graph.print_tabular() return gm.forward # Reset since we are using a different backend. torch._dynamo.reset() opt_model = torch.compile(init_model(), backend=custom_backend) opt_model(generate_data(16)[0]) 代码语
from typing import List def custom_backend(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): print("custom backend called with FX graph:") gm.graph.print_tabular() return gm.forward # Reset since we are using a different backend. torch._dynamo.reset() opt_model = torch.co...
克隆存储库git clone git@hcarlens/pytorch-tabular.git 运行基准脚本python3 pytorch-tabular/higgs_benchmark.py 如果您在使用GTX 1080Ti的实例（例如我使用的Genesis Cloud）上运行，则应获得以下结果：ubuntu@genesis:~$ python3 pytorch-tabular/higgs_benchmark.py2020-04-12 15:05:55.961134: I tensorflow/s...
python3 pytorch-tabular/higgs_benchmark.py 如果您在使用GTX 1080Ti的实例(例如我使用的Genesis Cloud)上运行,则应获得以下结果: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 ubuntu@genesis:~$ python3 pytorch-tabular/higgs_benchmark.py2020-04-1215:05:55.961134:Itensorflow/stream_executor/platform/...
gitclone git@github.com:hcarlens/pytorch-tabular.git 运行基准脚本 python3pytorch-tabular/higgs_benchmark.py 如果您在使用GTX 1080Ti的实例(例如我使用的Genesis Cloud)上运行,则应获得以下结果: ubuntu@genesis:~$ python3 pytorch...
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git clone git@:hcarlens/pytorch-tabular.git 1. 运行基准脚本 python3 pytorch-tabular/higgs_benchmark.py 1. 如果您在使用GTX 1080Ti的实例(例如我使用的Genesis Cloud)上运行,则应获得以下结果: ubuntu@genesis:~$ python3 pytorch-tabular/higgs_benchmark.py ...
from typing import List import torch from torch import _dynamo as torchdynamo def my_compiler(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): print("my_compiler() called with FX graph:") gm.graph.print_tabular() return gm.forward # return a python callable import torch.nn...
graph.print_tabular() return gm.forward # Reset since we are using a different backend. torch._dynamo.reset() opt_model = torch.compile(init_model(), backend=custom_backend) opt_model(generate_data(16)[0]) Out: custom backend called with FX graph: opcode name target args kwargs ---...
在本教程中,我们将使用 Azure 开放数据集中的MNIST 数据集。 Python 复制 # Initialize SparkSession spark = SparkSession.builder.getOrCreate() # Download MNIST dataset from Azure Open Datasets from azureml.opendatasets import MNIST mnist = MNIST.get_tabular_dataset() mnist_df = mnist.to_pandas_...