pytorch

README

MLModelScope PyTorch Agent

This is the Pytorch 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 Pytorch Model Zoo built in, plus many models acquired from public repositories. Although the agent supports different modalities including Object Detection and Image Enhancement, most of the built-in models are for Image Classification. More built-in models are coming. One can evaluate the ~50 models on any system of interest with either local Pytorch installation or Pytorch docker images.

Check out MLModelScope and welcome to contribute.

Bare Minimum Installation

Prerequsite System Library Installation

We first discuss a bare minimum pytorch-agent installation without the tracing and profiling capabilities. To make this work, you will need to have the following system libraries preinstalled in your system.

• The CUDA library (required)
• The CUPTI library (required)
• The Pytorch C++ (libtorch) library (required)
• The libjpeg-turbo library (optional, but preferred)

The CUDA Library

Please refer to Nvidia CUDA library installation on this. Find the localation of your local CUDA installation, which is typically at /usr/local/cuda/, and setup the path to the libcublas.so library. Place the following in either your ~/.bashrc or ~/.zshrc file:

export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda/lib64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

The CUPTI Library

Please refer to Nvidia CUPTI library installation on this. Find the localation of your local CUPTI installation, which is typically at /usr/local/cuda/extras/CUPTI, and setup the path to the libcupti.so library. Place the following in either your ~/.bashrc or ~/.zshrc file:

export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64

The Pytorch C++ (libtorch) Library

The Pytorch C++ library is required for our Pytorch Go package. If you want to use Pytorch Docker Images (e.g. NVIDIA GPU CLOUD (NGC)) instead, skip this step for now and refer to our later section on this.

You can download pre-built Pytorch C++ (libtorch) library from Pytorch. Choose Pytorch Build = Stable (1.3), Your OS = <fill>, Package = LibTorch, Language = C++ and CUDA = <fill>. Download Pre-cxx11 ABI or cxx11 ABI version based on local gcc/g++ version.

Extract the downloaded archive to /opt/libtorch/.

tar -C /opt/libtorch -xzf (downloaded file)

Configure the linker environmental variables since the Pytorch C++ library is extracted to a non-system directory. Place the following in either your ~/.bashrc or ~/.zshrc file

Linux

export LIBRARY_PATH=$LIBRARY_PATH:/opt/libtorch/lib
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/libtorch/lib

macOS

export LIBRARY_PATH=$LIBRARY_PATH:/opt/libtorch/lib
export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/opt/libtorch/lib

You can test the installed Pytorch C++ library using an example C++ program, although we suggest running an example in github.com/c3sr/go-pytorch as per its documentation to confirm library installation.

There is an issue when using libtorch with version < 1.6.0, the work around here is to set LRU_CACHE_CAPACITY=1 in the environmental variable. This is already done in the docker images we are using, but needs to be done if building the agent locally.

Use libjpeg-turbo for Image Preprocessing

libjpeg-turbo is a JPEG image codec that uses SIMD instructions (MMX, SSE2, AVX2, NEON, AltiVec) to accelerate baseline JPEG compression and decompression. It outperforms libjpeg by a significant amount.

You need libjpeg installed.

sudo apt-get install libjpeg-dev

The default is to use libjpeg-turbo, to opt-out, use build tag nolibjpeg.

To install libjpeg-turbo, refer to libjpeg-turbo.

Linux

  export TURBO_VER=2.0.2
  cd /tmp
  wget https://cfhcable.dl.sourceforge.net/project/libjpeg-turbo/${TURBO_VER}/libjpeg-turbo-official_${TURBO_VER}_amd64.deb
  sudo dpkg -i libjpeg-turbo-official_${TURBO_VER}_amd64.deb

macOS

brew install jpeg-turbo

Installation of GO for Compilation

Since we use go for MLModelScope development, it's required to have go installed in your system before proceeding.

Please follow Installing Go Compiler to have go installed.

Bare Minimum Pytorch-agent Installation

Download and install the MLModelScope Pytorch Agent by running the following command in any location, assuming you have installed go following the above instruction.

go get -v github.com/c3sr/pytorch

You can then install the dependency packages through go get.

