# How to Manage Memory with Sequential, GPU-Intensive (e.g., PyTorch) Python Calls via PythonCall.jl

**URL:** <https://discourse.julialang.org/t/how-to-manage-memory-with-sequential-gpu-intensive-e-g-pytorch-python-calls-via-pythoncall-jl/129951>\
**Category:** General Usage\
**Tags:** gpu, pytorch, pythoncalljl\
**Created:** [June 17, 2025, 1:07am UTC](https://discourse.julialang.org/t/how-to-manage-memory-with-sequential-gpu-intensive-e-g-pytorch-python-calls-via-pythoncall-jl/129951 "2025-06-17T01:07:18Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Dale\_James\_Black](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dale_james_black/32/11595_2.png) [@Dale\_James\_Black](https://discourse.julialang.org/u/Dale_James_Black)\
**Post date:** [June 17, 2025, 1:07am UTC](https://discourse.julialang.org/t/how-to-manage-memory-with-sequential-gpu-intensive-e-g-pytorch-python-calls-via-pythoncall-jl/129951/1 "2025-06-17T01:07:18Z")

</div>

### My Question

I’m running into an issue where a long-running Python function seems to be terminated mid-execution. My question is:

Are there any lower-level features or design patterns within PythonCall.jl that could help with this problem? For example, is there a way to periodically run a cleanup function (like `torch.cuda.empty_cache()`) or something similar to manage resources during a single, long-running `pycall`?

### The Scenario

I’m using PythonCall to interface with `TotalSegmentator`, a deep learning library built on PyTorch. The library has a main function, `totalsegmentator()`, which, when run in its high-resolution mode (`fast=false`), executes a sequence of 5 different neural network models internally to segment a full CT scan.

My initial, straightforward code looks like this:

**Simple (Not Working) Script:**

```julia
using Pkg
Pkg.activate(".")
Pkg.instantiate()

import CUDA
CUDA.functional()

using PythonCall
np, pydicom, totalsegmentator, nib = pyimport(
    "numpy",
    "pydicom",
    "totalsegmentator.python_api",
    "nibabel"
)

# Assume `nifti_image` is a valid NIfTI object loaded from DICOMs
# e.g., Python: <nibabel.nifti1.Nifti1Image object at 0x7fee3d265670>
# nifti_image = create_nifti_from_dicom(...)

# This single call should run the full segmentation
segmentation_result = totalsegmentator.totalsegmentator(
    nifti_image,
    fast=false,
    device="gpu",
    ml=true,
    quiet=false
)

# This line is never reached
println("Segmentation complete!")

```

### Observed Behavior

When I run this on a machine with an NVIDIA A100 80GB GPU, the script prints the first stage of the Python tool’s output and then hangs indefinitely:

```julia-auto
Resampling...
  Resampled in 1.91s
Predicting part 1 of 5 ...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 12.29it/s]
# --- HANGS HERE ---

```

By monitoring `nvidia-smi`, I observed the following:

1. The Julia process (e.g., PID `258497`) is running and using a baseline amount of VRAM.
2. The moment the prediction starts, the GPU utilization spikes to 100%, but the Julia process **disappears** from the process list.
3. The GPU continues to run at 100% for a short time with no parent process before finishing its work.

I’ve also noted that since the hang happens _during_ the single `totalsegmentator()` call, a cleanup function after the call wouldn’t execute. Furthermore, I’ve observed that calling Julia’s `GC.gc()` can sometimes free the underlying Python object for my `nifti_image` input, which could cause issues in other contexts.