diff --git a/notebook/ConditionGAN.src.md b/notebook/ConditionGAN.src.md
new file mode 100644
index 00000000..0c9aa87c
--- /dev/null
+++ b/notebook/ConditionGAN.src.md
@@ -0,0 +1,170 @@
+# 使用Jittor实现Conditional GAN
+
+Generative Adversarial Nets（GAN）[1]提出了一种新的方法来训练生成模型。然而，GAN对于要生成的图片缺少控制。Conditional GAN（CGAN）[2]通过添加显式的条件或标签，来控制生成的图像。本教程讲解了CGAN的网络结构、损失函数设计、使用CGAN生成一串数字、从头训练CGAN、以及在mnist手写数字数据集上的训练结果。
+
+## CGAN网络架构
+
+通过在生成器generator和判别器discriminator中添加相同的额外信息y，GAN就可以扩展为一个conditional模型。y可以是任何形式的辅助信息，例如类别标签或者其他形式的数据。我们可以通过将y作为额外输入层，添加到生成器和判别器来完成条件控制。
+
+在生成器generator中，除了y之外，还额外输入随机一维噪声z，为结果生成提供更多灵活性。
+
+![](https://cg.cs.tsinghua.edu.cn/jittor/images/tutorial/2020-5-13-22-47-cgan/network.jpg)
+
+## 损失函数
+
+### GAN的损失函数
+
+在解释CGAN的损失函数之前，首先介绍GAN的损失函数。下面是GAN的损失函数设计。
+
+![](https://cg.cs.tsinghua.edu.cn/jittor/images/tutorial/2020-5-13-22-47-cgan/gan-loss.png)
+
+对于判别器D，我们要训练最大化这个loss。如果D的输入是来自真实样本的数据x,则D的输出D(x)要尽可能地大，log(D(x))也会尽可能大。如果D的输入是来自G生成的假图片G(z)，则D的输出D(G(z))应尽可能地小，从而log(1-D(G(z))会尽可能地大。这样可以达到max D的目的。
+
+对于生成器G，我们要训练最小化这个loss。对于G生成的假图片G(z)，我们希望尽可能地骗过D，让它觉得我们生成的图片就是真的图片，这样就达到了G“以假乱真”的目的。那么D的输出D(G(z))应尽可能地大，从而log(1-D(G(z))会尽可能地小。这样可以达到min G的目的。
+
+D和G以这样的方式联合训练，最终达到G的生成能力越来越强，D的判别能力越来越强的目的。
+
+### CGAN的损失函数
+
+下面是CGAN的损失函数设计。
+
+![](https://cg.cs.tsinghua.edu.cn/jittor/images/tutorial/2020-5-13-22-47-cgan/loss.png)
+
+
+很明显，CGAN的loss跟GAN的loss的区别就是多了条件限定y。D(x/y)代表在条件y下，x为真的概率。D(G(z/y))表示在条件y下，G生成的图片被D判别为真的概率。
+
+## Jittor代码数字生成
+
+首先，我们导入需要的包，并且设置好所需的超参数：
+
+```python
+import jittor as jt
+from jittor import nn
+import numpy as np
+import pylab as pl
+
+%matplotlib inline
+
+# 隐空间向量长度
+latent_dim = 100
+# 类别数量
+n_classes = 10
+# 图片大小
+img_size = 32
+# 图片通道数量
+channels = 1
+# 图片张量的形状
+img_shape = (channels, img_size, img_size)
+```
+
+第一步，定义生成器G。该生成器输入两个一维向量y和noise，生成一张图片。
+
+```python
+class Generator(nn.Module):
+    def __init__(self):
+        super(Generator, self).__init__()
+        self.label_emb = nn.Embedding(n_classes, n_classes)
+
+        def block(in_feat, out_feat, normalize=True):
+            layers = [nn.Linear(in_feat, out_feat)]
+            if normalize:
+                layers.append(nn.BatchNorm1d(out_feat, 0.8))
+            layers.append(nn.LeakyReLU(0.2))
+            return layers
+        self.model = nn.Sequential(
+            *block((latent_dim + n_classes), 128, normalize=False), 
+            *block(128, 256), 
+            *block(256, 512), 
+            *block(512, 1024), 
+            nn.Linear(1024, int(np.prod(img_shape))), 
+            nn.Tanh())
+
+    def execute(self, noise, labels):
+        gen_input = jt.contrib.concat((self.label_emb(labels), noise), dim=1)
+        img = self.model(gen_input)
+        img = img.view((img.shape[0], *img_shape))
+        return img
+```
+
+第二步，定义判别器D。D输入一张图片和对应的y，输出是真图片的概率。
+
+```python
+class Discriminator(nn.Module):
+    def __init__(self):
+        super(Discriminator, self).__init__()
+        self.label_embedding = nn.Embedding(n_classes, n_classes)
+        self.model = nn.Sequential(
+            nn.Linear((n_classes + int(np.prod(img_shape))), 512), 
+            nn.LeakyReLU(0.2), 
+            nn.Linear(512, 512), 
+            nn.Dropout(0.4), 
+            nn.LeakyReLU(0.2), 
+            nn.Linear(512, 512), 
+            nn.Dropout(0.4), 
+            nn.LeakyReLU(0.2), 
+            nn.Linear(512, 1))
+
+    def execute(self, img, labels):
+        d_in = jt.contrib.concat((img.view((img.shape[0], (- 1))), self.label_embedding(labels)), dim=1)
+        validity = self.model(d_in)
+        return validity
+```
+
+第三步，使用CGAN生成一串数字。
+
+代码如下。您可以使用您训练好的模型来生成图片，也可以使用我们提供的预训练参数： 模型预训练参数下载：<https://cloud.tsinghua.edu.cn/d/fbe30ae0967942f6991c/>。
+
+```python
+# 下载提供的预训练参数
+!wget https://cg.cs.tsinghua.edu.cn/jittor/assets/build/generator_last.pkl
+!wget https://cg.cs.tsinghua.edu.cn/jittor/assets/build/discriminator_last.pkl
+```
+
+生成自定义的数字：
+
+```python
+# 定义模型
+generator = Generator()
+discriminator = Discriminator()
+generator.eval()
+discriminator.eval()
+
+# 加载参数
+generator.load('./generator_last.pkl')
+discriminator.load('./discriminator_last.pkl')
+
+# 定义一串数字
+number = "201962517"
+n_row = len(number)
+z = jt.array(np.random.normal(0, 1, (n_row, latent_dim))).float32().stop_grad()
+labels = jt.array(np.array([int(number[num]) for num in range(n_row)])).float32().stop_grad()
+gen_imgs = generator(z,labels)
+
+pl.imshow(gen_imgs.data.transpose((1,2,0,3))[0].reshape((gen_imgs.shape[2], -1)))
+```
+
+## 从头训练Condition GAN
+
+从头训练 Condition GAN 的完整代码在<https://github.com/Jittor/gan-jittor/blob/master/models/cgan/cgan.py>， 让我们把他下载下来看看！
+
+```python
+!wget https://raw.githubusercontent.com/Jittor/gan-jittor/master/models/cgan/cgan.py
+!python3.7 ./cgan.py --help
+
+# 选择合适的batch size，运行试试
+# 运行命令： !python3.7 ./cgan.py --batch_size 64
+```
+
+## MNIST数据集训练结果
+
+下面展示了Jittor版CGAN在MNIST数据集的训练结果。下面分别是训练0 epoch和90 epoches的结果。 
+
+![](https://cg.cs.tsinghua.edu.cn/jittor/images/tutorial/2020-5-13-22-47-cgan/0-epoch.png)
+
+![](https://cg.cs.tsinghua.edu.cn/jittor/images/tutorial/2020-5-13-22-47-cgan/90-epoch.png)
+
+## 参考文献
+
+1. Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in neural information processing systems. 2014.
+
+2. Mirza, Mehdi, and Simon Osindero. “Conditional generative adversarial nets.” arXiv preprint arXiv:1411.1784 (2014).
\ No newline at end of file
diff --git a/python/jittor/compiler.py b/python/jittor/compiler.py
index a622ce2b..9c3b086f 100644
--- a/python/jittor/compiler.py
+++ b/python/jittor/compiler.py
@@ -815,12 +815,22 @@ with jit_utils.import_scope(import_flags):
     jit_utils.try_import_jit_utils_core()
 
