| Name | jittor JSON |
| Version |
1.3.8.5
JSON |
| download |
| home_page | http://jittor.org |
| Summary | a Just-in-time(JIT) deep learning framework |
| upload_time | 2023-06-30 18:44:01 |
| maintainer | |
| docs_url | None |
| author | Jittor Group |
| requires_python | >=3.7 |
| license | |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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No coveralls.
|
# Jittor: a Just-in-time(JIT) deep learning framework
[Quickstart](#quickstart) | [Install](#install) | [Tutorial](#tutorial) | [简体中文](./README.cn.md)
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just-in-time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code with specialized for your model. Jittor also contains a wealth of high-performance model libraries, including: image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. .
The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.
Related Links:
* [Jittor Website](https://cg.cs.tsinghua.edu.cn/jittor/)
* [Jittor Tutorials](https://cg.cs.tsinghua.edu.cn/jittor/tutorial/)
* [Jittor Models](https://cg.cs.tsinghua.edu.cn/jittor/resources/)
* [Jittor Documents](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)
* [Github](https://github.com/jittor/jittor), [GitLink](https://www.gitlink.org.cn/jittor/jittor), [Gitee](https://gitee.com/jittor/jittor)
* [Jittor Forum](https://discuss.jittor.org/)
* [Awesome Jittor List](https://github.com/Jittor/jittor/blob/master/AWESOME-JITTOR-LIST.md)
* IM: QQ Group(761222083)
The following example shows how to model a two-layer neural network step by step and train from scratch In a few lines of Python code.
```python
import jittor as jt
from jittor import Module
from jittor import nn
import numpy as np
class Model(Module):
def __init__(self):
self.layer1 = nn.Linear(1, 10)
self.relu = nn.Relu()
self.layer2 = nn.Linear(10, 1)
def execute (self,x) :
x = self.layer1(x)
x = self.relu(x)
x = self.layer2(x)
return x
def get_data(n): # generate random data for training test.
for i in range(n):
x = np.random.rand(batch_size, 1)
y = x*x
yield jt.float32(x), jt.float32(y)
learning_rate = 0.1
batch_size = 50
n = 1000
model = Model()
optim = nn.SGD(model.parameters(), learning_rate)
for i,(x,y) in enumerate(get_data(n)):
pred_y = model(x)
dy = pred_y - y
loss = dy * dy
loss_mean = loss.mean()
optim.step(loss_mean)
print(f"step {i}, loss = {loss_mean.data.sum()}")
```
## Contents
* [Quickstart](#quickstart)
* [Install](#install)
* [Tutorial](#tutorial)
* [Contributing](#contributing)
* [The Team](#theteam)
* [License](#license)
## Quickstart
We provide some jupyter notebooks to help you quick start with Jittor.
- [Example: Model definition and training][1]
- [Basics: Op, Var][2]
- [Meta-operator: Implement your own convolution with Meta-operator][3]
## Install
Jittor environment requirements:
| OS | CPU | Python | Compiler | (Optional) GPU platform |
|--------------------------------------------------------|-------------------------------------|--------|--------------|---------------------------------------------|
| Linux<br>(Ubuntu, CentOS, Arch, <br>UOS, KylinOS, ...) | x86 <br>x86_64 <br>ARM <br>loongson | >= 3.7 | g++ >=5.4 | Nvidia CUDA >= 10.0, [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar) <br> or [AMD ROCm](https://docs.amd.com/) >= 4.0 <br> or [Hygon DCU DTK](https://tycloud.hpccube.com/doc/1.0.6/11277/general-handbook/software-tutorial/jittor.html) >= 22.04 |
| macOS <br>(>= 10.14 Mojave) | intel<br>Apple Silicon | >= 3.7 | clang >= 8.0 | - |
| Windows 10 & 11 | x86_64 | [>= 3.8](https://www.python.org/downloads/windows/) | - | Nvidia CUDA >= 10.2 [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows) |
Jittor offers three ways to install: pip, docker, or manual.
## Pip install
```bash
sudo apt install python3.7-dev libomp-dev
python3.7 -m pip install jittor
# or install from github(latest version)
# python3.7 -m pip install git+https://github.com/Jittor/jittor.git
python3.7 -m jittor.test.test_example
```
### macOS install
Please first install additional dependencies with [homebrew](https://brew.sh).
```bash
brew install libomp
```
Then you can install jittor through pip and run the example.
```bash
python3.7 -m pip install jittor
python3.7 -m jittor.test.test_example
```
Currently jittor only supports CPU on macOS.
### Windows install
```bash
# check your python version(>=3.8)
python --version
python -m pip install jittor
# if conda is used
conda install pywin32
```
In Windows, jittor will automatically detect and install CUDA, please make sure your NVIDIA driver support CUDA 10.2 or above, or you can manually let jittor install CUDA for you:
```bash
python -m jittor_utils.install_cuda
```
## Docker Install
We provide a Docker installation method to save you from configuring the environment. The Docker installation method is as follows:
```
# CPU only(Linux)
docker run -it --network host jittor/jittor
# CPU and CUDA(Linux)
docker run -it --network host --gpus all jittor/jittor-cuda
# CPU only(Mac and Windows)
docker run -it -p 8888:8888 jittor/jittor
```
## manual install
We will show how to install Jittor in Ubuntu 16.04 step by step, Other Linux distributions may have similar commands.
### Step 1: Choose your back-end compiler
```bash
# g++
sudo apt install g++ build-essential libomp-dev
# OR clang++-8
wget -O - https://raw.githubusercontent.com/Jittor/jittor/master/script/install_llvm.sh > /tmp/llvm.sh
bash /tmp/llvm.sh 8
```
### Step 2: Install Python and python-dev
Jittor need python version >= 3.7.
```bash
sudo apt install python3.7 python3.7-dev
```
### Step 3: Run Jittor
The whole framework is compiled Just-in-time. Let's install jittor via pip
```bash
git clone https://github.com/Jittor/jittor.git
sudo pip3.7 install ./jittor
export cc_path="clang++-8"
# if other compiler is used, change cc_path
# export cc_path="g++"
# export cc_path="icc"
# run a simple test
python3.7 -m jittor.test.test_example
```
if the test is passed, your Jittor is ready.
### Optional Step 4: Enable CUDA
Using CUDA in Jittor is very simple, Just setup environment value `nvcc_path`
```bash
# replace this var with your nvcc location
export nvcc_path="/usr/local/cuda/bin/nvcc"
# run a simple cuda test
python3.7 -m jittor.test.test_cuda
```
if the test is passed, your can use Jittor with CUDA by setting `use_cuda` flag.
```python
import jittor as jt
jt.flags.use_cuda = 1
```
### Optional Step 5: Test Resnet18 training
To check the integrity of Jittor, you can run Resnet18 training test. Note: 6G GPU RAM is requires in this test.
```bash
python3.7 -m jittor.test.test_resnet
```
if those tests are failed, please report bugs for us, and feel free to contribute ^_^
## Tutorial
In the tutorial section, we will briefly explain the basic concept of Jittor.
To train your model with Jittor, there are only three main concepts you need to know:
* Var: basic data type of jittor
* Operations: Jittor'op is simular with numpy
### 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.
```python
import jittor as jt
a = jt.float32([1,2,3])
print (a)
print (a.data)
# Output: float32[3,]
# Output: [ 1. 2. 3.]
```
And we can give the variable a name.
```python
a.name('a')
print(a.name())
# Output: a
```
### Operations
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.
```python
import jittor as jt
a = jt.float32([1,2,3])
b = jt.float32([4,5,6])
c = a*b
print(a,b,c)
print(type(a), type(b), type(c))
# Output: float32[3,] float32[3,] float32[3,]
# Output: <class 'jittor_core.Var'> <class 'jittor_core.Var'> <class 'jittor_core.Var'>
```
Beside that, All the operators we used `jt.xxx(Var, ...)` have alias `Var.xxx(...)`. For example:
```python
c.max() # alias of jt.max(c)
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`.
```python
help(jt.ops)
# Output:
# abs(x: core.Var) -> core.Var
# add(x: core.Var, y: core.Var) -> core.Var
# array(data: array) -> core.Var
# binary(x: core.Var, y: core.Var, op: str) -> core.Var
# ......
```
### More
If you want to know more about Jittor, please check out the notebooks below:
* Quickstart
- [Example: Model definition and training][1]
- [Basics: Op, Var][2]
- [Meta-operator: Implement your own convolution with Meta-operator][3]
* Advanced
- [Custom Op: write your operator with C++ and CUDA and JIT compile it][4]
- [Profiler: Profiling your model][5]
- Jtune: Tool for performance tuning
[1]: python/jittor/notebook/example.src.md "example"
[2]: python/jittor/notebook/basics.src.md "basics"
[3]: python/jittor/notebook/meta_op.src.md "meta_op"
[4]: python/jittor/notebook/custom_op.src.md "custom_op"
[5]: python/jittor/notebook/profiler.src.md "profiler"
Those notebooks can be started in your own computer by `python3.7 -m jittor.notebook`
## Contributing
Jittor is still young. It may contain bugs and issues. Please report them in our bug track system. Contributions are welcome. Besides, if you have any ideas about Jittor, please let us know.
