英文:
Deativate Pytorch Lightning Module Model Logging During Prediction
问题
我正在尝试使用FastAPI提供PyTorch Forecasting模型。在启动时，我使用以下代码从检查点加载模型：
model = BaseModel.load_from_checkpoint(model_path)
model.eval()
尽管预测结果看起来正常，但每次进行预测后，lightning_logs文件夹中都会生成一个新版本，其中包含每次预测后存储的新文件中的超参数。我在进行预测时使用以下代码：
raw_predictions = model.predict(df, mode="raw", return_x=True)
如何在提供模型进行预测时停止记录日志？
英文:
I am trying to serve a Pytorch Forecasting model using FastAPI. I am loading the model from a checkpoint using the following code on startup:
model = BaseModel.load_from_checkpoint(model_path)
model.eval()
Although the predictions do come up fine, every time there's a new version generated in the lightining_logs folder with the hyperparameters stored in a new file after each prediction. I use the following code for the predictions:
raw_predictions = model.predict(df, mode="raw", return_x=True)
How can I stop logging when I serve the model for predictions?
答案1
得分: 1
Hi heres what i normally do
- 通常我会将模型保存为普通的pt文件，PyTorch Lightning与PyTorch完全兼容（当然，你需要将它从LightningModule重新设计为普通的nn.Module类）
- 将模型保存为ONNX模型
from model import Model
import pytorch_lightning as pl
import torch
model: pl.LightningModule = Model()
torch.save(model.state_dict(), 'weights.pt')
# 或保存为ONNX
torch.onnx.export(model, (inputs), fname)
英文:
Hi heres what i normally do
- Save as a normal pt file pytorch lighthning is fully compatible with pytorch (of course you have to redesign from a LightningModule to a normal nn.Module class)
- Save as onnx model
from model import Model
import pytorch_lightning as pl
import torch
model:pl.LightningModule = Model()
torch.save(model.state_dict(), 'weights.pt')
# Or save to onnx
torch.onnx.export(model, (inputs), fname))
答案2
得分: 0
以下是您要翻译的内容：
有人在GitHub上发布了答案，与我在进行了大量阅读后发现它的时间差不多。对我来说，这并不那么明显：
trainer_kwargs={'logger': False}
对于我的问题中的代码，预测部分将变成：
raw_predictions = model.predict(df, mode="raw", return_x=False, trainer_kwargs=dict(accelerator="cpu|gpu", logger=False))
英文:
Someone posted the answer on GitHub around the same time I discovered it after doing lots of reading. It's not that evident, at least for me:
trainer_kwargs={'logger':False}
In the case of the code in my question the prediction part would turn into:
raw_predictions = model.predict(df, mode="raw", return_x=False, trainer_kwardgs=dict(accelarator="cpu|gpu", logger=False))
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