# 基础安装 pip install deepspeed # 完整功能安装(包含CUDA扩展) DS_BUILD_OPS=1 pip install deepspeed 2.2 基础代码改造 import deepspeed # 初始化引擎 model, optimizer, _, _ = deepspeed.initialize( model=model, model_parameters=model.parameters(), config_params="ds_config.json" ) # 训练循环改造...
您可以通过设置 DS_BUILD_OPS 环境变量为 1 来指示我们的安装程序(install.sh 或 pip install)尝试安装所有 ops,例如: DS_BUILD_OPS=1 pip install deepspeed DeepSpeed 只会安装与你的机器兼容的 ops。有关系统兼容性的更多详细信息,请尝试上面描述的 ds_report 工具。 如果你只想安装特定的 op(例如 FusedLam...
DS_BUILD_FUSED_LAMB=1pip install deepspeed 可用的DS_BUILD选项包含: DS_BUILD_OPS切换所有 ops DS_BUILD_CPU_ADAM构建 CPUAdam op DS_BUILD_FUSED_ADAM构建 FusedAdam op (from apex) DS_BUILD_FUSED_LAMB构建 FusedLamb op DS_BUILD_SPARSE_ATTN构建 sparse attention op DS_BUILD_TRANSFORMER构建 transforme...
DS_BUILD_FUSED_LAMB=1 pip install deepspeed 可用的DS_BUILD选项包含: DS_BUILD_OPS切换所有 ops DS_BUILD_CPU_ADAM构建 CPUAdam op DS_BUILD_FUSED_ADAM构建 FusedAdam op (from apex) DS_BUILD_FUSED_LAMB构建 FusedLamb op DS_BUILD_SPARSE_ATTN构建 sparse attention op DS_BUILD_TRANSFORMER构建 transfor...
DS_BUILD_OPS ```▍ 验证安装 通过以下Python命令验证DeepSpeed是否正确安装：```bash python -c "import deepspeed; print(deepspeed.__version__)"```此外，运行DeepSpeed的基础测试，以确认安装无误。▍ 准备训练脚本 假设已经有一个训练脚本train.py，需要在该脚本中添加DeepSpeed的配置参数。首先，导入DeepSpeed...
After installation, you can validate your install and see which extensions/ops your machine is compatible with via the DeepSpeed environment report. ds_report If you would like to pre-install any of the DeepSpeed extensions/ops (instead of JIT compiling) or install pre-compiled ops via PyPI ple...
DS_BUILD_CPU_ADAMbuilds the CPUAdam op. 我们只需要在安装deepspeed时设置DS_BUILD_CPU_ADAM=1即可。因此,我们卸载并重新安装deepspeed copy pip uninstall deepspeed pipcacheremove deepspeed DS_BUILD_CPU_ADAM=1pipinstalldeepspeed 然后就会自动编译出cpu_adam.so文件。
Use conda environment provided by #3943, and try to build deepspeed with pytorch-nightly by DS_BUILD_OPS=1 DS_BUILD_SPARSE_ATTN=0 \ pip install . --global-option="build_ext" \ 2>&1 | tee out # GOOD for release give me /home/rok/.conda/en...
{ds_model.module.device}") from deepspeed.ops.transformer.inference import DeepSpeedTransformerInference assert isinstance(ds_model.module.transformer.h[0], DeepSpeedTransformerInference) == True, "Model not sucessfully initalized" # Test model example = "My name is Philipp and I" input_ids = ...