pytorch · AlannaBurke · Apr 30, 2025 · Apr 30, 2025 · Apr 30, 2025 · Apr 30, 2025
@@ -23,7 +23,6 @@ See `build.sh` for valid build environments (it's the giant switch).
 
 ### Docker CD builds
 
-* `conda` - Dockerfile and build.sh to build Docker images used in nightly conda builds
 * `manywheel` - Dockerfile and build.sh to build Docker images used in nightly manywheel builds
 * `libtorch` - Dockerfile and build.sh to build Docker images used in nightly libtorch builds
 

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -103,7 +103,6 @@ source venv/bin/activate  # or `& .\venv\Scripts\Activate.ps1` on Windows
    Afterwards rebuilding a library (for example to rebuild `libtorch_cpu.so` issue `ninja torch_cpu` from `build` folder),
    would be sufficient to make change visible in `torch` package.
 
-
   To reinstall, first uninstall all existing PyTorch installs. You may need to run `pip
   uninstall torch` multiple times. You'll know `torch` is fully
   uninstalled when you see `WARNING: Skipping torch as it is not
@@ -183,6 +182,7 @@ source venv/bin/activate  # or `& .\venv\Scripts\Activate.ps1` on Windows
 Or if you would like to re-use an existing conda environment, you can pass in
 the prefix argument (`--prefix`):
 
+
 ```bash
 ./tools/nightly.py checkout -b my-nightly-branch -p my-env
 source my-env/bin/activate  # or `& .\my-env\Scripts\Activate.ps1` on Windows
@@ -754,7 +754,6 @@ same. Using ccache in a situation like this is a real time-saver.
 Before building pytorch, install ccache from your package manager of choice:
 
 ```bash
-conda install ccache -c conda-forge
 sudo apt install ccache
 sudo yum install ccache
 brew install ccache

diff --git a/README.md b/README.md
@@ -147,7 +147,7 @@ No wrapper code needs to be written. You can see [a tutorial here](https://pytor
 ## Installation
 
 ### Binaries
-Commands to install binaries via Conda or pip wheels are on our website: [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)
+Commands to install binaries via pip wheels are on our website: [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/)
 
 
 #### NVIDIA Jetson Platforms

diff --git a/RELEASE.md b/RELEASE.md
@@ -220,7 +220,6 @@ To view the state of the release build, please navigate to [HUD](https://hud.pyt
 Release candidates are currently stored in the following places:
 
 * Wheels: https://download.pytorch.org/whl/test/
-* Conda: https://anaconda.org/pytorch-test
 * Libtorch: https://download.pytorch.org/libtorch/test <!-- @lint-ignore -->
 
 Backups are stored in a non-public S3 bucket at [`s3://pytorch-backup`](https://s3.console.aws.amazon.com/s3/buckets/pytorch-backup?region=us-east-1&tab=objects)
@@ -319,7 +318,7 @@ Promotion of RCs to stable is done with this script:
 Users of that script should take care to update the versions necessary for the specific packages you are attempting to promote.
 
 Promotion should occur in two steps:
-* Promote S3 artifacts (wheels, libtorch) and Conda packages
+* Promote S3 artifacts (wheels, libtorch)
 * Promote S3 wheels to PyPI
 
 **NOTE**: The promotion of wheels to PyPI can only be done once so take caution when attempting to promote wheels to PyPI, (see https://github.com/pypi/warehouse/issues/726 for a discussion on potential draft releases within PyPI)
@@ -474,15 +473,13 @@ An example of this process can be found here:
 
 ## Triton dependency for the release
 
-In nightly builds for conda and wheels pytorch depend on Triton build by this workflow: https://hud.pytorch.org/hud/pytorch/pytorch/nightly/1?per_page=50&name_filter=Build%20Triton%20Wheel. The pinned version of triton used by this workflow is specified here:  https://github.com/pytorch/pytorch/blob/main/.ci/docker/ci_commit_pins/triton.txt .
+In nightly builds for wheels, PyTorch depends on Triton build by this workflow: https://hud.pytorch.org/hud/pytorch/pytorch/nightly/1?per_page=50&name_filter=Build%20Triton%20Wheel. The pinned version of Triton used by this workflow is specified here:  https://github.com/pytorch/pytorch/blob/main/.ci/docker/ci_commit_pins/triton.txt .
 
