From d37a7fbdd551440937c4846ef2f44f715f046ff9 Mon Sep 17 00:00:00 2001 From: Parag Ekbote Date: Thu, 12 Jun 2025 19:16:18 +0000 Subject: [PATCH 1/8] update. --- beginner_source/basics/autogradqs_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/basics/autogradqs_tutorial.py b/beginner_source/basics/autogradqs_tutorial.py index 8eff127ddee..e702b29810f 100644 --- a/beginner_source/basics/autogradqs_tutorial.py +++ b/beginner_source/basics/autogradqs_tutorial.py @@ -116,7 +116,7 @@ with torch.no_grad(): z = torch.matmul(x, w)+b -print(z.requires_grad) +prin(z.requires_grad) ###################################################################### From ad5eb25b139fc3bfd9a01a020b8d220093b13f3d Mon Sep 17 00:00:00 2001 From: Parag Ekbote Date: Fri, 13 Jun 2025 09:38:48 +0000 Subject: [PATCH 2/8] update the autograd tutorial. --- beginner_source/basics/autogradqs_tutorial.py | 38 ++++++++++++++++++- 1 file changed, 36 insertions(+), 2 deletions(-) diff --git a/beginner_source/basics/autogradqs_tutorial.py b/beginner_source/basics/autogradqs_tutorial.py index e702b29810f..418de49cc6f 100644 --- a/beginner_source/basics/autogradqs_tutorial.py +++ b/beginner_source/basics/autogradqs_tutorial.py @@ -116,7 +116,7 @@ with torch.no_grad(): z = torch.matmul(x, w)+b -prin(z.requires_grad) +print(z.requires_grad) ###################################################################### @@ -133,7 +133,8 @@ # - To mark some parameters in your neural network as **frozen parameters**. # - To **speed up computations** when you are only doing forward pass, because computations on tensors that do # not track gradients would be more efficient. - +# For additional reference, you can view the autograd mechanics +# documentation:https://docs.pytorch.org/docs/stable/notes/autograd.html#locally-disabling-gradient-computation ###################################################################### @@ -160,6 +161,39 @@ # - accumulates them in the respective tensor’s ``.grad`` attribute # - using the chain rule, propagates all the way to the leaf tensors. # +# We can also visualize the computational graph by the following 2 methods: +# +# 1. TORCH_LOGS="+autograd" +# By setting the TORCH_LOGS="+autograd" environment variable, we can enable runtime autograd logs for debugging. +# +# We can perform the logging in the following manner: +# TORCH_LOGS="+autograd" python test.py +# +# 2. Torchviz +# Torchviz is a package to render the computational graph visually. +# +# We can generate an image for the computational graph in the example given below: +# +# import torch +# from torch import nn +# from torchviz import make_dot +# +# model = nn.Sequential( +# nn.Linear(8, 16), +# nn.ReLU(), +# nn.Linear(16, 1) +# ) + +# x = torch.randn(1, 8, requires_grad=True) +# y = model(x).mean() + +# log the internal operations using torchviz +# import os +# os.environ['TORCH_LOGS'] = "+autograd" + +# dot = make_dot(y, params=dict(model.named_parameters()), show_attrs=True, show_saved=True) +# dot.render('simple_graph', format='png') +# # .. note:: # **DAGs are dynamic in PyTorch** # An important thing to note is that the graph is recreated from scratch; after each From 1532c0de875b79f541371438d8d1d08be18f87de Mon Sep 17 00:00:00 2001 From: Parag Ekbote Date: Fri, 13 Jun 2025 13:40:05 +0000 Subject: [PATCH 3/8] update the tutorial. --- beginner_source/basics/autogradqs_tutorial.