58 lines
2.1 KiB
Markdown
58 lines
2.1 KiB
Markdown
# Basics: Op, Var
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# 基本概念：Op, Var
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To train your model with jittor, there are only two main concept you need to know:
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要使用jittor训练模型，您需要了解两个主要概念：
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* Var: basic data type of jittor
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* Var：Jittor的基本数据类型
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* Operations: Jittor'op is simular with numpy
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* Operations：Jittor的算子与numpy类似
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## Var
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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.
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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。
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```
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import jittor as jt
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a = jt.float32([1,2,3])
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print (a)
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print (a.data)
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# Output: float32[3,]
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# Output: [ 1. 2. 3.]
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```
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## Op
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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.
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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。
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```
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import jittor as jt
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a = jt.float32([1,2,3])
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b = jt.float32([4,5,6])
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c = a+b
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print(a,b,c)
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```
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Beside that, All the operators we used `jt.xxx(Var, ...)` have alias `Var.xxx(...)`. For example:
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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：
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```
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c.max() # alias of jt.max(a)
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c.add(a) # alias of jt.add(c, a)
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c.min(keepdims=True) # alias of jt.min(c, keepdims=True)
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```
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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`.
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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`。
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```
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help(jt.ops)
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```
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