Awesome
Tensorflow-Project-Demo
For this project, I used the classic datasets iris to do the project demo. My personal goal for this project is to not only explore the data and build models, but to also build an API server with retrainable model. To achieve this goal, I used fastapi, tensorflow and ray tune.
The project contain three services db
, serving
and training
. When you train the model. the training
module will query the db
to get the iris data and start training the model. After the training process finish, it will give the model to serving
. So that any client service can connect to serving to get the prediction with given features.
Also, I decided to develop this project to be the same as how data-related projects are developed in real-world scenarios, wherein the end goal of development is a project that is feasible for production. Therefore, I have put efforts on creating:
- Exploratory Data Analysis (EDA) in the
notebooks/
folder; - An API Server inside the
api/
folder; - Files for deployment such as Dockerfile and docker-compose.yml;
- Documentations in the
docs/
folder; and - Some necessary scripts in
scripts/
folder; and - Configurations, included hyperparameters space in
config.py
.
In the tensorflow part, I used very powerful tools:
- tfrecord
- tensorboard
- tensorflow serving
for building the project.
Services
I have three services: db
, serving
and training
.
Database
I use the PostgreSQL as this demo's database. After you run all the service Training
will initial the database, then you can check the data in the database.
docker-compose exec db psql -d database -U user
select * from iris;
Training
The training service is a machine learning API that is open on port 8000. I used fastapi for the API server, so you can check it on http://localhost:8000/docs after you run the
training
service. The hyperparameters search space is in the src/config.py
so you can change them if you want.
The Retraining process start from query the db
and then it will write the data on local as tfrecord format. This move is able to reduce lots of memory usage during training. And tfrecord is a binary file therefore in normal case, it can also reduce the data storage problem.
Serving
I use Tensorflow Serving
to build the prediction server. It is a very powerful tools amount TensorFlow Extended (TFX) . See more detail on tensorflow document.
How to run this demo
1. Install requirement
- docker
- docker-compose
2. Build the image
make build
3. Start the services
docker-compose up -d
4. Try to update the model
curl -X PUT "http://localhost:8000/model" -H "accept: application/json"
5. Check Training Result
5.1 Activate tensorboard
docker-compose exec training make activate-tensorboard
5.2 Check the Result
Turn on your browser and go to http://localhost:6006/
6. Let's make some prediction
Feel free to change the number from 0 to any number you want
curl -X POST "http://localhost:8501/v1/models/tensorflow-project-demo:predict"
-H "accept: applicationjson"
-d
'{
"inputs":{
"sepal_length":[[0]],
"sepal_width":[[0]],
"petal_width":[[0]],
"petal_length":[[0]]
}
}'
The response will be like:
{
"outputs": [
[
0.607119262,
0.392192692,
0.000688050292
]
]
}
The output means the estimation in probability of categories Setosa, Versicolor and Virginica respectively.
Note
If you want to change the model structure, you can change the model.py
. Also, If you want to change the hyperparameters search space, you can go to config.py
.
About the hyperparameter search algorithm, I am using the random search for this demo but if you want to try other searching algorithm, you can change train.py
.
I have another project use lightgbm as the back bone model. Take a look about the project lightgbm-project-demo
Here are some documentations
How to set up the working environment for this project