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/beginner_source/basics/autogradqs_tutorial.py b/beginner_source/basics/autogradqs_tutorial.py index 418de49cc6f..671ed67c817 100644 --- a/beginner_source/basics/autogradqs_tutorial.py +++ b/beginner_source/basics/autogradqs_tutorial.py @@ -32,7 +32,7 @@ y = torch.zeros(3) # expected output w = torch.randn(5, 3, requires_grad=True) b = torch.randn(3, requires_grad=True) -z = torch.matmul(x, w)+b +z = torch.matmul(x, w) + b loss = torch.nn.functional.binary_cross_entropy_with_logits(z, y) @@ -133,7 +133,7 @@ # - To mark some parameters in your neural network as **frozen parameters**. # - To **speed up computations** when you are only doing forward pass, because computations on tensors that do # not track gradients would be more efficient. -# For additional reference, you can view the autograd mechanics +# For additional reference, you can view the autograd mechanics # documentation:https://docs.pytorch.org/docs/stable/notes/autograd.html#locally-disabling-gradient-computation ###################################################################### @@ -171,7 +171,7 @@ # # 2. Torchviz # Torchviz is a package to render the computational graph visually. -# +# # We can generate an image for the computational graph in the example given below: # # import torch From c11b361253044c1fbca78d4a5915a271c070b5e1 Mon Sep 17 00:00:00 2001 From: Parag Ekbote Date: Thu, 26 Jun 2025 06:13:30 +0000 Subject: [PATCH 4/8] update the tutorial. --- beginner_source/basics/autogradqs_tutorial.py | 37 +++---------------- 1 file changed, 6 insertions(+), 31 deletions(-) diff --git a/beginner_source/basics/autogradqs_tutorial.py b/beginner_source/basics/autogradqs_tutorial.py index 671ed67c817..a3c89789086 100644 --- a/beginner_source/basics/autogradqs_tutorial.py +++ b/beginner_source/basics/autogradqs_tutorial.py @@ -133,8 +133,7 @@ # - To mark some parameters in your neural network as **frozen parameters**. # - To **speed up computations** when you are only doing forward pass, because computations on tensors that do # not track gradients would be more efficient. -# For additional reference, you can view the autograd mechanics -# documentation:https://docs.pytorch.org/docs/stable/notes/autograd.html#locally-disabling-gradient-computation +# See this `note` for additional reference. ###################################################################### @@ -161,38 +160,14 @@ # - accumulates them in the respective tensor’s ``.grad`` attribute # - using the chain rule, propagates all the way to the leaf tensors. # -# We can also visualize the computational graph by the following 2 methods: +# To get a sense of what this computational graph looks like we can use the following tools: # -# 1. TORCH_LOGS="+autograd" -# By setting the TORCH_LOGS="+autograd" environment variable, we can enable runtime autograd logs for debugging. +# 1. torchviz is a package to visualize computational graphs +# # -# We can perform the logging in the following manner: -# TORCH_LOGS="+autograd" python test.py +# 2. TORCH_LOGS="+autograd" enables logging for the backward pass. +# # -# 2. Torchviz -# Torchviz is a package to render the computational graph visually. -# -# We can generate an image for the computational graph in the example given below: -# -# import torch -# from torch import nn -# from torchviz import make_dot -# -# model = nn.Sequential( -# nn.Linear(8, 16), -# nn.ReLU(), -# nn.Linear(16, 1) -# ) - -# x = torch.randn(1, 8, requires_grad=True) -# y = model(x).mean() - -# log the internal operations using torchviz -# import os -# os.environ['TORCH_LOGS'] = "+autograd" - -# dot = make_dot(y, params=dict(model.named_parameters()), show_attrs=True, show_saved=True) -# dot.render('simple_graph', format='png') # # .. note:: # **DAGs are dynamic in PyTorch** From 86cf7022004d4f218bbfa9cfdba48769c384d871 Mon Sep 17 00:00:00 2001 From: Parag Ekbote Date: Thu, 26 Jun 2025 06:14:29 +0000 Subject: [PATCH 5/8] update. --- beginner_source/basics/autogradqs_tutorial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/beginner_source/basics/autogradqs_tutorial.py b/beginner_source/basics/autogradqs_tutorial.py index a3c89789086..107ff3cd2bc 100644 --- a/beginner_source/basics/autogradqs_tutorial.py +++ b/beginner_source/basics/autogradqs_tutorial.py @@ -32,7 +32,7 @@ y = torch.zeros(3) # expected output w = torch.randn(5, 3, requires_grad=True) b = torch.randn(3, requires_grad=True) -z = torch.matmul(x, w) + b +z = torch.matmul(x, w)+b loss = torch.nn.functional.binary_cross_entropy_with_logits(z, y) From a77d137f9b8f2adc45a573688f6be04427e03471 Mon Sep 17 00:00:00 2001 From: Parag Ekbote Date: Mon, 30 Jun 2025 13:52:56 +0000 Subject: [PATCH 6/8] update link syntax. --- beginner_source/basics/autogradqs_tutorial.