You can help Jittor in the following ways:
* Citing Jittor in your paper
* recommend Jittor to your friends
* Contributing code
* Contributed tutorials and documentation
* File an issue
* Answer jittor related questions
* Light up the stars
* Keep an eye on jittor
* ......
## Contact Us
Website: http://cg.cs.tsinghua.edu.cn/jittor/
Email: jittor@qq.com
File an issue: https://github.com/Jittor/jittor/issues
QQ Group: 761222083
<img src="https://cg.cs.tsinghua.edu.cn/jittor/images/news/2020-12-8-21-19-1_2_2/fig4.png" width="200"/>
## The Team
Jittor is currently maintained by the [Tsinghua CSCG Group](https://cg.cs.tsinghua.edu.cn/). If you are also interested in Jittor and want to improve it, Please join us!
## Citation
```
@article{hu2020jittor,
title={Jittor: a novel deep learning framework with meta-operators and unified graph execution},
author={Hu, Shi-Min and Liang, Dun and Yang, Guo-Ye and Yang, Guo-Wei and Zhou, Wen-Yang},
journal={Science China Information Sciences},
volume={63},
number={222103},
pages={1--21},
year={2020}
}
```
## License
Jittor is Apache 2.0 licensed, as found in the LICENSE.txt file.
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"description": "# Jittor: a Just-in-time(JIT) deep learning framework\n\n\n\n[Quickstart](#quickstart) | [Install](#install) | [Tutorial](#tutorial) | [\u7b80\u4f53\u4e2d\u6587](./README.cn.md)\n\n\nJittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just-in-time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code with specialized for your model. Jittor also contains a wealth of high-performance model libraries, including: image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. .\n\n\nThe front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.\n\n\nRelated Links:\n* [Jittor Website](https://cg.cs.tsinghua.edu.cn/jittor/)\n* [Jittor Tutorials](https://cg.cs.tsinghua.edu.cn/jittor/tutorial/)\n* [Jittor Models](https://cg.cs.tsinghua.edu.cn/jittor/resources/)\n* [Jittor Documents](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)\n* [Github](https://github.com/jittor/jittor), [GitLink](https://www.gitlink.org.cn/jittor/jittor), [Gitee](https://gitee.com/jittor/jittor)\n* [Jittor Forum](https://discuss.jittor.org/)\n* [Awesome Jittor List](https://github.com/Jittor/jittor/blob/master/AWESOME-JITTOR-LIST.md)\n* IM: QQ Group(761222083)\n\n\n\nThe following example shows how to model a two-layer neural network step by step and train from scratch In a few lines of Python code.\n\n\n```python\nimport jittor as jt\nfrom jittor import Module\nfrom jittor import nn\nimport numpy as np\n\nclass Model(Module):\n def __init__(self):\n self.layer1 = nn.Linear(1, 10)\n self.relu = nn.Relu() \n self.layer2 = nn.Linear(10, 1)\n def execute (self,x) :\n x = self.layer1(x)\n x = self.relu(x)\n x = self.layer2(x)\n return x\n\ndef get_data(n): # generate random data for training test.\n for i in range(n):\n x = np.random.rand(batch_size, 1)\n y = x*x\n yield jt.float32(x), jt.float32(y)\n\n\nlearning_rate = 0.1\nbatch_size = 50\nn = 1000\n\nmodel = Model()\noptim = nn.SGD(model.parameters(), learning_rate)\n\nfor i,(x,y) in enumerate(get_data(n)):\n pred_y = model(x)\n dy = pred_y - y\n loss = dy * dy\n loss_mean = loss.mean()\n optim.step(loss_mean)\n print(f\"step {i}, loss = {loss_mean.data.sum()}\")\n```\n\n## Contents\n\n* [Quickstart](#quickstart)\n* [Install](#install)\n* [Tutorial](#tutorial)\n* [Contributing](#contributing)\n* [The Team](#theteam)\n* [License](#license)\n\n\n\n## Quickstart\n\n\nWe provide some jupyter notebooks to help you quick start with Jittor.