model_garden/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb

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# Copyright 2022 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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Open in Vertex AI Workbench

Vertex AI: Track parameters and metrics for locally trained models

Overview

This notebook demonstrates how to track metrics and parameters for ML training jobs and analyze this metadata using Vertex SDK for Python.

Dataset

In this notebook, we will train a simple distributed neural network (DNN) model to predict automobile's miles per gallon (MPG) based on automobile information in the auto-mpg dataset.

Objective

In this notebook, you will learn how to use Vertex SDK for Python to:

* Track parameters and metrics for a locally trained model.
* Extract and perform analysis for all parameters and metrics within an Experiment.

Costs

This tutorial uses billable components of Google Cloud:

• Vertex AI
• Cloud Storage

Learn about Vertex AI pricing and Cloud Storage pricing, and use the Pricing Calculator to generate a cost estimate based on your projected usage.

Set up your local development environment

If you are using Colab or Vertex AI Workbench notebooks, your environment already meets all the requirements to run this notebook. You can skip this step.

Otherwise, make sure your environment meets this notebook's requirements. You need the following:

• The Google Cloud SDK
• Git
• Python 3
• virtualenv
• Jupyter notebook running in a virtual environment with Python 3

The Google Cloud guide to Setting up a Python development environment and the Jupyter installation guide provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:

1. Install and initialize the Cloud SDK.

2. Install Python 3.

3. Install virtualenv and create a virtual environment that uses Python 3. Activate the virtual environment.

4. To install Jupyter, run pip install jupyter on the command-line in a terminal shell.

5. To launch Jupyter, run jupyter notebook on the command-line in a terminal shell.

6. Open this notebook in the Jupyter Notebook Dashboard.

Install additional packages

Run the following commands to install the Vertex SDK for Python.

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import sys

if "google.colab" in sys.modules:
    USER_FLAG = ""
else:
    USER_FLAG = "--user"

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! pip3 install -U tensorflow==2.8 $USER_FLAG
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG

Restart the kernel

After you install the additional packages, you need to restart the notebook kernel so it can find the packages.

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# Automatically restart kernel after installs
import os

if not os.getenv("IS_TESTING"):
    # Automatically restart kernel after installs
    import IPython

    app = IPython.Application.instance()
    app.kernel.do_shutdown(True)

Before you begin

Select a GPU runtime

Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select "Runtime --> Change runtime type > GPU"

Set up your Google Cloud project

The following steps are required, regardless of your notebook environment.

1. Select or create a Google Cloud project. When you first create an account, you get a $300 free credit towards your compute/storage costs.

2. Make sure that billing is enabled for your project.

3. Enable the Vertex AI API.

4. If you are running this notebook locally, you will need to install the Cloud SDK.

5. Enter your project ID in the cell below. Then run the cell to make sure the Cloud SDK uses the right project for all the commands in this notebook.

Note: Jupyter runs lines prefixed with ! as shell commands, and it interpolates Python variables prefixed with $ into these commands.

Set your project ID

If you don't know your project ID, you may be able to get your project ID using gcloud.

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import os

PROJECT_ID = ""

# Get your Google Cloud project ID from gcloud
if not os.getenv("IS_TESTING"):
    shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null
    PROJECT_ID = shell_output[0]
    print("Project ID: ", PROJECT_ID)

Otherwise, set your project ID here.

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if PROJECT_ID == "" or PROJECT_ID is None:
    PROJECT_ID = "[your-project-id]"  # @param {type:"string"}

Timestamp

If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append it onto the name of resources you create in this tutorial.

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from datetime import datetime

TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")

Authenticate your Google Cloud account

If you are using Vertex AI Workbench notebooks, your environment is already authenticated. Skip this step.

If you are using Colab, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.

Otherwise, follow these steps:

1. In the Cloud Console, go to the Create service account key page.

2. Click Create service account.

3. In the Service account name field, enter a name, and click Create.

4. In the Grant this service account access to project section, click the Role drop-down list. Type "Vertex AI" into the filter box, and select Vertex AI Administrator. Type "Storage Object Admin" into the filter box, and select Storage Object Admin.

5. Click Create. A JSON file that contains your key downloads to your local environment.

6. Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell.

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import os
import sys

# If you are running this notebook in Colab, run this cell and follow the
# instructions to authenticate your GCP account. This provides access to your
# Cloud Storage bucket and lets you submit training jobs and prediction
# requests.

# If on Google Cloud Notebooks, then don't execute this code
if not os.path.exists("/opt/deeplearning/metadata/env_version"):
    if "google.colab" in sys.modules:
        from google.colab import auth as google_auth

        google_auth.authenticate_user()

    # If you are running this notebook locally, replace the string below with the
    # path to your service account key and run this cell to authenticate your GCP
    # account.
    elif not os.getenv("IS_TESTING"):
        %env GOOGLE_APPLICATION_CREDENTIALS ''

Import libraries and define constants

Import required libraries.

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import matplotlib.pyplot as plt
import pandas as pd
from google.cloud import aiplatform
from tensorflow.python.keras import Sequential, layers
from tensorflow.python.keras.utils import data_utils

Define some constants

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EXPERIMENT_NAME = ""  # @param {type:"string"}
REGION = "[your-region]"  # @param {type:"string"}
if REGION == "[your-region]":
    REGION = "us-central1"

If EXEPERIMENT_NAME is not set, set a default one below:

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if EXPERIMENT_NAME == "" or EXPERIMENT_NAME is None:
    EXPERIMENT_NAME = "my-experiment-" + TIMESTAMP

Concepts

To better understanding how parameters and metrics are stored and organized, we'd like to introduce the following concepts:

Experiment

Experiments describe a context that groups your runs and the artifacts you create into a logical session. For example, in this notebook you create an Experiment and log data to that experiment.

