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Posted to issues@iceberg.apache.org by "dependabot[bot] (via GitHub)" <gi...@apache.org> on 2023/04/30 05:00:08 UTC
[GitHub] [iceberg] dependabot[bot] opened a new pull request, #7479: Build: Bump ray from 2.3.1 to 2.4.0 in /python
dependabot[bot] opened a new pull request, #7479:
URL: https://github.com/apache/iceberg/pull/7479
Bumps [ray](https://github.com/ray-project/ray) from 2.3.1 to 2.4.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/ray-project/ray/releases">ray's releases</a>.</em></p>
<blockquote>
<h2>Ray-2.4.0</h2>
<h1>Ray 2.4 - Generative AI and LLM support</h1>
<p>Over the last few months, we have seen a flurry of innovative activity around <a href="https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai">generative AI models</a> and <a href="https://en.wikipedia.org/wiki/Large_language_model">large language models (LLM)</a>. To continue our effort to ensure Ray provides a pivotal compute substrate for <a href="https://www.anyscale.com/blog/ray-common-production-challenges-for-generative-ai-infrastructure">generative AI workloads</a> and addresses the challenges (as explained in our <a href="https://www.anyscale.com/blog/ray-common-production-challenges-for-generative-ai-infrastructure">blog series</a>), we have invested engineering efforts in this release to ensure that these open source LLM models and workloads are accessible to the open source community and performant with Ray.</p>
<p>This release includes new examples for training, batch inference, and serving with your own LLM.</p>
<h2>Generative AI and LLM Examples</h2>
<ul>
<li><a href="https://docs.ray.io/en/releases-2.4.0/ray-air/examples/gptj_deepspeed_fine_tuning.html">GPT-J (LLM) fine-tuning with Microsoft DeepSpeed and Ray Train</a></li>
<li><a href="https://docs.ray.io/en/releases-2.4.0/ray-air/examples/gptj_batch_prediction.html">GPT-J-6B Batch Prediction with Ray Data</a></li>
<li><a href="https://docs.ray.io/en/releases-2.4.0/ray-air/examples/gptj_serving.html">GPT-J-6B Serving with Ray Serve</a></li>
<li><a href="https://docs.ray.io/en/releases-2.4.0/ray-air/examples/dreambooth_finetuning.html">Stable Diffusion (Dreambooth) fine-tuning with Ray Train</a></li>
<li><a href="https://docs.ray.io/en/releases-2.4.0/ray-air/examples/stablediffusion_batch_prediction.html">Stable Diffusion Batch Prediction with Ray Data </a></li>
<li><a href="https://docs.ray.io/en/releases-2.4.0/serve/tutorials/stable-diffusion.html">Stable Diffusion Serving with Ray Serve</a></li>
</ul>
<h2>Ray Train enhancements</h2>
<ul>
<li>We're introducing the <a href="https://docs.ray.io/en/releases-2.4.0/train/api/doc/ray.train.lightning.LightningTrainer.html">LightningTrainer</a>, allowing you to scale your <a href="https://lightning.ai/docs/pytorch/stable//index.html">PyTorch Lightning</a> on Ray. As part of our continued effort for seamless integration and ease of use, we have enhanced and replaced our existing ray_lightning integration, which was widely adopted, with the latest changes to Pytorch Lighting.</li>
<li>we’re releasing an <a href="https://docs.ray.io/en/releases-2.4.0/train/api/doc/ray.train.huggingface.accelerate.AccelerateTrainer.html">AccelerateTrainer</a>, allowing you to run <a href="https://huggingface.co/docs/accelerate">HuggingFace Accelerate</a> and <a href="https://huggingface.co/docs/accelerate/usage_guides/deepspeed">DeepSpeed</a> on Ray with minimal code changes. This Trainer integrates with the rest of the Ray ecosystem—including the ability to run distributed <a href="https://docs.ray.io/en/latest/tune/index.html">hyperparameter tuning</a> with each trial being a distributed training job.</li>
</ul>
<h2>Ray Data highlights</h2>
<ul>
