Merge branch 'site' into credits

Author: cjyabraham

_events/pytorch-conference-2025.md

@@ -0,0 +1,13 @@
+---
+category: event
+title: "PyTorch Conference 2025"
+date: October 23, 2025
+poster: assets/images/pytorch-conf-2025.jpg
+---
+**Date**: October 22 - 23, 2025
+
+<a href="https://events.linuxfoundation.org/pytorch-conference-2025/">
+<img style="width:100%" src="/assets/images/pytorch-conf-2025.jpg" alt="PyTorch Conference banner">
+</a>
+
+Join us in San Francisco on **October 22-23, 2025** to learn about AI and PyTorch, the cutting-edge renowned open source machine learning framework. This two-day event that brings together top-tier researchers, developers, and academic communities, fostering collaboration and advancing end-to-end machine learning.

_get_started/installation/linux.md

@@ -25,7 +25,7 @@ PyTorch is supported on Linux distributions that use [glibc](https://www.gnu.org
 ### Python
 {: #linux-python}
 
-Python 3.8-3.11 is generally installed by default on any of our supported Linux distributions, which meets our recommendation.
+Python 3.9-3.12 is generally installed by default on any of our supported Linux distributions, which meets our recommendation.
 
 > Tip: By default, you will have to use the command `python3` to run Python. If you want to use just the command `python`, instead of `python3`, you can symlink `python` to the `python3` binary.
 

_get_started/installation/mac.md

@@ -13,29 +13,19 @@ PyTorch is supported on macOS 10.15 (Catalina) or above.
 ### Python
 {: #mac-python}
 
-It is recommended that you use Python 3.8 - 3.11.
+It is recommended that you use Python 3.9 - 3.12.
 You can install Python either through the Anaconda
 package manager (see [below](#anaconda)), [Homebrew](https://brew.sh/), or
 the [Python website](https://www.python.org/downloads/mac-osx/).
 
-In one of the upcoming PyTorch releases, support for Python 3.8 will be deprecated.
-
 ### Package Manager
 {: #mac-package-manager}
 
-To install the PyTorch binaries, you will need to use one of two supported package managers: [Anaconda](https://www.anaconda.com/download/#macos) or [pip](https://pypi.org/project/pip/). Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python.
-
+To install the PyTorch binaries, you will need to use one of two supported package managers: [pip](https://pypi.org/project/pip/) or [Anaconda](https://www.anaconda.com/download/#macos). 
 #### Anaconda
 
-To install Anaconda, you can [download graphical installer](https://www.anaconda.com/download/#macos) or use the command-line installer. If you use the command-line installer, you can right-click on the installer link, select `Copy Link Address`, or use the following commands on Intel Mac:
+To install Anaconda, you can [download graphical installer](https://www.anaconda.com/download/#macos) or use the command-line installer. If you use the command-line installer, you can right-click on the installer link, select `Copy Link Address`, or use the following commands on Mac computer with Apple silicon:
 
