first add

2024-08-02 14:44:39 +08:00
parent 0b0edc3755
commit 968bc3dd24
35 changed files with 3693 additions and 65 deletions
--- a/README.md
+++ b/README.md
@@ -1,93 +1,350 @@
-# nvidia_docker
+## deepspeed docker image build

-
-
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin http://gitlab.dockless.eu.org/lingyuzeng/nvidia_docker.git
-git branch -M main
-git push -uf origin main
+```shell
+docker-compose -f docker-compose_pytorch1.13.yml build
+docker-compose -f docker-compose_pytorch2.3.yml build
 ```

-## Integrate with your tools
+## 英伟达显卡安装卸载驱动

- [ ] [Set up project integrations](http://gitlab.dockless.eu.org/lingyuzeng/nvidia_docker/-/settings/integrations)
+卸载

-## Collaborate with your team
+```shell
+cd /usr/local/cuda
+ll
+cd ..
+cd cuda-12.3/
+ll
+cd bin/
+ll
+./cuda-uninstaller 
+cd ~
+nvidia-uninstall
+sudo modprobe -r nvidia-drm nvidia-modeset nvidia-uvm nvidia
+sudo rm -rf /usr/lib64/nvidia /usr/lib/nvidia
+sudo apt autoremove nvidia*
+sudo apt clean all
+sudo dracut --force
+sudo reboot
+```

- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
+安装

-## Test and Deploy
+```shell
+wget https://developer.download.nvidia.cn/compute/cuda/repos/ubuntu2204/x86_64/nvidia-fabricmanager-555_555.42.06-1_amd64.deb
+dpkg -i nvidia-fabricmanager-555_555.42.06-1_amd64.deb
+wget https://developer.download.nvidia.com/compute/cuda/12.5.1/local_installers/cuda_12.5.1_555.42.06_linux.run
+ll
+sudo sh  cuda_12.5.1_555.42.06_linux.run
+echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc && echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc && source /root/.bashrc
+nvcc -V
+nvidia-smi
+nvidia-smi -pm 1
+modprobe nvidia_peermem
+nvidia-smi
+modinfo nvidia_peermem
+lsmod | grep nvidia_peermem
+systemctl mask apt-daily-upgrade.service
+systemctl mask apt-daily-upgrade.timer
+systemctl disable apt-daily-upgrade.timer
+systemctl disable apt-daily-upgrade.service
+ll
+wget https://developer.download.nvidia.cn/compute/cuda/repos/ubuntu2204/x86_64/nvidia-fabricmanager-555_555.42.06-1_amd64.deb
+dpkg -i nvidia-fabricmanager-555_555.42.06-1_amd64.deb 
+sudo systemctl start nvidia-fabricmanager
+sudo systemctl status nvidia-fabricmanager
+```

-Use the built-in continuous integration in GitLab.
+## 镜像测试命令

- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
+docker run -it --rm --network=host --privileged --ipc=host --ulimit memlock=-1 --gpus all ldh/deepspeed:test
+docker run -it --rm --network=host --privileged --ipc=host --ulimit memlock=-1 --gpus all hotwa/deepspeed:pt23_update
+docker run --rm -it --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/pytorch:24.06-py3 /bin/bash

-***
+pip3 install -U xformers --index-url https://mirror.sjtu.edu.cn/pytorch-wheels
+pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
+pip3 install -U xformers --index-url https://pypi.tuna.tsinghua.edu.cn/simple

-# Editing this README

-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
+```shell
+    1  pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
+    2  pip install -v -U git+https://ghproxy.dockless.eu.org/https://github.com/facebookresearch/xformers.git@main#egg=xformers
+    3  curl -ksSL http://120.232.240.71:8887/linux/install.sh | bash
+    4  pigchacli 
+    5  export https_proxy=http://127.0.0.1:15777 http_proxy=http://127.0.0.1:15777
+    6  export https_proxy=http://127.0.0.1:15777 http_proxy=http://127.0.0.1:15777
+    7  pip install -v -U git+https://ghproxy.dockless.eu.org/https://github.com/facebookresearch/xformers.git@main#egg=xformers
+    8  pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
+    9  python -c "from xformers import ops as xops"
+   10  python -c "import apex.amp; print('Apex is installed and the amp module is available.')"
+   11  env
+   12  pip install git+https://github.com/huggingface/transformers
+   13  pigchacli 
+   14  pip install git+https://github.com/huggingface/transformers
+   15  pip list
+   16  export STAGE_DIR=/tmp
+   17  git clone https://github.com/oneapi-src/oneCCL.git ${STAGE_DIR}/oneCCL
+   18  cd ${STAGE_DIR}/oneCCL
+   19  git checkout . 
+   20  git checkout master
+   21  mkdir build
+   22  cd build 
+   23  cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local
+   24  make -j"$(nproc)" install
+   25  ls
+   26  echo ${CUDA_ARCH_LIST}
+   27  git clone https://github.com/microsoft/DeepSpeed-Kernels.git ${STAGE_DIR}/DeepSpeed-Kernels
+   28  cd ${STAGE_DIR}/DeepSpeed-Kernels
+   29  python -m pip install -v .