\n\n\n- [Example: Model definition and training][1]\n- [Basics: Op, Var][2]\n- [Meta-operator: Implement your own convolution with Meta-operator][3]\n\n## Install\n\n\n\nJittor environment requirements:\n\n| OS | CPU | Python | Compiler | (Optional) GPU platform |\n|--------------------------------------------------------|-------------------------------------|--------|--------------|---------------------------------------------|\n| Linux<br>(Ubuntu, CentOS, Arch, <br>UOS, KylinOS, ...) | x86 <br>x86_64 <br>ARM <br>loongson | >= 3.7 | g++ >=5.4 | Nvidia CUDA >= 10.0, [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar) <br> or [AMD ROCm](https://docs.amd.com/) >= 4.0 <br> or [Hygon DCU DTK](https://tycloud.hpccube.com/doc/1.0.6/11277/general-handbook/software-tutorial/jittor.html) >= 22.04 |\n| macOS <br>(>= 10.14 Mojave) | intel<br>Apple Silicon | >= 3.7 | clang >= 8.0 | - |\n| Windows 10 & 11 | x86_64 | [>= 3.8](https://www.python.org/downloads/windows/) | - | Nvidia CUDA >= 10.2 [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows) |\n\n\nJittor offers three ways to install: pip, docker, or manual.\n\n\n## Pip install\n\n\n```bash\nsudo apt install python3.7-dev libomp-dev\npython3.7 -m pip install jittor\n# or install from github(latest version)\n# python3.7 -m pip install git+https://github.com/Jittor/jittor.git\npython3.7 -m jittor.test.test_example\n```\n\n\n\n### macOS install\n\n\nPlease first install additional dependencies with [homebrew](https://brew.sh).\n\n```bash\nbrew install libomp\n```\n\n\nThen you can install jittor through pip and run the example.\n\n```bash\npython3.7 -m pip install jittor\npython3.7 -m jittor.test.test_example\n```\n\n\nCurrently jittor only supports CPU on macOS.\n\n\n### Windows install\n\n\n\n```bash\n# check your python version(>=3.8)\npython --version\npython -m pip install jittor\n# if conda is used\nconda install pywin32\n```\n\n\nIn Windows, jittor will automatically detect and install CUDA, please make sure your NVIDIA driver support CUDA 10.2 or above, or you can manually let jittor install CUDA for you:\n\n```bash\npython -m jittor_utils.install_cuda\n```\n\n\n## Docker Install\n\n\n\nWe provide a Docker installation method to save you from configuring the environment. The Docker installation method is as follows:\n\n```\n# CPU only(Linux)\ndocker run -it --network host jittor/jittor\n# CPU and CUDA(Linux)\ndocker run -it --network host --gpus all jittor/jittor-cuda\n# CPU only(Mac and Windows)\ndocker run -it -p 8888:8888 jittor/jittor\n```\n\n\n## manual install\n\nWe will show how to install Jittor in Ubuntu 16.04 step by step, Other Linux distributions may have similar commands.\n\n\n### Step 1: Choose your back-end compiler\n\n\n```bash\n# g++\nsudo apt install g++ build-essential libomp-dev\n\n# OR clang++-8\nwget -O - https://raw.githubusercontent.com/Jittor/jittor/master/script/install_llvm.sh > /tmp/llvm.sh\nbash /tmp/llvm.sh 8\n```\n### Step 2: Install Python and python-dev\n\n\nJittor need python version >= 3.7.\n\n\n```bash\nsudo apt install python3.7 python3.7-dev\n```\n\n### Step 3: Run Jittor\n\n\nThe whole framework is compiled Just-in-time. Let's install jittor via pip\n\n\n```bash\ngit clone https://github.com/Jittor/jittor.git\nsudo pip3.7 install ./jittor\nexport cc_path=\"clang++-8\"\n# if other compiler is used, change cc_path\n# export cc_path=\"g++\"\n# export cc_path=\"icc\"\n\n# run a simple test\npython3.7 -m jittor.test.test_example\n```\nif the test is passed, your Jittor is ready.\n\n\n### Optional Step 4: Enable CUDA\n\n\nUsing CUDA in Jittor is very simple, Just setup environment value `nvcc_path`\n\n\n```bash\n# replace this var with your nvcc location \nexport nvcc_path=\"/usr/local/cuda/bin/nvcc\" \n# run a simple cuda test\npython3.7 -m jittor.test.test_cuda \n```\nif the test is passed, your can use Jittor with CUDA by setting `use_cuda` flag.\n\n\n```python\nimport jittor as jt\njt.flags.use_cuda = 1\n```\n\n### Optional Step 5: Test Resnet18 training\n\n\nTo check the integrity of Jittor, you can run Resnet18 training test. Note: 6G GPU RAM is requires in this test.