pytorch-lightning | lightweight PyTorch wrapper for high-performance | Machine Learning library
kandi X-RAY | pytorch-lightning Summary
kandi X-RAY | pytorch-lightning Summary
Lightning disentangles PyTorch code to decouple the science from the engineering.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Configure optimizers .
- Check and set final flags .
- Adds argparse arguments to cls .
- Reset the train dataloader .
- Apply a function to two collections .
- Register DDP comm wrapper .
- Broadcast a list of objects into a tensor .
- Dump a checkpoint .
- Apply a function to a collection .
- Initialize the meta device .
pytorch-lightning Key Features
pytorch-lightning Examples and Code Snippets
Lines of Code : 130License : Permissive (MIT)
Copyimport torch
import torch.nn as nn
class SimpsonsNet(nn.Module):
def __init__(self):
super(SimpsonsNet, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3 def train_mnist(config):
# Create your PTL model.
model = MNISTClassifier(config)
# Create the Tune Reporting Callback
metrics = {"loss": "ptl/val_loss", "acc": "ptl/val_accuracy"}
callbacks = [TuneReportCallback(metrics, on 
License : No LicenseCopyfrom typing import Optional
def my_func(param_a: int, param_b: Optional[float] = None) -> str:
"""Sample function.
Args:
param_a: first parameter
param_b: second parameter
Return:
sum of both numbers
Ex import argparse
import os
import subprocess
import sys
from packaging import version
import numpy as np
import pyspark
import pyspark.sql.types as T
from pyspark import SparkConf
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
i import argparse
import os
from filelock import FileLock
import tempfile
import torch
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
# import argparse
import os
import subprocess
import sys
from packaging import version
import numpy as np
import pyspark
import pyspark.sql.types as T
from pyspark import SparkConf
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
i model_test = model_test.load_from_checkpoint(path)
model_test = BYOL.load_from_checkpoint(path)
# criterion is passed if you want to register the validation loss too
def validate_model(model, eval_loader, criterion):
...
def validation_step(self, batch, batch_idx):
input_ids, attention_mask, targets = batch['input_ids'], batch['attention_mask'], batch['label'].squeeze()
logits = self(batch)
loss = F.cross_entropy(logits, targets)
acc = accu 29 def validation_step(self, val_batch, batch_idx):
30 x = val_batch
31 x = x.view(x.size(0), -1) # here is your problem
x = torch.tensor(val_batch)
Community Discussions
Trending Discussions on pytorch-lightning
QUESTION
I use PyTorch Lightning for model training, during which I use ModelCheckpoint to save loading points. Finally, I would like to know whether the model is loaded correctly. Let me know if you require further information?
ANSWER
Answered 2022-Mar-21 at 16:05load_from_checkpoint() will return a model with trained weights, so you need to assign it to a new variable.
QUESTION
I'm using the CIFAR-10 pre-trained VAE from lightning-bolts. It should be able to regenerate images with the quality shown on this picture taken from the docs (LHS are the real images, RHS are the generated)
However, when I write a simple script that loads the model, the weights, and tests it over the training set, I get a much worse reconstruction (top row are real images, bottom row are the generated ones):
Here is a link to a self-contained colab notebook that reproduces the steps I've followed to produce the pictures.
Am I doing something wrong on my inference process? Could it be that the weights are not as "good" as the docs claim?
Thanks!
...ANSWER
Answered 2022-Feb-01 at 20:11First, the image from the docs you show is for the AE, not the VAE. The results for the VAE look much worse:
https://pl-bolts-weights.s3.us-east-2.amazonaws.com/vae/vae-cifar10/vae_output.png
Second, the docs state "Both input and generated images are normalized versions as the training was done with such images." So when you load the data you should specify normalize=True. When you plot your data, you will need to 'unnormalize' the data as well:
QUESTION
Im trying to run a loop over a set of parameters and I wan't to make a new network for each parameter and let it learn a few epochs.
Currently my code looks like this:
...ANSWER
Answered 2021-Dec-09 at 14:53I think, in your settings, you want to disable automatic checkpointing:
QUESTION
On Google Colaboratory, I have tried all 3 runtimes: CPU, GPU, TPU. All give the same error.
Cells:
...ANSWER
Answered 2021-Aug-19 at 14:08Searching online; there semes to be many causes for this same problem.
In my case, setting Accelerator to None in Google Colaboratory solved this.
QUESTION
I have installed pytorch version 1.10.0 alongside torchtext, torchvision and torchaudio using conda. My PyTorch is cpu-only, and I have experimented with both conda install pytorch-mutex -c pytorch and conda install pytorch cpuonly -c pytorch to install the cpuonly version, both yielding the same eror that I will describe in the following lines.
I have also installed pytorch-lightning in conda, alongside jsonargparse[summaries via pip in the environment.
I have written this code to see whether LightningCLI works or not.
ANSWER
Answered 2021-Nov-24 at 22:00So in order to fix the problem, I had to change my environment.yaml in order to force pytorch to install from the pytorch channel.
So this is my environment.yaml now:
QUESTION
I use a deep learning models written in pytorch_lightning (pytorch) and train them on slurm clusters. I submit job like this:
...ANSWER
Answered 2021-Oct-05 at 11:43You can use Slurm's signalling mechanism to pass a signal to your application when it's within a certain number of seconds of the timelimit (see man sbatch). In your submission script use --signal=USR1@30 to send USR1 30 seconds before the timelimit is reached. Your submit script would contain these lines:
QUESTION
Doing things on Google Colab.
- transformers: 4.10.2
- pytorch-lightning: 1.2.7
ANSWER
Answered 2021-Sep-20 at 13:25The Trainer needs to call its .fit() in order to set up a lot of things and then only you can do .test() or other methods.
You are right about putting a .fit() just before .test() but the fit call needs to a valid one. You have to feed a dataloader/datamodule to it. But since you don't want to do a training/validation in this fit call, just pass limit_[train/val]_batches=0 while Trainer construction.
QUESTION
I would like to to multiply following two tensors x (of shape (BS, N, C)) and y (of shape (BS,1,C)) in the following way:
...ANSWER
Answered 2021-Sep-10 at 14:08Is there anything wrong with x*y? As you can see in the code below, it yields exactly the same output as your function:
QUESTION
Unable to use Automatic Logging (self.log) when calling training_step() on Pytorch Lightning, what am I missing? Here is a minimal example:
ANSWER
Answered 2021-Aug-25 at 17:45This is NOT the correct usage of LightningModule class. You can't call a hook (namely .training_step()) manually and expect everything to work fine.
You need to setup a Trainer as suggested by PyTorch Lightning at the very start of its tutorial - it is a requirement. The functions (or hooks) that you define in a LightningModule merely tells Lightning "what to do" in a specific situation (in this case, at each training step). It is the Trainer that actually "orchestrates" the training by instantiating the necessary environment (including Logging functionality) and feeding it into the Lightning Module whenever needed.
So, do it the way Lightning suggests and it will work.
QUESTION
Logger in PyTorch-Lightning prints information about the model to be trained (or evaluated) and the progress during the training,
However, in my case I would like to hide all messages from the logger in order not to flood the output in Jupyter Notebook.
I've looked into the API of the Trainer class on the official docs page https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags and it seems like there is no option to turn off the messages from the logger.
There is a parameter log_every_n_steps which can be set to big value, but nevertheless, the logging result after each epoch is displayed.
How can one disable the logging?
...ANSWER
Answered 2021-Aug-16 at 19:23Maybe try like that?
logging.getLogger("package").propagate = False
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install pytorch-lightning
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
