infery


Nameinfery JSON
Version 3.9.0 PyPI version JSON
download
home_pagehttps://github.com/Deci-AI/infery-examples
SummaryDeci Run-Time Engine
upload_time2023-01-12 08:53:48
maintainer
docs_urlNone
authorDeci AI
requires_python>=3.7.5
licenseDeci Infery License
keywords deci ai inference deep learning
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            # Infery
`infery` is a runtime engine that simplifies inference, benchmark and profiling.<br>
`infery` supports all major deep learning frameworks, with a <b>unified and simple API</b>.

## Quickstart and Examples
<a href="https://github.com/Deci-AI/infery-examples">
    <img src="https://img.shields.io/badge/Public-Infery Examples-green">
</a>

### Installation Instructions
https://docs.deci.ai/docs/installing-infery-1

## Usage
![Infery Usage](https://deci.ai/wp-content/uploads/2021/06/Infery-Fig1.png)


#### 1. Load an example model
```python
>>> import infery, numpy as np
>>> model = infery.load(model_path='MyNewTask_1_0.onnx', framework_type='onnx', inference_hardware='gpu')
```

#### 2. Predict with a random numpy input

```python
>>> inputs = [np.random.random(shape).astype('float32') for shape in [[1, 243, 30, 40], [1, 172, 60, 80], [1, 102, 120, 160], [1, 64, 240, 320], [1, 153, 15, 20]]]
>>> model.predict(inputs)
[array([[[[-3.5920768, -3.5920792, -3.592102 , ..., -3.592099 ,
           -3.5920944, -3.5920882],
          [-3.592076 , -3.5919113, -3.592086 , ..., -3.5921211,
           -3.5921066, -3.5920937],
          [-3.592083 , -3.592073 , -3.5920823, ..., -3.5921297,
           -3.592109 , -3.5920937],
          ...,
          [-3.5920753, -3.5917826, -3.591444 , ..., -3.580754 ,
           -3.5816329, -3.582549 ],
          [-3.592073 , -3.5917459, -3.591257 , ..., -3.5817945,
           -3.5820704, -3.5835373],
          [-3.592073 , -3.5920737, -3.5920737, ..., -3.5853324,
           -3.5845006, -3.5856297]],
 
         [[-5.862198 , -5.8567815, -5.851764 , ..., -5.86396  ,
           -5.8639617, -5.865011 ],
          [-5.858771 , -5.8493323, -5.841462 , ..., -5.8617773,
           -5.8614554, -5.8633246],
          [-5.8560567, -5.844124 , -5.8351245, ..., -5.8598166,
           -5.859674 , -5.8624067],
          ...,
          [-5.8608136, -5.854358 , -5.8467784, ..., -5.8504496,
           -5.8563104, -5.8615303],
          [-5.86313  , -5.8587003, -5.8531966, ..., -5.8534794,
           -5.8581944, -5.8625536],
          [-5.865306 , -5.8623176, -5.8593984, ..., -5.8581495,
           -5.861572 , -5.865295 ]],
 
         [[-8.843734 , -8.840406 , -8.837172 , ..., -8.840413 ,
           -8.842026 , -8.843931 ],
          [-8.840792 , -8.836787 , -8.831037 , ..., -8.836103 ,
           -8.839954 , -8.842534 ],
          [-8.838855 , -8.833998 , -8.82706  , ..., -8.835106 ,
           -8.839087 , -8.841538 ],
          ...,
          [-8.842419 , -8.840865 , -8.838625 , ..., -8.83943  ,
           -8.843677 , -8.845087 ],
          [-8.844379 , -8.84402  , -8.843141 , ..., -8.84185  ,
           -8.844696 , -8.845202 ],
          [-8.844775 , -8.845572 , -8.845177 , ..., -8.843876 ,
           -8.8444395, -8.845926 ]]]], dtype=float32)]
```

#### 3. Benchmark the model on the current hardware
```python
>>> model.benchmark(batch_size=1)
-INFO- Benchmarking the model in batch size 1 and dimensions [(243, 30, 40), (172, 60, 80), (102, 120, 160), (64, 240, 320), (153, 15, 20)]... 
<ModelBenchmarks: {
    "batch_size": 1,
    "batch_inf_time": "6.57 ms",
    "batch_inf_time_variance": "0.02 ms",
    "model_memory_used": "1536.00 mb",
    "system_startpoint_memory_used": "1536.00 mb",
    "post_inference_memory_used": "1536.00 mb",
    "total_memory_size": "7982.00 mb",
    "throughput": "152.32 fps",
    "sample_inf_time": "6.57 ms",
    "include_io": true,
    "framework_type": "onnx",
    "framework_version": "1.10.0",
    "inference_hardware": "GPU",
    "date": "11:16:55__02-03-2022",
    "ctime": 1643879815,
    "h_to_d_mean": null,
    "d_to_h_mean": null,
    "h_to_d_variance": null,
    "d_to_h_variance": null
}>
```