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

An alternative to install the dependency packages is to use Dep.

dep ensure -v

This installs the dependency in vendor/.

The CGO interface passes go pointers to the C API. There is an error in the CGO runtime. We can disable the error by placing

export GODEBUG=cgocheck=0

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

Build the Pytorch agent with GPU enabled

cd $GOPATH/src/github.com/c3sr/pytorch/pytorch-agent
go build

Build the Pytorch agent without GPU or libjpeg-turbo

cd $GOPATH/src/github.com/c3sr/pytorch/pytorch-agent
go build -tags="nogpu nolibjpeg"

If everything is successful, you should have an executable pytorch-agent binary in the current directory.

Configuration Setup

To run the agent, you need to setup the correct configuration file for the agent. Some of the information may not make perfect sense for all testing scenarios, but they are required and will be needed for later stage testing. Some of the port numbers as specified below can be changed depending on your later setup for those service.

So let's just set them up as is, and worry about the detailed configuration parameter values later.

You must have a carml config file called .carml_config.yml under your home directory. An example config file carml_config.yml.example is in github.com/c3sr/MLModelScope . You can move it to ~/.carml_config.yml.

The following configuration file can be placed in $HOME/.carml_config.yml or can be specified via the --config="path" option.

app:
  name: carml
  debug: true
  verbose: true
  tempdir: ~/data/carml
registry:
  provider: consul
  endpoints:
    - localhost:8500
  timeout: 20s
  serializer: jsonpb
database:
  provider: mongodb
  endpoints:
    - localhost
tracer:
  enabled: true
  provider: jaeger
  endpoints:
    - localhost:9411
  level: FULL_TRACE
logger:
  hooks:
    - syslog

Test Installation

With the configuration and the above bare minimumn installation, you should be ready to test the installation and see how things works.

Here are a few examples. First, make sure we are in the right location

cd $GOPATH/src/github.com/c3sr/pytorch/pytorch-agent

To see a list of help

./pytorch-agent -h

To see a list of models that we can run with this agent

./pytorch-agent info models

To run an inference using the default DNN model alexnet with a default input image.

./pytorch-agent predict urls --model_name TorchVision_Alexnet --profile=false --publish=false

The above --profile=false --publish=false command parameters tell the agent that we do not want to use profiling capability and publish the results, as we haven't installed the MongoDB database to store profiling data and the tracer service to accept tracing information.

External Service Installation to Enable Tracing and Profiling

We now discuss how to install a few external services that make the agent fully useful in terms of collecting tracing and profiling data.

External Srvices

MLModelScope relies on a few external services. These services provide tracing, registry, and database servers.

These services can be installed and enabled in different ways. We discuss how we use docker below to show how this can be done. You can also not use docker but install those services from either binaries or source codes directly.

Installing Docker

Refer to Install Docker.

On Ubuntu, an easy way is using

curl -fsSL get.docker.com -o get-docker.sh | sudo sh
sudo usermod -aG docker $USER

On macOS, intsall Docker Destop

Starting Trace Server

This service is required.

• On x86 (e.g. intel) machines, start jaeger by

docker run -d -e COLLECTOR_ZIPKIN_HTTP_PORT=9411 -p5775:5775/udp -p6831:6831/udp -p6832:6832/udp \
  -p5778:5778 -p16686:16686 -p14268:14268 -p9411:9411 jaegertracing/all-in-one:latest

• On ppc64le (e.g. minsky) machines, start jaeger machine by

docker run -d -e COLLECTOR_ZIPKIN_HTTP_PORT=9411 -p5775:5775/udp -p6831:6831/udp -p6832:6832/udp \
  -p5778:5778 -p16686:16686 -p14268:14268 -p9411:9411 carml/jaeger:ppc64le-latest

The trace server runs on http://localhost:16686

Starting Registry Server

This service is not required if using pytorch-agent for local evaluation.

• On x86 (e.g. intel) machines, start consul by

docker run -p 8500:8500 -p 8600:8600 -d consul

• On ppc64le (e.g. minsky) machines, start consul by

docker run -p 8500:8500 -p 8600:8600 -d carml/consul:ppc64le-latest

The registry server runs on http://localhost:8500

Starting Database Server

This service is not required if not using database to publish evaluation results.