<li>Streaming execution is enabled by default, providing users with a more efficient data processing pipeline that can handle larger datasets and minimize memory consumption. Check out the docs here: (<a href="https://docs.ray.io/en/releases-2.4.0/data/dataset-internals.html#streaming-execution">doc</a>)</li>
<li>We've implemented asynchronous batch prefetching of Dataset.iter_batches (<a href="https://docs.ray.io/en/releases-2.4.0/data/api/doc/ray.data.DatasetIterator.iter_batches.html">doc</a>), improving performance by fetching data in parallel while the main thread continues processing, thus reducing waiting time.</li>
<li>Support reading SQL databases (<a href="https://docs.ray.io/en/releases-2.4.0/data/creating-datasets.html#reading-from-sql-databases">doc</a>), enabling users to seamlessly integrate relational databases into their Ray Data workflows.</li>
<li>Introduced support for reading WebDataset (<a href="https://docs.ray.io/en/releases-2.4.0/data/api/doc/ray.data.read_webdataset.html">doc</a>), a common format for high-performance deep learning training jobs.</li>
</ul>
<h2>Ray Serve highlights</h2>
<ul>
<li>Multi-app CLI & REST API support is now available, allowing users to manage multiple applications with different configurations within a single Ray Serve deployment. This simplifies deployment and scaling processes for users with multiple applications. (<a href="https://docs.ray.io/en/releases-2.4.0/serve/multi-app.html">doc</a>)</li>
<li>Enhanced logging and metrics for Serve applications, giving users better visibility into their application's performance and facilitating easier debugging and monitoring.
(<a href="https://docs.ray.io/en/releases-2.4.0/serve/production-guide/monitoring.html#monitoring-ray-serve">doc</a>)</li>
</ul>
<h2>Other enhancements</h2>
<ul>
<li><a href="https://redirect.github.com/ray-project/ray/issues/32904">Ray 2.4 is the last version that supports Python 3.6</a></li>
<li>We've also added a brand new <a href="https://docs.ray.io/en/releases-2.4.0/">landing page</a></li>
</ul>
<h1>Ray Libraries</h1>
<h2>Ray AIR</h2>
<p>💫Enhancements:</p>
<ul>
<li>Add nightly test for alpa opt 30b inference. (<a href="https://redirect.github.com/ray-project/ray/issues/33419">#33419</a>)</li>
<li>Add a sanity checking release test for Alpa and ray nightly. (<a href="https://redirect.github.com/ray-project/ray/issues/32995">#32995</a>)</li>
<li>Add <code>TorchDetectionPredictor</code> (<a href="https://redirect.github.com/ray-project/ray/issues/32199">#32199</a>)</li>
<li>Add <code>artifact_location</code>, <code>run_name</code> to MLFlow integration (<a href="https://redirect.github.com/ray-project/ray/issues/33641">#33641</a>)</li>
<li>Add <code>*path</code> properties to <code>Result</code> and <code>ResultGrid</code> (<a href="https://redirect.github.com/ray-project/ray/issues/33410">#33410</a>)</li>
<li>Make <code>Preprocessor.transform</code> lazy by default (<a href="https://redirect.github.com/ray-project/ray/issues/32872">#32872</a>)</li>
<li>Make <code>BatchPredictor</code> lazy (<a href="https://redirect.github.com/ray-project/ray/issues/32510">#32510</a>, <a href="https://redirect.github.com/ray-project/ray/issues/32796">#32796</a>)</li>
<li>Use a configurable ray temp directory for the <code>TempFileLock</code> util (<a href="https://redirect.github.com/ray-project/ray/issues/32862">#32862</a>)</li>
<li>Add <code>collate_fn</code> to <code>iter_torch_batches</code> (<a href="https://redirect.github.com/ray-project/ray/issues/32412">#32412</a>)</li>
<li>Allow users to pass <code>Callable[[torch.Tensor], torch.Tensor]</code> to <code>TorchVisionTransform</code> (<a href="https://redirect.github.com/ray-project/ray/issues/32383">#32383</a>)</li>
<li>Automatically move <code>DatasetIterator</code> torch tensors to correct device (<a href="https://redirect.github.com/ray-project/ray/issues/31753">#31753</a>)</li>