## Documentation:
https://docs.deci.ai/docs/infery

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    "description": "# Infery\n`infery` is a runtime engine that simplifies inference, benchmark and profiling.<br>\n`infery` supports all major deep learning frameworks, with a <b>unified and simple API</b>.\n\n## Quickstart and Examples\n<a href=\"https://github.com/Deci-AI/infery-examples\">\n    <img src=\"https://img.shields.io/badge/Public-Infery Examples-green\">\n</a>\n\n### Installation Instructions\nhttps://docs.deci.ai/docs/installing-infery-1\n\n## Usage\n![Infery Usage](https://deci.ai/wp-content/uploads/2021/06/Infery-Fig1.png)\n\n\n#### 1. Load an example model\n```python\n>>> import infery, numpy as np\n>>> model = infery.load(model_path='MyNewTask_1_0.onnx', framework_type='onnx', inference_hardware='gpu')\n```\n\n#### 2. Predict with a random numpy input\n\n```python\n>>> inputs = [np.random.random(shape).astype('float32') for shape in [[1, 243, 30, 40], [1, 172, 60, 80], [1, 102, 120, 160], [1, 64, 240, 320], [1, 153, 15, 20]]]\n>>> model.predict(inputs)\n[array([[[[-3.5920768, -3.5920792, -3.592102 , ..., -3.592099 ,\n           -3.5920944, -3.5920882],\n          [-3.592076 , -3.5919113, -3.592086 , ..., -3.5921211,\n           -3.5921066, -3.5920937],\n          [-3.592083 , -3.592073 , -3.5920823, ..., -3.5921297,\n           -3.592109 , -3.5920937],\n          ...,\n          [-3.5920753, -3.5917826, -3.591444 , ..., -3.580754 ,\n           -3.5816329, -3.582549 ],\n          [-3.592073 , -3.5917459, -3.591257 , ..., -3.5817945,\n           -3.5820704, -3.5835373],\n          [-3.592073 , -3.5920737, -3.5920737, ..., -3.5853324,\n           -3.5845006, -3.5856297]],\n \n         [[-5.862198 , -5.8567815, -5.851764 , ..., -5.86396  ,\n           -5.8639617, -5.865011 ],\n          [-5.858771 , -5.8493323, -5.841462 , ..., -5.8617773,\n           -5.8614554, -5.8633246],\n          [-5.8560567, -5.844124 , -5.8351245, ..., -5.8598166,\n           -5.859674 , -5.8624067],\n          ...,\n          [-5.8608136, -5.854358 , -5.8467784, ..., -5.8504496,\n           -5.8563104, -5.8615303],\n          [-5.86313  , -5.8587003, -5.8531966, ..., -5.8534794,\n           -5.8581944, -5.8625536],\n          [-5.865306 , -5.8623176, -5.8593984, ..., -5.8581495,\n           -5.861572 , -5.865295 ]],\n \n         [[-8.843734 , -8.840406 , -8.837172 , ..., -8.840413 ,\n           -8.842026 , -8.843931 ],\n          [-8.840792 , -8.836787 , -8.831037 , ..., -8.836103 ,\n           -8.839954 , -8.842534 ],\n          [-8.838855 , -8.833998 , -8.82706  , ..., -8.835106 ,\n           -8.839087 , -8.841538 ],\n          ...,\n          [-8.842419 , -8.840865 , -8.838625 , ..., -8.83943  ,\n           -8.843677 , -8.845087 ],\n          [-8.844379 , -8.84402  , -8.843141 , ..., -8.84185  ,\n           -8.844696 , -8.845202 ],\n          [-8.844775 , -8.845572 , -8.845177 , ..., -8.843876 ,\n           -8.8444395, -8.845926 ]]]], dtype=float32)]\n```\n\n#### 3. Benchmark the model on the current hardware\n```python\n>>> model.benchmark(batch_size=1)\n-INFO- Benchmarking the model in batch size 1 and dimensions [(243, 30, 40), (172, 60, 80), (102, 120, 160), (64, 240, 320), (153, 15, 20)]... \n<ModelBenchmarks: {\n    \"batch_size\": 1,\n    \"batch_inf_time\": \"6.57 ms\",\n    \"batch_inf_time_variance\": \"0.02 ms\",\n    \"model_memory_used\": \"1536.00 mb\",\n    \"system_startpoint_memory_used\": \"1536.00 mb\",\n    \"post_inference_memory_used\": \"1536.00 mb\",\n    \"total_memory_size\": \"7982.00 mb\",\n    \"throughput\": \"152.32 fps\",\n    \"sample_inf_time\": \"6.57 ms\",\n    \"include_io\": true,\n    \"framework_type\": \"onnx\",\n    \"framework_version\": \"1.10.0\",\n    \"inference_hardware\": \"GPU\",\n    \"date\": \"11:16:55__02-03-2022\",\n    \"ctime\": 1643879815,\n    \"h_to_d_mean\": null,\n    \"d_to_h_mean\": null,\n    \"h_to_d_variance\": null,\n    \"d_to_h_variance\": null\n}>\n```\n\n## Documentation:\nhttps://docs.deci.ai/docs/infery\n\n\n\n",
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