Anurag1734/cuda-error-resolution-analysis
07
1[2 {3 "post_stream": {4 "posts": [5 {6 "id": 369159,7 "name": "narges poorkamali",8 "username": "narges_poorkamali",9 "avatar_template": "/user_avatar/discuss.pytorch.org/narges_poorkamali/{size}/53792_2.png",10 "created_at": "2022-10-06T06:54:35.722Z",11 "cooked": "<p>I use sigmoid function in my last layer. we know that sigmoid function, limits the outputs of network in range (0,1). I want <strong>most of the outputs</strong> to be in range (0, 0.5) and <strong>very few of them</strong> to be in range [0.5, 1). How can I do this in Pytorch to get the desired output?</p>\n<p>The following Pytorch code snippet is related to this question:</p>\n<pre><code class=\"lang-auto\">class Generator(nn.Module):\ndef __init__(self):\n super(Generator, self).__init__()\n #\n def block(in_feat, out_feat, normalize=True):\n layers = [nn.Linear(in_features=in_feat, out_features=out_feat)]\n if normalize:\n layers.append(nn.BatchNorm1d(out_feat))\n layers.append(nn.LeakyReLU(0.2, inplace=True))\n return layers\n # now we can use this function like below:\n self.model = nn.Sequential(*block(params.input_dim_generator, 500, normalize=False),\n *block(500, 350),\n *block(350, 256),\n nn.Linear(256, 564),\n nn.Sigmoid())\n\n# forward\n def forward(self, old_vector, z):\n vector_app = torch.cat((old_vector, z), dim=1)\n new_vector = self.model(vector_app)\n new_result = torch.max(new_vector, old_vector).float()\n return new_result\n</code></pre>\n<pre><code class=\"lang-auto\">\n\n\n</code></pre>\n<p>z is a random noise vector in range (0,1) and old_vector is a binary vector(the values are 0 or 1).<br>\nI use uniform distribution to generate random noise vector. z = torch.rand(old_vector.shape[0], params.noise_dim). The shape of old_vector is [160, 564] and the size of params.noise_dim is 70.</p>\n<p>the output of this model is:</p>\n<pre><code class=\"lang-auto\">\n\n</code></pre>\n<pre><code class=\"lang-auto\">new_result = torch.tensor([0.5167, 0.5281, 0.5804, 0.4372, 1.0000, 1.0000, 1.0000, 0.5501, 1.0000,\n 0.6154, 1.0000, 1.0000, 0.4699, 0.5536, 0.5005, 0.4318, 0.5302, 0.4830,\n 0.5404, 0.3597, 0.4639, 0.5885, 0.4997, 0.5881, 0.5046, 0.5670, 0.3977,\n 0.5186, 0.5859, 0.5398, 0.3954, 0.4839, 0.3310, 0.5208, 0.5420, 0.5056,\n 0.5022, 0.6316, 0.6185, 0.5142, 0.5536, 0.4988, 0.5250, 0.4813, 0.5150,\n 0.4080, 1.0000, 1.0000, 1.0000, 0.6054, 0.4766, 0.4423, 0.4520, 0.4816,\n 0.5159, 0.4582, 1.0000, 0.4550, 0.4956, 1.0000, 0.5934, 1.0000, 0.4809,\n 0.5512, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.4024, 0.4822, 1.0000,\n 0.5310, 1.0000, 0.5127, 1.0000, 0.5441, 0.5063, 1.0000, 0.5511, 0.5544,\n 1.0000, 0.4585, 0.5211, 0.5758, 0.4355, 1.0000, 0.5297, 0.4582, 0.4170,\n 1.0000, 1.0000, 0.5257, 0.4194, 0.3583, 0.5087, 0.5936, 0.4851, 0.5697,\n 0.4261, 0.4736, 0.4551, 1.0000, 0.5667, 0.5650, 1.0000, 0.5069, 0.5901,\n 0.4980, 0.5184, 1.0000, 1.0000, 0.5435, 1.0000, 1.0000, 1.0000, 1.0000,\n 0.4521, 1.0000, 0.4509, 1.0000, 0.5067, 1.0000, 0.4152, 0.5034, 0.5735,\n 0.4040, 1.0000, 0.4492, 1.0000, 0.4405, 1.0000, 1.0000, 0.5667, 0.5639,\n 0.4013, 0.4357, 0.4437, 0.4510, 0.4225, 0.5091, 0.5057, 1.0000, 0.5237,\n 0.5098, 1.0000, 0.4216, 0.5242, 0.5335, 0.3916, 0.4938, 1.0000, 0.4070,\n 0.5210, 1.0000, 1.0000, 0.4050, 0.3960, 0.5750, 0.4906, 0.4991, 1.0000,\n 0.3149, 0.2949, 1.0000, 0.4515, 0.3627, 0.4348, 