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[Docs][Train] Replace Ray Tune + Train example with vanilla Ray Tune in homepage#60229

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matthewdeng merged 1 commit intoray-project:masterfrom
xinyuangui2:fix-ray_io
Jan 20, 2026
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[Docs][Train] Replace Ray Tune + Train example with vanilla Ray Tune in homepage#60229
matthewdeng merged 1 commit intoray-project:masterfrom
xinyuangui2:fix-ray_io

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Summary

Replaced the Ray Tune example in the homepage (index.html) to show vanilla Ray Tune usage instead of V1 tune+train integration.

Changes:

  • Removed ScalingConfig and LightGBMTrainer imports (Ray Train components)
  • Added a pure Ray Tune example demonstrating:
    • An objective function that trains a model with hyperparameters and reports metrics
    • Hyperparameter search space using common Tune methods (loguniform, choice, randint)
    • Running 1000 trials with the Tuner API
    • Retrieving the best result

This makes the example clearer for users who want to learn Ray Tune's hyperparameter optimization capabilities without the complexity of Ray Train integration.

Signed-off-by: xgui <xgui@anyscale.com>
@xinyuangui2 xinyuangui2 requested a review from a team as a code owner January 16, 2026 18:24
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Code Review

This pull request effectively simplifies the Ray Tune example on the homepage by replacing a combined Ray Tune + Ray Train example with a vanilla Ray Tune one. This makes the core functionality of Ray Tune much clearer for new users. The new code snippet is well-structured, covering the definition of an objective function, a search space, and the execution of a tuning job. I have one suggestion to use tune.qrandint to avoid a common point of confusion with integer ranges, which will improve the example's clarity.

@ray-gardener ray-gardener bot added tune Tune-related issues docs An issue or change related to documentation labels Jan 16, 2026
@xinyuangui2 xinyuangui2 added the go add ONLY when ready to merge, run all tests label Jan 16, 2026
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thx

@matthewdeng matthewdeng merged commit 1f2a892 into ray-project:master Jan 20, 2026
6 checks passed
jinbum-kim pushed a commit to jinbum-kim/ray that referenced this pull request Jan 29, 2026
…in homepage (ray-project#60229)

## Summary

Replaced the Ray Tune example in the homepage (`index.html`) to show
vanilla Ray Tune usage instead of V1 tune+train integration.

  **Changes:**
- Removed `ScalingConfig` and `LightGBMTrainer` imports (Ray Train
components)
  - Added a pure Ray Tune example demonstrating:
- An objective function that trains a model with hyperparameters and
reports metrics
- Hyperparameter search space using common Tune methods (`loguniform`,
`choice`, `randint`)
    - Running 1000 trials with the `Tuner` API
    - Retrieving the best result

This makes the example clearer for users who want to learn Ray Tune's
hyperparameter optimization capabilities without the complexity of Ray
Train integration.

Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: jinbum-kim <jinbum9958@gmail.com>
400Ping pushed a commit to 400Ping/ray that referenced this pull request Feb 1, 2026
…in homepage (ray-project#60229)

## Summary

Replaced the Ray Tune example in the homepage (`index.html`) to show
vanilla Ray Tune usage instead of V1 tune+train integration.

  **Changes:**
- Removed `ScalingConfig` and `LightGBMTrainer` imports (Ray Train
components)
  - Added a pure Ray Tune example demonstrating:
- An objective function that trains a model with hyperparameters and
reports metrics
- Hyperparameter search space using common Tune methods (`loguniform`,
`choice`, `randint`)
    - Running 1000 trials with the `Tuner` API
    - Retrieving the best result

This makes the example clearer for users who want to learn Ray Tune's
hyperparameter optimization capabilities without the complexity of Ray
Train integration.

Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: 400Ping <jiekaichang@apache.org>
ryanaoleary pushed a commit to ryanaoleary/ray that referenced this pull request Feb 3, 2026
…in homepage (ray-project#60229)

## Summary

Replaced the Ray Tune example in the homepage (`index.html`) to show
vanilla Ray Tune usage instead of V1 tune+train integration.

  **Changes:**
- Removed `ScalingConfig` and `LightGBMTrainer` imports (Ray Train
components)
  - Added a pure Ray Tune example demonstrating:
- An objective function that trains a model with hyperparameters and
reports metrics
- Hyperparameter search space using common Tune methods (`loguniform`,
`choice`, `randint`)
    - Running 1000 trials with the `Tuner` API
    - Retrieving the best result

This makes the example clearer for users who want to learn Ray Tune's
hyperparameter optimization capabilities without the complexity of Ray
Train integration.

Signed-off-by: xgui <xgui@anyscale.com>
peterxcli pushed a commit to peterxcli/ray that referenced this pull request Feb 25, 2026
…in homepage (ray-project#60229)

## Summary

Replaced the Ray Tune example in the homepage (`index.html`) to show
vanilla Ray Tune usage instead of V1 tune+train integration.

  **Changes:**
- Removed `ScalingConfig` and `LightGBMTrainer` imports (Ray Train
components)
  - Added a pure Ray Tune example demonstrating:
- An objective function that trains a model with hyperparameters and
reports metrics
- Hyperparameter search space using common Tune methods (`loguniform`,
`choice`, `randint`)
    - Running 1000 trials with the `Tuner` API
    - Retrieving the best result

This makes the example clearer for users who want to learn Ray Tune's
hyperparameter optimization capabilities without the complexity of Ray
Train integration.

Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: peterxcli <peterxcli@gmail.com>
peterxcli pushed a commit to peterxcli/ray that referenced this pull request Feb 25, 2026
…in homepage (ray-project#60229)

## Summary

Replaced the Ray Tune example in the homepage (`index.html`) to show
vanilla Ray Tune usage instead of V1 tune+train integration.

  **Changes:**
- Removed `ScalingConfig` and `LightGBMTrainer` imports (Ray Train
components)
  - Added a pure Ray Tune example demonstrating:
- An objective function that trains a model with hyperparameters and
reports metrics
- Hyperparameter search space using common Tune methods (`loguniform`,
`choice`, `randint`)
    - Running 1000 trials with the `Tuner` API
    - Retrieving the best result

This makes the example clearer for users who want to learn Ray Tune's
hyperparameter optimization capabilities without the complexity of Ray
Train integration.

Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: peterxcli <peterxcli@gmail.com>
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2 participants