add description for config.yaml
@@ -26,9 +26,10 @@ Template
|__ model.py # pre-processing and machine learning model pipeline definition
|__ utils.py # utility functions including e.g. loading data from bigquery and cloud storage
|__ task.py # training job entry point, handling the parameters passed from command line
- |__ hptuning_config.yaml # config file for running hyperparameter tunning
- |__ setup.py # specify necessary dependency of the training job
- |__ requirements.txt # specify necessary dependency of the training job
+ |__ config.yaml # for running normal training job on CMLE
+ |__ hptuning_config.yaml # for running hyperparameter tunning job on CMLE
+ |__ setup.py # specify necessary dependency for running job on CMLE
+ |__ requirements.txt # specify necessary dependency, helper for setup environemnt for local development
|__ Dockerfile
```
@@ -87,7 +88,23 @@ items need to be modified, in order to adapt to the target dataset.
- **CATEGORICAL_FEATURES**: columns those will be treated as categorical features
- **LABEL**: column that will be treated as label
-## Step 2. Submit ML training job
+## Step 3. Modify yaml file
+There are two yaml files, where
+- config.yaml: for running normal training job on CMLE
+- hptuning_config.yaml: for running hyperparameter tunning job on CMLE
+
+There is a common portion in both of the yaml file defining critical configurations for training ML model on CMLE. The
+code snippets is an example. In particular, the runtimeVersion and scikit-learn version correspondence
+can be check [here](https://cloud.google.com/ml-engine/docs/tensorflow/runtime-version-list).
+```yaml
+trainingInput:
+ scaleTier: STANDARD_1 # Machine type
+ region: "us-central1" # GCP region
+ runtimeVersion: "1.13" # Scikit-learn version
+ pythonVersion: "2.7" # Note: Python 3 is also supported
+```
+
+## Step 3. Submit ML training job
```shell
bash scripts/train.sh [INPUT_PATH] [RUN_ENV] [BUCKET_NAME]
```
