本地运行正常但gcloud-ml无法生成savedModel技术求助

I’ve dealt with similar SavedModel export headaches when using Cloud ML Engine’s local training, so let’s walk through the most likely fixes and checks for your scenario:

• Verify your training script’s SavedModel export logic

  • Double-check if your code actually triggers the SavedModel export in local training mode. Sometimes scripts are written to only export when running in distributed Cloud ML Engine jobs, not local ones. Look for conditional logic around tf.saved_model.save() (or the older tf.saved_model.builder.SavedModelBuilder.save()) to make sure it runs in local training.
  • Confirm the export path is correctly tied to your --job-dir parameter. Even in local training, writing to a GCS bucket requires proper authentication—try temporarily exporting to a local directory (like ./local-model) instead of the GCS path to rule out permission issues.

• Fix command-line parameter formatting

  • Your --train-files argument has an unintended space: "gs://bucket-ml/data/treinamento/train/part *.csv" (between part and *). This will cause the parameter parser to split the path incorrectly, meaning your script might not load any training data, exit early, and never reach the SavedModel export step. Update it to "gs://bucket-ml/data/treinamento/train/part*.csv" (remove the space).
  • Double-check that your --job-dir path is correct and that the script is using this path for both checkpoints and SavedModel exports. Sometimes scripts hardcode export paths instead of using the provided job-dir value.

• Inspect detailed training logs

  • Re-run your training command with the --verbosity=debug flag to get more granular log output. Look for any hidden errors (like data loading failures, missing dependencies, or training step limits that are too low) that might be causing the script to exit before exporting the model.
  • Search the logs for lines mentioning SavedModel export (e.g., "Exporting SavedModel to..."). If you don’t see these lines, your code isn’t reaching the export logic; if there’s an error message, address that specific issue first.

• Check checkpoint file integrity

  • Even though checkpoints were generated, make sure all required files are present: .index, .data-00000-of-00001, and .meta. Missing any of these means the checkpoint is corrupted, which would prevent the script from using it to export a SavedModel. If the checkpoints are incomplete, re-run the training to ensure it finishes without interruptions.

内容的提问来源于stack exchange，提问作者miguel brito