170 lines
4.7 KiB
Markdown
170 lines
4.7 KiB
Markdown
## deepspeed docker image build
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```shell
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docker-compose -f docker-compose_pytorch1.13.yml build
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docker-compose -f docker-compose_pytorch2.3.yml build
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```
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## 物理机更新内核
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```shell
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uname -r
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sudo apt-get update # This will update the repositories list
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sudo apt-get upgrade # This will update all the necessary packages on your system
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sudo apt-get dist-upgrade # This will add/remove any needed packages
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reboot # You may need this since sometimes after a upgrade/dist-upgrade, there are some left over entries that get fixed after a reboot
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sudo apt-get install linux-headers-$(uname -r) # This should work now
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```
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## test command
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```shell
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docker run -it --gpus all --name deepspeed_test --shm-size=1gb --rm hotwa/deepspeed:latest /bin/bash
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```
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## [查询GPU 架构 给变量赋值](https://blog.csdn.net/zong596568821xp/article/details/106411024)
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```shell
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git clone https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps.git
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cd deepstream_tlt_apps/TRT-OSS/x86
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nvcc deviceQuery.cpp -o deviceQuery
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./deviceQuery
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```
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H100 输出
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```shell
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(base) root@node19:~/bgpt/deepstream_tlt_apps/TRT-OSS/x86# ./deviceQuery
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Detected 8 CUDA Capable device(s)
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Device 0: "NVIDIA H100 80GB HBM3"
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CUDA Driver Version / Runtime Version 12.4 / 10.1
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CUDA Capability Major/Minor version number: 9.0
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Device 1: "NVIDIA H100 80GB HBM3"
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CUDA Driver Version / Runtime Version 12.4 / 10.1
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CUDA Capability Major/Minor version number: 9.0
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Device 2: "NVIDIA H100 80GB HBM3"
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CUDA Driver Version / Runtime Version 12.4 / 10.1
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CUDA Capability Major/Minor version number: 9.0
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Device 3: "NVIDIA H100 80GB HBM3"
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CUDA Driver Version / Runtime Version 12.4 / 10.1
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CUDA Capability Major/Minor version number: 9.0
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Device 4: "NVIDIA H100 80GB HBM3"
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CUDA Driver Version / Runtime Version 12.4 / 10.1
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CUDA Capability Major/Minor version number: 9.0
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Device 5: "NVIDIA H100 80GB HBM3"
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CUDA Driver Version / Runtime Version 12.4 / 10.1
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CUDA Capability Major/Minor version number: 9.0
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Device 6: "NVIDIA H100 80GB HBM3"
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CUDA Driver Version / Runtime Version 12.4 / 10.1
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CUDA Capability Major/Minor version number: 9.0
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Device 7: "NVIDIA H100 80GB HBM3"
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CUDA Driver Version / Runtime Version 12.4 / 10.1
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CUDA Capability Major/Minor version number: 9.0
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```
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## DeepSpeed hostfile 分发
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要手动分发 hostfile 并进行分布式安装，你需要以下几个步骤：
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1. 准备 hostfile
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确保 hostfile 文件包含所有参与的主机及其配置。
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示例 hostfile 内容：
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```plaintext
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host1 slots=4
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host2 slots=4
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host3 slots=8
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```
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2. 确保 SSH 配置正确
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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。
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生成 SSH 密钥（如果尚未生成）：
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```shell
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ssh-keygen -t rsa
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```
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将 SSH 公钥复制到每个主机：
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```shell
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ssh-copy-id user@host1
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ssh-copy-id user@host2
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ssh-copy-id user@host3
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```
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3. 创建临时目录并复制 wheel 文件
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在所有主机上创建一个临时目录，用于存放分发的 wheel 文件。
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```shell
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export PDSH_RCMD_TYPE=ssh
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hosts=$(cat /path/to/your/hostfile | awk '{print $1}' | paste -sd ",")
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tmp_wheel_path="/tmp/deepspeed_wheels"
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pdsh -w $hosts "mkdir -pv ${tmp_wheel_path}"
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pdcp -w $hosts dist/deepspeed*.whl ${tmp_wheel_path}/
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pdcp -w $hosts requirements/requirements.txt ${tmp_wheel_path}/
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```
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4. 在每个主机上安装 DeepSpeed 和依赖项
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在所有主机上安装 DeepSpeed 和所需的依赖项。
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```shell
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pdsh -w $hosts "pip install ${tmp_wheel_path}/deepspeed*.whl"
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pdsh -w $hosts "pip install -r ${tmp_wheel_path}/requirements.txt"
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```
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5. 清理临时文件
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安装完成后，删除所有主机上的临时文件。
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```shell
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pdsh -w $hosts "rm -rf ${tmp_wheel_path}"
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```
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详细步骤
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确保 SSH 配置正确：
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```shell
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ssh-keygen -t rsa
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ssh-copy-id user@host1
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ssh-copy-id user@host2
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ssh-copy-id user@host3
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```
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创建临时目录并复制文件：
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```shell
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export PDSH_RCMD_TYPE=ssh
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hosts=$(cat /path/to/your/hostfile | awk '{print $1}' | paste -sd ",")
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tmp_wheel_path="/tmp/deepspeed_wheels"
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pdsh -w $hosts "mkdir -pv ${tmp_wheel_path}"
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pdcp -w $hosts dist/deepspeed*.whl ${tmp_wheel_path}/
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pdcp -w $hosts requirements/requirements.txt ${tmp_wheel_path}/
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```
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在所有主机上安装 DeepSpeed 和依赖项：
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```shell
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pdsh -w $hosts "pip install ${tmp_wheel_path}/deepspeed*.whl"
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pdsh -w $hosts "pip install -r ${tmp_wheel_path}/requirements.txt"
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```
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清理临时文件：
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```shell
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pdsh -w $hosts "rm -rf ${tmp_wheel_path}"
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```
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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。 |