Installation Details

The quickest way to get started with DeepSpeed is via pip, this will install the latest release of DeepSpeed which is not tied to specific PyTorch or CUDA versions. DeepSpeed includes several C++/CUDA extensions that we commonly refer to as our ‘ops’. By default, all of these extensions/ops will be built just-in-time (JIT) using torch’s JIT C++ extension loader that relies on ninja to build and dynamically link them at runtime.

pip install deepspeed

After installation, you can validate your installation and see which ops your machine is compatible with via the DeepSpeed environment report with ds_report or python -m deepspeed.env_report. We’ve found this report useful when debugging DeepSpeed install or compatibility issues.

ds_report

Pre-install DeepSpeed Ops

Note: PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops. However, this is not required if using the default mode of JIT compilation of ops.

Sometimes we have found it useful to pre-install either some or all DeepSpeed C++/CUDA ops instead of using the JIT compiled path. In order to support pre-installation we introduce build environment flags to turn on/off building specific ops.

You can indicate to our installer (either install.sh or pip install) that you want to attempt to install all of our ops by setting the DS_BUILD_OPS environment variable to 1, for example:

DS_BUILD_OPS=1 pip install deepspeed

DeepSpeed will only install any ops that are compatible with your machine. For more details on which ops are compatible with your system please try our ds_report tool described above.

If you want to install only a specific op (e.g., FusedLamb), you can toggle with DS_BUILD environment variables at installation time. For example, to install DeepSpeed with only the FusedLamb op use:

DS_BUILD_FUSED_LAMB=1 pip install deepspeed

Available DS_BUILD options include:

  • DS_BUILD_OPS toggles all ops
  • DS_BUILD_CPU_ADAM builds the CPUAdam op
  • DS_BUILD_FUSED_ADAM builds the FusedAdam op (from apex)
  • DS_BUILD_FUSED_LAMB builds the FusedLamb op
  • DS_BUILD_SPARSE_ATTN builds the sparse attention op
  • DS_BUILD_TRANSFORMER builds the transformer op
  • DS_BUILD_TRANSFORMER_INFERENCE builds the transformer-inference op
  • DS_BUILD_STOCHASTIC_TRANSFORMER builds the stochastic transformer op
  • DS_BUILD_UTILS builds various optimized utilities
  • DS_BUILD_AIO builds asynchronous (NVMe) I/O op

To speed up the build-all process, you can parallelize the compilation process with:

DS_BUILD_OPS=1 pip install deepspeed --global-option="build_ext" --global-option="-j8"

This should complete the full build 2-3 times faster. You can adjust -j to specify how many cpu-cores are to be used during the build. In the example it is set to 8 cores.

You can also build a binary wheel and install it on multiple machines that have the same type of GPUs and the same software environment (CUDA toolkit, pytorch, python, etc.)

DS_BUILD_OPS=1 python setup.py build_ext -j8 bdist_wheel

This will create a pypi binary wheel under dist, e.g., dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl and then you can install it directly on multiple machines, in our example:

pip install dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl

Install DeepSpeed from source

After cloning the DeepSpeed repo from GitHub, you can install DeepSpeed in JIT mode via pip (see below). This installation should complete quickly since it is not compiling any C++/CUDA source files.

pip install .

For installs spanning multiple nodes we find it useful to install DeepSpeed using the install.sh script in the repo. This will build a python wheel locally and copy it to all the nodes listed in your hostfile (either given via --hostfile, or defaults to /job/hostfile).

When the code using DeepSpeed is used for the first time it’ll automatically build only the CUDA extensions, required for the run, and by default it’ll place them under ~/.cache/torch_extensions/. The next time the same program is executed these now precompiled extensions will be loaded form that directory.

If you use multiple virtual environments this could be a problem, since by default there is only one torch_extensions directory, but different virtual environments may use different setups (e.g., different python or cuda versions) and then the loading of a CUDA extension built by another environment will fail. Therefore, if you need to you can override the default location with the help of the TORCH_EXTENSIONS_DIR environment variable. So in each virtual environment you can point it to a unique directory and DeepSpeed will use it to save and load CUDA extensions.

You can also change it just for a specific run with:

 TORCH_EXTENSIONS_DIR=./torch-extensions deepspeed ...

Building for the correct architectures

If you’re getting the following error:

RuntimeError: CUDA error: no kernel image is available for execution on the device

when running deepspeed, that means that the cuda extensions weren’t built for the card you’re trying to use it for.

When building from source deepspeed will try to support a wide range of architectures, but under jit-mode it’ll only support the architectures visible at the time of building.

You can build specifically for a desired range of architectures by setting a TORCH_CUDA_ARCH_LIST env variable:

TORCH_CUDA_ARCH_LIST="6.1;7.5;8.6" pip install ...

It will also make the build faster when you only build for a few architectures.

This is also recommended to ensure your exact architecture is used. Due to a variety of technical reasons, a distributed pytorch binary isn’t built to fully support all architectures, skipping binary compatible ones, at a potential cost of underutilizing your full card’s compute capabilities. To see which architectures get included during the deepspeed build from source - save the log and grep for -gencode arguments.

The full list of nvidia GPUs and their compute capabilities can be found here.

Feature specific dependencies

Some DeepSpeed features require specific dependencies outside the general dependencies of DeepSpeed.

  • Python package dependencies per feature/op please see our requirements directory.

  • We attempt to keep the system level dependencies to a minimum, however some features do require special system-level packages. Please see our ds_report tool output to see if you are missing any system-level packages for a given feature.

Pre-compiled DeepSpeed builds from PyPI

Coming soon

Updated: