Skip to content

Latest commit

 

History

History
82 lines (66 loc) · 2.36 KB

README.md

File metadata and controls

82 lines (66 loc) · 2.36 KB
Raw

Retrain flower data with tensorflow-hub

This is an example code to reproduce an issue of tensorflow-hub with Google ML Engine. The reason why I made the repository is to reproduce the issue that tensorflow-hub doesn't work on Google ML Engine at the time I am making the repository.

OP_REQUIRES failed at save_restore_tensor.cc:170 : Invalid argument: Unsuccessful TensorSliceReader constructor: Failed to get matching files on /tmp/tfhub_modules/11d9faf945d073033780fd924b2b09ff42155763/variables/variables: Not found: /tmp/tfhub_modules/11d9faf945d073033780fd924b2b09ff42155763/variables; No such file or directory

Requirements

  • Anaconda
  • Google Cloud SDK

Prepare for the python environment

# Create conda environment
make create-conda

# Remove conda environment
make remove-conda

Hot wo run

  1. Download flower data. The script downloads the flower data in data/flower_photos
bash ./dev/prepare-dataset.sh
  1. Create TFRecord data.
python crete_tfrecord.py \
  --input ./data/flower_photos \
  --train_output ./train.tfrecord \
  --eval_output ./eval.tfrecord
  1. Upload TFRecord data to Google Cloud Storage.
PROJECT_ID=...
GCS_BUCKET=...

# Create a bucket if necessary.
gsutil mb -p "$PROJECT_ID" "gs://${GCS_BUCKET}"

# Copy TFRecord files.
gsutil cp -p train.tfrecord "gs://${GCS_BUCKET}/train.tfrecord"
gsutil cp -p eval.tfrecord "gs://${GCS_BUCKET}/eval.tfrecord"

# Check the uploaded files.
gsutil ls "gs://${GCS_BUCKET}/train.tfrecord"
gsutil ls "gs://${GCS_BUCKET}/eval.tfrecord"
  1. Train a model.
# Run on your local machine to test.
GCS_BUCKET=...
TRAIN_DATA="gs://${GCS_BUCKET}/train.tfrecord"
EVAL_DATA="gs://${GCS_BUCKET}/eval.tfrecord"
bash run_local.sh "${TRAIN_DATA}" "${EVAL_DATA}"

# Run on ML Engine
PROJECT_ID=...
GCS_BUCKET=...
MODEL_DIR="gs://${GCS_BUCKET}/model/"
TRAIN_DATA="gs://${GCS_BUCKET}/train.tfrecord"
EVAL_DATA="gs://${GCS_BUCKET}/eval.tfrecord"
bash run_cloud.sh \
  "$PROJECT_ID" \
  "$GCS_BUCKET" \
  "$MODEL_DIR" \
  "$TRAIN_DATA" \
  "$EVAL_DATA"

# Monitor the training
tensorboard --logdir="$MODEL_DIR"