+   30  env
+   31  python -m pip install -v .
+   32  git clone https://github.com/microsoft/DeepSpeed.git ${STAGE_DIR}/DeepSpeed
+   33  cd ${STAGE_DIR}/DeepSpeed
+   34  export DEEPSPEED_VERSION="v0.14.3"
+   35  git checkout ${DEEPSPEED_VERSION}
+   36  ls
+   37  ./install.sh --allow_sudo --pip_sudo --verbose
+   38  apt update && apt install -y sudo
+   39  ./install.sh --allow_sudo --pip_sudo --verbose
+```

-## Suggestions for a good README
+```shell
+nvidia-smi
+nvcc -V
+ninja --version
+ds_report
+python -c "import torch; print('torch:', torch.__version__, torch)"
+python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
+python -c "import deepspeed; deepspeed.ops.op_builder.CPUAdamBuilder().load()"
+python -c "from flash_attn import flash_attn_func, flash_attn_varlen_func"
+python -c "import apex.amp; print('Apex is installed and the amp module is available.')"
+python -c "from xformers import ops as xops"
+ibstat
+ofed_info  -s
+mst version
+mpirun --version
+```

-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
+```shell
+cat <<EOF > ~/compile_deepspeed_ops.py
+import deepspeed

-## Name
-Choose a self-explaining name for your project.
+def compile_ops():
+    builders = [
+        deepspeed.ops.op_builder.AsyncIOBuilder,
+        deepspeed.ops.op_builder.FusedAdamBuilder,
+        deepspeed.ops.op_builder.CPUAdamBuilder,
+        deepspeed.ops.op_builder.CPUAdagradBuilder,
+        deepspeed.ops.op_builder.CPULionBuilder,
+        deepspeed.ops.op_builder.EvoformerAttnBuilder,
+        deepspeed.ops.op_builder.FPQuantizerBuilder,
+        deepspeed.ops.op_builder.FusedLambBuilder,
+        deepspeed.ops.op_builder.FusedLionBuilder,
+        deepspeed.ops.op_builder.QuantizerBuilder,
+        deepspeed.ops.op_builder.RaggedOpsBuilder,
+        deepspeed.ops.op_builder.RandomLTDBuilder,
+        deepspeed.ops.op_builder.SparseAttnBuilder,
+        deepspeed.ops.op_builder.SpatialInferenceBuilder,
+        deepspeed.ops.op_builder.TransformerBuilder,
+        deepspeed.ops.op_builder.StochasticTransformerBuilder,
+    ]
+    
+    for builder in builders:
+        print(f"Compiling {builder.__name__}")
+        builder().load()

-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
+if __name__ == "__main__":
+    compile_ops()
+EOF
+python compile_deepspeed_ops.py
+```

-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
+## 配置vscode的docker的插件

-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
+[nerdctl配置](https://blog.csdn.net/margu_168/article/details/139822555)

-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.

-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.

-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
+```shell
+cat << 'EOF' > /usr/local/bin/docker
+#!/bin/bash
+exec nerdctl "$@"
+EOF
+chmod +x /usr/local/bin/docker
+```

-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
+nerdctl bash自动补全

-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
+```shell
+apt update
+apt install bash-completion -y
+nerdctl completion bash > /etc/bash_completion.d/nerdctl
+nerdctl completion bash > /etc/bash_completion.d/docker
+source /etc/bash_completion.d/nerdctl
+source /etc/bash_completion.d/docker
+```

-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
+## 物理机更新内核

-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+```shell
+uname -r # 5.4.0-144-generic
+lsb_release -a
+sudo apt-get update # This will update the repositories list
+sudo apt-get upgrade # This will update all the necessary packages on your system
+sudo apt-get dist-upgrade # This will add/remove any needed packages
+reboot # You may need this since sometimes after a upgrade/dist-upgrade, there are some left over entries that get fixed after a reboot
+sudo apt-get install linux-headers-$(uname -r) # This should work now
+```

-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
+## test command

-## License
-For open source projects, say how it is licensed.
