Auction Price Regression Tutorial

Estimated time: 30 minutes

Find code for this tutorial here.


This tutorial demonstrates how to make use of the features of Foundations Atlas. Note that any machine learning job can be run in Atlas without modification. However, with minimal changes to the code we can take advantage of Atlas features that will enable us to:

  • view artifacts such as plots and tensorboard logs, alongside model performance metrics
  • launch many training jobs at once
  • organize model experiments more systematically

This tutorial assumes that you have already installed Foundations Atlas. If you have not, then you can download Foundations Atlas from this link.

The Data

In this tutorial we make use of this data from a Kaggle competition. However don't worry about manually downloading the data as it will be downloaded automatically by running the provided scripts. This dataset contains the sale price of heavy machinery (such as bulldozers) as well as it's usage, equipment type, and configuration. In this tutorial we'll train regression models to predict the sale price from the other provided information. Note that the target (the sale price) has been mapped to a log scale.

Start Atlas

Activate the conda environment in which Foundations Atlas is installed. Then run atlas-server start. Validate that the GUI has been started by accessing it at http://localhost:5555/projects

Clone the Tutorial

Clone this repository and make it your current directory by running:

git clone auction_price_regression_tutorial
cd auction_price_regression_tutorial

Enabling Atlas Features

You are provided with the following python scripts:

  • A driver script which downloads the dataset, prepares it for model training and evaluation, trains a
    fully connected network with entity embeddings, then evaluates the model on the test set

  • Code to implement the neural network

Note that this code runs without any modification.

To enable Atlas features, we only to need to make a few changes. Firstly add the following line to the top of and

import foundations

Logging Metrics and Parameters

The last line of prints the test mean squared error. We'll replace this print statement with a call to the function foundations.log_metric().This function takes two arguments, a key and a value. Once a job successfully completes, logged metrics for each job will be visible from the Foundations GUI. Copy the following line and replace the print statement with it.

Line 46 in

foundations.log_metric('test mean squared error', float(mse))

Saving Artifacts

Currently, we create a matplotlib graph of validation mean squared error at the end of
With Atlas, we can save any artifact to the GUI with just one line. Add the following lines after "plt.savefig()" to send the locally saved plot to the Atlas GUI.

Line 38 in

foundations.save_artifact('plots/validation_mse.png', "validation_mse")

TensorBoard Integration

TensorBoard is a super powerful data visualization tool that makes visualizing your training extremely easy. Foundations Atlas has full TensorBoard integration. To access TensorBoard directly from the Atlas GUI, add the following line of code to start of

Line 8 in


The function set_tensorboard_logdir() take one argument, the directory that your TensorBoard files will be saved to. TensorBoard files are generated at each epoch through a callback, you can find the code in train() function


Lastly, create a file in the project directory named "job.config.yaml", and copy the text from below into the file.

project_name: 'bulldozer-demo'
log_level: INFO

Running a Job

Activate the environment in which you have foundations installed, then from inside the project directory (bulldozer-demo) run the following command:

foundations submit scheduler .

This will schedule a job to be run. Now open the Atlas GUI in your browser: http://localhost:5555/projects. Click into the project 'bulldozer-demo', then click on the "Job Details" tab. Here, you'll see the running job. Once it completes, it will have a green status and you will see your logged metrics.

To view your saved artifacts, you can click on the expansion icon to the right of the running job, then click on the "Artifacts" tab, and select the artifact you want to view from the menu below.

To view your model training on TensorBoard, simply select the running job, and click the "Send to TensorBoard" button on the GUI.

Atlas makes running and tracking the results of a set of hyperparameters easy. Create a new file called '' and paste in the following code:

import os
os.environ['FOUNDATIONS_COMMAND_LINE'] = 'True'
import foundations
import numpy as np

def generate_params():
  hyperparameters = {'n_epochs': int(np.random.choice([2,4])),
                     'batch_size': int(np.random.choice([64,128])),
                     'validation_percentage': 0.1,
                     'dense_blocks': [{'size': int(np.random.choice([64,128,512])), 'dropout_rate': np.random.uniform(0,0.5)}],
                     'embedding_factor': np.random.uniform(0.2,0.6),

  return hyperparameters

# A loop that calls the submit method from the Foundations SDK which takes the hyperparameters and the entrypoint script for our code (

num_jobs = 5
for _ in range(num_jobs):
  hyperparameters = generate_params()
  foundations.submit(scheduler_config='scheduler', job_dir='.', command='', params=hyperparameters, stream_job_logs=True)

This script samples hyperparameters uniformly from pre-defined ranges, then submits jobs using those hyperparameters. For a script that exerts more control over the hyperparameter sampling, check the end of the tutorial. The job execution code is still coming from; i.e. each experiment is submitted to and ran with the driver.

In order to get this to work, a small modification needs to be made to In the code block where the hyperparameters are defined (indicated by the comment 'define hyperparameters'), we'll load the sampled hyperparameters instead of defining a fixed set of hyperparameters explictely.

