PaddlePaddle/r

• R support
• • Install
  • • Use docker
    • Local installation
  • Use Paddle inference in R
  • Quick start

R support

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Use paddle in R.

Install

Use docker

Download Dockerfile, run

docker build -t paddle-rapi:latest .

Local installation

First, make sure Python is installed, assuming that the path is /opt/python3.7.

python -m pip install paddlepaddle # CPU version
python -m pip install paddlepaddle-gpu # GPU version

Install the R libraries needed to use paddle.

install.packages("reticulate") # call Python in R
install.packages("RcppCNPy") # use numpy.ndarray in R

Use Paddle inference in R

First, load PaddlePaddle in R.

library(reticulate)
library(RcppCNPy)

use_python("/opt/python3.7/bin/python3.7")
paddle <- import("paddle.fluid.core")

Create an AnalysisConfig, which is the configuration of the paddle inference engine.

config <- paddle$AnalysisConfig("")

Set model path.

config$set_model("model/__model__", "model/__params__")

Use zero copy inference.

config$switch_use_feed_fetch_ops(FALSE)
config$switch_specify_input_names(TRUE)

Other configuration options and descriptions are as fallows.

config$enable_profile() # turn on inference profile
config$enable_use_gpu(gpu_memory_mb, gpu_id) # use GPU
config$disable_gpu() # disable GPU 
config$gpu_device_id() # get GPU id
config$switch_ir_optim(TRUE) # turn on IR optimize(default is TRUE)
config$enable_tensorrt_engine(workspace_size,
                              max_batch_size,
                              min_subgraph_size,
                              paddle$AnalysisConfig$Precision$FLOAT32,
                              use_static,
                              use_calib_mode
                              ) # use TensorRT
config$enable_mkldnn() # use MKLDNN
config$delete_pass(pass_name) # delete IR pass

Create inference engine.

predictor <- paddle$create_paddle_predictor(config)

Get input tensor(assume single input), and set input data

input_names <- predictor$get_input_names()
input_tensor <- predictor$get_input_tensor(input_names[1])
input_shape <- as.integer(c(1, 3, 300, 300)) # shape has integer type
input_data <- np_array(data, dtype="float32")$reshape(input_shape)
input_tensor$copy_from_cpu(input_data)

Run inference.

predictor$zero_copy_run()

Get output tensor(assume single output).

output_names <- predictor$get_output_names()
output_tensor <- predictor$get_output_tensor(output_names[1])

Parse output data, and convert to numpy.ndarray

output_data <- output_tensor$copy_to_cpu()
output_data <- np_array(output_data)

Click to see the full R mobilenet example and the corresponding Python mobilenet example the above. For more examples, see R inference example.

Quick start

Download Dockerfile and example to local directory, and build docker image

docker build -t paddle-rapi:latest .

Create and enter container

docker run --rm -it paddle-rapi:latest bash

Run the following command in th container

cd example
chmod +x mobilenet.r
./mobilenet.r