Scaling distributed machine learning with the parameter server
當今越來越多的機器學習系統都採用了分佈式，在分佈式領域裏，少帥的第三代PS框架深受歡迎。本文是在研讀論文「Scaling distributed machine learning with the parameter server」時，按照自己理解對其進行翻譯，不免有很多細節不對之處，希望各位看官能夠見諒。 論文下載地址 我們爲分佈式機器學習問題提出了參數服務器框架。 數據和工作負載分發到各工作節
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相關文章
- 1. Distributed Training using Apache MXNet with Horovod
- 2. Running Deep Learning on Distributed GPUs With Spark
- 3. Accelerating TensorFlow with the Google Machine Learning Engine 如何加速Google Machine Learning Engine
- 4. Machine and Deep Learning with Python
- 5. [Machine Learning]--Improving classification with the AdaBoost meta-algorithm
- 6. Machine Learning (2) Parameter Learning & Linear Algebra Review
- 7. Classification and inference with machine learning
- 8. parameter server介紹
- 9. Machine Learning 資料
- 10. 【論文閱讀】SketchML: Accelerating Distributed Machine Learning with Data Sketches