 python_path = sys.executable
-py3_config_path = sys.executable+"-config"
-assert os.path.isfile(python_path)
-if not os.path.isfile(py3_config_path) :
-    py3_config_path = sys.executable + '3-config'
+py3_config_paths = [
+    sys.executable + "-config",
+    os.path.dirname(sys.executable) + f"/python3.{sys.version_info.minor}-config",
+    f"/usr/bin/python3.{sys.version_info.minor}-config",
+    os.path.dirname(sys.executable) + "/python3-config",
+]
+if "python_config_path" in os.environ:
+    py3_config_paths.insert(0, os.environ["python_config_path"])
 
-assert os.path.isfile(py3_config_path)
+for py3_config_path in py3_config_paths:
+    if os.path.isfile(py3_config_path):
+        break
+else:
+    raise RuntimeError(f"python3.{sys.version_info.minor}-config "
+        "not found in {py3_config_paths}, please specify "
+        "enviroment variable 'python_config_path'")
 nvcc_path = env_or_try_find('nvcc_path', '/usr/local/cuda/bin/nvcc')
 gdb_path = try_find_exe('gdb')
 addr2line_path = try_find_exe('addr2line')
diff --git a/python/jittor/dataset/mnist.py b/python/jittor/dataset/mnist.py
index 6d237a42..ea55a883 100644
--- a/python/jittor/dataset/mnist.py
+++ b/python/jittor/dataset/mnist.py
@@ -17,12 +17,19 @@ import jittor as jt
 import jittor.transform as trans
 
 class MNIST(Dataset):
-    def __init__(self, data_root=dataset_root+"/mnist_data/", train=True ,download=True, transform=None):
+    def __init__(self, data_root=dataset_root+"/mnist_data/", 
+                 train=True, 
+                 download=True, 
+                 batch_size = 16,
+                 shuffle = False,
+                 transform=None):
         # if you want to test resnet etc you should set input_channel = 3, because the net set 3 as the input dimensions
         super().__init__()
         self.data_root = data_root
         self.is_train = train
         self.transform = transform
+        self.batch_size = batch_size
+        self.shuffle = shuffle
         if download == True:
             self.download_url()
 
diff --git a/python/jittor/test/test_notebooks.py b/python/jittor/test/test_notebooks.py
index df52230b..c1c0cb33 100644
--- a/python/jittor/test/test_notebooks.py
+++ b/python/jittor/test/test_notebooks.py
@@ -15,7 +15,7 @@ tests = []
 for mdname in os.listdir(dirname):
     if not mdname.endswith(".src.md"): continue
     # temporary disable model_test
-    if "LSGAN" in mdname: continue
+    if "GAN" in mdname: continue
     tests.append(mdname[:-3])
 
 try: