‣ Ubuntu 22.04 including Python 3.10 ‣ NVIDIA CUDA® 12.1.1 ‣ NVIDIA cuBLAS 12.1.3.1 ‣ NVIDIA cuDNN 8.9.3 ‣ NVIDIA NCCL 2.18.3 ‣ NVIDIA RAPIDS™ 23.06 ‣ Apex ‣ rdma-core 39.0 ‣ NVIDIA HPC-X 2.15 ‣ Ubuntu 22.04 including Python 3.10 ‣ NVIDIA CUDA® 12.1.1 ‣ NVIDIA cuBLAS 12.1.3.1 ‣ NVIDIA cuDNN 8.9.2 ‣ NVIDIA NCCL 2.18.1 ‣ NVIDIA RAPIDS™ 23.04 ‣ Apex ‣ rdma-core 39.0 ‣ NVIDIA HPC-X 2.15 ‣ Ubuntu 22.04 including Python 3.10 ‣ NVIDIA CUDA® 12.1.1 ‣ NVIDIA cuBLAS 12.1.3.1 ‣ NVIDIA cuDNN 8.9.1.23 ‣ NVIDIA NCCL 2.18.1 ‣ NVIDIA RAPIDS™ 23.04 ‣ Apex ‣ rdma-core 36.0 ‣ NVIDIA HPC-X 2

CNTK。我们 推荐 TensorFlow 后端。 • TensorFlow 安装指引。 • Theano 安装指引。 • CNTK 安装指引。 你也可以考虑安装以下可选依赖： • cuDNN (如果你计划在 GPU 上运行 Keras，建议安装)。 • HDF5 和 h5py (如果你需要将 Keras 模型保存到磁盘，则需要这些)。 • graphviz 和 pydot (用于可视化工具绘制模型图)。 RNN，但它往往会占用更多的内存。展开只适用于短序列。 • reset_after: GRU 公约 (是否在矩阵乘法之前或者之后使用重置门)。False =「之前」(默认)， Ture =「之后」( CuDNN 兼容)。 参考文献 • Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Transla- recurrent_constraint=None, bias_constraint=None, return_sequences=False, return_state=False, stateful=False) 由 CuDNN 支持的快速 GRU 实现。 只能以 TensorFlow 后端运行在 GPU 上。 参数 • units: 正整数，输出空间的维度。 • kernel_initializer: kernel

Epoch 1/100 2021-11-09 14:44:20.431426: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005 32/32 [==============================] - 366s 12s/step - loss: 0.6981 - accuracy: 0 Epoch 1/100 2021-11-09 15:38:34.694059: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005 63/63 [==============================] - 380s 6s/step - loss: 0.6932 - accuracy: 0

hosts can benefit from GPU acceleration. GPUs and deep learning libraries such as Keras, TensorFlow, cudNN, and CUDA are managed separately from MADlib. For more information see the MADlib wiki instructions

hosts can benefit from GPU acceleration. GPUs and deep learning libraries such as Keras, TensorFlow, cudNN, and CUDA are managed separately from MADlib. For more information see the MADlib wiki instructions

hosts can benefit from GPU acceleration. GPUs and deep learning libraries such as Keras, TensorFlow, cudNN, and CUDA are managed separately from MADlib. For more information see the MADlib wiki instructions

hosts can benefit from GPU acceleration. GPUs and deep learning libraries such as Keras, TensorFlow, cudNN, and CUDA are managed separately from MADlib. For more information see the MADlib wiki instructions

hosts can benefit from GPU acceleration. GPUs and deep learning libraries such as Keras, TensorFlow, cudNN, and CUDA are managed separately from MADlib. For more information see the MADlib wiki instructions

hosts can benefit from GPU acceleration. GPUs and deep learning libraries such as Keras, TensorFlow, cudNN, and CUDA are managed separately from MADlib. For more information see the MADlib wiki instructions

hosts can benefit from GPU acceleration. GPUs and deep learning libraries such as Keras, TensorFlow, cudNN, and CUDA are managed separately from MADlib. For more information see the MADlib wiki instructions