Better performance with the tf.data API Â |Â TensorFlow Core
Overview GPUs and TPUs can radically reduce the time required to execute a single training step. Achieving peak performance requires an efficient input pipeline that delivers data for the next step before the current step has finished. The tf.data API helps to build flexible and efficient input pipelines. This document demonstrates how to use the tf.data API to build highly performant TensorFlow i
Speeding up Keras with tfrecord datasets
Alright. So you just got started with Keras with Tensorflow as a backend. Introducing GPU computing was quite simple so you started increasing the size of your datasets. Everything works fantastic, your GPU is happy and hungry for more, so you increase the dataset size even more to improve the robustness of your model. At a certain size, you hit the limit of your RAM and naturally you write a quic
Meet Horovod: Uber's Open Source Distributed Deep Learning Framework for TensorFlow
Data / ML, EngineeringMeet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlowOctober 17, 2017 / Global Over the past few years, advances in deep learning have driven tremendous progress in image processing, speech recognition, and forecasting. At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep l
Raspberry Pi上ã§TensorFlowã§ç°¡å˜ã«ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングを試ã›ã‚‹ãƒ„ール「tensorflow-piã€ã§ãƒãƒœãƒ›ãƒ³ã®å¹»ã®é¡”èªè˜æ©Ÿèƒ½ã«æŒ‘戦ã—ã¦ã¿ãŸ - karaage. [ã‹ã‚‰ã‚ã’]
Raspberry Piã¨TensorFlowを使ã£ãŸãƒ‡ã‚£ãƒ¼ãƒ—ラーニング開発環境 以下ã®è¨˜äº‹ã§Raspberry Piã¨TensorFlowを使ã£ãŸãƒ‡ã‚£ãƒ¼ãƒ—ラーニングã®é–‹ç™ºç’°å¢ƒã®æ§‹ç¯‰æ–¹æ³•ã‚’紹介ã—ã¾ã—ãŸã€‚ 記事ã®æœ€å¾Œã®æ–¹ã«ã€è‡ªå‰ãƒ‡ãƒ¼ã‚¿ã®å¦ç¿’ã‹ã‚‰ã€å¦ç¿’ã—ãŸãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã§åˆ¤åˆ¥ã¾ã§å®Ÿæ–½ã§ãる自å‰ã®ãƒ‘ッケージ「tensorflow-piã€ã‚’紹介ã•ã›ã¦ã„ãŸã ãã¾ã—ãŸã€‚ ãŸã ã€READMEã ã‘見ã¦ã‚‚ã€æ„味ä¸æ˜Žãªè‹±èªžã§è‰¯ãã‚ã‹ã‚‰ãªã„ã¨æ€ã„ã¾ã™ã®ã§ã€ä»Šå›žã¯å®Ÿä¾‹ã‚’交ãˆãªãŒã‚‰ã“ã®ã‚½ãƒ•ãƒˆã®ä½¿ã„方を紹介ã—よã†ã¨æ€ã„ã¾ã™ã€‚ 例題ã§ã™ãŒã€ä»¥å‰ãƒãƒƒãƒˆã§è©±é¡Œã«ãªã£ãŸã€Œãƒãƒœãƒ›ãƒ³ã€ã®å¹»ã®é¡”èªè˜æ©Ÿèƒ½ãŒã‚ã‚Šã¾ã™ã€‚詳ã—ãã¯ä»¥ä¸‹å‚照下ã•ã„。 開発決定ï¼ã¨ã„ã†ãƒ‹ãƒ¥ãƒ¼ã‚¹ã¯è©±é¡Œã«ãªã£ãŸã‚‚ã®ã®ã€ãã®å¾Œç¶šå ±ãŒã¨ã‚“ã¨èžã‹ã‚Œã¾ã›ã‚“。ãã‚‚ãも技術的ã«ä¸å¯èƒ½ãªã‚“ã˜ã‚ƒã¨ã„ã†å™‚ã‚‚èžã“ãˆã¦ããŸã‚Šã—ã¾ã—ãŸã€‚ 奇ã—ãã‚‚ãƒãƒœãƒ›ãƒ³ã¨Raspberry Pi
![Raspberry Pi上ã§TensorFlowã§ç°¡å˜ã«ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングを試ã›ã‚‹ãƒ„ール「tensorflow-piã€ã§ãƒãƒœãƒ›ãƒ³ã®å¹»ã®é¡”èªè˜æ©Ÿèƒ½ã«æŒ‘戦ã—ã¦ã¿ãŸ - karaage. [ã‹ã‚‰ã‚ã’]](https://cdn-ak-scissors.b.st-hatena.com/image/square/10adf88ff1495f3fbce26be21cda9215a10d1a47/height=288;version=1;width=512/https%3A%2F%2Fcdn-ak.f.st-hatena.com%2Fimages%2Ffotolife%2Fk%2Fkaraage%2F20170726%2F20170726015040.jpg)
Benchmarking CNTK on Keras: is it Better at Deep Learning than TensorFlow?
Benchmarking CNTK on Keras: is it Better at Deep Learning than TensorFlow? June 12, 2017 - Keras is a high-level open-source framework for deep learning, maintained by François Chollet, that abstracts the massive amounts of configuration and matrix algebra needed to build production-quality deep learning models. The Keras API abstracts a lower-level deep learning framework like Theano or Google’s
XLA: TensorFlow, Compiled! (TensorFlow Dev Summit 2017)
Speed is everything for effective machine learning, and XLA was developed to reduce training and inference time. In this talk, Chris Leary and Todd Wang describe how TensorFlow can make use of XLA, JIT, AOT, and other compilation techniques to minimize execution time and maximize computing resources. Visit the TensorFlow website for all session recordings: https://goo.gl/bsYmza Subscribe to the
1
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
Training on datasets too big to fit in RAM
{{#tags}}- {{label}}
{{/tags}}