Running error for all mini-batch runs within examples/pytorch/gcmc
🐛 Bug
Mini-batch sampling pipline update, one of the major features released in v0.8, has not been propagated to examples/pytorch/gcmc/train_sampling.py.
To Reproduce
Step(s) to reproduce the behavior:
(single GPU run)
- python3 train_sampling.py --data_name=ml-100k --use_one_hot_fea --gcn_agg_accum=stack --gpu 0
Traceback (most recent call last):
File "/workspace/examples/gcmc/train_sampling.py", line 377, in
Expected behavior
The case should run through (with single/multi-GPU settings)
Environment
- DGL Version (e.g., 1.0): 0.9
- Backend Library & Version (e.g., PyTorch 0.4.1, MXNet/Gluon 1.3): 1.12
- OS (e.g., Linux): Ubuntu 20.04
- How you installed DGL (
conda,pip, source): source - Build command you used (if compiling from source):
- Python version: 3.8.13
- CUDA/cuDNN version (if applicable): 11.7
- GPU models and configuration (e.g. V100): A100
- Any other relevant information: torchtext 0.13.0
Additional context
The same type of error (mini-batch sampling update) is also happened to all TGN examples (examples/pytorch/tgn/train.py), where EdgeCollator is no longer a valid class name from latest dgl-v0.8x release...
Fixed by #4082
@jermainewang
In fact, this issue has not been resolved because of the outdated DGL dataloader APIs. It seems train_sampling.py uses at least two features that have been deprecated:
(1) Set return_eids=True in MultiLayerNeighborSampler to use dgl.EID later (no longer support in 0.8x)
(2) Specify g_sampling in dataloader constructor (no longer support in 0.8x).
This issue has been automatically marked as stale due to lack of activity. It will be closed if no further activity occurs. Thank you