Replace that block (line 19 - 28) with the following:

# define hyperparameters
hyperparameters = foundations.load_parameters()

Now, to run the hyperparameter search, from the project directory (bulldozer-demo) simply run


What if you're interested in slightly more sophisticated techniques than just a random search over the entire parameters space?

A nice, yet simple technique to optimize your hyperparameters, is to start by randomly exploring a portion of the total Hparams space, greedily select the best scoring job. From this best job A, you can make random small tweaks or "steps" of the hyperparameters, and look for a new best. We keep repeating this process to slightly tweak the hyperparameters over and over until we reach the optimal setup.

The following graph illustrates this optimization behavior: greedy-random-walk-1

It is important to note that:

  • All jobs within a same batch of jobs are executed at the same time (provided enough resources/GPUs are available)
  • Every new batch of jobs is compared against the best job A from which the sampling is done. This assures that we never move back to a worse Hparams setup
  • If all jobs in a batch of jobs do not beat the previous best, we resample a new batch until we find a new best or we stop the search
  • Intuitively, the bigger the size of the batch of jobs, the more exploration you can do, although even a batch of size 1 is enough to hit a descent set of Hparams.
  • Also, the bigger the size of the batch of jobs, the faster it is to find the optimal hyperparameters

A simple code example to do this is as follows, which can be created in a file

import foundations
import numpy as np


def totally_random_parameters():
    hyperparameters = {'n_epochs': int(np.random.choice([2, 4])),
                       'batch_size': int(np.random.choice([64, 128])),
                       'validation_percentage': 0.1,
                       'dense_blocks': [{'size': int(np.random.choice([64, 128, 512])), 'dropout_rate': np.random.uniform(0, 0.5)}],
                       'embedding_factor': np.random.uniform(0.2, 0.6),
                       'learning_rate': float(np.random.choice([0.0001, 0.001, 0.0005])),
                       'lr_plateau_factor': 0.1,
                       'lr_plateau_patience': 3,
                       'early_stopping_min_delta': float(np.random.choice([0.0001, 0.001])),
                       'early_stopping_patience': 5}
    return hyperparameters

def random_tweak(init_value, as_int=False, factor=0.1, a_min=None, a_max=None):
    eps = 1e-6
    new_value = float(np.random.normal(loc=float(init_value), scale=factor * (init_value + eps)))

    if as_int:
        new_value = int(new_value)

    if a_min is not None:
        new_value = max(a_min, new_value)

    if a_max is not None:
        new_value = min(a_max, new_value)

    return new_value

def randomize_params(params):
    if params is None:
        return totally_random_parameters()

    params['n_epochs'] = random_tweak(params['n_epochs'], as_int=True, factor=.5, a_min=2, a_max=10)
    params['batch_size'] = random_tweak(params['batch_size'], as_int=True, factor=.3, a_min=64, a_max=128)
    params['dense_blocks'][0]['size'] = random_tweak(params['dense_blocks'][0]['size'], as_int=True, factor=.1, a_min=64, a_max=512)
    params['dense_blocks'][0]['dropout_rate'] = random_tweak(params['dense_blocks'][0]['dropout_rate'], as_int=False, factor=.1, a_min=0., a_max=.5)
    params['embedding_factor'] = random_tweak(params['embedding_factor'], as_int=False, factor=.2, a_min=.2, a_max=.6)
    params['learning_rate'] = random_tweak(params['learning_rate'], as_int=False, factor=.2, a_min=1e-4, a_max=1e-3)
    params['early_stopping_min_delta'] = random_tweak(params['early_stopping_min_delta'], as_int=False, factor=.1, a_min=1e-4, a_max=1e-3)
    return params

# Initialize best job placeholders
best_params = None
best_loss = 2 ** 32 - 1

for _ in range(RANDOM_WALK_STEPS):
    print('Random walk step: {}'.format(_))
    batch_params = []
    deployed_jobs = []

    for i in range(JOB_BATCH_SIZE):
        # Random params tweak
        current_params = randomize_params(best_params)

        # Run job and await results
        deployed_jobs.append(foundations.submit(scheduler_config="scheduler", job_directory='.', command=[""], params=current_params))

    batch_losses = [job.get_metric('test mean squared error') for job in deployed_jobs]

    # Reassign best job
    for current_loss, current_params in zip(batch_losses, batch_params):
        if current_loss < best_loss:
            best_loss = current_loss
            best_params = current_params

After a simple random walk Hparams optimization, one might think about doing a more sophisticated search, using evolutionary algorithms or bayesian optimization.


That's it! You've completed the Foundations Atlas Tutorial. Now, you should be able to go to the GUI and see your running and completed jobs, compare model hyperparameters and performance, as well as view artifacts and training visualizations on TensorBoard.

Do you have any thoughts or feedback for Foundations Atlas? Join the Dessa Slack community!