• On x86 (e.g. intel) machines, start mongodb by

docker run -p 27017:27017 --restart always -d mongo:3.0

You can also mount the database volume to a local directory using

docker run -p 27017:27017 --restart always -d  -v $HOME/data/carml/mongo:/data/db mongo:3.0

Configuration

You must have a carml config file called .carml_config.yml under your home directory. An example config file ~/.carml_config.yml is already discussed above. Please update the port numbers for the above external services accordingly if you decide to choose a different ports above.

Testing

The testing steps are very similar to those testing we discussed above, except that you can now safely use both the profiling and publishing services.

Use the Agent with the MLModelScope Web UI

./pytorch-agent serve -l -d -v

Refer to [TODO] to run the web UI to interact with the agent.

Use the Agent through Command Line

Run ./pytorch-agent -h to list the available commands.

Run ./pytorch-agent info models to list the available models.

Run ./pytorch-agent predict to evaluate a model. This runs the default evaluation. ./pytorch-agent predict -h shows the available flags you can set.

An example run is

./pytorch-agent predict urls --model_name TorchVision_Alexnet --profile=false --publish=false

Refer to [TODO] to run the web UI to interact with the agent.

Use the Agent through Pre-built Docker Images

We have pre-built docker images on Dockerhub. The images are c3sr/pytorch-agent:amd64-cpu-latest and c3sr/pytorch-agent:amd64-gpu-latest. The entrypoint is set as pytorch-agent thus these images act similar as the command line above.

An example run is

docker run --gpus=all --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 --privileged=true \
    --network host \
    -v ~/.carml_config.yml:/root/.carml_config.yml \
    -v ~/results:/go/src/github.com/c3sr/pytorch/results \
    c3sr/pytorch-agent:amd64-gpu-latest predict urls --model_name TorchVision_Alexnet --profile=false --publish=false

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

NOTE: To run with GPU, you need to meet following requirements:

• Docker >= 19.03 with nvidia-container-toolkit (otherwise need to use nvidia-docker)
• CUDA >= 10.1
• NVIDIA Driver >= 418.39

Notes on installing Pytorch C++ from source

To build the Pytorch C++ library from source, refer to https://github.com/pytorch/pytorch#installation and the code for building go-pytorch dockerfiles.

Documentation

Index

• Variables
• func Asset(name string) ([]byte, error)
• func AssetDir(name string) ([]string, error)
• func AssetNames() []string
• func Register()

Variables

var (
	Version   = "1.1.5"
	BuildDate = "undefined"
	GitCommit = "undefined"
)

Version ...

var FrameworkManifest = dlframework.FrameworkManifest{
	Name:    "PyTorch",
	Version: "1.8.1",
	Container: map[string]*dlframework.ContainerHardware{
		"amd64": {
			Cpu: "raiproject/carml-pytorch:amd64-cpu",
			Gpu: "raiproject/carml-pytorch:amd64-gpu",
		},
		"ppc64le": {
			Cpu: "raiproject/carml-pytorch:ppc64le-gpu",
			Gpu: "raiproject/carml-pytorch:ppc64le-gpu",
		},
	},
}

FrameworkManifest ...

Functions

func Asset

func Asset(name string) ([]byte, error)

Asset loads and returns the asset for the given name. It returns an error if the asset could not be found or could not be loaded.

func AssetDir

func AssetDir(name string) ([]string, error)

AssetDir returns the file names below a certain directory embedded in the file by go-bindata. For example if you run go-bindata on data/... and data contains the following hierarchy:

data/
  foo.txt
  img/
    a.png
    b.png

then AssetDir("data") would return []string{"foo.txt", "img"} AssetDir("data/img") would return []string{"a.png", "b.png"} AssetDir("foo.txt") and AssetDir("notexist") would return an error AssetDir("") will return []string{"data"}.

func AssetNames

func AssetNames() []string

AssetNames returns the names of the assets.

func Register

func Register()

Register ...