• with setup.py

python setup.py install

• with pip

pip install utensor_cgen

• with pip

pip instal -e .[dev]

• with pipenv
  1. $ PIPENV_VENV_IN_PROJECT=1 pipenv install -d
  2. $ pipenv shell
     • this will spawn a subshell and activate the virtual environment for you
     • You should be able to use the cli now

You can go to this repo for detail information about pipenv.

Note: If you have trouble with installation using pipenv, try PIPENV_VENV_IN_PROJECT=1 pipenv install -d --skip-lock

1. You can use docker
   • run docker pull dboyliao/utensor-cli for a pre-build docker image
   • or run docker build -t <user_name>/utensor-cli . to build the docker image by yourself, where <user_name> can be any user name you want.
2. Run the docker image
   • docker run -it dboyliao/utensor-cli
   • or docker run -it <user_name>/utensor-cli if you want to use the image you just build.

Please refer to docker documentation for detail.

Please refer to tests/deep_mlp for detailed example

Following steps are a general guild for user how to porting a TensorFlow protobuf file into a uTensor implementation:

1. install utensor_cgent
   • run python3 setupt.py install
2. run utensor-cli convert --output-nodes='NODE,NODE,...' graph.pb
   • run utensor-cli -h for help
   • the graph.pb is the pb file of original graph (not quantized)
3. If you want to see what ops/nodes are in the pb file, you can run utensor-cli show <pbfile>

1. follow the steps in setup section
2. run make tests
   • Or you can use pipenv run pytest tests instead

• If you want to use dropout with placeholders for the keep_prob, you have to name the keep_prob placeholder by any name that starts with "keep_prob".
  • You can still use any input tensor with name starts with "keep_prob" as long as it's not the output tensor of a placeholder node.
  • You can't wrap dropout in any namescope

1. (done?) core code generator implementation
   • We need some refactoring, PRs are welcomed!
2. type alias in C/C++
   • ex: use uint8_t or unsigned char?
   • a lot more about this....
3. Relation among snippets/containers
   • shared template variables? (headers, shared placeholders...etc)
4. Better configuration schema
   • json
   • yaml
   • or ?