-```bash
-# The version of Anaconda may be different depending on when you are installing`
-curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
-sh Miniconda3-latest-MacOSX-x86_64.sh
-# and follow the prompts. The defaults are generally good.`
-```
-or following commands on M1 Mac:
 ```bash
 # The version of Anaconda may be different depending on when you are installing`
 curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh

_get_started/installation/windows.md

@@ -18,7 +18,7 @@ PyTorch is supported on the following Windows distributions:
 ### Python
 {: #windows-python}
 
-Currently, PyTorch on Windows only supports Python 3.8-3.11; Python 2.x is not supported.
+Currently, PyTorch on Windows only supports Python 3.9-3.12; Python 2.x is not supported.
 
 As it is not installed by default on Windows, there are multiple ways to install Python:
 

_get_started/previous-versions.md

@@ -17,6 +17,43 @@ your convenience.
 
 ## Commands for Versions >= 1.0.0
 
+### v2.4.1
+#### Conda
+##### OSX
+```
+# conda
+conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 -c pytorch
+```
+#####  Linux and Windows
+```
+# CUDA 11.8
+conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1  pytorch-cuda=11.8 -c pytorch -c nvidia
+# CUDA 12.1
+conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.1 -c pytorch -c nvidia
+# CUDA 12.4
+conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.4 -c pytorch -c nvidia
+# CPU Only
+conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 cpuonly -c pytorch
+```
+#### Wheel
+##### OSX
+```
+pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1
+```
+##### Linux and Windows
+```
+# ROCM 6.1 (Linux only)
+pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/rocm6.1
+# CUDA 11.8
+pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118
+# CUDA 12.1
+pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu121
+# CUDA 12.4
+pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
+# CPU only
+pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cpu
+```
+
 ### v2.4.0
 
 #### Conda

_get_started/pytorch.md

@@ -1,6 +1,6 @@
 ---
 layout: get_started
-title: PyTorch 2.0
+title: PyTorch 2.x
 permalink: /get-started/pytorch-2.0/
 featured-img: "assets/images/featured-img-pytorch-2.png"
 background-class: get-started-background

_includes/main_menu.html

@@ -52,8 +52,8 @@
             <span class=dropdown-title>Developer Resources</span>
             <p>Find resources and get questions answered</p>
           </a>
-          <a class="nav-dropdown-item" href="{{ site.baseurl}}/ecosystem/contributor-awards-2023">
-            <span class="dropdown-title">Contributor Awards - 2023</span>
+          <a class="nav-dropdown-item" href="{{ site.baseurl}}/ecosystem/contributor-awards-2024">
+            <span class="dropdown-title">Contributor Awards - 2024</span>
             <p>Award winners announced at this year's PyTorch Conference</p>
           </a>
         </div>
@@ -141,6 +141,11 @@
           </a>
           <a class="nav-dropdown-item" href="{{ site.baseurl }}/credits">
             <span class=dropdown-title>Cloud Credit Program</span>
+          <a class="nav-dropdown-item" href="{{ site.baseurl }}/tac">
+            <span class=dropdown-title>Technical Advisory Council</span>
+          </a>
+          <a class="nav-dropdown-item" href="{{ site.baseurl }}/staff">
+            <span class=dropdown-title>Staff</span>
           </a>
           <a class="nav-dropdown-item" href="{{ site.baseurl }}/contact-us">
             <span class=dropdown-title>Contact Us</span>

_includes/mobile_menu.html

@@ -60,7 +60,7 @@
             <a href="{{ site.baseurl }}/resources">Developer Resources</a>
           </li>
           <li>
-            <a href="{{ site.baseurl}}/ecosystem/contributor-awards-2023">Contributor Awards - 2023</a>
+            <a href="{{ site.baseurl}}/ecosystem/contributor-awards-2024">Contributor Awards - 2024</a>
           </li>
         </ul>
         <li class="resources-mobile-menu-title">
@@ -119,6 +119,12 @@
           <li>
             <a href="{{ site.baseurl }}/credits">Cloud Credit Program</a>
           </li>
+          <li>          
+            <a href="{{ site.baseurl }}/tac">Technical Advisory Council</a>
+          </li>
+          <li>
+            <a href="{{ site.baseurl }}/staff">Staff</a>
+          </li>
           <li>
             <a href="{{ site.baseurl }}/contact-us">Contact Us</a>
           </li>

_includes/quick_start_local.html

@@ -5,7 +5,7 @@
   <a href="{{ site.baseurl }}/get-started/previous-versions">install previous versions of PyTorch</a>. Note that LibTorch is only available for C++.
 </p>
 
-<p><b>NOTE:</b> Latest PyTorch requires Python 3.8 or later.</p>
+<p><b>NOTE:</b> Latest PyTorch requires Python 3.9 or later.</p>
 
 <div class="row">
   <div class="col-md-3 headings">

_posts/2021-6-8-overview-of-pytorch-autograd-engine.md

@@ -37,7 +37,7 @@ In the example above, when multiplying x and y to obtain v, the engine will exte
   <p>Figure 2: Computational graph extended after executing the logarithm</p>
 </div>
 
-Continuing, the engine now calculates the <a href="https://www.codecogs.com/eqnedit.php?latex=log(v)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?log(v)" title="log(v)" /></a> operation and extends the graph again with the log derivative that it knows to be <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{1}{v}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{1}{v}" title="\frac{1}{v}" /></a>. This is shown in figure 3. This operation generates the result <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial&space;w}{\partial&space;v}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial&space;w}{\partial&space;v}" title="\frac{\partial w}{\partial v}" /></a> that when propagated backward and multiplied by the multiplication derivative as in the chain rule, generates the derivatives <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial&space;w}{\partial&space;x}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial&space;w}{\partial&space;x}" title="\frac{\partial w}{\partial x}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial&space;w}{\partial&space;x}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial&space;w}{\partial&space;x}" title="\frac{\partial w}{\partial x}" /></a>.
