MindSpore Serving Installation

Installation

MindSpore Serving wheel packages are common to various hardware platforms(Nvidia GPU, Ascend 910/310P/310, CPU). The inference task depends on the MindSpore or MindSpore Lite inference framework. We need to select one of them as the Serving Inference backend. When these two inference backend both exist, Mindspore Lite inference framework will be used.

MindSpore and MindSpore Lite have different build packages for different hardware platforms. The following table lists the target devices and model formats supported by each build package.

Inference backend	Build platform	Target device	Supported model formats
MindSpore	Nvidia GPU	Nvidia GPU	`MindIR`
	Ascend	Ascend 910	`MindIR`
		Ascend 310P/310	`MindIR`, `OM`
MindSpore Lite	Nvidia GPU	Nvidia GPU, CPU	`MindIR_Opt`
	Ascend	Ascend 310, CPU	`MindIR_Opt`
	CPU	CPU	`MindIR_Opt`

When MindSpore is used as the inference backend, MindSpore Serving supports the Ascend 910/310P/310 and Nvidia GPU environments. The Ascend 310P/310 environment supports both OM and MindIR model formats, and the Ascend 910 and GPU environment only supports the MindIR model format.

For details about how to install and configure MindSpore, see Installing MindSpore and Configuring MindSpore.

When MindSpore Lite is used as the inference backend, MindSpore Serving supports Ascend 310, Nvidia GPU and CPU environments. Only the MindIR_Opt model format is supported. Currently, models in MindIR format exported from MindSpore and models exported from other frameworks need to be converted to MindIR_Opt format using MindSpore Lite conversion tool. During model conversion, if the target device is set to Ascend310, the generated MindIR_Opt model can be used only in the Ascend 310 environment. Otherwise, the generated MindIR_Opt model can be used only in the Nvidia GPU and CPU environments.

For details about how to compile and install MindSpore Lite, see the MindSpore Lite Documentation. We should configure the environment variable LD_LIBRARY_PATH to indicates the installation path of libmindspore-lite.so.

We can install MindSpore Serving either by pip or by source code.

Installation by pip

Perform the following steps to install Serving:

If use the pip command, download the .whl package from the MindSpore Serving page and install it.

pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/Serving/{arch}/mindspore_serving-{version}-{python_version}-linux_{arch}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple

{version} denotes the version of MindSpore Serving. For example, when you are downloading MindSpore Serving 1.1.0, {version} should be 1.1.0.

{arch} denotes the system architecture. For example, the Linux system you are using is x86 architecture 64-bit, {arch} should be x86_64. If the system is ARM architecture 64-bit, then it should be aarch64.

{python_version} spcecifies the python version for which MindSpore Serving is built. If you wish to use Python3.7.5,{python_version} should be cp37-cp37m. If Python3.9.0 is used, it should be cp39-cp39. Please use the same Python environment whereby MindSpore Serving is installed.

Installation by Source Code

Install Serving using the source code.

git clone https://gitee.com/mindspore/serving.git -b master
cd serving
bash build.sh

For the bash build.sh above, we can add -jn, for example -j16, to accelerate compilation. By adding -S on option, third-party dependencies can be downloaded from gitee instead of github.

After the build is complete, find the .whl installation package of Serving in the serving/build/package/ directory and install it.

pip install mindspore_serving-{version}-{python_version}-linux_{arch}.whl

Installation Verification

Run the following commands to verify the installation. Import the Python module. If no error is reported, the installation is successful.

from mindspore_serving import server