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torch-mlir
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Update README.md

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powderluv 2021-09-29 09:59:39 -07:00 committed by Sean Silva
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@ -38,7 +38,7 @@ cd torch-mlir
git submodule update --init
```
## Setup your Python Environment
## Setup your Python VirtualEnvironment and Dependencies
```
python -m venv mlir_venv
@ -71,9 +71,9 @@ cmake --build build
```
## Demos
## Setup ENV
## Setup Python Environment
```
export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir
export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples
```
### TorchScript
@ -96,7 +96,7 @@ 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/anush/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /home/mlir/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
100.0%
PyTorch prediction
[('Labrador retriever', 70.66319274902344), ('golden retriever', 4.956596374511719), ('Chesapeake Bay retriever', 4.195662975311279)]
@ -119,7 +119,7 @@ The `examples` folder includes the Python package `torchfx`, which is a function
#### Example usage of `torchfx`
The `examples` folder includes scripts `torchfx_*.py` showing how to use the TorchFX to MLIR pipeline. In order to run the examples, make sure you've setup your `PYTHONPATH` by following [these](#setup-env) instructions, and add `/path/to/torch-mlir/examples` to your `PYTHONPATH`.
The `examples` folder includes scripts `torchfx_*.py` showing how to use the TorchFX to MLIR pipeline. In order to run the examples, make sure you've setup your `PYTHONPATH` by following [these](#setup-your-python-environment) instructions.
Then, run
@ -138,13 +138,10 @@ The `examples` folder includes the Python package `lazytensor`, which implements
The `examples` folder includes scripts `lazytensor_*.py` showing how to use the Lazy Tensor to MLIR pipeline. The examples depend on the Lazy Tensor Core (LTC) of PyTorch. For information on how to obtain LTC, see [here](https://github.com/pytorch/pytorch/blob/lazy_tensor_staging/lazy_tensor_core/QUICKSTART.md).
In order to run the examples, make sure you've setup your `PYTHONPATH` by following [these](#setup-env) instructions, and also add the following to your `PYTHONPATH`:
1. `/path/to/torch-mlir/examples`
2. `/path/to/pytorch/lazy_tensor_core`
Then, run
In order to run the examples, make sure you've setup your `PYTHONPATH` by following [these](#setup-your-python-environment) instructions, and also add `/path/to/pytorch/lazy_tensor_core` to your `PYTHONPATH` as shown below:
```
export PYTHONPATH=$PYTHONPATH:`/replace/with/path/to/pytorch/lazy_tensor_core`
python lazytensor_example_name.py
```