From 16742e79e715c6f0ba39e19b8a184c2eebdeeb59 Mon Sep 17 00:00:00 2001 From: ludatabricks <38018689+ludatabricks@users.noreply.github.com> Date: Thu, 6 Sep 2018 13:13:00 -0700 Subject: [PATCH] update README for version v1.2.0 (#155) * update README * remove release section and add the link to github release note --- README.md | 18 ++++-------------- 1 file changed, 4 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 9c16fb9f..7389ab8a 100644 --- a/README.md +++ b/README.md @@ -71,19 +71,7 @@ You can also post bug reports and feature requests in Github issues. ## Releases -- [1.0.0](https://github.com/databricks/spark-deep-learning/releases/tag/v1.0.0) release: Spark 2.3.0 required. Python 3.6 & Scala 2.11 recommended. TensorFlow 1.6.0 required. - 1. Using the definition of images from Spark 2.3.0. The new definition uses the BGR channel ordering - for 3-channel images instead of the RGB ordering used in this project before the change. - 2. Persistence for DeepImageFeaturizer (both Python and Scala). -- [0.3.0](https://github.com/databricks/spark-deep-learning/releases/tag/v0.3.0) release: Spark 2.2.0, Python 3.6 & Scala 2.11 recommended. TensorFlow 1.4.1- required. - 1. KerasTransformer & TFTransformer for large-scale batch inference on non-image (tensor) data. - 2. Scala API for transfer learning (`DeepImageFeaturizer`). InceptionV3 is supported. - 3. Added VGG16, VGG19 models to DeepImageFeaturizer & DeepImagePredictor (Python). -- [0.2.0](https://github.com/databricks/spark-deep-learning/releases/tag/v0.2.0) release: Spark 2.1.1 & Python 2.7 recommended. - 1. KerasImageFileEstimator API (train a Keras model on image files) - 2. SQL UDF support for Keras models - 3. Added Xception, Resnet50 models to DeepImageFeaturizer & DeepImagePredictor. -- 0.1.0 Alpha release: Spark 2.1.1 & Python 2.7 recommended. +Visit [Github Release Page](https://github.com/databricks/spark-deep-learning/releases) to check the release notes. ## Downloads and installation @@ -104,7 +92,9 @@ To try running the examples below, check out the Databricks notebook in the [Dat [0.1.0](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/5669198905533692/3647723071348946/3983381308530741/latest.html), [0.2.0](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/5669198905533692/1674891575666800/3983381308530741/latest.html), [0.3.0](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/4856334613426202/3381529530484660/4079725938146156/latest.html), -[1.0.0](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6026450283250196/3874201704285756/7409402632610251/latest.html). +[1.0.0](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6026450283250196/3874201704285756/7409402632610251/latest.html), +[1.1.0](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6026450283250196/3874201704285756/7409402632610251/latest.html), +[1.2.0](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6026450283250196/2720471487429801/7409402632610251/latest.html). ### Working with images in Spark