diff --git a/README.md b/README.md index 8bd2f638..876b9473 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ We summarize the contributions of CogDL as follows: ## โ— News -- The new **v0.5.0-alpha0 pre-release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. +- The new **v0.5.0 release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. ๐ŸŽ‰ - The new **v0.4.1 release** adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. ๐ŸŽ‰ diff --git a/README_CN.md b/README_CN.md index 73b894f3..39b398ea 100644 --- a/README_CN.md +++ b/README_CN.md @@ -21,7 +21,7 @@ CogDL็š„็‰นๆ€งๅŒ…ๆ‹ฌ๏ผš ## โ— ๆœ€ๆ–ฐ -- ๆœ€ๆ–ฐ็š„ **v0.5.0-alpha0 pre-release** ไธบๅ›พ็ฅž็ป็ฝ‘็ปœ็š„่ฎญ็ปƒ่ฎพ่ฎกไบ†ไธ€ๅฅ—็ปŸไธ€็š„ๆต็จ‹. ่ฟ™ไธช็‰ˆๆœฌๅŽป้™คไบ†ๅŽŸๅ…ˆ็š„`Task`็ฑป๏ผŒๅผ•ๅ…ฅไบ†`DataWrapper`ๆฅๅ‡†ๅค‡training/validation/test่ฟ‡็จ‹ไธญๆ‰€้œ€็š„ๆ•ฐๆฎ๏ผŒๅผ•ๅ…ฅไบ†`ModelWrapper`ๆฅๅฎšไน‰ๆจกๅž‹training/validation/test็š„ๆญฅ้ชค. +- ๆœ€ๆ–ฐ็š„ **v0.5.0 release** ไธบๅ›พ็ฅž็ป็ฝ‘็ปœ็š„่ฎญ็ปƒ่ฎพ่ฎกไบ†ไธ€ๅฅ—็ปŸไธ€็š„ๆต็จ‹. ่ฟ™ไธช็‰ˆๆœฌๅŽป้™คไบ†ๅŽŸๅ…ˆ็š„`Task`็ฑป๏ผŒๅผ•ๅ…ฅไบ†`DataWrapper`ๆฅๅ‡†ๅค‡training/validation/test่ฟ‡็จ‹ไธญๆ‰€้œ€็š„ๆ•ฐๆฎ๏ผŒๅผ•ๅ…ฅไบ†`ModelWrapper`ๆฅๅฎšไน‰ๆจกๅž‹training/validation/test็š„ๆญฅ้ชค. ๐ŸŽ‰ - ๆœ€ๆ–ฐ็š„ **v0.4.1 release** ๅขžๅŠ ไบ†ๆทฑๅฑ‚GNN็š„ๅฎž็Žฐๅ’ŒๆŽจ่ไปปๅŠกใ€‚่ฟ™ไธช็‰ˆๆœฌๅŒๆ—ถๆไพ›ไบ†ๆ–ฐ็š„ไธ€ไบ›pipeline็”จไบŽ็›ดๆŽฅ่Žทๅ–ๅ›พ่กจ็คบๅ’ŒๆญๅปบๆŽจ่ๅบ”็”จใ€‚ๆฌข่ฟŽๅคงๅฎถๅ‚ๅŠ ๆˆ‘ไปฌๅœจKDD 2021ไธŠ็š„tutorial๏ผŒๆ—ถ้—ดๆ˜ฏ8ๆœˆ14ๅทไธŠๅˆ10:30 - 12:00๏ผˆๅŒ—ไบฌๆ—ถ้—ด๏ผ‰ใ€‚ ๆ›ดๅคš็š„ๅ†…ๅฎนๅฏไปฅๆŸฅ็œ‹ https://kdd2021graph.github.io/. ๐ŸŽ‰ diff --git a/cogdl/__init__.py b/cogdl/__init__.py index 9018dea9..1407ccee 100644 --- a/cogdl/__init__.py +++ b/cogdl/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.5.0-alpha1" +__version__ = "0.5.0" from .experiments import experiment from .oag import oagbert diff --git a/docs/source/index.rst b/docs/source/index.rst index 5848584b..2481e698 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,7 +17,7 @@ We summarize the contributions of CogDL as follows: โ— News ------------ -- The new **v0.5.0-alpha0 pre-release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. +- The new **v0.5.0 release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. - The new **v0.4.1 release** adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. ๐ŸŽ‰ - The new **v0.4.0 release** refactors the data storage (from ``Data`` to ``Graph``) and provides more fast operators to speed up GNN training. It also includes many self-supervised learning methods on graphs. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see this `link `_ for more details. ๐ŸŽ‰ - The new **v0.3.0 release** provides a fast spmm operator to speed up GNN training. We also release the first version of `CogDL paper `_ in arXiv. You can join `our slack `_ for discussion. ๐ŸŽ‰๐ŸŽ‰๐ŸŽ‰ diff --git a/examples/notebooks/quickstart.ipynb b/examples/notebooks/quickstart.ipynb index 9bdec9d8..2774c40e 100644 --- a/examples/notebooks/quickstart.ipynb +++ b/examples/notebooks/quickstart.ipynb @@ -18,56 +18,6 