0.3887, 0.5807, 0.5787,\n 0.5781, 1.0000, 1.0000, 1.0000, 1.0000, 0.4919, 1.0000, 1.0000, 0.5554,\n 0.5515, 1.0000, 0.5472, 0.3342, 0.5705, 0.5076, 0.6348, 0.4436, 0.4683,\n 0.4228, 0.6506, 0.4540, 0.5333, 0.4512, 0.6037, 0.5173, 1.0000, 0.4466,\n 0.5644, 0.5565, 0.5141, 0.4771, 0.5822, 0.4888, 1.0000, 0.6331, 0.6435,\n 1.0000, 0.5012, 1.0000, 0.4864, 1.0000, 0.4994, 0.4326, 0.4347, 0.3606,\n 0.5829, 0.5229, 1.0000, 0.5992, 0.5883, 0.4825, 0.6254, 0.4951, 0.4285,\n 0.4982, 1.0000, 0.5847, 0.4131, 0.5194, 0.5270, 0.4856, 0.6182, 0.5578,\n 1.0000, 0.5460, 0.5023, 0.6279, 0.5727, 0.5997, 0.4903, 0.5633, 0.5070,\n 0.5013, 1.0000, 0.4179, 0.5529, 0.6254, 0.5767, 0.3939, 0.5791, 0.4936,\n 0.4714, 0.5150, 0.5717, 0.4570, 0.4463, 0.5493, 0.5179, 1.0000, 0.5682,\n 0.5451, 0.5266, 0.5571, 1.0000, 1.0000, 0.5506, 0.4710, 0.5951, 1.0000,\n 0.5027, 1.0000, 1.0000, 0.4960, 0.6269, 0.4817, 1.0000, 0.4059, 0.4787,\n 0.4419, 0.5479, 0.4830, 0.4709, 0.6106, 0.6154, 0.3958, 0.6434, 0.4626,\n 0.5954, 0.5083, 0.5121, 1.0000, 0.5139, 1.0000, 0.5428, 1.0000, 0.5278,\n 0.5255, 0.5854, 0.4400, 0.4774, 0.4431, 0.4871, 0.3854, 0.6217, 0.5562,\n 0.4461, 0.5191, 0.5654, 0.4428, 0.5503, 0.5742, 1.0000, 0.4899, 1.0000,\n 0.5229, 0.5428, 0.4285, 0.3038, 0.3029, 0.5145, 0.6747, 0.5685, 0.5268,\n 0.4888, 0.6431, 0.5308, 0.6249, 0.4531, 0.5631, 0.4498, 0.4465, 0.5125,\n 0.5610, 1.0000, 0.5033, 0.5517, 1.0000, 0.4625, 0.5095, 1.0000, 0.3415,\n 0.4749, 1.0000, 0.4567, 1.0000, 0.4417, 0.5623, 1.0000, 0.4780, 0.4218,\n 1.0000, 0.5474, 0.6514, 0.5725, 0.4219, 0.5303, 0.3375, 0.5710, 0.5507,\n 0.3698, 0.4902, 0.6082, 0.5212, 0.5606, 0.5320, 0.4893, 0.3831, 0.4605,\n 0.5409, 0.4605, 0.5774, 0.5709, 0.5020, 0.5771, 0.4032, 0.5832, 0.4454,\n 0.4572, 0.4651, 0.4752, 0.5786, 0.4700, 0.3398, 0.4143, 0.4413, 0.4020,\n 0.6390, 0.5165, 0.4871, 0.6229, 0.4915, 1.0000, 0.4780, 0.5900, 0.4847,\n 0.4583, 0.5889, 0.4291, 0.4095, 0.5258, 1.0000, 0.4875, 1.0000, 0.5174,\n 0.4302, 1.0000, 0.5058, 0.5917, 0.5395, 0.3915, 0.4775, 0.4688, 0.4860,\n 0.4869, 0.4189, 1.0000, 0.6453, 0.4652, 0.5106, 0.4336, 0.4959, 0.5144,\n 1.0000, 1.0000, 0.4382, 0.5917, 1.0000, 0.5123, 0.4299, 0.5447, 1.0000,\n 0.5316, 0.4145, 0.5741, 1.0000, 0.4581, 0.5953, 1.0000, 0.4909, 0.3703,\n 0.3851, 0.5324, 1.0000, 0.6660, 1.0000, 0.5687, 0.4825, 0.5081, 0.5052,\n 0.6288, 0.5371, 0.4286, 1.0000, 0.6535, 0.5556, 0.5390, 0.3320, 1.0000,\n 0.6431, 0.5405, 1.0000, 0.3641, 0.4390, 0.6196, 0.4720, 0.5114, 0.4844,\n 0.4184, 0.6269, 1.0000, 0.4077, 0.3950, 0.4502, 1.0000, 0.4417, 0.4329,\n 0.5803, 0.4967, 0.5248, 0.5182, 0.4417, 0.4066, 0.6219, 0.3435, 1.0000,\n 0.4680, 1.0000, 0.5403, 0.4570, 1.0000, 0.5805, 1.0000, 0.5796, 0.5100,\n 0.6487, 0.4752, 0.4579, 0.6026, 0.5964, 0.5842, 0.3423, 0.5475, 0.4467,\n 0.4494, 0.4782, 0.6054, 0.4499, 0.4691, 0.4700, 0.5006, 0.5895, 0.3947,\n 0.5517, 0.4240, 0.5286, 0.4796, 0.5116, 0.5696, 0.4369, 0.4761, 0.5444,\n 0.4490, 0.6399, 0.5469, 0.5155, 0.5339, 0.5860, 0.6092, 0.4000, 0.4622,\n 0.4235, 0.5554, 0.4088, 0.5798, 0.5034, 0.4752, 0.4337, 0.4786, 0.5766,\n 0.4569, 0.5401, 0.4903, 0.4243, 0.3825, 0.6652, 0.4780, 0.5335, 0.4415,\n 0.5478, 0.3797, 1.0000, 0.6133, 0.5824, 0.4292, 0.5182, 0.3953, 0.5071,\n 0.5131, 0.4735, 1.0000, 0.3457, 0.5933, 0.5329])\n</code></pre>\n<pre><code class=\"lang-auto\"></code></pre>",12 "post_number": 1,13 "post_type": 1,14 "posts_count": 