Python Inference Script is a Python package that enables developers to author machine learning workflows in Python and deploy without Python.
Various tools could be available for fast experimentation, for example sklearn, CNTK, Tensorflow, PyTorch and etc. However, when it comes to deployement, problems will emerge:
- Is it optimized, fast or memory efficient?
- Is the runtime or model compact enough for edge devices?
- Is it easy to learn or cross-platform?
To solve those puzzles, the Python Inference Script(PyIS) is introduced.
python -m pip install pyis-python --upgrade # Python backend
from pyis.python import ops
from pyis.python.model_context import save, load
# create trie op
trie = ops.CedarTrie()
trie.insert('what time is it in Seattle?')
trie.insert('what is the time in US?')
# run trie match
query = 'what is the time in US?'
is_matched = trie.contains(query)
# serialize
save(trie, 'tmp/trie.pkl')
# load and run
trie = load('tmp/trie.pkl')
is_matched = trie.contains(query) # LibTorch backend
import torch
from pyis.torch import ops
from pyis.torch.model_context import save, load
# define torch model
class TrieMatcher(torch.nn.Module):
def __init__(self):
super().__init__()
self.trie = ops.CedarTrie()
self.trie.insert('what time is it in Seattle?')
self.trie.insert('what is the time in US?')
def forward(self, query: str) -> bool:
return self.trie.contains(query)
# create torch model
model = torch.jit.script(TrieMatcher())
# run trie match
query = 'what is the time in US?'
is_matched = model.forward(query)
# serialize
save(model, 'tmp/trie.pt')
# load and run
model = load('tmp/trie.pt')
is_matched = model.forward(query)COMING SOON...
Run the following commands and open docs/_build/html/index.html in browser.
pip install sphinx myst-parser sphinx-rtd-theme sphinxemoji
cd docs/
make html # for linux
.\make.bat html # for windows
Please refer to CONTRIBUTING.md for the agreement and the instructions if you want to participate in this project.
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Licensed under the MIT license.