Comments (2)
Hi, this looks like a generic PyTorch installation error that isn't related to the spacetimeformer code or PyTorch lightning (which is a third-party library that basically handles training loop boilerplate). Installing PyTorch can be surprisingly tricky at times, especially with cuda version conflicts and so on. I recommend making a new environment and installing the latest version of PyTorch (1.11), which has been tested with the latest version of this repo. I've had to set up PyTorch on a bunch of different servers and in my experience it's usually easier to fix cuda compatibility issues by installing with conda rather than pip. https://pytorch.org/get-started/locally/
You'll know it worked if you can do
import torch
torch.cuda.is_available()
and get "True"
from spacetimeformer.
Thanks for reaching back!
I've recreated the environment with python 3.8, torch 1.11.0, cuda 10.2 and installed the requirements via pip. However, the error still persists.
from spacetimeformer.
Related Issues (20)
- AttributeError: module 'tqdm' has no attribute 'auto' HOT 8
- how should I find the datas for example
- Colab installation
- Random seed
- TypeError: accuracy() missing 1 required positional argument: 'task' HOT 1
- MisconfigurationException: `configure_optimizers` must include a monitor when a `ReduceLROnPlateau` scheduler is used HOT 5
- Training with custom dataset. Error: object is not callable HOT 2
- colab
- Pip installation
- Error while resuming training from saved checkpoint
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- Memory requirements to replicate on Pems-Bay HOT 2
- Working with custom dataset, IndexError: index out of range in self HOT 1
- ValueError: SyncBatchNorm layers only work with GPU modules HOT 2
- prediction vs labels
- Unable to reproduce results for spacetimeformer HOT 2
- `configure_optimizers` must include a monitor when a `ReduceLROnPlateau` scheduler is used. For example: {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "metric_to_track"} HOT 2
- Question regarding the use of projected exogenous variable values
- using own dataset
- Publication of Training Commands
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