How to deploy

Data Wrangling

gradient experiments run singlenode \
--name airbnb_data \
--projectId <ProjectID in gradient> \
--experimentEnv "{\"API_KEY\":\"<Google Maps Places API key>\",\"RADIUS\":300}" \
--container tensorflow/tensorflow:latest-gpu-py3 \
--machineType P4000 \
--command "pip install luigi jpholiday requests "dask[complete]" && python data/wrangling.py -o /storage/airbnb/dataset/marged_data.pkl" \
--modelType Tensorflow \
--workspaceUrl https://github.com/furuta/springboard_capstone_gradient

Model Learning

gradient experiments run singlenode \
--name airbnb_model \
--projectId <ProjectID in gradient> \
--experimentEnv "{\"EPOCHS\":100,\"BATCH_SIZE\":500}" \
--container tensorflow/tensorflow:latest-gpu-py3 \
--machineType P4000 \
--command "pip install sklearn && pip install "dask[complete]" && python train/train_model.py -i /storage/airbnb/dataset/marged_data.pkl --modelPath /storage/airbnb/model --version 1" \
--modelType Tensorflow \
--modelPath "/storage/airbnb/model" \
--workspaceUrl https://github.com/furuta/springboard_capstone_gradient

Deploy API

gradient jobs create \
--name deploy api \
--projectId <ProjectID in gradient> \
--jobEnv "{\"API_KEY\":\"<Google Maps Places API key>\",\"RADIUS\":300}" \
--container tensorflow/tensorflow:latest-py3 \
--machineType C3 \
--ports 8080:8080 \
--command "pip install flask jpholiday "dask[complete]" requests marshmallow && python deploy/infer.py -m /storage/airbnb/model/1 -d /storage/airbnb/dataset" \
--workspaceUrl https://github.com/furuta/springboard_capstone_gradient

*You can change the directories for data and model

How to use API

URL

http://{URL that the job in gradient made}/price

Query Paramater

key	type	required	detail
start	Date	YES	Set in YYYY-MM-DD format. This is the start date of the accommodation date for which you want to get a price. It must be in or after 2009.
end	Date	YES	Set in YYYY-MM-DD format. This is the last day of the accommodation date for which you want to get a price. It must be in or before 2029.
latitude	Float	YES	Set the latitude of the accommodation. It must be between 35.5014 and 35.8981 because learned data was Tokyo-to.
longitude	Float	YES	Set the longitude of the accommodation. It must be between 138.9257 and 139.9156 because learned data was Tokyo-to.
accommodates	Int	YES	Set the number of guests allowed. It must be a positive value.
bedrooms	Int	YES	Set the number of bedrooms. It must be a positive value.
beds	Int	YES	Set the number of beds. It must be a positive value.
room_type	Int	YES	Set the room type numerically. 1: Private room 2: Entire home/apt 3:Shared room
property_type	Int	YES	Set the accommodation type numerically. 1: Apartment 2: House 3: Serviced apartment 4: Condominium 5: Guest suite 6: Hut 7: Tiny house 8: Townhouse 9: Villa 10: Aparthotel 11: Cabin 12: Bed and breakfast 13: Loft 14: Hostel 15: Guesthouse 16: Boutique hotel 17: Nature lodge 18: Ryokan (Japan) 19: Tent 20: Hotel 21: Bungalow 22: Other 23: Camper/RV 24: Boat 25: Dome house 26: Dorm 27: Resort 28: Barn
cancellation_policy	Int	YES	Set the cancellation policy numerically. 1: strict_14_with_grace_period 2: moderate 3: flexible 4: super_strict_30 5: super_strict_60 6: strict

For Example

http://{URL that the job in gradient made}/price?start=2019-12-20&end=2019-12-30&latitude=35.67152&longitude=139.71203&accommodates=2&bedrooms=1&beds=2&room_type=1&property_type=2&cancellation_policy=1