update (#97)

README.md

@@ -59,7 +59,7 @@
     |   排序   |                  [xDeepFM](models/rank/xdeepfm/model.py)                  |    ✓    |    x    |     ✓     |     x     | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)                                                       |
     |   排序   |                      [DIN](models/rank/din/model.py)                      |    ✓    |    x    |     ✓     |     x     | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)                                                                                     |
     |   排序   |                [Wide&Deep](models/rank/wide_deep/model.py)                |    ✓    |    x    |     ✓     |     x     | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)                                                                                               |
-    |   排序   |                    [FGCNN](models/rank/fgcnn/model.py)                    |    ✓    |    ✓    |     ✓     |     ✓     |[WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)|
+    |   排序   |                    [FGCNN](models/rank/fgcnn/model.py)                    |    ✓    |    ✓    |     ✓     |     ✓     | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf)                                                                      |
     |  多任务  |                  [ESMM](models/multitask/esmm/model.py)                   |    ✓    |    ✓    |     ✓     |     ✓     | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)                                                              |
     |  多任务  |                  [MMOE](models/multitask/mmoe/model.py)                   |    ✓    |    ✓    |     ✓     |     ✓     | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)                                                       |
     |  多任务  |           [ShareBottom](models/multitask/share-bottom/model.py)           |    ✓    |    ✓    |     ✓     |     ✓     | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf)                                                                                                               |
@@ -120,7 +120,7 @@ python -m paddlerec.run -m paddlerec.models.rank.dnn
 * [分布式深度学习介绍](doc/ps_background.md)
 
 ### 快速开始
-* [十分钟上手PaddleRec](https://aistudio.baidu.com/aistudio/projectdetail/523965)
+* [十分钟上手PaddleRec](https://aistudio.baidu.com/aistudio/projectdetail/559336)
 
 ### 入门教程
 * [数据准备](doc/slot_reader.md)

core/trainers/framework/startup.py

@@ -39,6 +39,9 @@ class StartupBase(object):
             return
         print("going to load ", dirname)
         if is_fleet:
+            if context["fleet_mode"].upper() == "PS":
+                return
+            # For Pslib
             context["fleet"].load_persistables(context["exe"], dirname)
         else:
             fluid.io.load_persistables(