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chronos-t5-large

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Hi Shemna,

Thank you for your interest.

Chronos is not a text LLM, so it doesn't actually have an associated T5TokenizerFast. At the moment, you won't be able to deploy Chronos to Sagemaker in a one-click fashion (with only supports text LLMs currently). You can either test Chronos locally or manually setup a Sagemaker endpoint. However, soon you should be able to one-click deploy Chronos using AutoGluon cloud.

cc @canerturkmen

As @abdulfatir mentioned, AutoGluon Cloud will soon add support for Sagemaker deployment in a few lines of codes. Before that, if you would like to test or prototype this, you can try with unofficial docker images at DockerHub

Below are sample codes with AG-Cloud to make it work:

import pandas as pd
from autogluon.cloud import TimeSeriesCloudPredictor
import os
from datetime import datetime
 
data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/cloud/timeseries_train.csv")
id_column="item_id"
timestamp_column="timestamp"
target="target"
 
predictor_init_args = {
    "target": target
}  # init args you would pass to AG TimeSeriesCloudPredictor
predictor_fit_args = {
    "train_data": data,
    "time_limit": 600,
    "presets": "chronos_small",
}  # fit args you would pass to AG TimeSeriesCloudPredictor
cloud_predictor = TimeSeriesCloudPredictor(cloud_output_path="<s3://your_s3_path>")
cloud_predictor.fit(
    predictor_init_args=predictor_init_args,
    predictor_fit_args=predictor_fit_args,
    id_column=id_column,
    timestamp_column=timestamp_column,
    instance_type="ml.g4dn.2xlarge",
    custom_image_uri="<your_training_ecr_image_path>"
 
cloud_predictor.deploy(
    custom_image_uri="<your_inference_ecr_image_path>"
)
result = cloud_predictor.predict_real_time(
    test_data=data,
    id_column=id_column,
    timestamp_column=timestamp_column,
    target=target
)
print(result)

To ensure above codes working, you need following:

• pip install autogluon.cloud in your Python env
• Pull and push the training image from dockerhub to the AWS ECR account. Details on how to do that can be found at here
• Pull and push the inference image to AWS ECR following the same procedure as above step
• Replace <s3://your_s3_path>, <your_training_ecr_image_path>, <your_inference_ecr_image_path> in above codes with your own parameters

Let me know if you have any further questiokns