Unverified Commit 460e784b authored by Quan (Andy) Gan's avatar Quan (Andy) Gan Committed by GitHub
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Update README.md (#3170)

parent de174ada
...@@ -21,12 +21,12 @@ DGL is an easy-to-use, high performance and scalable Python package for deep lea ...@@ -21,12 +21,12 @@ DGL is an easy-to-use, high performance and scalable Python package for deep lea
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## <img src="http://data.dgl.ai/asset/image/new.png" width="30">DGL News ## <img src="http://data.dgl.ai/asset/image/new.png" width="30">DGL News
**07/22/2021**: The new **v0.7.0 release** includes a number of system optimizations, new models, new features and enhancements and bugfixes. See our [release note](https://github.com/dmlc/dgl/releases/tag/v0.7.0) for more details.
**02/26/2021**: The new **v0.6.0 release** includes distributed heterogeneous graph support, 13 more model examples, a Chinese translation of user guide thank to community support, and a new tutorial. See our [release note](https://github.com/dmlc/dgl/releases/tag/v0.6.0) for more details. **02/26/2021**: The new **v0.6.0 release** includes distributed heterogeneous graph support, 13 more model examples, a Chinese translation of user guide thank to community support, and a new tutorial. See our [release note](https://github.com/dmlc/dgl/releases/tag/v0.6.0) for more details.
**09/05/2020**: We invite you to participate in the survey [here](https://forms.gle/Ej3jHCocACmb49Gp8) to make DGL better fit for your needs. Thanks! **09/05/2020**: We invite you to participate in the survey [here](https://forms.gle/Ej3jHCocACmb49Gp8) to make DGL better fit for your needs. Thanks!
**08/21/2020**: The new **v0.5.0 release** includes distributed GNN training, overhauled documentation and user guide, and several more features. We have also submitted some models to the [OGB](https://ogb.stanford.edu) leaderboard. See our [release note](https://github.com/dmlc/dgl/releases/tag/0.5.0) for more details.
## Using DGL ## Using DGL
**A data scientist** may want to apply a pre-trained model to your data right away. For this you can use DGL's [Application packages, formally *Model Zoo*](https://github.com/dmlc/dgl/tree/master/apps). Application packages are developed for domain applications, as is the case for [DGL-LifeScience](https://github.com/awslabs/dgl-lifesci). We will soon add model zoo for knowledge graph embedding learning and recommender systems. Here's how you will use a pretrained model: **A data scientist** may want to apply a pre-trained model to your data right away. For this you can use DGL's [Application packages, formally *Model Zoo*](https://github.com/dmlc/dgl/tree/master/apps). Application packages are developed for domain applications, as is the case for [DGL-LifeScience](https://github.com/awslabs/dgl-lifesci). We will soon add model zoo for knowledge graph embedding learning and recommender systems. Here's how you will use a pretrained model:
...@@ -390,12 +390,12 @@ conda install -c dglteam dgl-cuda11.1 # CUDA 11.1 ...@@ -390,12 +390,12 @@ conda install -c dglteam dgl-cuda11.1 # CUDA 11.1
| | Latest Nightly Build Version | Stable Version | | | Latest Nightly Build Version | Stable Version |
|-----------|-------------------------------|-------------------------| |-----------|-------------------------------|-------------------------|
| CPU | `pip install --pre dgl -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl` | | CPU | `pip install --pre dgl -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl -f https://data.dgl.ai/wheels-test/repo.html` |
| CUDA 9.2 | `pip install --pre dgl-cu92 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu92` | | CUDA 9.2 | `pip install --pre dgl-cu92 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu92 -f https://data.dgl.ai/wheels-test/repo.html` |
| CUDA 10.1 | `pip install --pre dgl-cu101 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu101` | | CUDA 10.1 | `pip install --pre dgl-cu101 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu101 -f https://data.dgl.ai/wheels-test/repo.html` |
| CUDA 10.2 | `pip install --pre dgl-cu102 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu102` | | CUDA 10.2 | `pip install --pre dgl-cu102 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu102 -f https://data.dgl.ai/wheels-test/repo.html` |
| CUDA 11.0 | `pip install --pre dgl-cu110 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu110` | | CUDA 11.0 | `pip install --pre dgl-cu110 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu110 -f https://data.dgl.ai/wheels-test/repo.html` |
| CUDA 11.1 | `pip install --pre dgl-cu111 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu111` | | CUDA 11.1 | `pip install --pre dgl-cu111 -f https://data.dgl.ai/wheels-test/repo.html` | `pip install dgl-cu111 -f https://data.dgl.ai/wheels-test/repo.html` |
### Built from source code ### Built from source code
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