Updated README and slightly changed conda env

README.md

@@ -17,6 +17,64 @@ for how to build, install, and use HPVM.
 
 HPVM is currently at **version 1.0**.
 
+## Getting Started
+
+### Dependencies
+
+The following components are mandatory for building HPVM:
+- GCC (>=5.1)
+- CMake (>=3.17)
+- GNU Make (>=3.79) or Ninja (>=1.10)
+
+### Building HPVM
+
+1. Install [Anaconda](https://www.anaconda.com/), a Python package manager. This provides the command `conda`.
+
+1. Clone this branch (`hpvm-release-epochs0`) of this repository:
+
+    ```bash
+    git clone -b hpvm-release-epochs0 --single-branch https://gitlab.engr.illinois.edu/llvm/hpvm.git
+    ```
+
+1. Find `env.yaml` in the repo's root, and use conda to create a virtual environment with Python 3.7 and all required packages installed:
+
+    ```bash
+    cd hpvm/
+    conda env create -n hpvm -f ./env.yaml
+    conda activate hpvm
+    ```
+
+1. Change directory to `./hpvm` in the repo and run the installer with recommended options:
+
+    ```bash
+    cd hpvm/; ./install.sh -t "X86"
+    ```
+
+    This will download additional dependencies, configure HPVM and build HPVM.
+
+    - If you have `ninja` installed (in the place of `make`),
+      you may use `./install.sh --ninja -t "X86"` to build HPVM with ninja.
+
+### Generating NVDLA Buffer from PyTorch DNN
+
+Run `test/epoch_dnn/main.py` to generate the PyTorch DNN defined at `test/epoch_dnn/torch_dnn/miniera.py` into NVDLA,
+and evaluate it on real hardware:
+
+```bash
+cd test/epoch_dnn
+# rm -r /tmp/miniera  # Do this if you have run main.py before
+python main.py
+```
+
+This script will run through the whole code generation (putting everything under `/tmp/miniera`) and evaluation, including
+1. Generate quantization scale `calib.txt` for INT8 quantization;
+1. Export DNN to NVDLA buffer `hpvm-mod.nvdla`;
+1. Select a sample of images from the dataset and put them as JPG files (`images/$idx_$label.jpg`);
+   for example, the 0th image which has label 1 will be `images/0000_1.jpg`;
+1. Copy NVDLA model and images to an NVDLA device via SCP;
+1. Run the NVDLA model on every image we sampled;
+1. Display the accuracy of this model.
+
 ## HPVM Scheduler Backend
 
 We provide the HPVM Backend pass `DFG2LLVM_EPOCHS` which converts those Dataflow Graph (DFG) nodes which represent specific

hpvm/env.yaml

@@ -7,6 +7,7 @@ dependencies:
   - jinja2=2.11
   - matplotlib=3.3
   - networkx=2.5
+  - numpy==1.19.4  # This version doesn't have some warning compared to 1.20
   - onnx==1.8.0
   - pandas=1.1
   - pip
@@ -26,3 +27,4 @@ dependencies:
     - graphviz==0.10
     - onnx-simplifier==0.3
     - torchnet==0.0.4
+    - pexpect

hpvm/test/epoch_dnn/README.md

-# Environment setup 
-
-Add path to `libprotobuf.so` and `libnvdla_compiler.so` in LD_LIBRARY_PATH
-
-
-
-# Compilation Command 
-
-Compilation script : `miniera-hpvm/src/compile_benchmark.sh`
-
-Command:
-
-```
-python3 /home/hsharif3/Gitlab/hpvm_release_nvdla/hpvm/build/tools/hpvm/tools/hpvm-clang/main.py -t nvdla --conf-file data/tuner_confs.txt -DMODEL_PARAMS_DIR=${PATH_TO_MODELPARAMS} -fno-exceptions miniera-hpvm.cpp miniera-hpvm
-
-```
-
-*NOTE*: by default hpvm-clang is configured to read calibration inputs from a `calib.txt` file plaed in the current build directoy (see main.py.in in hpvm-clang). Default compilation mode is INT8 - modify `main.py` in build directory to use FP16 mode (easily configurable)
-
-
-# Running on EPOCHS chip 
-
-The compilation creates an nvdla module named `hpvm-mod.nvdla` 
-
-Connect to EPOCHS chip:
-
-```
-ssh -q -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -p 5506 root@ espgate.cs.columbia.edu
-
-```
-
-*password*: `openesp` 
-
-More instructions on EPOCHS chip here: 
-
-https://docs.google.com/document/d/1rQa6sIDKtYHGKmWg5bPROJ_FijDUnaVEWkckLWtpHFU/edit?usp=sharing
-
-
-Running on EPOCHS chip:
-
-```
-
-cd NV_NVDLA/
-
-./nvdla_runtime  --loadable  hpvm-mod.nvdla --image  ${test_image}  --rawdump
-
-```
-
-*NOTE*  --normalize not neeed here -- input should be INT8
-
-# Images to use for Testing 
-
-Images to use for testing under `/benchmarks/miniera-hpvm/data/images` 
-
-The ground truth class of the image is part of the file name. For instance, `*_1.jpg` indicates file with target class `1`.
-
-
-# Frontend/Source changes required
-
-* Weights must be FP16!
-
-* Weights for FC layers need to be tranposed 
-
-* Modify paths to readTrainedWeights to pass constant file path -- use MODEL_PARAMS_STR as part of string (see `miniera-hpvm.cpp` as example) 
-
-
-
-# Missing Support 
-
-** BatchNorm support missing (better to try on small example first)