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Removing conda references from PyTorch Docs (#152702)
Addresses #148339 Pull Request resolved: https://github.com/pytorch/pytorch/pull/152702 Approved by: https://github.com/svekars, https://github.com/albanD, https://github.com/atalman
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Anita Katahoire
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PyTorch MergeBot
PyTorch MergeBot
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.github/requirements/README.md
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8
.github/requirements/README.md
vendored
@ -11,14 +11,6 @@ jobs, but it also allows them to be cached properly to improve CI
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reliability.
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The list of support files are as follows:
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* Conda:
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* conda-env-iOS. This is used by iOS build and test jobs to setup the
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conda environment
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* conda-env-macOS-ARM64. This is used by MacOS (m1, arm64) build and
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test jobs to setup the conda environment
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* conda-env-Linux-X64. This is used by Linux buck build and test jobs
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to setup the conda environment
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* Pip:
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* pip-requirements-iOS.txt. This is used by iOS build and test jobs to
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setup the pip environment
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@ -112,8 +112,7 @@ source venv/bin/activate # or `& .\venv\Scripts\Activate.ps1` on Windows
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lazy.)
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```bash
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conda uninstall pytorch -y
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yes | pip uninstall torch
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pip uninstall torch
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```
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Next run `python setup.py clean`. After that, you can install in `develop` mode again.
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@ -180,14 +179,6 @@ You can use this script to check out a new nightly branch with the following:
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source venv/bin/activate # or `& .\venv\Scripts\Activate.ps1` on Windows
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```
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Or if you would like to re-use an existing conda environment, you can pass in
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the prefix argument (`--prefix`):
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```bash
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./tools/nightly.py checkout -b my-nightly-branch -p my-env
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source my-env/bin/activate # or `& .\my-env\Scripts\Activate.ps1` on Windows
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```
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To install the nightly binaries built with CUDA, you can pass in the flag `--cuda`:
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```bash
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@ -289,7 +280,7 @@ dependencies as well as the nightly binaries into the repo directory.
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### Python Unit Testing
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**Prerequisites**:
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The following packages should be installed with either `conda` or `pip`:
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The following packages should be installed with `pip`:
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- `expecttest` and `hypothesis` - required to run tests
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- `mypy` - recommended for linting
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- `pytest` - recommended to run tests more selectively
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@ -497,8 +488,7 @@ pip install -r requirements.txt
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# Or if you prefer an uncontaminated global executable environment or do not want to go through the node configuration:
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# npm install katex && export PATH="$PATH:$(pwd)/node_modules/.bin"
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```
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> Note: if you installed `nodejs` with a different package manager (e.g.,
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`conda`) then `npm` will probably install a version of `katex` that is not
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> Note: if you installed `nodejs` with a different package manager then `npm` will probably install a version of `katex` that is not
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compatible with your version of `nodejs` and doc builds will fail.
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A combination of versions that is known to work is `node@6.13.1` and
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`katex@0.13.18`. To install the latter with `npm` you can run
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@ -670,13 +660,13 @@ you run `import torch` anywhere else, the development version will be
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used).
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If you want to manage multiple builds of PyTorch, you can make use of
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[conda environments](https://conda.io/docs/using/envs.html) to maintain
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[venv environments](https://docs.python.org/3/library/venv.html) to maintain
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separate Python package environments, each of which can be tied to a
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specific build of PyTorch. To set one up:
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```bash
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conda create -n pytorch-myfeature
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source activate pytorch-myfeature
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python -m venv pytorch-myfeature
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source pytorch-myfeature/bin/activate # or `& .\pytorch-myfeature\Scripts\Activate.ps1` on Windows
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# if you run python now, torch will NOT be installed
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python setup.py develop
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```
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@ -754,7 +744,6 @@ same. Using ccache in a situation like this is a real time-saver.
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Before building pytorch, install ccache from your package manager of choice:
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```bash
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conda install ccache -c conda-forge
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sudo apt install ccache
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sudo yum install ccache
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brew install ccache
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@ -1046,8 +1035,7 @@ than Linux, which are worth keeping in mind when fixing these problems.
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3. If you have a Windows box (we have a few on EC2 which you can request access to) and
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you want to run the build, the easiest way is to just run `.ci/pytorch/win-build.sh`.
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If you need to rebuild, run `REBUILD=1 .ci/pytorch/win-build.sh` (this will avoid
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blowing away your Conda environment.)
