Tutorials/3d_segmentation/spleen_segmentation_3d.ipynb repruductive problem

[lanslotttTT]

Thansk for your excellent great job! When I tried to run the spleen_segmentation_3d.ipynb on the colab I found that every time I got different output ,why this happen and how to solve it thanks in advance!

And I run same code on my local machine my loss start at 0.6591

Here is my config

MONAI version: 0.8.1
Numpy version: 1.21.4
Pytorch version: 1.8.1+cu111
MONAI flags: HAS_EXT = False, USE_COMPILED = False
MONAI rev id: 71ff399a3ea07aef667b23653620a290364095b1

Optional dependencies:
Pytorch Ignite version: 0.4.8
Nibabel version: 3.2.1
scikit-image version: 0.18.3
Pillow version: 8.4.0
Tensorboard version: 2.8.0
gdown version: NOT INSTALLED or UNKNOWN VERSION.
TorchVision version: 0.9.1+cu111
tqdm version: 4.62.3
lmdb version: 1.3.0
psutil version: 5.9.0
pandas version: 1.3.4
einops version: 0.4.0
transformers version: NOT INSTALLED or UNKNOWN VERSION.
mlflow version: NOT INSTALLED or UNKNOWN VERSION.

Here is the config on colab:

MONAI version: 0.9.dev2220
Numpy version: 1.21.6
Pytorch version: 1.11.0+cu113
MONAI flags: HAS_EXT = False, USE_COMPILED = False
MONAI rev id: 1e9b0084b1d32b4a72f6e309bacde1390526d38f
MONAI __file__: /usr/local/lib/python3.7/dist-packages/monai/__init__.py

Optional dependencies:
Pytorch Ignite version: 0.4.8
Nibabel version: 3.0.2
scikit-image version: 0.18.3
Pillow version: 7.1.2
Tensorboard version: 2.8.0
gdown version: 4.4.0
TorchVision version: 0.12.0+cu113
tqdm version: 4.64.0
lmdb version: 0.99
psutil version: 5.4.8
pandas version: 1.3.5
einops version: NOT INSTALLED or UNKNOWN VERSION.
transformers version: NOT INSTALLED or UNKNOWN VERSION.
mlflow version: NOT INSTALLED or UNKNOWN VERSION.
pynrrd version: NOT INSTALLED or UNKNOWN VERSION.

[lanslotttTT]

I guess it's because of the notebook execution logic of the collab that cause the computing graph will be saved all the time.
But there still a problem in Colab ,when I run the cell below ,I got different input

[wyli]

mainly as it's commented on the pytorch doc page, https://pytorch.org/docs/stable/notes/randomness.html#reproducibility

Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms.