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@ -112,21 +112,23 @@ We provide some jupyter notebooks to help you quick start with Jittor.
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Jittor框架对环境要求如下:
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* 操作系统: **Linux**(e.g. Ubuntu/CentOS/Arch), **macOS**（x86_64）或 **Windows Subsystem of Linux（WSL）**
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Jittor 支持**Linux**(e.g. Ubuntu/CentOS/Arch), **macOS**,**Windows**， 其中**Linux**和**macOS**的依赖如下：
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* Python：版本 >= 3.7
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* C++编译器 （需要下列至少一个）
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- g++ （>=5.4.0）
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- clang （>=8.0）
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- g++ （>=5.4.0 for linux）
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- clang （>=8.0 for mac）
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* GPU 编译器（可选）：nvcc >=10.0
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* GPU 加速库（可选）：cudnn-dev (cudnn开发版, 推荐使用tar安装方法，[参考链接](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar))
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**Windows**对环境的要求为：
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* Python：版本 >= 3.8
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* x86_64处理器
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* Windows 10及以上。
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如果您不希望手动配置环境，我们推荐使用 Docker 进行安装。
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除此之外，您还可以使用 pip 安装和手动安装。
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注意1：目前Jittor通过WSL的方式在Windows操作系统上运行，WSL的安装方法请参考[微软官网](https://docs.microsoft.com/en-us/windows/wsl/install-win10)，WSL版本目前尚不支持CUDA。
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注意2：macOS 用户需要安装额外依赖，请参考 [macOS 安装](#macOS-安装)。
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注意1：macOS 用户需要安装额外依赖，请参考 [macOS 安装](#macOS-安装)。
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Jittor 提供了三种安装方法：docker，pip和手动安装：
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@ -142,10 +144,7 @@ Jittor environment requirements:
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* GPU library: cudnn-dev (recommend tar file installation, [reference link](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar))
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Note#1: Currently Jittor runs on the Windows operating system through WSL. For the installation method of WSL, please refer to [Microsoft official website](https://docs.microsoft.com/en-us/windows/wsl/install-win10). WSL does not yet support CUDA.
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Note#2: macOS users have to install additional dependencies, see [macOS install](#macOS-install).
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Note#1: macOS users have to install additional dependencies, see [macOS install](#macOS-install).
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Jittor offers three ways to install: docker, pip, or manual.
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@ -212,6 +211,24 @@ python3.7 -m jittor.test.test_example
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Currently jittor only supports CPU in macOS.
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## Windows安装
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Windows 请准备好Python>=3.8，安装方法如下(conda安装需要额外命令)：
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```bash
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# check your python version(>=3.8)
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python --version
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python -m pip install jittor
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# if conda is used
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conda install pywin32
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```
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Windows 下，jittor会自动检测显卡并安装对应的 CUDA， 请确保您的NVIDIA驱动支持CUDA 10.2 以上，您还可以使用如下命令手动为Jittor安装CUDA：
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```bash
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python -m jittor_utils.install_cuda
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```
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## 手动安装
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## manual install
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@ -216,23 +216,21 @@ def setup_cuda_extern():
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try:
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setup_cuda_lib(lib_name, extra_flags=link_cuda_extern)
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except Exception as e:
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import traceback
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line = traceback.format_exc()
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LOG.w(f"CUDA found but {lib_name} is not loaded:\n{line}")
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msg = f"CUDA found but {lib_name} is not loaded:\n"
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if lib_name == "cudnn":
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msg = """Develop version of CUDNN not found,
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msg += """Develop version of CUDNN not found,
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please refer to CUDA offical tar file installation:
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https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar"""
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if platform.machine() == "x86_64":
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msg += """
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if platform.machine() in ["x86_64", "AMD64"]:
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msg += f"""
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or you can let jittor install cuda and cudnn for you:
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>>> python3.{sys.version_info.minor} -m jittor_utils.install_cuda
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"""
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LOG.w(msg)
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LOG.f(msg)
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def setup_cuda_lib(lib_name, link=True, extra_flags=""):
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arch_key = "x86_64"
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if platform.machine() != "x86_64":
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if platform.machine() not in ["x86_64", "AMD64"]:
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arch_key = "aarch64"
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globals()[lib_name+"_ops"] = None
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globals()[lib_name] = None
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@ -953,7 +953,7 @@ if os.path.isfile(ex_python_path):
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# if jtcuda is already installed
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nvcc_path = None
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if install_cuda.has_installation():
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if install_cuda.has_installation() or os.name == 'nt':
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nvcc_path = install_cuda.install_cuda()
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if nvcc_path:
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nvcc_path = try_find_exe(nvcc_path)
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@ -984,7 +984,7 @@ def check_clang_latest_supported_cpu():
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# cc_flags += " -Wall -Werror -Wno-unknown-pragmas -std=c++14 -fPIC "
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cc_flags += " -Wall -Wno-unknown-pragmas -std=c++14 -fPIC "
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# 1. Arch/CPU specific optimization
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if platform.machine() == "x86_64":
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if platform.machine() in ["x86_64", "AMD64"]:
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cc_flags += " -march=native "
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elif platform.machine() == 'arm64' and platform.system() == "Darwin":
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cc_flags += f" -mcpu={check_clang_latest_supported_cpu()} "
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@ -76,7 +76,7 @@ for os_name, os_type in os_name_system_dict.items():
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env += cname
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# use core2 arch, avoid using avx instructions
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# TODO: support more archs, such as arm, or use ir(GIMPLE or LLVM)
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if platform.machine() == "x86_64":
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if platform.machine() in ["x86_64", "AMD64"]:
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env += " cc_flags='-march=core2' "
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if device == "cpu":
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env += " nvcc_path='' "
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