@orian hindi​ : I shall provide you a framework to test and try and please see if it works out for you!

1. Make sure that the log directory specified in SummaryWriter is valid and accessible. In Databricks, you should use the Databricks File System (DBFS) path instead of a local file system path. You can use a path like /dbfs/<your-directory>/ to log your TensorBoard data to DBFS.
2. Check the permissions of the directory you are logging to. Make sure that the user running the training job has write permissions to the directory.
3. Ensure that you are calling SummaryWriter.flush() after each epoch to write the buffered data to disk.
4. Make sure that you have the correct port number when launching TensorBoard. In Databricks, the default port number for TensorBoard is 6006.
5. Verify that you are pointing TensorBoard to the correct log directory when launching it. You can use a command like tensorboard --logdir=/dbfs/<your-directory>/ to launch TensorBoard and view your logs.

If you are still having trouble, you may want to try logging your TensorBoard data to the local file system of the cluster, and then copying it to DBFS after the training job has completed. This can help ensure that the logs are being written correctly, and can also make it easier to organize and analyze the data.