diff --git a/README.md b/README.md
index 38dd8d0..4d6f1fd 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Habitat
+# DeepView.Predict
[](https://github.com/CentML/habitat/blob/main/LICENSE)
[](https://codeclimate.com/github/CentML/DeepView.Predict/maintainability)
@@ -17,11 +17,11 @@ A Runtime-Based Computational Performance Predictor for Deep Neural Network Trai
- [Research paper](#paper)
- [Contributing](#contributing)
-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).
+DeepView.Predict 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 DeepView.Predict works, please see our [research paper](https://arxiv.org/abs/2102.00527).
Installation
-To run Habitat, you need:
+To run DeepView.Predict, you need:
- [Python 3.6+](https://www.python.org/)
- [Pytorch 1.1.0+](https://pytorch.org/)
- A system equiped with an Nvidia GPU with properly configured CUDA
@@ -37,12 +37,17 @@ Currently, we have predictors for the following Nvidia GPUs:
| 2080Ti | Turing | 11 GB | GDDR6 | 68 |
| T4 | Turing | 16 GB | GDDR6 | 40 |
| 3090 | Ampere | 24 GB | GDDR6X | 82 |
+| A100 | Ampere | 40 GB | HBM2 | 108 |
+| A40 | Ampere | 48 GB | GDDR6 | 84 |
+| A4000 | Ampere | 16 GB | GDDR6 | 48 |
+| 4000 | Turing | 8 GB | GDDR6 | 36 |
+
Building locally
### 1. Install CUPTI
-CUPTI is a profiling interface required by Habitat. Select your version of CUDA [here](https://developer.nvidia.com/cuda-toolkit-archive) and follow the instructions to add NVIDIA's repository. Then, install CUPTI with:
+CUPTI is a profiling interface required by DeepView.Predict. Select your version of CUDA [here](https://developer.nvidia.com/cuda-toolkit-archive) and follow the instructions to add NVIDIA's repository. Then, install CUPTI with:
```bash
sudo apt-get install cuda-cupti-xx-x
```
@@ -54,7 +59,7 @@ Alternatively, if you do not have root access on your machine, you can use `cond
```
After installing CUPTI, add `$CONDA_HOME/extras/CUPTI/lib64/` to `LD_LIBRARY_PATH` to ensure the library is linked.
-### 2. Install Habitat
+### 2. Install DeepView.Predict
You can install via pip if you have the following versions of CUDA and Python
@@ -77,12 +82,12 @@ For example, if you are using CUDA 10.2 and Python 3.7):
pip install http://centml-releases.s3-website.us-east-2.amazonaws.com/habitat/wheels/habitat_predict-1.0.0-20221123+cu102-py37-none-any.whl
```
-If you do not find matching version of CUDA and Python above, you need to build Habitat from source with the following instructions
+If you do not find matching version of CUDA and Python above, you need to build DeepView.Predict from source with the following instructions
### Installing from source
1. Install CMake 3.17+.
- - Note that CMake 3.24.0 and 3.24.1 has a bug that breaks Habitat as it is not able to find the CUPTI directory and you should not use those versions
+ - Note that CMake 3.24.0 and 3.24.1 has a bug that breaks DeepView.Predict as it is not able to find the CUPTI directory and you should not use those versions
- [https://gitlab.kitware.com/cmake/cmake/-/merge_requests/7608/diffs](https://gitlab.kitware.com/cmake/cmake/-/merge_requests/7608/diffs)
- Run the following commands to download and install a precompiled version of CMake 3.24.2
@@ -101,20 +106,20 @@ If you do not find matching version of CUDA and Python above, you need to build
```
2. Install [Git Large File Storage](https://git-lfs.github.com/)
-3. Clone the Habitat package
+3. Clone the DeepView.Predict package
```bash
- git clone https://github.com/centml/habitat
+ git clone https://github.com/CentML/DeepView.Predict
```
-4. Get the pre-trained models used by Habitat
+4. Get the pre-trained models used by DeepView.Predict
```bash
git submodule init && git submodule update
git lfs pull
```
-5. Finally build habitat with the following command
+5. Finally build DeepView.Predict with the following command
```bash
./analyzer/install-dev.sh
@@ -122,23 +127,23 @@ If you do not find matching version of CUDA and Python above, you need to build
Building with Docker
-Habitat has been tested to work on the latest version of [NVIDIA NGC PyTorch containers](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch).
+DeepView.Predict has been tested to work on the latest version of [NVIDIA NGC PyTorch containers](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch).
-1. To build Habitat with Docker, first run the NGC container where
+1. To build DeepView.Predict with Docker, first run the NGC container where
```bash
docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:XX.XX-py3
```
-2. Inside the container, clone the repository then build and install the Habitat Python package:
+2. Inside the container, clone the repository then build and install DeepView.Predict Python package:
```bash
-git clone --recursive https://github.com/centml/habitat
+git clone --recursive https://github.com/CentML/DeepView.Predict
./habitat/analyzer/install-dev.sh
```
-**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).
+**Note:** DeepView.Predict 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 DeepView.Predict, please follow the instructions [here](https://developer.nvidia.com/ERR_NVGPUCTRPERM) and in [issue #5](https://github.com/geoffxy/habitat/issues/5).
Usage example
-You can verify your Habitat installation by running the simple usage example:
+You can verify your DeepView.Predict installation by running the simple usage example:
```python
# example.py
import habitat
@@ -166,7 +171,7 @@ print("Predicted time on V100:", pred.run_time_ms)
python3 example.py
```
-See [experiments/run_experiment.py](https://github.com/CentML/habitat/tree/main/experiments) for other examples of Habitat usage.
+See [experiments/run_experiment.py](https://github.com/CentML/DeepView.Predict/tree/main/experiments) for other examples of Habitat usage.
Release History
@@ -195,11 +200,11 @@ more information.
Research Paper
-Habitat began as a research project in the [EcoSystem Group](https://www.cs.toronto.edu/ecosystem) at the [University of Toronto](https://cs.toronto.edu). The accompanying research paper appeared in the proceedings of [USENIX
+DeepView.Profile began as a research project in the [EcoSystem Group](https://www.cs.toronto.edu/ecosystem) at the [University of Toronto](https://cs.toronto.edu). The accompanying research paper appeared in the proceedings of [USENIX
ATC'21](https://www.usenix.org/conference/atc21/presentation/yu). If you are
interested, you can read a preprint of the paper [here](https://arxiv.org/abs/2102.00527).
-If you use Habitat in your research, please consider citing our paper:
+If you use DeepView.Profile in your research, please consider citing our paper:
```bibtex
@inproceedings{habitat-yu21,