</ul>
<p>🔨 Fixes:</p>
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</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/ray-project/ray/commit/cd1ba65e239360c8a7b130f991ed414eccc063ce"><code>cd1ba65</code></a> [docker] Disable docker builds for code cherry picks (<a href="https://redirect.github.com/ray-project/ray/issues/34744">#34744</a>)</li>
<li><a href="https://github.com/ray-project/ray/commit/4479f66d4db967d3c9dd0af2572061276ba926ba"><code>4479f66</code></a> Cherry pick doc PRs <a href="https://redirect.github.com/ray-project/ray/issues/34614">#34614</a> <a href="https://redirect.github.com/ray-project/ray/issues/34615">#34615</a> <a href="https://redirect.github.com/ray-project/ray/issues/34435">#34435</a> <a href="https://redirect.github.com/ray-project/ray/issues/34505">#34505</a> <a href="https://redirect.github.com/ray-project/ray/issues/34617">#34617</a> <a href="https://redirect.github.com/ray-project/ray/issues/34623">#34623</a> <a href="https://redirect.github.com/ray-project/ray/issues/34660">#34660</a> (<a href="https://redirect.github.com/ray-project/ray/issues/34676">#34676</a>)</li>
<li><a href="https://github.com/ray-project/ray/commit/fb34fc32fec610ae9e309493f83adc316b295d49"><code>fb34fc3</code></a> [train] Add AccelerateTrainer as valid AIR_TRAINER (<a href="https://redirect.github.com/ray-project/ray/issues/34639">#34639</a>) (<a href="https://redirect.github.com/ray-project/ray/issues/34657">#34657</a>)</li>
<li><a href="https://github.com/ray-project/ray/commit/b0c23a912daee0d0aad9c41223bfe40bd4d81695"><code>b0c23a9</code></a> [CI] fix virtualenv version to deflake linux://python/ray/tests:test_runtime_...</li>
<li><a href="https://github.com/ray-project/ray/commit/558b26b5dcb15f77aadb344add8c63b8ac90b49f"><code>558b26b</code></a> [Ci] fix pip version to deflake minimal install 3.10</li>
<li><a href="https://github.com/ray-project/ray/commit/e935be9b13166f5d87cbcbf74129aad7324ee933"><code>e935be9</code></a> [docker] Enable docker builds for code cherry picks (<a href="https://redirect.github.com/ray-project/ray/issues/34649">#34649</a>)</li>
<li><a href="https://github.com/ray-project/ray/commit/d5d34c1ea29557e9a3478101e727e23a0919d60e"><code>d5d34c1</code></a> Revert "[core]Turn on light weight resource broadcasting. (<a href="https://redirect.github.com/ray-project/ray/issues/32625">#32625</a>)" (<a href="https://redirect.github.com/ray-project/ray/issues/34636">#34636</a>)</li>
<li><a href="https://github.com/ray-project/ray/commit/a8d7c9cfcf5f4c96ddf7aa3baf68a292de544b0d"><code>a8d7c9c</code></a> [Doc] Add missing links for LightningTrainer and HuggingfaceTrainer (<a href="https://redirect.github.com/ray-project/ray/issues/34612">#34612</a>)</li>
<li><a href="https://github.com/ray-project/ray/commit/6fc9f70e801ac42edcf4de2d9ace6865ee51d85d"><code>6fc9f70</code></a> [Doc] Fix AIR benchmark configuration link failure(with pinned commit id). <a href="https://redirect.github.com/ray-project/ray/issues/3">#3</a>...</li>
<li><a href="https://github.com/ray-project/ray/commit/d2804d953e6ebc98957074cef7d9994f329bc825"><code>d2804d9</code></a> [cherry pick][docs] for new landing page for 2.4.0 (<a href="https://redirect.github.com/ray-project/ray/issues/34546">#34546</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/ray-project/ray/compare/ray-2.3.1...ray-2.4.0">compare view</a></li>
</ul>
</details>
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[GitHub] [iceberg] Fokko merged pull request #7479: Build: Bump ray from 2.3.1 to 2.4.0 in /python
Posted by "Fokko (via GitHub)" <gi...@apache.org>.
Fokko merged PR #7479:
URL: https://github.com/apache/iceberg/pull/7479
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