 In Nightly builds we have following configuration:
-* Conda builds, depend on: https://anaconda.org/pytorch-nightly/torchtriton
 * Wheel builds, depend on : https://download.pytorch.org/whl/nightly/pytorch-triton/
 * Rocm wheel builds, depend on : https://download.pytorch.org/whl/nightly/pytorch-triton-rocm/
 
 However for release we have following :
-* Conda builds, depend on: https://anaconda.org/pytorch-test/torchtriton for test and https://anaconda.org/pytorch/torchtriton for release
 * Wheel builds, depend only triton pypi package: https://pypi.org/project/triton/ for both test and release
 * Rocm wheel builds, depend on : https://download.pytorch.org/whl/test/pytorch-triton-rocm/ for test and https://download.pytorch.org/whl/pytorch-triton-rocm/ for release
 

diff --git a/SECURITY.md b/SECURITY.md
@@ -88,5 +88,5 @@ To ensure safe binary releases, PyTorch release pipelines are built on the follo
  - All binary builds/upload jobs must be run on ephemeral runners, i.e., on a machine that is allocated from the cloud to do the build and released back to the cloud after the build is finished. This protects those builds from interference from external actors, who potentially can get reverse shell access to a non-ephemeral runner and wait there for a binary build.
  - All binary builds are cold-start builds, i.e., distributed caching/incremental builds are not permitted. This renders builds much slower than incremental CI builds but isolates them from potential compromises of the intermediate artifacts caching systems.
  - All upload jobs are executed in a [deployment environments](https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment) that are restricted to protected branches
- - Security credentials needed to upload binaries to PyPI/conda or stable indexes `download.pytorch.org/whl` are never uploaded to repo secrets storage/environment. This requires an extra manual step to publish the release but ensures that access to those would not be compromised by deliberate/accidental leaks of secrets stored in the cloud.
+ - Security credentials needed to upload binaries to PyPI or stable indexes `download.pytorch.org/whl` are never uploaded to repo secrets storage/environment. This requires an extra manual step to publish the release but ensures that access to those would not be compromised by deliberate/accidental leaks of secrets stored in the cloud.
  - No binary artifacts should be published to GitHub releases pages, as these are overwritable by anyone with write permission to the repo.
diff --git a/benchmarks/README.md b/benchmarks/README.md
@@ -5,17 +5,20 @@ This folder contains scripts that produce reproducible timings of various PyTorc
 It also provides mechanisms to compare PyTorch with other frameworks.
 
 ## Setup environment
-Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order:
+Make sure you're on a machine with CUDA, torchvision, and PyTorch installed.
+For the latest installation instructions, please refer to the [PyTorch installation page](https://pytorch.org/get-started/locally).
+
+Install in the following order:
 ```
-# Install torchvision. It comes with the pytorch stable release binary
-conda install pytorch torchvision -c pytorch
+# Install torchvision. It comes with the PyTorch stable release binary.
+pip install torch torchvision
 
-# Install the latest pytorch master from source.
+# Install the latest PyTorch master from source.
 # It should supersede the installation from the release binary.
 cd $PYTORCH_HOME
 python setup.py build develop
 
-# Check the pytorch installation version
+# Check the PyTorch installation version.
 python -c "import torch; print(torch.__version__)"
 ```
 

diff --git a/docs/source/notes/windows.rst b/docs/source/notes/windows.rst
@@ -105,8 +105,6 @@ Package not found in win-32 channel.
     - pytorch
 
     Current channels:
-    - https://conda.anaconda.org/pytorch/win-32
-    - https://conda.anaconda.org/pytorch/noarch
     - https://repo.continuum.io/pkgs/main/win-32
     - https://repo.continuum.io/pkgs/main/noarch
     - https://repo.continuum.io/pkgs/free/win-32
@@ -135,13 +133,15 @@ Import error
 The problem is caused by the missing of the essential files. Actually,
 we include almost all the essential files that PyTorch need for the conda
 package except VC2017 redistributable and some mkl libraries.
-You can resolve this by typing the following command.
+You can resolve this by typing the following command:
 
 .. code-block:: bat
-
     conda install -c peterjc123 vc vs2017_runtime
     conda install mkl_fft intel_openmp numpy mkl
 
+Please note that as of 2.6, conda builds have been deprecated.
+Please refer to the [PyTorch install guide](https://pytorch.org/get-started/locally/) for the latest installation instructions.
+
 As for the wheels package, since we didn't pack some libraries and VS2017
 redistributable files in, please make sure you install them manually.
 The `VS 2017 redistributable installer

diff --git a/tools/README.md b/tools/README.md
@@ -46,9 +46,6 @@ Important if you want to run on AMD GPU:
 
 Tools which are only situationally useful:
 
-* [docker](docker) - Dockerfile for running (but not developing)
-  PyTorch, using the official conda binary distribution.  Context:
-  https://github.com/pytorch/pytorch/issues/1619
 * [download_mnist.py](download_mnist.py) - Download the MNIST
   dataset; this is necessary if you want to run the C++ API tests.