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First things first, let's install Polaris!

We highly recommend using a Conda Python distribution, such as mamba:

mamba install -c conda-forge polaris
Other installation options

You can replace mamba by conda. The package is also pip installable if you need it: pip install polaris-lib.

Benchmarking API

At its core, Polaris is a benchmarking library. It provides a simple API to run benchmarks. While it can be used independently, it is built to easily integrate with the Polaris Hub. The hub hosts a variety of high-quality datasets, benchmarks and associated results.

If all you care about is to partake in a benchmark that is hosted on the hub, it is as simple as:

import polaris as po

# Load the benchmark from the Hub
benchmark = po.load_benchmark("polaris/hello-world-benchmark")

# Get the train and test data-loaders
train, test = benchmark.get_train_test_split()

# Use the training data to train your model
# Get the input as an array with 'train.inputs' and 'train.targets'  
# Or simply iterate over the train object.
for x, y in train:

# Work your magic to accurately predict the test set
predictions = [0.0 for x in test]

# Evaluate your predictions
results = benchmark.evaluate(predictions)

# Submit your results

That's all there is to it to partake in a benchmark. No complicated, custom data-loaders or evaluation protocol. With just a few lines of code, you can feel confident that you are properly evaluating your model and focus on what you do best: Solving the hard problems in our domain!

Similarly, you can easily access a dataset.

import polaris as po

# Load the dataset from the hub
dataset = po.load_dataset("polaris/hello-world")

# Get information on the dataset size

# Load a datapoint in memory

# Or, similarly:
dataset[dataset.rows[0], dataset.columns[0]]

# Get the first 10 rows in memory

Core concepts

At the core of our API are 4 core concepts, each associated with a class:

  1. Dataset: The dataset class is carefully designed data-structure, stress-tested on terra-bytes of data, to ensure whatever dataset you can think of, you can easily create, store and use it.
  2. BenchmarkSpecification: The benchmark specification class wraps a Dataset with additional meta-data to produce a the benchmark. Specifically, it specifies how to evaluate a model's performance on the underlying dataset (e.g. the train-test split and metrics). It provides a simple API to run said evaluation protocol.
  3. Subset: The subset class should be used as a starting-point for any framework-specific (e.g. PyTorch or Tensorflow) data loaders. To facilitate this, it abstracts away the non-trivial logic of accessing the data and provides several style of access to built upon.
  4. BenchmarkResults: The benchmark results class stores the results of a benchmark, along with additional meta-data. This object can be easily uploaded to the Polaris Hub and shared with the broader community.

Where to next?

Now that you've seen how easy it is to use Polaris, let's dive into the details through a set of tutorials!