Prepare v0.6.0 release

.github/workflows/continuous_integration.yml

@@ -16,8 +16,8 @@ on:
   pull_request:
 
 concurrency:
-  # Per-ref so a tag/branch push never cancels a build for a different ref
-  group: ${{ github.workflow }}-${{ github.event.number || github.ref }}
+  # Cancel superseded commits for the same PR without mixing PRs, refs, or trigger types.
+  group: ${{ github.workflow }}-${{ github.event_name }}-${{ github.event.pull_request.number || github.ref }}
   cancel-in-progress: true
 
 permissions:
@@ -239,4 +239,4 @@ jobs:
           issue-number: ${{ github.event.number }}
           body: |
             Job PR-${{ github.event.number }}-${{ env.SHORT_SHA }} is done.
-            Docs are uploaded to https://d12sc05jpx1wj5.cloudfront.net/PR-${{ github.event.number }}/${{ env.SHORT_SHA }}/index.html
+            Docs are uploaded to https://d12sc05jpx1wj5.cloudfront.net/PR-${{ github.event.number }}/${{ env.SHORT_SHA }}/index.html

docs/conf.py

@@ -4,7 +4,7 @@
 sys.path = [".", ".."] + sys.path
 
 project = "AutoGluon-Cloud"
-release = "0.5.1"
+release = "0.6.0"
 copyright = "2026, All authors. Licensed under Apache 2.0."
 author = "AutoGluon contributors"
 

pyproject.toml

@@ -38,7 +38,7 @@ classifiers = [
 dynamic = ["version"]
 dependencies = [
     # common module provides utils with stable api across minor version
-    "autogluon.common>=0.7,<1.6",
+    "autogluon.common>=0.7,<1.7",
     # <2 because unlikely to introduce breaking changes in minor releases.
     # TransformAmiVersion was added to the SageMaker service model in botocore 1.37.23.
     "boto3>=1.37.23,<2",
@@ -56,9 +56,9 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-ray = ["ray[default]>=2.10.0,<2.56"]
+ray = ["ray[default]>=2.10.0,<2.59"]
 all = [  # To allow user to pass ag objects
-    "autogluon>=0.7,<1.6",
+    "autogluon>=0.7,<1.7",
     "autogluon.cloud[ray]",
 ]
 tests = [

setup.py

@@ -24,7 +24,7 @@ def update_version(version):
     return version
 
 
-version = "0.5.1"
+version = "0.6.0"
 version = update_version(version)
 
 if __name__ == "__main__":

src/autogluon/cloud/__init__.py

@@ -1,5 +1,7 @@
 import logging
+import os
 
+os.environ.setdefault("SAGEMAKER_SUPPRESS_V2_WARNING", "1")
 import sagemaker
 from packaging.version import Version
 

src/autogluon/cloud/predictor/cloud_predictor.py

@@ -4,7 +4,6 @@
 import logging
 import os
 import tarfile
-import warnings
 from abc import ABC, abstractmethod
 from datetime import datetime
 from pathlib import Path
@@ -245,19 +244,10 @@ def fit(
         data_channels = {"train_data": train_data, "tuning_data": tuning_data, "test_data": test_data}
         for key in ("train_data", "tuning_data"):
             if key in predictor_fit_args:
-                warnings.warn(
-                    f"Passing `{key}` via `predictor_fit_args` is deprecated and will be removed in autogluon.cloud 0.6.0. "
-                    f"Pass `{key}` as an explicit argument to `fit()` instead.",
-                    FutureWarning,
-                    stacklevel=2,
+                raise TypeError(
+                    f"`{key}` can no longer be passed via `predictor_fit_args`. "
+                    f"Pass `{key}` as an explicit argument to `fit()` instead."
                 )
-                if data_channels[key] is None:
-                    data_channels[key] = predictor_fit_args.pop(key)
-                else:
-                    raise TypeError(
-                        f"`{key}` was passed both as an explicit argument and via `predictor_fit_args`. "
-                        f"Pass it only as an explicit argument."
-                    )
         if data_channels["train_data"] is None:
             raise TypeError("fit() missing required argument: 'train_data'")
         if image_column is not None and self.predictor_type == "tabular":

src/autogluon/cloud/predictor/timeseries_cloud_predictor.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import logging
-import warnings
 from pathlib import Path
 from typing import Any, Dict, Optional, Union
 
@@ -135,19 +134,10 @@ def fit(
         }
         for key in ("train_data", "tuning_data", "known_covariates"):
             if key in predictor_fit_args:
-                warnings.warn(
-                    f"Passing `{key}` via `predictor_fit_args` is deprecated and will be removed in autogluon.cloud 0.6.0. "
-                    f"Pass `{key}` as an explicit argument to `fit()` instead.",
-                    FutureWarning,
-                    stacklevel=2,
+                raise TypeError(
+                    f"`{key}` can no longer be passed via `predictor_fit_args`. "
+                    f"Pass `{key}` as an explicit argument to `fit()` instead."
                 )
-                if data_channels[key] is None:
-                    data_channels[key] = predictor_fit_args.pop(key)
-                else:
-                    raise TypeError(
-                        f"`{key}` was passed both as an explicit argument and via `predictor_fit_args`. "
-                        f"Pass it only as an explicit argument."
-                    )
         if data_channels["train_data"] is None:
             raise TypeError("fit() missing required argument: 'train_data'")
 

tests/unittests/timeseries/test_timeseries.py

@@ -402,8 +402,9 @@ def test_timeseries_endpoint_payload_formats(test_helper, framework_version, pla
         inference_custom_image_uri = test_helper.get_custom_image_uri(framework_version, type="inference", gpu=False)
 
         cloud_predictor.fit(
+            train_data=plain_dataset,
             predictor_init_args=dict(target="target", prediction_length=_PLAIN_PREDICTION_LENGTH),
-            predictor_fit_args=dict(train_data=plain_dataset, presets="medium_quality", time_limit=60),
+            predictor_fit_args=dict(presets="medium_quality", time_limit=60),
             framework_version=framework_version,
             custom_image_uri=training_custom_image_uri,
         )