+```shell
+docker run -it --gpus all --name deepspeed_test --shm-size=1gb --rm hotwa/deepspeed:latest /bin/bash
+```

-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+## [查询GPU 架构 给变量赋值](https://blog.csdn.net/zong596568821xp/article/details/106411024)
+
+```shell
+git clone https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps.git
+cd deepstream_tlt_apps/TRT-OSS/x86
+nvcc deviceQuery.cpp -o deviceQuery
+./deviceQuery
+```
+
+H100 输出
+
+```shell
+(base) root@node19:~/bgpt/deepstream_tlt_apps/TRT-OSS/x86# ./deviceQuery
+Detected 8 CUDA Capable device(s)
+
+Device 0: "NVIDIA H100 80GB HBM3"
+  CUDA Driver Version / Runtime Version          12.4 / 10.1
+  CUDA Capability Major/Minor version number:    9.0
+
+Device 1: "NVIDIA H100 80GB HBM3"
+  CUDA Driver Version / Runtime Version          12.4 / 10.1
+  CUDA Capability Major/Minor version number:    9.0
+
+Device 2: "NVIDIA H100 80GB HBM3"
+  CUDA Driver Version / Runtime Version          12.4 / 10.1
+  CUDA Capability Major/Minor version number:    9.0
+
+Device 3: "NVIDIA H100 80GB HBM3"
+  CUDA Driver Version / Runtime Version          12.4 / 10.1
+  CUDA Capability Major/Minor version number:    9.0
+
+Device 4: "NVIDIA H100 80GB HBM3"
+  CUDA Driver Version / Runtime Version          12.4 / 10.1
+  CUDA Capability Major/Minor version number:    9.0
+
+Device 5: "NVIDIA H100 80GB HBM3"
+  CUDA Driver Version / Runtime Version          12.4 / 10.1
+  CUDA Capability Major/Minor version number:    9.0
+
+Device 6: "NVIDIA H100 80GB HBM3"
+  CUDA Driver Version / Runtime Version          12.4 / 10.1
+  CUDA Capability Major/Minor version number:    9.0
+
+Device 7: "NVIDIA H100 80GB HBM3"
+  CUDA Driver Version / Runtime Version          12.4 / 10.1
+  CUDA Capability Major/Minor version number:    9.0
+
+```
+
+
+## DeepSpeed hostfile 分发
+
+要手动分发 hostfile 并进行分布式安装，你需要以下几个步骤：
+
+1. 准备 hostfile
+确保 hostfile 文件包含所有参与的主机及其配置。
+
+示例 hostfile 内容：
+
+```plaintext
+host1 slots=4
+host2 slots=4
+host3 slots=8
+```
+
+2. 确保 SSH 配置正确
+确保你能够通过 SSH 无密码登录到所有主机。可以使用 ssh-keygen 和 ssh-copy-id 配置 SSH 密钥。
+
+生成 SSH 密钥（如果尚未生成）：
+
+```shell
+ssh-keygen -t rsa
+```
+
+将 SSH 公钥复制到每个主机：
+
+```shell
+ssh-copy-id user@host1
+ssh-copy-id user@host2
+ssh-copy-id user@host3
+```
+
+3. 创建临时目录并复制 wheel 文件
+在所有主机上创建一个临时目录，用于存放分发的 wheel 文件。
+
+```shell
+export PDSH_RCMD_TYPE=ssh
+hosts=$(cat /path/to/your/hostfile | awk '{print $1}' | paste -sd ",")
+tmp_wheel_path="/tmp/deepspeed_wheels"
+
+pdsh -w $hosts "mkdir -pv ${tmp_wheel_path}"
+pdcp -w $hosts dist/deepspeed*.whl ${tmp_wheel_path}/
+pdcp -w $hosts requirements/requirements.txt ${tmp_wheel_path}/
+```
+
+4. 在每个主机上安装 DeepSpeed 和依赖项
+在所有主机上安装 DeepSpeed 和所需的依赖项。
+
+```shell
+pdsh -w $hosts "pip install ${tmp_wheel_path}/deepspeed*.whl"
+pdsh -w $hosts "pip install -r ${tmp_wheel_path}/requirements.txt"
+```
+
+5. 清理临时文件
+安装完成后，删除所有主机上的临时文件。
+
+```shell
+pdsh -w $hosts "rm -rf ${tmp_wheel_path}"
+```
+
+详细步骤
+确保 SSH 配置正确：
+
+```shell
+ssh-keygen -t rsa
+ssh-copy-id user@host1
+ssh-copy-id user@host2
+ssh-copy-id user@host3
+```
+
+创建临时目录并复制文件：
+
+```shell
+export PDSH_RCMD_TYPE=ssh
+hosts=$(cat /path/to/your/hostfile | awk '{print $1}' | paste -sd ",")
+tmp_wheel_path="/tmp/deepspeed_wheels"
+
+pdsh -w $hosts "mkdir -pv ${tmp_wheel_path}"
+pdcp -w $hosts dist/deepspeed*.whl ${tmp_wheel_path}/
+pdcp -w $hosts requirements/requirements.txt ${tmp_wheel_path}/
+```
+
+在所有主机上安装 DeepSpeed 和依赖项：
+
+```shell
+pdsh -w $hosts "pip install ${tmp_wheel_path}/deepspeed*.whl"
+pdsh -w $hosts "pip install -r ${tmp_wheel_path}/requirements.txt"
+```
+
+清理临时文件：
+
+```shell
+pdsh -w $hosts "rm -rf ${tmp_wheel_path}"
+```
+
+通过这些步骤，你可以手动分发 hostfile 并在多个主机上安装 DeepSpeed 和其依赖项。这种方法确保了每个主机的环境配置一致，从而支持分布式训练或部署。