Basics: Op, Var

基本概念：Op, Var

To train your model with jittor, there are only two main concept you need to know:

要使用jittor训练模型，您需要了解两个主要概念：

• Var: basic data type of jittor
• Var：Jittor的基本数据类型
• Operations: Jittor'op is simular with numpy
• Operations：Jittor的算子与numpy类似

Var

First, let's get started with Var. Var is the basic data type of jittor. Computation process in Jittor is asynchronous for optimization. If you want to access the data, Var.data can be used for synchronous data accessing.

首先，让我们开始使用Var。Var是jittor的基本数据类型，为了运算更加高效Jittor中的计算过程是异步的。 如果要访问数据，可以使用Var.data进行同步数据访问。

import jittor as jt
a = jt.float32([1,2,3])
print (a)
print (a.data)
# Output: float32[3,]
# Output: [ 1. 2. 3.]

Op

Jittor'op is simular with numpy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. Printing those variables shows they have the same shape and dtype.

Jittor的算子与numpy类似。 让我们尝试一些操作， 我们通过操作jt.float32创建Var a和b，并将它们相加。 输出这些变量相关信息，可以看出它们具有相同的形状和类型。

import jittor as jt
a = jt.float32([1,2,3])
b = jt.float32([4,5,6])
c = a+b
print(a,b,c)

Beside that, All the operators we used jt.xxx(Var, ...) have alias Var.xxx(...). For example:

除此之外，我们使用的所有算子jt.xxx(Var,...)都具有别名Var.xxx(...)。 例如：

c.max() # alias of jt.max(a)
c.add(a) # alias of jt.add(c, a)
c.min(keepdims=True) # alias of jt.min(c, keepdims=True)

if you want to know all the operation which Jittor supports. try help(jt.ops). All the operation you found in jt.ops.xxx, can be used via alias jt.xxx.

如果您想知道Jittor支持的所有操作，可以运行help(jt.ops)。 您在jt.ops.xxx中找到的所有操作都可以通过别名jt.xxx。

help(jt.ops)