Run

A run represents a single path/avenue that you executed while performing an experiment. A run includes artifacts that you used as inputs or outputs, and parameters that you used in this execution. An Experiment can contain multiple runs.

Getting started tracking parameters and metrics

You can use the Vertex SDK for Python to track metrics and parameters for models trained locally.

In the following example, you train a simple distributed neural network (DNN) model to predict automobile's miles per gallon (MPG) based on automobile information in the auto-mpg dataset.

Load and process the training dataset

Download and process the dataset.

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def read_data(uri):
    dataset_path = data_utils.get_file("auto-mpg.data", uri)
    column_names = [
        "MPG",
        "Cylinders",
        "Displacement",
        "Horsepower",
        "Weight",
        "Acceleration",
        "Model Year",
        "Origin",
    ]
    raw_dataset = pd.read_csv(
        dataset_path,
        names=column_names,
        na_values="?",
        comment="\t",
        sep=" ",
        skipinitialspace=True,
    )
    dataset = raw_dataset.dropna()
    dataset["Origin"] = dataset["Origin"].map(
        lambda x: {1: "USA", 2: "Europe", 3: "Japan"}.get(x)
    )
    dataset = pd.get_dummies(dataset, prefix="", prefix_sep="")
    return dataset


dataset = read_data(
    "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data"
)

Split dataset for training and testing.

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def train_test_split(dataset, split_frac=0.8, random_state=0):
    train_dataset = dataset.sample(frac=split_frac, random_state=random_state)
    test_dataset = dataset.drop(train_dataset.index)
    train_labels = train_dataset.pop("MPG")
    test_labels = test_dataset.pop("MPG")

    return train_dataset, test_dataset, train_labels, test_labels


train_dataset, test_dataset, train_labels, test_labels = train_test_split(dataset)

Normalize the features in the dataset for better model performance.

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def normalize_dataset(train_dataset, test_dataset):
    train_stats = train_dataset.describe()
    train_stats = train_stats.transpose()

    def norm(x):
        return (x - train_stats["mean"]) / train_stats["std"]

    normed_train_data = norm(train_dataset)
    normed_test_data = norm(test_dataset)

    return normed_train_data, normed_test_data


normed_train_data, normed_test_data = normalize_dataset(train_dataset, test_dataset)

Define ML model and training function

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def train(
    train_data,
    train_labels,
    num_units=64,
    activation="relu",
    dropout_rate=0.0,
    validation_split=0.2,
    epochs=1000,
):

    model = Sequential(
        [
            layers.Dense(
                num_units,
                activation=activation,
                input_shape=[len(train_dataset.keys())],
            ),
            layers.Dropout(rate=dropout_rate),
            layers.Dense(num_units, activation=activation),
            layers.Dense(1),
        ]
    )

    model.compile(loss="mse", optimizer="adam", metrics=["mae", "mse"])
    print(model.summary())

    history = model.fit(
        train_data, train_labels, epochs=epochs, validation_split=validation_split
    )

    return model, history

Initialize the Vertex AI SDK for Python and create an Experiment

Initialize the client for Vertex AI and create an experiment.

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aiplatform.init(project=PROJECT_ID, location=REGION, experiment=EXPERIMENT_NAME)

Start several model training runs

Training parameters and metrics are logged for each run.

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parameters = [
    {"num_units": 16, "epochs": 3, "dropout_rate": 0.1},
    {"num_units": 16, "epochs": 10, "dropout_rate": 0.1},
    {"num_units": 16, "epochs": 10, "dropout_rate": 0.2},
    {"num_units": 32, "epochs": 10, "dropout_rate": 0.1},
    {"num_units": 32, "epochs": 10, "dropout_rate": 0.2},
]

for i, params in enumerate(parameters):
    aiplatform.start_run(run=f"auto-mpg-local-run-{i}")
    aiplatform.log_params(params)
    model, history = train(
        normed_train_data,
        train_labels,
        num_units=params["num_units"],
        activation="relu",
        epochs=params["epochs"],
        dropout_rate=params["dropout_rate"],
    )
    aiplatform.log_metrics(
        {metric: values[-1] for metric, values in history.history.items()}
    )

    loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)
    aiplatform.log_metrics({"eval_loss": loss, "eval_mae": mae, "eval_mse": mse})

Extract parameters and metrics into a dataframe for analysis

We can also extract all parameters and metrics associated with any Experiment into a dataframe for further analysis.

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experiment_df = aiplatform.get_experiment_df()
experiment_df

Visualizing an experiment's parameters and metrics

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plt.rcParams["figure.figsize"] = [15, 5]

ax = pd.plotting.parallel_coordinates(
    experiment_df.reset_index(level=0),
    "run_name",
    cols=[
        "param.num_units",
        "param.dropout_rate",
        "param.epochs",
        "metric.loss",
        "metric.val_loss",
        "metric.eval_loss",
    ],
    color=["blue", "green", "pink", "red"],
)
ax.set_yscale("symlog")
ax.legend(bbox_to_anchor=(1.0, 0.5))

Visualizing experiments in Cloud Console

Run the following to get the URL of Vertex AI Experiments for your project.

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print("Vertex AI Experiments:")
print(
    f"https://console.cloud.google.com/ai/platform/experiments/experiments?folder=&organizationId=&project={PROJECT_ID}"
)

Cleaning up

To clean up all Google Cloud resources used in this project, you can delete the Google Cloud project you used for the tutorial.

Otherwise, you can delete the individual resources you created in this tutorial:

• Experiment (Can be deleted manually in the GCP Console UI)