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/beginner_source/basics/autogradqs_tutorial.py b/beginner_source/basics/autogradqs_tutorial.py index 107ff3cd2bc..e7ea62b2def 100644 --- a/beginner_source/basics/autogradqs_tutorial.py +++ b/beginner_source/basics/autogradqs_tutorial.py @@ -163,10 +163,10 @@ # To get a sense of what this computational graph looks like we can use the following tools: # # 1. torchviz is a package to visualize computational graphs -# +# `` # # 2. TORCH_LOGS="+autograd" enables logging for the backward pass. -# +# `` # # # .. note:: From 4b736a5837d0f437fae7123af52b70e7dff22ccf Mon Sep 17 00:00:00 2001 From: Parag Ekbote Date: Mon, 30 Jun 2025 15:16:58 +0000 Subject: [PATCH 7/8] use the rst syntax. --- beginner_source/basics/autogradqs_tutorial.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/beginner_source/basics/autogradqs_tutorial.py b/beginner_source/basics/autogradqs_tutorial.py index e7ea62b2def..7289d0084ad 100644 --- a/beginner_source/basics/autogradqs_tutorial.py +++ b/beginner_source/basics/autogradqs_tutorial.py @@ -133,7 +133,8 @@ # - To mark some parameters in your neural network as **frozen parameters**. # - To **speed up computations** when you are only doing forward pass, because computations on tensors that do # not track gradients would be more efficient. -# See this `note` for additional reference. +# See this `note `_ +# for additional reference. ###################################################################### @@ -163,10 +164,10 @@ # To get a sense of what this computational graph looks like we can use the following tools: # # 1. torchviz is a package to visualize computational graphs -# `` +# `pytorchviz `_ # # 2. TORCH_LOGS="+autograd" enables logging for the backward pass. -# `` +# ``_ # # # .. note:: From b9576f7baccd59ecc7d191c0c942225f527bd993 Mon Sep 17 00:00:00 2001 From: Parag Ekbote Date: Tue, 1 Jul 2025 15:05:29 +0000 Subject: [PATCH 8/8] fix:link syntax. --- beginner_source/basics/autogradqs_tutorial.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/beginner_source/basics/autogradqs_tutorial.py b/beginner_source/basics/autogradqs_tutorial.py index 7289d0084ad..2753103eaa8 100644 --- a/beginner_source/basics/autogradqs_tutorial.py +++ b/beginner_source/basics/autogradqs_tutorial.py @@ -133,7 +133,7 @@ # - To mark some parameters in your neural network as **frozen parameters**. # - To **speed up computations** when you are only doing forward pass, because computations on tensors that do # not track gradients would be more efficient. -# See this `note `_ +# See this `note `_ # for additional reference. ###################################################################### @@ -163,11 +163,12 @@ # # To get a sense of what this computational graph looks like we can use the following tools: # -# 1. torchviz is a package to visualize computational graphs -# `pytorchviz `_ +#1. torchviz is a package to visualize computational graphs. +# See the repository here: `https://github.com/szagoruyko/pytorchviz `_ +# +#2. Setting ``TORCH_LOGS="+autograd"`` enables logging for the backward pass. See details in this +# discussion: `https://dev-discuss.pytorch.org/t/highlighting-a-few-recent-autograd-features-h2-2023/1787 `_ # -# 2. TORCH_LOGS="+autograd" enables logging for the backward pass. -# ``_ # # # .. note:: pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy