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Installation

Requirements

  • Linux with Python ≥ 3.10, PyTorch ≥ 2.3.1 and torchvision that matches the PyTorch installation. Install them together at https://pytorch.org to ensure this.
    • Note older versions of Python or PyTorch may also work. However, the versions above are strongly recommended to provide all features such as torch.compile.
  • CUDA toolkits that match the CUDA version for your PyTorch installation. This should typically be CUDA 12.1 if you follow the default installation command.

Then, install SAM 2 from the root of this repository via

pip install -e ".[demo]"

Note that you may skip building the SAM 2 CUDA extension during installation via environment variable SAM2_BUILD_CUDA=0, as follows:

# skip the SAM 2 CUDA extension
SAM2_BUILD_CUDA=0 pip install -e ".[demo]"

This would also skip the post-processing step at runtime (removing small holes and sprinkles in the output masks, which requires the CUDA extension), but shouldn't affect the results in most cases.

Building the SAM 2 CUDA extension

By default, we allow the installation to proceed even if the SAM 2 CUDA extension fails to build. (In this case, the build errors are hidden unless using -v for verbose output in pip install.)

If you see a message like Skipping the post-processing step due to the error above at runtime or Failed to build the SAM 2 CUDA extension due to the error above during installation, it indicates that the SAM 2 CUDA extension failed to build in your environment. In this case, you can still use SAM 2 for both image and video applications, but the post-processing step (removing small holes and sprinkles in the output masks) will be skipped. This shouldn't affect the results in most cases.

If you would like to enable this post-processing step, you can reinstall SAM 2 on a GPU machine with environment variable SAM2_BUILD_ALLOW_ERRORS=0 to force building the CUDA extension (and raise errors if it fails to build), as follows

pip uninstall -y SAM-2; rm -f sam2/*.so; SAM2_BUILD_ALLOW_ERRORS=0 pip install -v -e ".[demo]"

Note that PyTorch needs to be installed first before building the SAM 2 CUDA extension. It's also necessary to install CUDA toolkits that match the CUDA version for your PyTorch installation. (This should typically be CUDA 12.1 if you follow the default installation command.) After installing the CUDA toolkits, you can check its version via nvcc --version.

Please check the section below on common installation issues if the CUDA extension fails to build during installation or load at runtime.

Common Installation Issues

Click each issue for its solutions:

I got `ImportError: cannot import name '_C' from 'sam2'`

This is usually because you haven't run the pip install -e ".[demo]" step above or the installation failed. Please install SAM 2 first, and see the other issues if your installation fails.

In some systems, you may need to run python setup.py build_ext --inplace in the SAM 2 repo root as suggested in facebookresearch#77.

I got `MissingConfigException: Cannot find primary config 'sam2_hiera_l.yaml'`

This is usually because you haven't run the pip install -e . step above, so sam2_configs isn't in your Python's sys.path. Please run this installation step. In case it still fails after the installation step, you may try manually adding the root of this repo to PYTHONPATH via

export SAM2_REPO_ROOT=/path/to/segment-anything-2  # path to this repo
export PYTHONPATH="${SAM2_REPO_ROOT}:${PYTHONPATH}"

to manually add sam2_configs into your Python's sys.path.

My installation failed with `CUDA_HOME environment variable is not set`

This usually happens because the installation step cannot find the CUDA toolkits (that contain the NVCC compiler) to build a custom CUDA kernel in SAM 2. Please install CUDA toolkits or the version that matches the CUDA version for your PyTorch installation. If the error persists after installing CUDA toolkits, you may explicitly specify CUDA_HOME via

export CUDA_HOME=/usr/local/cuda  # change to your CUDA toolkit path

and rerun the installation.

Also, you should make sure

python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'

print (True, a directory with cuda) to verify that the CUDA toolkits are correctly set up.

If you are still having problems after verifying that the CUDA toolkit is installed and the CUDA_HOME environment variable is set properly, you may have to add the --no-build-isolation flag to the pip command:

pip install --no-build-isolation -e .
I got `undefined symbol: _ZN3c1015SmallVectorBaseIjE8grow_podEPKvmm` (or similar errors)

This usually happens because you have multiple versions of dependencies (PyTorch or CUDA) in your environment. During installation, the SAM 2 library is compiled against one version library while at run time it links against another version. This might be due to that you have different versions of PyTorch or CUDA installed separately via pip or conda. You may delete one of the duplicates to only keep a single PyTorch and CUDA version.

In particular, if you have a lower PyTorch version than 2.3.1, it's recommended to upgrade to PyTorch 2.3.1 or higher first. Otherwise, the installation script will try to upgrade to the latest PyTorch using pip, which could sometimes lead to duplicated PyTorch installation if you have previously installed another PyTorch version using conda.

We have been building SAM 2 against PyTorch 2.3.1 internally. However, a few user comments (e.g. facebookresearch#22, facebookresearch#14) suggested that downgrading to PyTorch 2.1.0 might resolve this problem. In case the error persists, you may try changing the restriction from torch>=2.3.1 to torch>=2.1.0 in both pyproject.toml and setup.py to allow PyTorch 2.1.0.

I got `RuntimeError: No available kernel. Aborting execution.` (or similar errors)

This is probably because your machine doesn't have a GPU or a compatible PyTorch version for Flash Attention (see also https://discuss.pytorch.org/t/using-f-scaled-dot-product-attention-gives-the-error-runtimeerror-no-available-kernel-aborting-execution/180900 for a discussion in PyTorch forum). You may be able to resolve this error by replacing the line

OLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings()

in sam2/modeling/sam/transformer.py with

OLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = True, True, True

to relax the attention kernel setting and use other kernels than Flash Attention.

I got `Error compiling objects for extension`

You may see error log of:

unsupported Microsoft Visual Studio version! Only the versions between 2017 and 2022 (inclusive) are supported! The nvcc flag '-allow-unsupported-compiler' can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk.

This is probably because your versions of CUDA and Visual Studio are incompatible. (see also https://stackoverflow.com/questions/78515942/cuda-compatibility-with-visual-studio-2022-version-17-10 for a discussion in stackoverflow).
You may be able to fix this by adding the -allow-unsupported-compiler argument to nvcc after L48 in the setup.py.
After adding the argument, get_extension() will look like this:

def get_extensions():
    srcs = ["sam2/csrc/connected_components.cu"]
    compile_args = {
        "cxx": [],
        "nvcc": [
            "-DCUDA_HAS_FP16=1",
            "-D__CUDA_NO_HALF_OPERATORS__",
            "-D__CUDA_NO_HALF_CONVERSIONS__",
            "-D__CUDA_NO_HALF2_OPERATORS__",
            "-allow-unsupported-compiler"  # Add this argument
        ],
    }
    ext_modules = [CUDAExtension("sam2._C", srcs, extra_compile_args=compile_args)]
    return ext_modules