This is the MXNet agent for MLModelScope, an open-source framework and hardware agnostic, extensible and customizable platform for evaluating and profiling ML models across datasets / frameworks / systems, and within AI application pipelines.

Currently it has most of the models from Gluon Model Zoo built in. More built-in models are comming. One can evaluate the ~80 models on any systems of insterest with either local MXNet installation or MXNet docker images.

Check out MLModelScope and welcome to contribute.

Install go if you have not done so. Please follow Go Installation.

Download and install the MLModelScope MXNet Agent:

go get -v github.com/c3sr/mxnet

The agent requires The MXNet C library and other Go packages.

You can install the dependency through go get.

cd $GOPATH/src/github.com/c3sr/mxnet
go get -u -v ./...

Or use Dep.

dep ensure -v

This installs the dependency in vendor/.

Note: The CGO interface passes go pointers to the C API. This is an error by the CGO runtime. Disable the error by placing

export GODEBUG=cgocheck=0

in your ~/.bashrc or ~/.zshrc file and then run either source ~/.bashrc or source ~/.zshrc

The MXNet C library is required.

If you use MXNet Docker Images (e.g. NVIDIA GPU CLOUD (NGC)), skip this step.

Refer to go-mxnet for mxnet installation.

Refer to External services.

Refer to Use within TensorFlow Docker Images.

Continue if you have

• installed all the dependencies
• downloaded carml_config_example.yml to $HOME as .carml_config.yml
• launched docker external services on the host machine of the docker container you are going to use

, otherwise read above

An example of using NGC MXNet docker image:

nvidia-docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -it --privileged=true --network host \
-v $GOPATH:/workspace/go1.12/global \
-v $GOROOT:/workspace/go1.12_root \
-v ~/.carml_config.yml:/root/.carml_config.yml \
nvcr.io/nvidia/mxnet:19.06-py3

NOTE: The SHMEM allocation limit is set to the default of 64MB. This may be insufficient for MXNet. NVIDIA recommends the use of the following flags: nvidia-docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 ...

Within the container, set up the environment so that the agent can find the MXNet installation.

export GOPATH=/workspace/go1.12/global
export GOROOT=/workspace/go1.12_root
export PATH=$GOROOT/bin:$PATH

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64/
export CGO_LDFLAGS="${CGO_LDFLAGS} -L /usr/local/cuda/lib64 -L /usr/local/cuda/extras/CUPTI/lib64/"

export PATH=$PATH:$(go env GOPATH)/bin  
export GODEBUG=cgocheck=0  
cd $GOPATH/src/github.com/c3sr/mxnet/mxnet-agent