 612.191406 1717615422.2421 -MEM 612.191406 1717615422.3425 -MEM 612.707031 1717615422.4428 -MEM 618.636719 1717615422.5431 -MEM 618.636719 1717615422.6434 -MEM 625.597656 1717615422.7437 -MEM 625.597656 1717615422.8441 -MEM 631.785156 1717615422.9444 -MEM 632.816406 1717615423.0447 -MEM 633.074219 1717615423.1450 -MEM 640.292969 1717615423.2453 -MEM 640.292969 1717615423.3456 -MEM 647.769531 1717615423.4459 -MEM 647.769531 1717615423.5461 -MEM 654.214844 1717615423.6465 -MEM 655.246094 1717615423.7468 -MEM 655.761719 1717615423.8471 -MEM 662.722656 1717615423.9474 -MEM 662.980469 1717615424.0477 -MEM 670.199219 1717615424.1480 -MEM 670.457031 1717615424.2483 -MEM 677.160156 1717615424.3486 -MEM 677.933594 1717615424.4489 -MEM 678.449219 1717615424.5492 -MEM 685.410156 1717615424.6495 -MEM 685.410156 1717615424.7498 -MEM 692.886719 1717615424.8501 -MEM 692.886719 1717615424.9504 -MEM 700.363281 1717615425.0506 -MEM 700.363281 1717615425.1509 -MEM 700.878906 1717615425.2512 -MEM 707.839844 1717615425.3515 -MEM 708.097656 1717615425.4518 -MEM 652.933594 1717615425.5522 diff --git a/profiling_results.prof b/profiling_results.prof deleted file mode 100644 index 1cc4be2..0000000 Binary files a/profiling_results.prof and /dev/null differ diff --git a/train_singlegpu.py b/train_singlegpu.py deleted file mode 100644 index 4d028eb..0000000 --- a/train_singlegpu.py +++ /dev/null @@ -1,97 +0,0 @@ -import norch -import norch.nn as nn -import norch.optim as optim -from norch.norchvision import transforms as T -import random -random.seed(1) -from memory_profiler import profile - -@profile -def main(): - - - BATCH_SIZE = 32 - device = "cpu" - epochs = 10 - - transform = T.Compose( - [ - T.ToTensor(), - T.Reshape([-1, 784, 1]) - ] - ) - - target_transform = T.Compose( - [ - T.ToTensor() - ] - ) - - - print("Loading data") - train_data, test_data = norch.norchvision.datasets.MNIST.splits(transform=transform, target_transform=target_transform) - train_loader = norch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE) - - class MyModel(nn.Module): - def __init__(self): - super(MyModel, self).__init__() - self.fc1 = nn.Linear(784, 30) - self.sigmoid1 = nn.Sigmoid() - self.fc2 = nn.Linear(30, 10) - self.sigmoid2 = nn.Sigmoid() - - def forward(self, x): - out = self.fc1(x) - out = self.sigmoid1(out) - out = self.fc2(out) - out = self.sigmoid2(out) - - return out - - print("Creating model") - model = MyModel().to(device) - criterion = nn.CrossEntropyLoss() - optimizer = optim.SGD(model.parameters(), lr=0.01) - loss_list = [] - - print("Starting training") - for epoch in range(epochs): - - avg_loss = 0 - num_steps = 0 - - for idx, batch in enumerate(train_loader): - - if idx % 100 == 0 and idx > 0: - print(f"Epoch: {epoch}/{epochs} - Step: {idx} / {len(train_loader)}") - break - - inputs, target = batch - - inputs = inputs.to(device) - target = target.to(device) - - outputs = model(inputs) - loss = criterion(outputs, target) - - optimizer.zero_grad() - - loss.backward() - - optimizer.step() - - avg_loss += loss[0] - num_steps += 1 - - avg_loss = avg_loss / num_steps - print(f'Epoch [{epoch + 1}/{epochs}], Loss: {avg_loss:.4f}') - loss_list.append(avg_loss) - - break - - - -if __name__ == "__main__": - main() - -