R Model Serving using Python TorchServe

Dear colleagues,

As you probably know the tensors store in R by pytorch are just pointers to the tensors allocated in the C++ library LibTorch.

However the extension of the models is still .pt and I was wandering if the same model that has been serialized in R can be served by the TorchServe.

Has someone tried this?

I am basically trying to avoid the creation of a plumber interface for serving torch models, specially when major cloud providers are already providing with some pytorch serving with prepackaged containers.


1 Like

Hi Edgar,

This is possible if you are able to jit_trace your model. Then your model is serialized as a ScriptModule and can be loaded in Python without any additional dependency. SInce torchserver use python in the backend this should work.

The main caveat here is that you wouldn't be able to use models that are not traceable ie. models that need to use the --model-file parameter in torchserve as R nn_modules wouldn't be readable by python. In this case you'd better using plumber or something that can load an R process and execute R logic.

Also, for now, you need to create a wrapper function to your model and detach all parameters, before tracing, but we will implement support for jit_tracing modules in the near future.

I did this quick example and it worked as expected. First I jit_traced an R torch model to to save it as a ScriptModule. THe model i traced is a pretrained alexnet from torchvision:


net <- torchvision::model_alexnet(pretrained = TRUE) 
# currently we need to detach all parameters in order to
# JIT compile. We need to support modules to avoid that.
for (p in net$parameters) {

# currently we can only JIT functions, not nn_modules, so we wrap
# the model into a function.
# this will be implemented soon
fn <- function(x) {

input <- torch_randn(100, 3, 224, 224)
out <- fn(input)

tr_fn <- jit_trace(fn, input)
jit_save(tr_fn, "models/model.pt")

Now, after installing torch-model-archiver with pip install torch-model-archiver I 'archived' that model to the .mar file with:

torch-model-archiver --model-name mynet \
                     --version 1.0 \
                     --serialized-file models/model.pt \
                     --export-path model-store \
                     --handler image_classifier \

Next I started torchserve using their docker image:

docker run \
        --rm --shm-size=1g \
        --ulimit memlock=-1 \
        --ulimit stack=67108864 \
        -p8080:8080 \
        -p8081:8081 \
        -p8082:8082 \
        -p7070:7070 \
        -p7071:7071 \
        --mount type=bind,source=/home/dfalbel/torchserve/model-store/,target=/tmp/models \
        pytorch/torchserve:latest \
        torchserve --model-store=/tmp/models --models mynet.mar

Finally I could run the predictions using:

curl -O https://raw.githubusercontent.com/pytorch/serve/master/docs/images/kitten_small.jpg
curl -T kitten_small.jpg
  "281": 0.5944757461547852,
  "285": 0.3166409432888031,
  "287": 0.052945494651794434,
  "282": 0.028301434591412544,
  "286": 0.004156926181167364

Hope this helps! And let me know if you have additional questions.


Huge thanks @dfalbel

This works really like charm. My next step will be to try this with the sagemaker pytorch serve container.



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