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If you need to rebuild, run `REBUILD=1 .ci/pytorch/win-build.sh`.
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Even if you don't know anything about MSVC, you can use cmake to build simple programs on
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Windows; this can be helpful if you want to learn more about some peculiar linking behavior
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@ -1264,7 +1252,7 @@ in the meantime there will be some separation.
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There are a few "unusual" directories which, for historical reasons,
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are Caffe2/PyTorch specific. Here they are:
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- `CMakeLists.txt`, `Makefile`, `binaries`, `cmake`, `conda`, `modules`,
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- `CMakeLists.txt`, `Makefile`, `binaries`, `cmake`, `modules`,
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`scripts` are Caffe2-specific. Don't put PyTorch code in them without
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extra coordination.
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@ -8,7 +8,7 @@ It also provides mechanisms to compare PyTorch with other frameworks.
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Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order:
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```
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# Install torchvision. It comes with the pytorch stable release binary
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conda install pytorch torchvision -c pytorch
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pip3 install torch torchvision
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# Install the latest pytorch master from source.
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# It should supersede the installation from the release binary.
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@ -92,7 +92,7 @@ We can now run the following commands to build the application from within the
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cmake --build . --config Release
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where ``/absolute/path/to/libtorch`` should be the absolute (!) path to the unzipped LibTorch
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distribution. If PyTorch was installed via conda or pip, `CMAKE_PREFIX_PATH` can be queried
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distribution. If PyTorch was installed via pip, `CMAKE_PREFIX_PATH` can be queried
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using `torch.utils.cmake_prefix_path` variable. In that case CMake configuration step would look something like follows:
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.. code-block:: sh
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@ -105,8 +105,6 @@ Package not found in win-32 channel.
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- pytorch
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Current channels:
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- https://conda.anaconda.org/pytorch/win-32
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- https://conda.anaconda.org/pytorch/noarch
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- https://repo.continuum.io/pkgs/main/win-32
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- https://repo.continuum.io/pkgs/main/noarch
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- https://repo.continuum.io/pkgs/free/win-32
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@ -132,17 +130,8 @@ Import error
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ImportError: DLL load failed: The specified module could not be found.
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The problem is caused by the missing of the essential files. Actually,
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we include almost all the essential files that PyTorch need for the conda
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package except VC2017 redistributable and some mkl libraries.
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You can resolve this by typing the following command.
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.. code-block:: bat
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conda install -c peterjc123 vc vs2017_runtime
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conda install mkl_fft intel_openmp numpy mkl
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As for the wheels package, since we didn't pack some libraries and VS2017
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The problem is caused by the missing of the essential files.
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For the wheels package, since we didn't pack some libraries and VS2017
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redistributable files in, please make sure you install them manually.
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The `VS 2017 redistributable installer
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<https://aka.ms/vs/15/release/VC_redist.x64.exe>`_ can be downloaded.
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@ -153,24 +142,6 @@ uses MKL instead of OpenBLAS. You may type in the following command.
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pip install numpy mkl intel-openmp mkl_fft
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Another possible cause may be you are using GPU version without NVIDIA
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graphics cards. Please replace your GPU package with the CPU one.
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.. code-block:: python
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from torch._C import *
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ImportError: DLL load failed: The operating system cannot run %1.
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This is actually an upstream issue of Anaconda. When you initialize your
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environment with conda-forge channel, this issue will emerge. You may fix
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the intel-openmp libraries through this command.
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.. code-block:: bat
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conda install -c defaults intel-openmp -f
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Usage (multiprocessing)
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-------------------------------------------------------
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@ -51,19 +51,18 @@ We may eventually upstream them into PyTorch itself along with `functorch`.
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We have to install a nightly build of PyTorch so first set up an environment:
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```sh
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conda create --name dim
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conda activate dim
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python -m venv dim
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source dim/bin/activate # or `& .\dim\Scripts\Activate.ps1` on Windows
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```
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First-class dims requires a fairly recent nightly build of PyTorch so that functorch will work. You can install it using one of these commands:
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```sh
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# For CUDA 10.2
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conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-nightly
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# For CUDA 11.3
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conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch-nightly
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# For CUDA
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu116
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# For CPU-only build
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conda install pytorch torchvision torchaudio cpuonly -c pytorch-nightly
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
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```
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Install dim. You will be asked for github credentials to access the fairinternal organization.
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