From f12b790f7552e1e8ed4e6ba306286467e613814b Mon Sep 17 00:00:00 2001
From: Bo Zhou <zhoubo01@baidu.com>
Date: Fri, 19 Apr 2019 11:09:53 +0800
Subject: [PATCH] Update README.md (#68)

* Update README.md

* Update Dockerfile

* Update build.sh
---
 .teamcity/Dockerfile | 2 +-
 .teamcity/build.sh   | 2 ++
 README.md            | 2 +-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/.teamcity/Dockerfile b/.teamcity/Dockerfile
index 85d8a2f..e5422e8 100644
--- a/.teamcity/Dockerfile
+++ b/.teamcity/Dockerfile
@@ -15,7 +15,7 @@
 
 # A dev image based on paddle production image
 
-FROM parl/parl-test:cuda9.0-cudnn7
+FROM parl/parl-test:1.1-cuda9.0-cudnn7
 
 COPY ./requirements.txt /root/
 
diff --git a/.teamcity/build.sh b/.teamcity/build.sh
index 34092a6..9b8b7a3 100755
--- a/.teamcity/build.sh
+++ b/.teamcity/build.sh
@@ -56,6 +56,8 @@ function check_style() {
 }
 
 function run_test_with_gpu() {
+    export FLAGS_fraction_of_gpu_memory_to_use=0.5
+    
     mkdir -p ${REPO_ROOT}/build
     cd ${REPO_ROOT}/build
     cmake ..
diff --git a/README.md b/README.md
index 4e7a956..971f918 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ agent = AtariAgent(algorithm)
 
 # Parallelization
 PARL provides a compact API for distributed training, allowing users to transfer the code into a parallelized version by simply adding a decorator.  
-Here is a `Hello World` example to demonstrate how easily it is to leverage outer computation resources.
+Here is a `Hello World` example to demonstrate how easy it is to leverage outer computation resources.
 ```python
 #============Agent.py=================
 @parl.remote_class
-- 
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