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YOjTWjGz9CZo", - "outputId": "a78c7d89-9dfd-4146-a0f8-ad6dd1139f53" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nvcc: NVIDIA (R) Cuda compiler driver\n", - "Copyright (c) 2005-2020 NVIDIA Corporation\n", - "Built on Wed_Jul_22_19:09:09_PDT_2020\n", - "Cuda compilation tools, release 11.0, V11.0.221\n", - "Build cuda_11.0_bu.TC445_37.28845127_0\n", - "Mon Jul 12 13:46:17 2021 \n", - "+-----------------------------------------------------------------------------+\n", - "| NVIDIA-SMI 470.42.01 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", - "|-------------------------------+----------------------+----------------------+\n", - "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", - "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", - "| | | MIG M. |\n", - "|===============================+======================+======================|\n", - "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", - "| N/A 66C P8 11W / 70W | 0MiB / 15109MiB | 0% Default |\n", - "| | | N/A |\n", - "+-------------------------------+----------------------+----------------------+\n", - " \n", - "+-----------------------------------------------------------------------------+\n", - "| Processes: |\n", - "| GPU GI CI PID Type Process name GPU Memory |\n", - "| ID ID Usage |\n", - "|=============================================================================|\n", - "| No running processes found |\n", - "+-----------------------------------------------------------------------------+\n" - ] - } - ], - "source": [ - "#@title see gpu card\n", - "\n", - "!nvcc -V\n", - "!nvidia-smi\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, "metadata": { 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already satisfied: click in /home/yukuo/anaconda3/envs/cu102/lib/python3.7/site-packages (from sacremoses->transformers->cogdl) (7.1.2)\n" ] } ], @@ -238,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -251,49 +132,41 @@ "name": "stdout", "output_type": "stream", "text": [ - "Failed to load C version of sampling, use python version instead.\n", - "Namespace(activation='relu', checkpoint=None, cpu=False, dataset='cora', device_id=[0], dropout=0.5, fast_spmm=False, hidden_size=64, inference=False, lr=0.01, max_epoch=500, missing_rate=0, model='gcn', norm=None, num_classes=None, num_features=None, num_layers=2, patience=100, residual=False, save_dir='.', save_model=None, seed=1, task='node_classification', trainer=None, use_best_config=False, weight_decay=0.0005)\n", - "Downloading https://cloud.tsinghua.edu.cn/d/6808093f7f8042bfa1f0/files/?p=%2Fcora.zip&dl=1\n", - "unpacking cora.zip\n", - "Processing...\n", - "Done!