The purpose of this directory is to provide a sample for how you can package a TensorFlow training model to submit it to AI Platform. The sample makes it easier to organise your code, and to adapt it to your dataset. In more details, the template covers the following functionality:
- Metadata to define your dataset, along with the problem type (Classification).
- Standard implementation of input, parsing, and serving functions.
- Automatic feature columns creation based on the metadata (and normalization stats).
- Wide & Deep model construction using canned estimators.
- Train, evaluate, and export the model.
- Parameterization of the experiment.
Although this sample provides standard implementation to different functionality, you can customise these parts with your own implementation.
- Setup your project by following the instructions in the setup folder. Run variables.sh in setup folder.
source ./variables.sh- Download the datasets using download-taxi.sh in datasets folder.
source ./download-taxi.sh /your_local_datasets_dir/-
Create a Python 3 virtual environment and activate it.
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Change the directory to this sample and run:
python setup.py installOptional: You can also run:
pip install -r requirements.txt
Note: These instructions are used for local testing. When you submit a training job, no code will be executed on your local machine.
trainerdirectory: with all the python modules to adapt to your datascriptsdirectory: command-line scripts to train the model locally or on AI Platformrequirements.txt: containing all the required python packages for this sampleconfig.yaml: for hyper-parameter tuning and specifying the AI Platform scale-tier
| File Name | Purpose | Do You Need to Change? |
|---|---|---|
| metadata.py | Defines: 1) task type, 2) input data header, 3) numeric and categorical feature names, and 4) target feature name (and labels, for a classification task) | Yes, as you will need to specify the metadata of your dataset. This might be the only module to change! |
| inputs.py | Includes: 1) data input functions to read data from csv and tfrecords files, 2) parsing functions to convert csv and tf.example to tensors, 3) function to implement your custom features processing and creation functionality, and 4) prediction functions (for serving the model) that accepts CSV, JSON, and tf.example instances. | Maybe, if you want to implement any custom pre-processing and feature creation during reading data. |
| featurizer.py | Creates: 1) TensorFlow feature_column(s) based on the dataset metadata (and other extended feature columns, e.g. bucketisation, crossing, embedding, etc.), and 2) deep and wide feature column lists. | Maybe, if you want to change your feature_column(s) and/or change how deep and wide columns are defined. |
| model.py | Includes: 1) function to create DNNLinearCombinedRegressor, and 2) DNNLinearCombinedClassifier. | No, unless you want to change something in the estimator, e.g., activation functions, optimizers, etc.. |
| experiment.py | Runs the model training and evaluation experiment, and exports the final model. | No, unless you want to add/remove parameters, or change parameter default values. |
| task.py | Includes: 1) Initialise and parse task arguments (hyper parameters), and 2) Entry point to the trainer. | No, unless you want to add/remove parameters, or change parameter default values. |
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train-local.sh This script trains a model locally. It generates a SavedModel in local folder and verifies predictions locally.
-
train-cloud.sh This script submits a training job to AI Platform.
Once the prerequisites are satisfied, you may:
- For local training run:
source ./scripts/train-local.sh
- For cloud training run:
source ./scripts/train-cloud.sh
TensorFlow v1.14.0+