Create CLI interface for tf-cloud

src/python/tensorflow_cloud/experimental/cli/README.md

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+# Tensorflow cloud CLI
+
+Is a feature of tfcloud that let you run tfcloud from your terminal using `tfc` command.
+
+
+### How to use it
+
+1. [Follow the configuration and installation instructions.](https://github.com/tensorflow/cloud#tensorflow-cloud-run-api-for-gcp-trainingtuning)
+2. Create a file with all that you need to train your model or a jupyter notebook. for example `train.py`:
+
+``` python
+import tensorflow_datasets as tfds
+import tensorflow as tf
+
+datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)
+mnist_train, mnist_test = datasets['train'], datasets['test']
+
+num_train_examples = info.splits['train'].num_examples
+num_test_examples = info.splits['test'].num_examples
+
+BUFFER_SIZE = 10000
+BATCH_SIZE = 64
+
+def scale(image, label):
+    image = tf.cast(image, tf.float32)
+    image /= 255
+    return image, label
+
+train_dataset = mnist_train.map(scale).cache()
+train_dataset = train_dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(
+        28, 28, 1)),
+    tf.keras.layers.MaxPooling2D(),
+    tf.keras.layers.Flatten(),
+    tf.keras.layers.Dense(64, activation='relu'),
+    tf.keras.layers.Dense(10, activation='softmax')
+])
+
+model.compile(loss='sparse_categorical_crossentropy',
+              optimizer=tf.keras.optimizers.Adam(),
+              metrics=['accuracy'])
+model.fit(train_dataset, epochs=12)
+```
+
+3. Train your model on the cloud with:
+
+``` bash
+tfc run train.py
+```
+
+### Contribution guidelines
+
+We use typer to build the CLI interface for tfcloud, if you want to contributed to this feature is important to follow typer ideology of write type annotations in the code. [Typer documentation.](https://typer.tiangolo.com/)

src/python/tensorflow_cloud/experimental/cli/cli.py

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+from typing import Optional
+from typing import Any
+from typing import Dict
+
+try:
+    import typer
+except (ImportError, ModuleNotFoundError) as inst:
+    print(type(inst))
+    print(inst)
+    print('You can try running "pip install colorama==0.4.4 typer[all]==0.3.2" on you terminal')
+
+from src.python.tensorflow_cloud.core.run import run
+from src.python.tensorflow_cloud.core.run import remote
+from src.python.tensorflow_cloud.core import docker_config as docker_config_module
+from src.python.tensorflow_cloud.core.machine_config import COMMON_MACHINE_CONFIGS
+
+app = typer.Typer()
+
+@app.command("remote")
+def remote_command():
+    """
+    To know is you code is running remote with TF Cloud.
+    """
+    if remote():
+        typer.echo("Running remotly")
+        typer.Exit()
+
+    return typer.echo("Running Localy")
+
+@app.command("run", help="Run your code in a remote cloud environment with TF Cloud.")
+def run_command(
+    entry_point: Optional[str] = typer.Argument(..., help="File path to the python file or iPython notebook that contains the TensorFlow code"),
+    requirements_txt: Optional[str] = typer.Option(None, help="File path to requirements.txt file containing additional pip dependencies if any"),
+    image_uri: Optional[str] = typer.Option(None, help="Docker image URI for the Docker image being built"),
+    parent_image: Optional[str] = typer.Option(None, help="Parent Docker image to use. Example value - 'gcr.io/my_gcp_project/deep_learning:v2' If a parent Docker image is not provided here, we will use a [TensorFlow Docker image](https://www.tensorflow.org/install/docker) as the parent image."),
+    cache_from: Optional[str] = typer.Option(None, help="Docker image URI to be used as a cache when building the new Docker image. This is especially useful if you are iteratively improving your model architecture/training code. If this parameter is not provided, then we will use `image` URI as cache."),
+    image_build_bucket: Optional[str] = typer.Option(None, help="GCS bucket name to be used for building a Docker image via [Google Cloud Build](https://cloud.google.com/cloud-build/). If it is not specified, then your local Docker daemon will be used for Docker build."),
+    distribution_strategy: str = typer.Option("auto", help="Tensorflow distribution strategy based on the machine config"),
+    chief_config: str = typer.Option("auto", help="`MachineConfig` that represents the configuration for the chief worker in a distribution cluster. Choose between (CPU, K80_1X, K80_4X, K80_8X, P100_1X, P100_4X, P4_1X, P4_4X, V100_1X, V100_4X, T4_1X, T4_4X, TPU)"),
+    worker_config: str = typer.Option("auto", help="`MachineConfig` that represents the configuration for the general workers in a distribution cluster. Choose between (CPU, K80_1X, K80_4X, K80_8X, P100_1X, P100_4X, P4_1X, P4_4X, V100_1X, V100_4X, T4_1X, T4_4X, TPU)"),
+    worker_count: int = typer.Option(0, help="Represents the number of general workers in a distribution cluster."),
+    entry_point_args: str = typer.Option(None, help="Command line arguments to pass to the `entry_point` program. Not implemented yet."), # review
+    stream_logs: bool = typer.Option(False, help="Boolean flag which when enabled streams logs back from the cloud job."),
+    job_labels: str = typer.Option(None, help="Labels to organize jobs. You can specify up to 64 key-value pairs in lowercase letters and numbers, where the first character must be lowercase letter. For more details see https://cloud.google.com/ai-platform/training/docs/resource-labels. Not implemented yet.") # review
+):
+    entry_point_args = None
+    job_labels = None
+
+    docker_config = docker_config_module.DockerConfig(image=image_uri, 
+                                                    parent_image=parent_image,
+                                                    cache_from=cache_from,
+                                                    image_build_bucket=image_build_bucket)
+
+    if chief_config != "auto":
+        try:
+            chief_config = COMMON_MACHINE_CONFIGS[chief_config]
+        except KeyError:
+            typer.BadParameter("You need to choose a between (CPU, K80_1X, K80_4X, K80_8X, P100_1X, P100_4X, P4_1X, P4_4X, V100_1X, V100_4X, T4_1X, T4_4X, TPU) options")
+
+    if worker_config != "auto":
+        try:
+            worker_config = COMMON_MACHINE_CONFIGS[worker_config]
+        except KeyError:
+            typer.BadParameter("You need to choose a between (CPU, K80_1X, K80_4X, K80_8X, P100_1X, P100_4X, P4_1X, P4_4X, V100_1X, V100_4X, T4_1X, T4_4X, TPU) options")
+
+    info = run(
+        entry_point=entry_point,
+        requirements_txt=requirements_txt,
+        docker_config=docker_config,
+        distribution_strategy=distribution_strategy,
+        chief_config=chief_config,
+        worker_config=worker_config,
+        worker_count=worker_count,
+        entry_point_args=entry_point_args,
+        stream_logs=stream_logs,
+        job_labels=job_labels
+    )
+
+    typer.echo(f"Job id: {info['job_id']}")
+    typer.echo(f"Docker image URI: {info['docker_image']}")
+
+if __name__ == '__main__':
+    app()