Set airflow directory
export AIRFLOW_HOME="/home/avnish/census_consumer_project/census_consumer_complaint/airflow"
To install airflow
pip install apache-airflow
To configure databse
airflow db init
To create login user for airflow
airflow users create -e [email protected] -f Avnish -l Yadav -p admin -r Admin -u admin
To start scheduler
airflow scheduler
To launch airflow server
airflow webserver -p <port_number>
pip install pandas-tfrecords
pip install \
--upgrade --ignore-installed \
python-snappy==0.5.1 \
--global-option=build_ext \
--global-option="-I/usr/local/include" \
--global-option="-L/usr/local/lib"
pip install twine python setup.py sdist bdist_wheel twine upload --repository-url https://test.pypi.org/legacy/ dist/* twine upload dist/*
pip install tensorflow-serving-api
to inspect model
saved_model_cli show --dir <dir_path>
Above command will return tag set
saved_model_cli show --dir <dir_path> --tag_set <tag_name>
Above command will show available model signatures
Next: with tag_set and signature_def info, we can inspect model input and output
saved_model_cli show --dir <dir_path> --tag_set <tag_name> --signature_def <SignatureDef Key>
To inspect all signature without tag_set and signature_def saved_model_cli show --dir <dir_path> --all
Testing the model
Test model prediction using saved_model_cli with sample input data
--input_examples: input data formatted as a tf.Example data structure
--outdir: by default output will be written in terminal
--overwrite: to write into a file
tf_debug: run in debug mode
To expose your model as an API using docker image tensorflow/serving
docker pull tensorflow/serving
sudo docker run -p 8500:8500 \
-p 8501:8501\
--volumn <model_dir>:<target_dir>\
-e MODEL_NAME=<model_name>\
-e model_base_path=<target_dir>\
-t tensorflow/serving:latest
sudo docker run -p 8500:8500 -p 8501:8501 \
-v /home/avnish/census_consumer_project/census_consumer_complaint/census_consumer_complaint_data/saved_models:/avnish/my_model \
-e MODEL_NAME=my_model \
-e MODEL_BASE_PATH=/avnish \
-t tensorflow/serving:latest
To inspect docker container directory
docker exec -it <conatiner_name> bash