Merge pull request #3 from CentML/may26-update-readme

Update README file

Author: anurlybayev

README.md

@@ -1,62 +1,46 @@
-# Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training
-
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4885489.svg)](https://doi.org/10.5281/zenodo.4885489)
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4876277.svg)](https://doi.org/10.5281/zenodo.4876277)
-
+# Habitat
 Habitat is a tool that predicts a deep neural network's training iteration
 execution time on a given GPU. It currently supports PyTorch. To learn more
 about how Habitat works, please see our [research
 paper](https://arxiv.org/abs/2102.00527).
+## Installation
+You can install Habitat using the prebuilt wheel files. To install, download the whl files from the [releases page](https://github.com/CentML/habitat/releases) then run:
+```sh
+pip install habitat*.whl
+```
 
+## Usage example
+You can verify your Habitat installation by running the simple usage example. This example measures a single inference iteration of Resnet50 on the RTX2080Ti and extrapolates the runtime to the V100. 
+```py
+import habitat
+import torch
+import torchvision.models as models
 
-## Running From Source
-
-Currently, the only way to run Habitat is to build it from source. You should
-use the Docker image provided in this repository to make sure that you can
-compile the code.
-
-1. Download the [Habitat pre-trained
-   models](https://doi.org/10.5281/zenodo.4876277).
-2. Run `extract-models.sh` under `analyzer` to extract and install the
-   pre-trained models.
-3. Run `setup.sh` under `docker/` to build the Habitat container image.
-4. Run `start.sh` to start a new container. By default, your home directory
-   will be mounted inside the container under `~/home`.
-5. Once inside the container, run `install-dev.sh` under `analyzer/` to build
-   and install the Habitat package.
-6. In your scripts, `import habitat` to get access to Habitat. See
-   `experiments/run_experiment.py` for an example showing how to use Habitat.
-
-**Note:** Habitat needs access to your GPU's performance counters, which
-requires special permissions if you are running with a recent driver (418.43 or
-later). If you encounter a `CUPTI_ERROR_INSUFFICIENT_PRIVILEGES` error when
-running Habitat, please follow the instructions
-[here](https://developer.nvidia.com/ERR_NVGPUCTRPERM)
-and in [issue #5](https://github.com/geoffxy/habitat/issues/5).
-
-
-## License
-
-The code in this repository is licensed under the Apache 2.0 license (see
-`LICENSE` and `NOTICE`), with the exception of the files mentioned below.
-
-This software contains source code provided by NVIDIA Corporation. These files
-are:
+# Define model and sample inputs
+model = models.resnet50().cuda()
+image = torch.rand(8, 3, 224, 224).cuda()
 
-- The code under `cpp/external/cupti_profilerhost_util/` (CUPTI sample code)
-- `cpp/src/cuda/cuda_occupancy.h`
+# Measure a single inference
+tracker = habitat.OperationTracker(device=habitat.Device.RTX2080Ti)
+with tracker.track():
+    out = model(image)
 
-The code mentioned above is licensed under the [NVIDIA Software Development
-Kit End User License Agreement](https://docs.nvidia.com/cuda/eula/index.html).
+trace = tracker.get_tracked_trace()
+print("Run time on source:", trace.run_time_ms)
 
-We include the implementations of several deep neural networks under
-`experiments/` for our evaluation. These implementations are copyrighted by
-their original authors and carry their original licenses. Please see the
-corresponding `README` files and license files inside the subdirectories for
-more information.
+# Perform prediction to a single target device
+pred = trace.to_device(habitat.Device.V100)
+print("Predicted time on V100:", pred.run_time_ms)
+```
+## Development Environment Setup
+Habitat requires both the native component and the Python binding to function. For detailed installation instructions, see [INSTALL.md](INSTALL.md).
 
+## Release process
+Run the `Build Habitat` GitHub action. This will build the wheel files for each platform.
 
-## Research Paper
+## Release history
+See [Releases](https://github.com/CentML/habitat/releases).
+## Meta
 
 Habitat began as a research project in the [EcoSystem
 Group](https://www.cs.toronto.edu/ecosystem) at the [University of
@@ -79,3 +63,5 @@ If you use Habitat in your research, please consider citing our paper:
   year = {2021},
 }
 ```
+## Contributing
+ - Guidelines on how to contribute to the project