\n\n\n```bash\npython3.7 -m jittor.test.test_resnet\n```\nif those tests are failed, please report bugs for us, and feel free to contribute ^_^\n\n\n## Tutorial\n\n\nIn the tutorial section, we will briefly explain the basic concept of Jittor.\n\n\nTo train your model with Jittor, there are only three main concepts you need to know:\n\n\n* Var: basic data type of jittor\n* Operations: Jittor'op is simular with numpy\n\n### Var\n\n\nFirst, 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.\n\n\n```python\nimport jittor as jt\na = jt.float32([1,2,3])\nprint (a)\nprint (a.data)\n# Output: float32[3,]\n# Output: [ 1. 2. 3.]\n```\n\nAnd we can give the variable a name.\n\n\n```python\na.name('a')\nprint(a.name())\n# Output: a\n```\n\n### Operations\n\n\nJittor'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.\n\n\n```python\nimport jittor as jt\na = jt.float32([1,2,3])\nb = jt.float32([4,5,6])\nc = a*b\nprint(a,b,c)\nprint(type(a), type(b), type(c))\n# Output: float32[3,] float32[3,] float32[3,]\n# Output: <class 'jittor_core.Var'> <class 'jittor_core.Var'> <class 'jittor_core.Var'>\n```\nBeside that, All the operators we used `jt.xxx(Var, ...)` have alias `Var.xxx(...)`. For example:\n\n\n```python\nc.max() # alias of jt.max(c)\nc.add(a) # alias of jt.add(c, a)\nc.min(keepdims=True) # alias of jt.min(c, keepdims=True)\n```\n\nif 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`.\n\n\n```python\nhelp(jt.ops)\n# Output:\n# abs(x: core.Var) -> core.Var\n# add(x: core.Var, y: core.Var) -> core.Var\n# array(data: array) -> core.Var\n# binary(x: core.Var, y: core.Var, op: str) -> core.Var\n# ......\n```\n### More\n\n\nIf you want to know more about Jittor, please check out the notebooks below:\n\n\n* Quickstart\n - [Example: Model definition and training][1]\n - [Basics: Op, Var][2]\n - [Meta-operator: Implement your own convolution with Meta-operator][3]\n* Advanced\n - [Custom Op: write your operator with C++ and CUDA and JIT compile it][4]\n - [Profiler: Profiling your model][5]\n - Jtune: Tool for performance tuning\n\n\n\n[1]: python/jittor/notebook/example.src.md\t\"example\"\n[2]: python/jittor/notebook/basics.src.md\t\"basics\"\n[3]: python/jittor/notebook/meta_op.src.md\t\"meta_op\"\n[4]: python/jittor/notebook/custom_op.src.md\t\"custom_op\"\n[5]: python/jittor/notebook/profiler.src.md\t\"profiler\"\n\nThose notebooks can be started in your own computer by `python3.7 -m jittor.notebook`\n\n\n## Contributing\n\n\nJittor is still young. It may contain bugs and issues. Please report them in our bug track system. Contributions are welcome. Besides, if you have any ideas about Jittor, please let us know.\n\n\n\n\nYou can help Jittor in the following ways:\n\n* Citing Jittor in your paper\n* recommend Jittor to your friends\n* Contributing code\n* Contributed tutorials and documentation\n* File an issue\n* Answer jittor related questions\n* Light up the stars\n* Keep an eye on jittor\n* ......\n\n## Contact Us\n\n\n\n\n\nWebsite: http://cg.cs.tsinghua.edu.cn/jittor/\n\nEmail: jittor@qq.com\n\nFile an issue: https://github.com/Jittor/jittor/issues\n\nQQ Group: 761222083\n\n\n<img src=\"https://cg.cs.tsinghua.edu.cn/jittor/images/news/2020-12-8-21-19-1_2_2/fig4.png\" width=\"200\"/>\n\n## The Team\n\n\nJittor is currently maintained by the [Tsinghua CSCG Group](https://cg.cs.tsinghua.edu.cn/). If you are also interested in Jittor and want to improve it, Please join us!\n\n\n## Citation\n\n\n```\n@article{hu2020jittor,\n title={Jittor: a novel deep learning framework with meta-operators and unified graph execution},\n author={Hu, Shi-Min and Liang, Dun and Yang, Guo-Ye and Yang, Guo-Wei and Zhou, Wen-Yang},\n journal={Science China Information Sciences},\n volume={63},\n number={222103},\n pages={1--21},\n year={2020}\n}\n```\n\n## License\n\n\nJittor is Apache 2.0 licensed, as found in the LICENSE.txt file.\n\n\n\n\n",
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