Dask

Dask is an open source parallel computing library for Python. In this guide, we will explain how to use TimeGPT on top of Dask.

Outline:

1. Installation

2. Load Your Data

3. Import Dask

4. Use TimeGPT on Dask

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1. Installation

Install Dask through Fugue. Fugue provides an easy-to-use interface for distributed computing that lets users execute Python code on top of several distributed computing frameworks, including Dask.

Note

You can install fugue with pip:

pip install fugue[dask]

If executing on a distributed Dask cluster, ensure that the nixtla library is installed across all the workers.

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2. Load Data

You can load your data as a pandas DataFrame. In this tutorial, we will use a dataset that contains hourly electricity prices from different markets.

import pandas as pd

df = pd.read_csv(
    'https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv',
    parse_dates=['ds'],
) 
df.head()

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3. Import Dask

Import Dask and convert the pandas DataFrame to a Dask DataFrame.

import dask.dataframe as dd

dask_df = dd.from_pandas(df, npartitions=2)
dask_df

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4. Use TimeGPT on Dask

Using TimeGPT on top of Dask is almost identical to the non-distributed case. The only difference is that you need to use a Dask DataFrame, which we already defined in the previous step.

First, instantiate the NixtlaClient class.

from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key = 'my_api_key_provided_by_nixtla'
)

👍 Use an Azure AI endpoint

To use an Azure AI endpoint, set the base_url argument:

nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key")

Then use any method from the NixtlaClient class such as forecast or cross_validation.

fcst_df = nixtla_client.forecast(dask_df, h=12)
fcst_df.compute().head()

📘 Available models in Azure AI

If you are using an Azure AI endpoint, please be sure to set model="azureai":

nixtla_client.forecast(..., model="azureai")

For the public API, we support two models: timegpt-1 and timegpt-1-long-horizon.

By default, timegpt-1 is used. Please see this tutorial on how and when to use timegpt-1-long-horizon.

cv_df = nixtla_client.cross_validation(dask_df, h=12, n_windows=5, step_size=2)
cv_df.compute().head()

You can also use exogenous variables with TimeGPT on top of Dask. To do this, please refer to the Exogenous Variables tutorial. Just keep in mind that instead of using a pandas DataFrame, you need to use a Dask DataFrame instead.