docker build --tag tmponnxleak .
docker run -it --rm tmponnxleak

docker build . --target=downloader --tag=onnx-downloader
docker run -it --rm -v "$(pwd)"/:/hostlib onnx-downloader cp -r /onnxruntime /hostlib/.

OMP_WAIT_POLICY=PASSIVE OMP_NUM_THREADS=1 LD_LIBRARY_PATH="$(pwd)"/onnxruntime/lib go run .

I can't achieve to cap memory consumption in my work flow.

I create and release sessions over time, from a single long running process (an HTTP real-time serving application), in order to infer on daily retrained models.

I create one new session per new trained model

Outdated model sessions are closed when replaced by newer ones, but memory usage keep growing.

I tried to replicate my work flow in a demo app : we can see memory increasing (despite go runtime memory is constant)

#1 New Session (6 threads, 10 MB resident, 71 MB go sys)
#2 New Session (7 threads, 46 MB resident, 73 MB go sys)
#3 New Session (22 threads, 55 MB resident, 73 MB go sys)
#4 New Session (22 threads, 62 MB resident, 73 MB go sys)
#5 New Session (25 threads, 65 MB resident, 73 MB go sys)
...
#20 New Session (28 threads, 136 MB resident, 73 MB go sys)
...
#50 New Session (30 threads, 238 MB resident, 74 MB go sys)
...
#100 New Session (35 threads, 292 MB resident, 74 MB go sys)
...
#200 New Session (43 threads, 301 MB resident, 74 MB go sys)
...
#500 New Session (50 threads, 321 MB resident, 74 MB go sys)
...
#750 New Session (50 threads, 326 MB resident, 74 MB go sys)
...
#1000 New Session (55 threads, 336 MB resident, 74 MB go sys)

here is running one session after the other (open, infer, close, open, infer, close, ...) with 10000 concurrent prediction for each session

the model is always the same (a 3MB onnx file, with 3 hidden dense layers, 1 input (shape [1, 1415]) , 1 output (shape [1, 100]))

the input is always the same "zero filled" vector