传统的数据湖泊通过启用时间旅行，运行SQL查询，使用酸性交易摄入数据以及可视化PBABYTE尺度数据集在云存储中，为分析工作负载提供了关键的数据基础架构。它们使组织能够分解数据孤岛，解锁数据驱动的决策，提高运营效率并降低成本。但是，随着深度学习接管常见的分析工作流程，传统数据湖泊对诸如自然语言处理（NLP），音频处理，计算机视觉和涉及非尾巴数据集的应用程序的有用程度降低。本文介绍了Deep Lake，这是一个开源湖泊，用于在Activeloop开发的深度学习应用程序。 Deep Lake保持了一项关键区别的香草数据湖的好处：它以张量的形式存储复杂数据，例如图像，视频，注释以及表格数据，并将数据迅速流式传输到网络上（a ）张量查询语言，（b）浏览器可视化引擎或（c）不牺牲GPU利用率的深度学习框架。可以从Pytorch，Tensorflow，Jax，与许多MLOPS工具集成在一起的数据集。
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We discover a robust self-supervised strategy tailored towards molecular representations for generative masked language models through a series of tailored, in-depth ablations. Using this pre-training strategy, we train BARTSmiles, a BART-like model with an order of magnitude more compute than previous self-supervised molecular representations. In-depth evaluations show that BARTSmiles consistently outperforms other self-supervised representations across classification, regression, and generation tasks setting a new state-of-the-art on 11 tasks. We then quantitatively show that when applied to the molecular domain, the BART objective learns representations that implicitly encode our downstream tasks of interest. For example, by selecting seven neurons from a frozen BARTSmiles, we can obtain a model having performance within two percentage points of the full fine-tuned model on task Clintox. Lastly, we show that standard attribution interpretability methods, when applied to BARTSmiles, highlight certain substructures that chemists use to explain specific properties of molecules. The code and the pretrained model are publicly available.
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