Metadata-Version: 2.1
Name: nn-sdk
Version: 1.2.5
Summary: nn_sdk推理tf1 tf2 pb nlp模型 , input tensor[input_ids , input_mask], output tensor[pred_ids]
Home-page: https://github.com/passion765
Author: tk
Author-email: 462304@qq.com
License: Apache 2.0
Keywords: nn-sdk,tensorflow,tf inference
Platform: linux_x86_64
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3, <4
Description-Content-Type: text/markdown

nn-sdk是一个基于tf1 tf2神经网络推理开发包

```py
# -*- coding: utf-8 -*-
from nn_sdk.py_tf_csdk import csdk_object

'''
    支持多子图,支持图多输入多输出.
    支持tensorflow 1 pb , tensorflow 2 pb , tensorflow ckpt
    net_stage 
'''
config = {
    "model_dir": r'./model.ckpt',
    "log_level": 4, # 0 fatal , 2 error , 4 info , 8 debug
    "model_type": 1,  # 0 pb format   if 1 ckpt format
    "ConfigProto": {
        "log_device_placement": False,
        "allow_soft_placement": True,
        "gpu_options": {
            "allow_growth": True
        },
    },
    "graph_inf_version": 1,  # the format of tensorflow pb model [1,2]
    "graph": [
        {
            "input": ["input_ids:0","input_mask:0"], #tf1 sample ["input_ids:0"],  tf2 sample ["input_ids"]
            "output": ["pred_ids:0"], #tf1 sample ["pred_ids:0"],  tf2 sample ["pred_ids"]
        }
    ]}

seq_length = 4
input_ids = [[10.] * seq_length]
input_mask = [[1] * seq_length]
sdk_inf = csdk_object(config)
if sdk_inf.valid():
    net_stage = 0
    ret, out = sdk_inf.process(net_stage, input_ids,input_mask)
    print(ret)
    print(out)
    sdk_inf.close()
```