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"show_read_indicator": false,399 "thumbnails": null,400 "slow_mode_enabled_until": null,401 "can_vote": false,402 "vote_count": 0,403 "user_voted": false,404 "discourse_zendesk_plugin_zendesk_id": null,405 "discourse_zendesk_plugin_zendesk_url": "https://your-url.zendesk.com/agent/tickets/",406 "details": {407 "can_edit": false,408 "notification_level": 1,409 "participants": [410 {411 "id": 59893,412 "username": "narges_poorkamali",413 "name": "narges poorkamali",414 "avatar_template": "/user_avatar/discuss.pytorch.org/narges_poorkamali/{size}/53792_2.png",415 "post_count": 1,416 "primary_group_name": null,417 "flair_name": null,418 "flair_url": null,419 "flair_color": null,420 "flair_bg_color": null,421 "flair_group_id": null,422 "trust_level": 1423 }424 ],425 "created_by": {426 "id": 59893,427 "username": "narges_poorkamali",428 "name": "narges poorkamali",429 "avatar_template": "/user_avatar/discuss.pytorch.org/narges_poorkamali/{size}/53792_2.png"430 },431 "last_poster": {432 "id": 59893,433 "username": "narges_poorkamali",434 "name": "narges poorkamali",435 "avatar_template": "/user_avatar/discuss.pytorch.org/narges_poorkamali/{size}/53792_2.png"436 }437 },438 "bookmarks": []439 },440 {441 "post_stream": {442 "posts": [443 {444 "id": 369085,445 "name": "Philippe JUHEL",446 "username": "Philippe_JUHEL",447 "avatar_template": "/user_avatar/discuss.pytorch.org/philippe_juhel/{size}/53773_2.png",448 "created_at": "2022-10-05T19:48:59.298Z",449 "cooked": "<p>Hi,</p>\n<p>Here’re 2 regression programs with the same sets of data and the same basic model (2 inputs and 1 outpu):</p>\n<p>Program 1 :</p>\n<pre><code class=\"lang-auto\">import numpy as np\nimport torch\nimport time\n\n# Define the model\ndef model(x):\n return x @ w.t() + b\n\n# MSE loss\ndef mse(t1, t2):\n diff = t1 - t2\n return torch.sum(diff * diff) / diff.numel()\n\ndef fit(num_epochs, model, loss_fn, w, b):\n for i in range(num_epochs):\n preds = model(inputs)\n loss = loss_fn(preds, targets)\n loss.backward()\n with torch.no_grad():\n w -= w.grad * lr\n b -= b.grad * lr\n w.grad.zero_()\n b.grad.zero_()\n\nlr = 1e-3\nnb_epochs = 1000\n\nnb_data = 1000\nmin_x = 2.0\nmax_x = 3.0\nmin_y = 5.0\nmax_y = 9.0\n\nX = np.linspace(min_x, max_x, num=nb_data, dtype=np.float32)\nY = np.linspace(min_y, max_y, num=nb_data, dtype=np.float32)\ninputs = np.stack((X, Y), axis=1)\ntargets = X + Y\ntargets = targets.reshape(targets.size, 1)\n\n# Convert inputs and targets to tensors\ninputs = torch.from_numpy(inputs)\ntargets = torch.from_numpy(targets)\n\n# Weights and biases\nw = torch.randn(1, 2, requires_grad=True)\nb = torch.randn(1, requires_grad=True)\n\nbegin = time.time()\nfit(nb_epochs, model, mse, w, b)\nend = time.time()\nprint(f\"Duration = {end-begin} s\")\n\n# Calculate loss\npreds = model(inputs)\nloss = mse(preds, targets)\nprint(f\"Loss = {loss}\")\n\n# Calculate a prediction\npred_y = model(torch.Tensor([[2.5, 6]]))\nprint(\"predict \", pred_y.item(), \" should be ===>\",8.5 )\ntype or paste code here\n</code></pre>\n<p>Program 2 :</p>\n<pre><code class=\"lang-auto\">import torch.nn as nn\nimport torch\nimport numpy as np\nfrom torch.utils.data import TensorDataset, DataLoader\nimport torch.nn.functional as F\nimport time\n\n# Define a utility function to train the model\ndef fit(num_epochs, model, loss_fn, opt):\n for epoch in