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.
- How to use TF Hub on a distributed setting? · Issue #48 · tensorflow/hub
- Tensorflow-Hub doesn't work on the runtime version 1.7. [78898344] - Visible to Public - Issue Tracker
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
- Anaconda
- Google Cloud SDK
# Create conda environment
make create-conda
# Remove conda environment
make remove-conda
- Download flower data.
The script downloads the flower data in
data/flower_photos
bash ./dev/prepare-dataset.sh
- Create TFRecord data.
python crete_tfrecord.py \
--input ./data/flower_photos \
--train_output ./train.tfrecord \
--eval_output ./eval.tfrecord
- 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"
- 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"