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deepview-predict-0.1.0
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مشاهده بیشترتوضیحات
Cross-GPU performance predictions for PyTorch neural network training.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepview-predict-0.1.0 |
| نام | deepview-predict |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | ['CentML'] |
| ایمیل نگهدارنده | ['support@centml.ai'] |
| نویسنده | CentML |
| ایمیل نویسنده | support@centml.ai |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/deepview-predict/ |
| مجوز | Apache-2.0 |
# DeepView.Predict
[](https://github.com/CentML/habitat/blob/main/LICENSE)
[](https://codeclimate.com/github/CentML/DeepView.Predict/maintainability)
[](https://codeclimate.com/github/CentML/DeepView.Predict/test_coverage)
A Runtime-Based Computational Performance Predictor for Deep Neural Network Training
- [Installation](#installation)
- [Building from source](#build)
- [Usage example](#getting-started)
- [Development Environment Setup](#dev-setup)
- [Release process](#release-process)
- [Release history](#release-history)
- [License](#license)
- [Research paper](#paper)
- [Contributing](#contributing)
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).
<h2 id="installation">Installation</h2>
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
Currently, we have predictors for the following Nvidia GPUs:
| GPU | Generation | Memory | Mem. Type | SMs |
| ---------- |:-----------:| ------:| :-------: | :-: |
| P4000 | Pascal | 8 GB | GDDR5 | 14 |
| P100 | Pascal | 16 GB | HBM2 | 56 |
| V100 | Volta | 16 GB | HBM2 | 80 |
| 2070 | Turing | 8 GB | GDDR6 | 36 |
| 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 |
<h2 id="building-locally">Building locally</h2>
### Installing from pip
Install via pip with the following command
```bash
pip install deepview-predict
```
### Installing from source
1. Install CUPTI
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
```
where `xx-x` represents the version of CUDA you have installed.
Alternatively, if you do not have root access on your machine, you can use `conda` to install CUPTI. Select your version of CUDA [here](https://anaconda.org/nvidia/cuda-cupti) and follow the instructions. For example if you have CUDA 11.6.0, you can install CUPTI with:
```bash
conda install -c "nvidia/label/cuda-11.6.0" cuda-cupti
```
After installing CUPTI, add `$CONDA_HOME/extras/CUPTI/lib64/` to `LD_LIBRARY_PATH` to ensure the library is linked.
2. Install CMake 3.17+.
- 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
```bash
wget https://github.com/Kitware/CMake/releases/download/v3.24.2/cmake-3.24.2-linux-x86_64.sh
chmod +x cmake-3.24.2-linux-x86_64.sh
mkdir /opt/cmake
sh cmake-3.24.2-linux-x86_64.sh --prefix=/opt/cmake --skip-license
ln -s /opt/cmake/bin/cmake /usr/local/bin/cmake
```
- You can verify the version of CMake you installed with the following command
```bash
cmake --version
```
3. Install [Git Large File Storage](https://git-lfs.github.com/)
4. Clone the DeepView.Predict package
```bash
git clone https://github.com/CentML/DeepView.Predict
cd DeepView.Predict
```
5. Get the pre-trained models used by DeepView.Predict
```bash
git submodule init && git submodule update
git lfs pull
```
6. Finally build DeepView.Predict with the following command
```bash
./analyzer/install-dev.sh
```
<h2 id="building-with-docker">Building with Docker</h2>
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 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 DeepView.Predict Python package:
```bash
git clone --recursive https://github.com/CentML/DeepView.Predict
./habitat/analyzer/install-dev.sh
```
**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).
<h2 id="usage-example">Usage example</h2>
You can verify your DeepView.Predict installation by running the simple usage example:
```python
# example.py
import habitat
import torch
import torchvision.models as models
# Define model and sample inputs
model = models.resnet50().cuda()
image = torch.rand(8, 3, 224, 224).cuda()
# Measure a single inference
tracker = habitat.OperationTracker(device=habitat.Device.RTX2080Ti)
with tracker.track():
out = model(image)
trace = tracker.get_tracked_trace()
print("Run time on source:", trace.run_time_ms)
# Perform prediction to a single target device
pred = trace.to_device(habitat.Device.V100)
print("Predicted time on V100:", pred.run_time_ms)
```
```bash
python3 example.py
```
See [experiments/run_experiment.py](https://github.com/CentML/DeepView.Predict/tree/main/experiments) for other examples of DeepView.Predict usage.
<h2 id="release-history">Release History</h2>
See [Releases](https://github.com/UofT-EcoSystem/habitat/releases)
<h2 id="license">License</h2>
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:
- The code under `cpp/external/cupti_profilerhost_util/` (CUPTI sample code)
- `cpp/src/cuda/cuda_occupancy.h`
The code mentioned above is licensed under the [NVIDIA Software Development
Kit End User License Agreement](https://docs.nvidia.com/cuda/eula/index.html).
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.
<h2 id="paper">Research Paper</h2>
DeepView.Predict 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 DeepView.Predict in your research, please consider citing our paper:
```bibtex
@inproceedings{habitat-yu21,
author = {Yu, Geoffrey X. and Gao, Yubo and Golikov, Pavel and Pekhimenko,
Gennady},
title = {{Habitat: A Runtime-Based Computational Performance Predictor for
Deep Neural Network Training}},
booktitle = {{Proceedings of the 2021 USENIX Annual Technical Conference
(USENIX ATC'21)}},
year = {2021},
}
```
<h2 id="contributing">Contributing</h2>
Check out [CONTRIBUTING.md](https://github.com/CentML/habitat/blob/main/CONTRIBUTING.md) for more information on how to help with Habitat.
نیازمندی
| مقدار | نام |
|---|---|
| - | pyyaml |
| >=1.4.0 | torch |
| >=1.1.2 | pandas |
| >=4.49.0 | tqdm |
| ==11.7.101 | nvidia-cuda-cupti-cu11 |
| ==11.7.99 | nvidia-cuda-runtime-cu11 |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl deepview-predict-0.1.0:
pip install deepview-predict-0.1.0.whl
نصب پکیج tar.gz deepview-predict-0.1.0:
pip install deepview-predict-0.1.0.tar.gz