range(num_epochs):\n for xb,yb in train_dl:\n # Generate predictions\n pred = model(xb)\n loss = loss_fn(pred, yb)\n # Perform gradient descent\n loss.backward()\n opt.step()\n opt.zero_grad()\n\ndevice = \"cpu\"\n#device = \"cuda:0\"\n\nlr = 1e-3\nbatch_size = 100\nnb_epochs = 1000\n\nnb_data = 1000\nmin_x = 2.0\nmax_x = 3.0\nmin_y = 5.0\nmax_y = 9.0\n\nX = np.linspace(min_x, max_x, num=nb_data, dtype=np.float32)\nY = np.linspace(min_y, max_y, num=nb_data, dtype=np.float32)\ninputs = np.stack((X, Y), axis=1)\ntargets = X + Y\ntargets = targets.reshape(targets.size, 1)\n\ninputs = torch.from_numpy(inputs).to(device)\ntargets = torch.from_numpy(targets).to(device)\n\ntrain_ds = TensorDataset(inputs, targets)\n# Define data loader\ntrain_dl = DataLoader(train_ds, batch_size, shuffle=True)\n\n# Define model, 2 inputs, 1 output\nmodel = nn.Linear(2, 1).to(device)\n# Define optimizer\nopt = torch.optim.SGD(model.parameters(), lr=lr)\n# Define loss function\nloss_fn = F.mse_loss\n\n# Train the model for some epochs\nbegin = time.time()\nfit(nb_epochs, model, loss_fn, opt)\nend = time.time()\nprint(f\"Duration = {end-begin} s\")\n\n# Calculate final loss\npreds = model(inputs)\nloss = loss_fn(preds, targets)\nprint(f\"Loss = {loss}\")\n\n# Evaluate a prediction\npred_y = model(torch.Tensor([[2.5, 6]]).to(device))\nprint(\"predict \", pred_y.item(), \" should be ===>\",8.5 )\n\n</code></pre>\n<p>They have the same number of data (1000) and the same number of epochs (1000)</p>\n<p>When I run these programs on the same machine (Ubuntu 20.04, 32Gb, core I7, NVIDIA 2080, torch 1.12.1 ), here are the duration for the training (<strong>fit</strong> function) :</p>\n<p>program 1 : 0.25s<br>\nprogram 2 (on CPU) : 8.5s<br>\nprogram 2 (on GPU) : 11.8s</p>\n<p>Why a so big difference between program 1 and program 2? And for program 2 with GPU, why is it worst than with CPU?</p>\n<p>Regards,</p>\n<p>Philippe</p>",450 "post_number": 1,451 "post_type": 1,452 "posts_count": 6,453 "updated_at": "2022-10-05T19:48:59.298Z",454 "reply_count": 0,455 "reply_to_post_number": null,456 "quote_count": 0,457 "incoming_link_count": 58,458 "reads": 8,459 "readers_count": 7,460 "score": 291.6,461 "yours": false,462 "topic_id": 162878,463 "topic_slug": "strange-difference-in-performance-between-2-regression-programs",464 "display_username": "Philippe JUHEL",465 "primary_group_name": null,466 "flair_name": null,467 "flair_url": null,468 "flair_bg_color": null,469 "flair_color": null,470 "flair_group_id": null,471 "badges_granted": [],472 "version": 1,473 "can_edit": false,474 "can_delete": false,475 "can_recover": false,476 "can_see_hidden_post": false,477 "can_wiki": false,478 "read": true,479 "user_title": null,480 "bookmarked": false,481 "actions_summary": [],482 "moderator": false,483 "admin": false,484 "staff": false,485 "user_id": 59880,486 "hidden": false,487 "trust_level": 1,488 "deleted_at": null,489 "user_deleted": false,490 "edit_reason": null,491 "can_view_edit_history": true,492 "wiki": false,493 "post_url": "/t/strange-difference-in-performance-between-2-regression-programs/162878/1",494 "can_accept_answer": false,495 "can_unaccept_answer": false,496 "accepted_answer": false,497 "topic_accepted_answer": null,498 "can_vote": false499 },500 {501 "id": 369137,502 "name": "",503 "username": "ptrblck",504 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",505 "created_at": "2022-10-06T06:10:58.082Z",506 "cooked": "<p>Your model is tiny as it’s a single operation/layer and you are most likely seeing the overhead of creating the <code>DataLoader</code>, shuffling the data, etc.