Ray is an open source unified compute framework to scale Python workloads. In this guide, we will explain how to use TimeGPT on top of Ray.

Outline:

1. Installation

2. Load Your Data

3. Initialize Ray

4. Use TimeGPT on Ray

5. Shutdown Ray

​

1. Installation

Install Ray 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 Ray.

Note

You can install fugue with pip:

Copy

Ask AI

pip install fugue[ray]

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

​

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()

​

3. Initialize Ray

Initialize Ray and convert the pandas DataFrame to a Ray DataFrame.

import ray
from ray.cluster_utils import Cluster

ray_cluster = Cluster(
    initialize_head=True,
    head_node_args={"num_cpus": 2}
)
ray.init(address=ray_cluster.address, ignore_reinit_error=True)

ray_df = ray.data.from_pandas(df)
ray_df

MaterializedDataset(
   num_blocks=1,
   num_rows=6720,
   schema={unique_id: object, ds: datetime64[ns], y: float64}
)

​

4. Use TimeGPT on Ray

Using TimeGPT on top of Ray is almost identical to the non-distributed case. The only difference is that you need to use a Ray DataFrame.

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.

ray_df

MaterializedDataset(
   num_blocks=1,
   num_rows=6720,
   schema={unique_id: object, ds: datetime64[ns], y: float64}
)

fcst_df = nixtla_client.forecast(ray_df, h=12)

📘 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.

To visualize the result, use the to_pandas method to convert the output of Ray to a pandas DataFrame.

fcst_df.to_pandas().tail()

cv_df = nixtla_client.cross_validation(ray_df, h=12, freq='H', n_windows=5, step_size=2)

cv_df.to_pandas().tail()

You can also use exogenous variables with TimeGPT on top of Ray. 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 Ray DataFrame instead.

​

5. Shutdown Ray

When you are done, shutdown the Ray session.

ray.shutdown()