ascend-model-migration

To migrate and train models on Ascend NPU, you need an end-to-end workflow that covers environment setup, repository cloning, applying NPU patches, linking datasets and weights, and launching distributed training execution.

Before migrating BEVFormer, you need SSH access to target servers, a configured environment with CANN and torch_npu, the DrivingSDK patch repository, and the MMLab stack installed to prepare for distributed training execution.

The model patching process works by cloning the target open-source repository, applying specific NPU patches from a DrivingSDK repository, and then linking the required datasets and weights before starting the training execution.

Yes, you can run distributed training for BEVFormer on Ascend NPU by using the training orchestration scripts to launch and monitor the training progress after completing the environment setup and data linking steps.

The best way to configure the environment is to install and set up CANN and torch_npu alongside the MMLab stack on your target Ascend server, ensuring all dependencies are resolved before applying patches and starting training.

Ascend NPU training execution can fail if the environment is not properly configured with CANN and torch_npu, or if datasets and weights are not correctly linked before launching the distributed training scripts.