<br>\nYou can profile parts of your code to narrow down where the slowdown is coming from.<br>\nJust executing the <code>DataLoader</code> on my system takes a few seconds for 1000 epochs.</p>",507 "post_number": 2,508 "post_type": 1,509 "posts_count": 6,510 "updated_at": "2022-10-06T06:10:58.082Z",511 "reply_count": 0,512 "reply_to_post_number": null,513 "quote_count": 0,514 "incoming_link_count": 1,515 "reads": 8,516 "readers_count": 7,517 "score": 6.6,518 "yours": false,519 "topic_id": 162878,520 "topic_slug": "strange-difference-in-performance-between-2-regression-programs",521 "display_username": "",522 "primary_group_name": null,523 "flair_name": null,524 "flair_url": null,525 "flair_bg_color": null,526 "flair_color": null,527 "flair_group_id": null,528 "badges_granted": [],529 "version": 1,530 "can_edit": false,531 "can_delete": false,532 "can_recover": false,533 "can_see_hidden_post": false,534 "can_wiki": false,535 "read": true,536 "user_title": "",537 "bookmarked": false,538 "actions_summary": [],539 "moderator": true,540 "admin": true,541 "staff": true,542 "user_id": 3534,543 "hidden": false,544 "trust_level": 2,545 "deleted_at": null,546 "user_deleted": false,547 "edit_reason": null,548 "can_view_edit_history": true,549 "wiki": false,550 "post_url": "/t/strange-difference-in-performance-between-2-regression-programs/162878/2",551 "can_accept_answer": false,552 "can_unaccept_answer": false,553 "accepted_answer": false,554 "topic_accepted_answer": null555 },556 {557 "id": 369155,558 "name": "Philippe JUHEL",559 "username": "Philippe_JUHEL",560 "avatar_template": "/user_avatar/discuss.pytorch.org/philippe_juhel/{size}/53773_2.png",561 "created_at": "2022-10-06T06:37:46.699Z",562 "cooked": "<p>Thank you for your answer.</p>\n<p>I measure the duration just for the <strong>fit</strong> function, so the preparation of the data (creation of DataLoader and shuffling) is <strong>not</strong> tacking into account for the duration. But, maybe this overconsumption of time is due to extraction of data from the DataLoader in the</p>\n<blockquote>\n<p>for xb,yb in train_dl:</p>\n</blockquote>\n<p>loop?</p>\n<p>Another strange thing is that it takes more time to process when I use a GPU but maybe it is due to the time to transfer data to the GPU?</p>\n<p>For information, at first, I wanted to understand why a simple regression example took so long to run with Pytorch Lighting. So to compare with a simpler solution, I started by creating this tiny models with just Pytorch, but that led me to ask myself this question about the difference in performance between these two solutions.</p>\n<p>Philippe</p>",563 "post_number": 3,564 "post_type": 1,565 "posts_count": 6,566 "updated_at": "2022-10-06T06:37:46.699Z",567 "reply_count": 1,568 "reply_to_post_number": null,569 "quote_count": 0,570 "incoming_link_count": 0,571 "reads": 8,572 "readers_count": 7,573 "score": 6.6,574 "yours": false,575 "topic_id": 162878,576 "topic_slug": "strange-difference-in-performance-between-2-regression-programs",577 "display_username": "Philippe JUHEL",578 "primary_group_name": null,579 "flair_name": null,580 "flair_url": null,581 "flair_bg_color": null,582 "flair_color": null,583 "flair_group_id": null,584 "badges_granted": [],585 "version": 1,586 "can_edit": false,587 "can_delete": false,588 "can_recover": false,589 "can_see_hidden_post": false,590 "can_wiki": false,591 "read": true,592 "user_title": null,593 "bookmarked": false,594 "actions_summary": [],595 "moderator": false,596 "admin": false,597 "staff": false,598 "user_id": 59880,599 "hidden": false,600 "trust_level": 1,601 "deleted_at": null,602 "user_deleted": false,603 "edit_reason": null,604 "can_view_edit_history": true,605 "wiki": false,606 "post_url": "/t/strange-difference-in-performance-between-2-regression-programs/162878/3",607 "can_accept_answer": false,608 "can_unaccept_answer": false,609 "accepted_answer": false,610 "topic_accepted_answer": null611 },612 {613 "id": 369158,614 "name": "",615 "username": "ptrblck",616 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",617 "created_at": "2022-10-06T06:53:02.190Z",618 "cooked": "<aside class=\"quote no-group\" data-username=\"Philippe_JUHEL\" data-post=\"3\" data-topic=\"162878\">\n<div class=\"title\">\n<div class=\"quote-controls\"></div>\n<img loading=\"lazy\" alt=\"\" width=\"24\" height=\"24\" src=\"https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/philippe_juhel/48/53773_2.png\" class=\"avatar\"> Philippe_JUHEL:</div>\n<blockquote>\n<p>I measure the duration just for the <strong>fit</strong> function, so the preparation of the data (creation of DataLoader and shuffling) is <strong>not</strong> tacking into account for the duration.</p>\n</blockquote>\n</aside>\n<p>That’s not true, since iterating the <code>DataLoader</code> will recreate it in each epoch to e.g. create a new sampler etc. If <code>num_workers>0</code> is used than also the workers will be re-spawned unless <code>persistent_workers=True</code> is used. Again, you can profile your code to narrow down the slowdown.</p>\n<aside class=\"quote no-group\" data-username=\"Philippe_JUHEL\" data-post=\"3\" data-topic=\"162878\">\n<div class=\"title\">\n<div class=\"quote-controls\"></div>\n<img loading=\"lazy\" alt=\"\" width=\"24\" height=\"24\" src=\"https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/philippe_juhel/48/53773_2.png\" class=\"avatar\"> Philippe_JUHEL:</div>\n<blockquote>\n<p>Another strange thing is that it takes more time to process when I use a GPU but maybe it is due to the time to transfer data to the GPU?</p>\n</blockquote>\n</aside>\n<p>Your GPU profiling is invalid, since CUDA operations are executed asynchronously, so you would need to synchronize the code before starting and stopping the timers. However, even with proper profiling I would not expect to see any speedup, since your entire code is already bottlenecked by the data loading. A single tiny linear layer with <code>in_features=2</code> and <code>out_features=1</code> would also not benefit lrgely from a GPU execution.