+Continuing, the engine now calculates the <a href="https://www.codecogs.com/eqnedit.php?latex=log(v)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?log(v)" title="log(v)" /></a> operation and extends the graph again with the log derivative that it knows to be <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{1}{v}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{1}{v}" title="\frac{1}{v}" /></a>. This is shown in figure 3. This operation generates the result <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial&space;w}{\partial&space;v}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial&space;w}{\partial&space;v}" title="\frac{\partial w}{\partial v}" /></a> that when propagated backward and multiplied by the multiplication derivative as in the chain rule, generates the derivatives <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial&space;w}{\partial&space;x}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial&space;w}{\partial&space;x}" title="\frac{\partial w}{\partial x}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial&space;w}{\partial&space;y}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial&space;w}{\partial&space;y}" title="\frac{\partial w}{\partial y}" /></a>.
 
 <div class="text-center">
   <img src="{{ site.baseurl }}/assets/images/extended_computational_graph.png" width="100%">
@@ -111,7 +111,7 @@ We can execute the same expression in PyTorch and calculate the gradient of the
   <pre>>>> y.backward(1.0)</pre>
   <pre>>>> x.grad</pre>
   tensor([1.3633,
-          0.1912])</pre>
+          0.1912])
 </div>
 
 The result is the same as our hand-calculated Jacobian-vector product!

_posts/2024-09-12-arm-joins-pytorch.md

@@ -0,0 +1,31 @@
+---
+layout: blog_detail
+title: "Arm Joins the PyTorch Foundation as a Premier Member"
+author: The PyTorch Foundation
+---
+
+The PyTorch Foundation, a neutral home for the deep learning community to collaborate on the open source PyTorch framework and ecosystem, is announcing today that [Arm](https://www.arm.com/) has joined as a premier member.
+
+Arm designs a high-performance, power-efficient compute platform with unmatched scalability, supporting a vast ecosystem of developers deploying AI at the edge and in the cloud, ranging from the Arm instances offered by all major cloud service providers to smartphones, laptops, software-defined vehicles and more.
+
+“Our continued investments in software are accelerating development and AI performance for over 20 million software developers, ensuring they can develop for Arm, on Arm,” said Alex Spinelli, VP Developer Technology at Arm. “PyTorch is a pivotal framework in advancing AI research and development. This membership demonstrates our strong commitment to open source - ensuring PyTorch just works on Arm and can leverage seamless acceleration for the most demanding AI models, now and in the future.”
+
+Last year at the PyTorch Conference, Arm partnered with Apple, Meta and Qualcomm to release [ExecuTorch](https://pytorch.org/blog/pytorch-edge/), an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers.
+
+"We're thrilled to welcome Arm to the PyTorch Foundation. As we look to the future of AI and machine learning, the role of specialized silicon and edge devices becomes increasingly crucial. Arm's expertise in these areas will be invaluable as we work to make PyTorch more efficient and accessible across a wider range of hardware,” said PyTorch Foundation Executive Director Matt White. “This collaboration underscores our commitment to fostering innovation and expanding PyTorch's capabilities to meet the evolving needs of developers and researchers worldwide."
+
+As a premier member, Arm is granted one seat to the PyTorch Foundation Governing Board. The Board sets policy through our bylaws, mission and vision statements, describing the overarching scope of foundation initiatives, technical vision, and direction.
+
+We’re happy to welcome Alex Spinelli, VP Developer Technology at Arm, to our board. Prior to Arm, Alex was VP of Product for Core Machine Learning at Google, where he led Google’s technology and infrastructure for building, training, and serving machine learning, including the TensorFlow stack.
+
+To learn more about how you can be a part of the PyTorch Foundation, visit our [website](https://pytorch.org/foundation).
+
+
+## About PyTorch Foundation
+
+The PyTorch Foundation is a neutral home for the deep learning community to collaborate on the open source PyTorch framework and ecosystem. The PyTorch Foundation is supported by its members and leading contributors to the PyTorch open source project. The Foundation leverages resources provided by members and contributors to enable community discussions and collaboration.
+
+
+## About The Linux Foundation
+
+The Linux Foundation is the world’s leading home for collaboration on open source software, hardware, standards, and data. Linux Foundation projects are critical to the world’s infrastructure including Linux, Kubernetes, Node.js, ONAP, PyTorch, RISC-V, SPDX, OpenChain, and more. The Linux Foundation focuses on leveraging best practices and addressing the needs of contributors, users, and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org. The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see its trademark usage page. Linux is a registered trademark of Linus Torvalds.