\n" + "Namespace(activation='relu', actnn=False, checkpoint_path='./checkpoints/model.pt', cpu=False, cpu_inference=False, dataset=['cora'], devices=[0], distributed=False, dropout=0.5, dw='node_classification_dw', epochs=500, eval_step=1, hidden_size=64, load_emb_path=None, local_rank=0, log_path='.', logger=None, lr=0.01, master_addr='localhost', master_port=13425, max_epoch=None, model=['gcn'], mw='node_classification_mw', n_trials=3, n_warmup_steps=0, no_test=False, norm=None, nstage=1, num_classes=None, num_features=None, num_layers=2, patience=100, progress_bar='epoch', project='cogdl-exp', residual=False, resume_training=False, rp_ratio=1, save_emb_path=None, seed=[1], split=[0], unsup=False, use_best_config=False, weight_decay=0)\n", + " \n", + "|----------------------------------------------------------------------------------------|\n", + " *** Running (`cora`, `gcn`, `node_classification_dw`, `node_classification_mw`)\n", + "|----------------------------------------------------------------------------------------|\n", + "Model Parameters: 92231\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch: 455, Train: 1.0000, Val: 0.7920, ValLoss: 0.7300: 89%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰ | 446/500 [00:03<00:00, 120.41it/s]" + "Epoch: 146, train_loss: 0.0109, val_acc: 0.7860: 29%|โ–ˆโ–ˆโ–‰ | 147/500 [00:02<00:05, 66.46it/s] \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Valid accurracy = 0.7940\n", - "Test accuracy = 0.8100\n", - "| Variant | Acc | ValAcc |\n", + "Saving 47-th model to ./checkpoints/model.pt ...\n", + "Loading model from ./checkpoints/model.pt ...\n", + "{'test_acc': 0.817, 'val_acc': 0.798}\n", + "| Variant | test_acc | val_acc |\n", "|-----------------|---------------|---------------|\n", - "| ('cora', 'gcn') | 0.8100ยฑ0.0000 | 0.7940ยฑ0.0000 |\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "| ('cora', 'gcn') | 0.8170ยฑ0.0000 | 0.7980ยฑ0.0000 |\n" ] }, { "data": { "text/plain": [ - "defaultdict(list, {('cora', 'gcn'): [{'Acc': 0.81, 'ValAcc': 0.794}]})" + "defaultdict(list, {('cora', 'gcn'): [{'test_acc': 0.817, 'val_acc': 0.798}]})" ] }, - "execution_count": 3, - "metadata": { - "tags": [] - }, + "execution_count": 2, + "metadata": {}, "output_type": "execute_result" } ], @@ -466,14 +339,14 @@ ], "source": [ "# automl usage\n", - "def func_search(trial):\n", + "def search_space(trial):\n", " return {\n", " \"lr\": trial.suggest_categorical(\"lr\", [1e-3, 5e-3, 1e-2]),\n", " \"hidden_size\": trial.suggest_categorical(\"hidden_size\", [32, 64, 128]),\n", " \"dropout\": trial.suggest_uniform(\"dropout\", 0.5, 0.8),\n", " }\n", "\n", - "experiment(dataset=\"cora\", model=\"gcn\", seed=[1, 2], func_search=func_search)" + "experiment(dataset=\"cora\", model=\"gcn\", seed=[1, 2], search_space=search_space)" ] } ], @@ -486,6 +359,18 @@ "kernelspec": { "display_name": "Python 3", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" } }, "nbformat": 4, diff --git a/third_party/dgNN b/third_party/dgNN index fc12618e..e1cbdc7c 160000 --- a/third_party/dgNN +++ b/third_party/dgNN @@ -1 +1 @@ -Subproject commit fc12618ed598a303fc824e660303d46b358d4849 +Subproject commit e1cbdc7c6c02c6ff0865837f6a2d02b24199108f diff --git a/third_party/fastmoe b/third_party/fastmoe index 3397bc19..b652e8d8 160000 --- a/third_party/fastmoe +++ b/third_party/fastmoe @@ -1 +1 @@ -Subproject commit 3397bc19ebe1568850980587a156abd79a5c5465 +Subproject commit b652e8d88bbe82171fa28a479f21ad1263fb2e1c