</p>",619 "post_number": 4,620 "post_type": 1,621 "posts_count": 6,622 "updated_at": "2022-10-06T06:53:02.190Z",623 "reply_count": 0,624 "reply_to_post_number": 3,625 "quote_count": 1,626 "incoming_link_count": 2,627 "reads": 8,628 "readers_count": 7,629 "score": 11.6,630 "yours": false,631 "topic_id": 162878,632 "topic_slug": "strange-difference-in-performance-between-2-regression-programs",633 "display_username": "",634 "primary_group_name": null,635 "flair_name": null,636 "flair_url": null,637 "flair_bg_color": null,638 "flair_color": null,639 "flair_group_id": null,640 "badges_granted": [],641 "version": 1,642 "can_edit": false,643 "can_delete": false,644 "can_recover": false,645 "can_see_hidden_post": false,646 "can_wiki": false,647 "read": true,648 "user_title": "",649 "bookmarked": false,650 "actions_summary": [],651 "moderator": true,652 "admin": true,653 "staff": true,654 "user_id": 3534,655 "hidden": false,656 "trust_level": 2,657 "deleted_at": null,658 "user_deleted": false,659 "edit_reason": null,660 "can_view_edit_history": true,661 "wiki": false,662 "post_url": "/t/strange-difference-in-performance-between-2-regression-programs/162878/4",663 "can_accept_answer": false,664 "can_unaccept_answer": false,665 "accepted_answer": false,666 "topic_accepted_answer": null667 },668 {669 "id": 369160,670 "name": "Philippe JUHEL",671 "username": "Philippe_JUHEL",672 "avatar_template": "/user_avatar/discuss.pytorch.org/philippe_juhel/{size}/53773_2.png",673 "created_at": "2022-10-06T07:02:32.483Z",674 "cooked": "<p>Thank you for all this information.</p>\n<p>I’ll continue my investigation with profiling and a bigger dataset.</p>\n<p>Philippe</p>",675 "post_number": 5,676 "post_type": 1,677 "posts_count": 6,678 "updated_at": "2022-10-06T07:02:32.483Z",679 "reply_count": 1,680 "reply_to_post_number": null,681 "quote_count": 0,682 "incoming_link_count": 0,683 "reads": 7,684 "readers_count": 6,685 "score": 51.4,686 "yours": false,687 "topic_id": 162878,688 "topic_slug": "strange-difference-in-performance-between-2-regression-programs",689 "display_username": "Philippe JUHEL",690 "primary_group_name": null,691 "flair_name": null,692 "flair_url": null,693 "flair_bg_color": null,694 "flair_color": null,695 "flair_group_id": null,696 "badges_granted": [],697 "version": 1,698 "can_edit": false,699 "can_delete": false,700 "can_recover": false,701 "can_see_hidden_post": false,702 "can_wiki": false,703 "read": true,704 "user_title": null,705 "bookmarked": false,706 "actions_summary": [707 {708 "id": 2,709 "count": 1710 }711 ],712 "moderator": false,713 "admin": false,714 "staff": false,715 "user_id": 59880,716 "hidden": false,717 "trust_level": 1,718 "deleted_at": null,719 "user_deleted": false,720 "edit_reason": null,721 "can_view_edit_history": true,722 "wiki": false,723 "post_url": "/t/strange-difference-in-performance-between-2-regression-programs/162878/5",724 "can_accept_answer": false,725 "can_unaccept_answer": false,726 "accepted_answer": false,727 "topic_accepted_answer": null728 },729 {730 "id": 369163,731 "name": "",732 "username": "ptrblck",733 "avatar_template": "/user_avatar/discuss.pytorch.org/ptrblck/{size}/1823_2.png",734 "created_at": "2022-10-06T07:14:19.360Z",735 "cooked": "<p>Sounds good!<br>\nAlso, try to increase the actual workload of the model and let me know how it goes.</p>",736 "post_number": 6,737 "post_type": 1,738 "posts_count": 6,739 "updated_at": "2022-10-06T07:14:19.360Z",740 "reply_count": 0,741 "reply_to_post_number": 5,742 "quote_count": 0,743 "incoming_link_count": 0,744 "reads": 6,745 "readers_count": 5,746 "score": 1.2,747 "yours": false,748 "topic_id": 162878,749 "topic_slug": "strange-difference-in-performance-between-2-regression-programs",750 "display_username": "",751 "primary_group_name": null,752 "flair_name": null,753 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