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torch-mlir
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Add shell markdown formatting to README.md

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Phoenix Meadowlark 2021-10-04 11:56:11 -07:00 committed by Sean Silva
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README.md
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@ -40,7 +40,7 @@ git submodule update --init
## Setup your Python VirtualEnvironment and Dependencies
```
```shell
python -m venv mlir_venv
source mlir_venv/bin/activate
python -m pip install --upgrade pip #Some older pip installs may not be able to handle the recent PyTorch deps
@ -49,7 +49,7 @@ python -m pip install --pre torch torchvision pybind11 -f https://download.pytor
```
## Build
```
```shell
cmake -GNinja -Bbuild \
-DCMAKE_C_COMPILER=clang \
-DCMAKE_CXX_COMPILER=clang++ \
@ -72,7 +72,7 @@ cmake --build build
## Demos
## Setup Python Environment
```
```shell
export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples
```
@ -80,7 +80,7 @@ export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/
Running execution (end-to-end) tests:
```
```shell
# Run E2E TorchScript tests. These compile and run the TorchScript program
# through torch-mlir with a simplified MLIR CPU backend we call RefBackend
python -m e2e_testing.torchscript.main --filter Conv2d --verbose
@ -90,13 +90,12 @@ python -m e2e_testing.torchscript.main --filter Conv2d --verbose
Standalone script to Convert a PyTorch ResNet18 model to MLIR and run it on the CPU Backend:
```
```shell
# The example uses PIL and requests to get the image.
pip install requests pillow
# Run ResNet18 as a standalone script.
python examples/torchscript_resnet18_e2e.py
(mlir_venv) mlir@torch-mlir:~$ python examples/torchscript_resnet18_e2e.py
load image from https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /home/mlir/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
100.0%
@ -104,11 +103,10 @@ PyTorch prediction
[('Labrador retriever', 70.66319274902344), ('golden retriever', 4.956596374511719), ('Chesapeake Bay retriever', 4.195662975311279)]
torch-mlir prediction
[('Labrador retriever', 70.66320037841797), ('golden retriever', 4.956601619720459), ('Chesapeake Bay retriever', 4.195651531219482)]
```
Jupyter notebook:
```
```shell
python -m ipykernel install --user --name=torch-mlir --env PYTHONPATH "$PYTHONPATH"
# Open in jupyter, and then navigate to
# `examples/resnet_inference.ipynb` and use the `torch-mlir` kernel to run.
@ -125,7 +123,7 @@ The `examples` folder includes scripts `torchfx_*.py` showing how to use the Tor
Then, run
```
```shell
python torchfx_example_name.py
```
@ -142,7 +140,7 @@ The `examples` folder includes scripts `lazytensor_*.py` showing how to use the
In order to run the examples, make sure you've setup your `PYTHONPATH` by following the [Setup Python Environment](#setup-python-environment) instructions, and also add `/path/to/pytorch/lazy_tensor_core` to your `PYTHONPATH` as shown below:
```
```shell
export PYTHONPATH=$PYTHONPATH:`/replace/with/path/to/pytorch/lazy_tensor_core`
python lazytensor_example_name.py
```