---
title: Quickstart
---

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This document gives you a quick view on the basic usage of Submarine platform. You can finish each step of ML model lifecycle on the platform without messing up with the troublesome environment problems.

## Installation

### Prepare a Kubernetes cluster

1. Prerequisite

- Check [dependency page](../devDocs/Dependencies.md) for the compatible version
- [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/)
- [helm](https://helm.sh/docs/intro/install/) (Helm v3 is minimum requirement.)
- [minikube](https://minikube.sigs.k8s.io/docs/start/).

2. Start minikube cluster
```
minikube start --vm-driver=docker --cpus 8 --memory 4096 --kubernetes-version v1.21.2
```

### Launch submarine in the cluster

1. Clone the project
```
git clone https://github.com/apache/submarine.git
```

2. Install the submarine operator and dependencies by helm chart
```
cd submarine
helm install submarine ./helm-charts/submarine
```

3. Create a Submarine custom resource and the operator will create the submarine server, database, etc. for us.
```
kubectl apply -f submarine-cloud-v2/artifacts/examples/example-submarine.yaml
```

### Ensure submarine is ready

1. Use kubectl to query the status of pods
```
kubectl get pods
```

2. Make sure each pod is `Running`
```
NAME                                              READY   STATUS    RESTARTS   AGE
notebook-controller-deployment-5d4f5f874c-mnbc8   1/1     Running   0          61m
pytorch-operator-844c866d54-xm8nl                 1/1     Running   2          61m
submarine-database-85bd68dbc5-qggtm               1/1     Running   0          11m
submarine-minio-76465444f6-hdgdp                  1/1     Running   0          11m
submarine-mlflow-75f86d8f4d-rj2z7                 1/1     Running   0          11m
submarine-operator-5dd79cdf86-gpm2p               1/1     Running   0          61m
submarine-server-68985b767-vjdvx                  1/1     Running   0          11m
submarine-tensorboard-5df8499fd4-vnklf            1/1     Running   0          11m
submarine-traefik-7cbcfd4bd9-wbf8b                1/1     Running   0          61m
tf-job-operator-6bb69fd44-zmlmr                   1/1     Running   1          61m
```

### Connect to workbench

1. Exposing service
  ```
  # Method 1 -- use minikube ip
  minikube ip  # you'll get the IP address of minikube, ex: 192.168.49.2

  # Method 2 -- use port-forwarding
  kubectl port-forward --address 0.0.0.0 service/submarine-traefik 32080:80
  ```

2. View workbench
  If you use method 1, go to `http://{minikube ip}:32080`. For example, `http://192.168.49.2:32080`. If you use method 2, go to `http://0.0.0.0:32080`.
  ![](/img/quickstart-worbench.png)

## Example: Submit a mnist distributed example

We put the code of this example [here](https://github.com/apache/submarine/tree/master/dev-support/examples/quickstart). `train.py` is our training script, and `build.sh` is the script to build a docker image.

### 1. Write a python script for distributed training

Take a simple mnist tensorflow script as an example. We choose `MultiWorkerMirroredStrategy` as our distributed strategy.

```python
"""
./dev-support/examples/quickstart/train.py
Reference: https://github.com/kubeflow/tf-operator/blob/master/examples/v1/distribution_strategy/keras-API/multi_worker_strategy-with-keras.py
"""

import tensorflow_datasets as tfds
import tensorflow as tf
from tensorflow.keras import layers, models
import submarine

def make_datasets_unbatched():
  BUFFER_SIZE = 10000

  # Scaling MNIST data from (0, 255] to (0., 1.]
  def scale(image, label):
    image = tf.cast(image, tf.float32)
    image /= 255
    return image, label

  datasets, _ = tfds.load(name='mnist', with_info=True, as_supervised=True)

  return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE)


def build_and_compile_cnn_model():
  model = models.Sequential()
  model.add(
      layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
  model.add(layers.MaxPooling2D((2, 2)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.MaxPooling2D((2, 2)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.Flatten())
  model.add(layers.Dense(64, activation='relu'))
  model.add(layers.Dense(10, activation='softmax'))

  model.summary()

  model.compile(optimizer='adam',
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])

  return model

def main():
  strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
      communication=tf.distribute.experimental.CollectiveCommunication.AUTO)

  BATCH_SIZE_PER_REPLICA = 4
  BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync

  with strategy.scope():
    ds_train = make_datasets_unbatched().batch(BATCH_SIZE).repeat()
    options = tf.data.Options()
    options.experimental_distribute.auto_shard_policy = \
        tf.data.experimental.AutoShardPolicy.DATA
    ds_train = ds_train.with_options(options)
    # Model building/compiling need to be within `strategy.scope()`.
    multi_worker_model = build_and_compile_cnn_model()

  class MyCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
      # monitor the loss and accuracy
      print(logs)
      submarine.log_metrics({"loss": logs["loss"], "accuracy": logs["accuracy"]}, epoch)

  multi_worker_model.fit(ds_train, epochs=10, steps_per_epoch=70, callbacks=[MyCallback()])


if __name__ == '__main__':
  main()
```

### 2. Prepare an environment compatible with the training
Build a docker image equipped with the requirement of the environment.

```bash
eval $(minikube docker-env)
./dev-support/examples/quickstart/build.sh
```

### 3. Submit the experiment

1. Open submarine workbench and click `+ New Experiment`
2. Choose `Define your experiment`
3. Fill the form accordingly. Here we set 3 workers.

    1. Step 1
    ![](/img/quickstart-submit-1-0-7-0.png)
    2. Step 2
    ![](/img/quickstart-submit-2-0-7-0.png)
    3. Step 3
    ![](/img/quickstart-submit-3-0-7-0.png)
    4. The experiment is successfully submitted
    ![](/img/quickstart-submit-4-0-7-0.png)

### 4. Monitor the process

1. In our code, we use `submarine` from `submarine-sdk` to record the metrics. To see the result, click corresponding experiment with name `mnist-example` in the workbench.
2. To see the metrics of each worker, you can select a worker from the left top list.

![](/img/quickstart-ui-0-7-0.png)

### 5. Serve the model (In development)