mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-09 11:32:07 +08:00
simplify examples structure (#1247)
* simplify examples structure * update changelog * fix imports * rename example * rename scripts * changelog
This commit is contained in:
Jirka Borovec
@@ -1,4 +1,4 @@
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# Basic Examples
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## Basic Examples
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Use these examples to test how lightning works.
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#### Test on CPU
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@@ -36,4 +36,27 @@ On a single node, uses all GPUs for 1 model. Then shares gradient information
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across nodes.
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```bash
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python gpu_template.py --gpus 2 --distributed_backend ddp2
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```
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```
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# Multi-node example
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This demo launches a job using 2 GPUs on 2 different nodes (4 GPUs total).
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To run this demo do the following:
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1. Log into the jumphost node of your SLURM-managed cluster.
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2. Create a conda environment with Lightning and a GPU PyTorch version.
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3. Choose a script to submit
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#### DDP
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Submit this job to run with DistributedDataParallel (2 nodes, 2 gpus each)
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```bash
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sbatch ddp_job_submit.sh YourEnv
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```
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#### DDP2
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Submit this job to run with a different implementation of DistributedDataParallel.
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In this version, each node acts like DataParallel but syncs across nodes like DDP.
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```bash
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sbatch ddp2_job_submit.sh YourEnv
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```
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@@ -8,7 +8,7 @@ import numpy as np
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import torch
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import pytorch_lightning as pl
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from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
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from pl_examples.models.lightning_template import LightningTemplateModel
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SEED = 2334
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torch.manual_seed(SEED)
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@@ -8,7 +8,7 @@ import numpy as np
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import torch
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import pytorch_lightning as pl
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from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
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from pl_examples.models.lightning_template import LightningTemplateModel
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SEED = 2334
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torch.manual_seed(SEED)
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@@ -1,282 +0,0 @@
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"""
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Example template for defining a system.
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"""
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import os
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from argparse import ArgumentParser
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.transforms as transforms
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from torch import optim
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from torch.utils.data import DataLoader
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from torchvision.datasets import MNIST
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from pytorch_lightning import _logger as log
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from pytorch_lightning.core import LightningModule
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class LightningTemplateModel(LightningModule):
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"""
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Sample model to show how to define a template.
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Example:
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>>> # define simple Net for MNIST dataset
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>>> params = dict(
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... drop_prob=0.2,
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... batch_size=2,
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... in_features=28 * 28,
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... learning_rate=0.001 * 8,
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... optimizer_name='adam',
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... data_root='./datasets',
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... out_features=10,
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... hidden_dim=1000,
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... )
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>>> from argparse import Namespace
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>>> hparams = Namespace(**params)
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>>> model = LightningTemplateModel(hparams)
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"""
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def __init__(self, hparams):
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"""
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Pass in hyperparameters as a `argparse.Namespace` or a `dict` to the model.
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"""
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# init superclass
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super().__init__()
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self.hparams = hparams
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self.batch_size = hparams.batch_size
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# if you specify an example input, the summary will show input/output for each layer
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self.example_input_array = torch.rand(5, 28 * 28)
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# build model
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self.__build_model()
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# ---------------------
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# MODEL SETUP
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# ---------------------
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def __build_model(self):
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"""
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Layout the model.
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"""
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self.c_d1 = nn.Linear(in_features=self.hparams.in_features,
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out_features=self.hparams.hidden_dim)
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self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
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self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
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self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,
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out_features=self.hparams.out_features)
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# ---------------------
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# TRAINING
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# ---------------------
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def forward(self, x):
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"""
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No special modification required for Lightning, define it as you normally would
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in the `nn.Module` in vanilla PyTorch.
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"""
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x = self.c_d1(x)
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x = torch.tanh(x)
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x = self.c_d1_bn(x)
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x = self.c_d1_drop(x)
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x = self.c_d2(x)
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logits = F.log_softmax(x, dim=1)
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return logits
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def loss(self, labels, logits):
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nll = F.nll_loss(logits, labels)
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return nll
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def training_step(self, batch, batch_idx):
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"""
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Lightning calls this inside the training loop with the data from the training dataloader
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passed in as `batch`.
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"""
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# forward pass
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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# calculate loss
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loss_val = self.loss(y, y_hat)
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tqdm_dict = {'train_loss': loss_val}
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output = OrderedDict({
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'loss': loss_val,
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'progress_bar': tqdm_dict,
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'log': tqdm_dict
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})
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# can also return just a scalar instead of a dict (return loss_val)
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return output
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def validation_step(self, batch, batch_idx):
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"""
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Lightning calls this inside the validation loop with the data from the validation dataloader
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passed in as `batch`.
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"""
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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loss_val = self.loss(y, y_hat)
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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val_acc = torch.tensor(val_acc)
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if self.on_gpu:
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val_acc = val_acc.cuda(loss_val.device.index)
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output = OrderedDict({
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'val_loss': loss_val,
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'val_acc': val_acc,
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})
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# can also return just a scalar instead of a dict (return loss_val)
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return output
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def validation_epoch_end(self, outputs):
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"""
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Called at the end of validation to aggregate outputs.
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:param outputs: list of individual outputs of each validation step.
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"""
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# if returned a scalar from validation_step, outputs is a list of tensor scalars
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# we return just the average in this case (if we want)
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# return torch.stack(outputs).mean()
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val_loss_mean = 0
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val_acc_mean = 0
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for output in outputs:
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val_loss = output['val_loss']
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# reduce manually when using dp
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if self.trainer.use_dp or self.trainer.use_ddp2:
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val_loss = torch.mean(val_loss)
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val_loss_mean += val_loss
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# reduce manually when using dp
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val_acc = output['val_acc']
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if self.trainer.use_dp or self.trainer.use_ddp2:
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val_acc = torch.mean(val_acc)
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val_acc_mean += val_acc
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val_loss_mean /= len(outputs)
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val_acc_mean /= len(outputs)
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tqdm_dict = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}
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result = {'progress_bar': tqdm_dict, 'log': tqdm_dict, 'val_loss': val_loss_mean}
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return result
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# ---------------------
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# TRAINING SETUP
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# ---------------------
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def configure_optimizers(self):
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"""
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Return whatever optimizers and learning rate schedulers you want here.
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At least one optimizer is required.
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"""
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optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
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return [optimizer], [scheduler]
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def __dataloader(self, train):
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# this is neede when you want some info about dataset before binding to trainer
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self.prepare_data()
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# init data generators
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transform = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.5,), (1.0,))])
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dataset = MNIST(root=self.hparams.data_root, train=train,
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transform=transform, download=False)
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# when using multi-node (ddp) we need to add the datasampler
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batch_size = self.hparams.batch_size
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loader = DataLoader(
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dataset=dataset,
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batch_size=batch_size,
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num_workers=0
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)
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return loader
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def prepare_data(self):
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transform = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.5,), (1.0,))])
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_ = MNIST(root=self.hparams.data_root, train=True,
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transform=transform, download=True)
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def train_dataloader(self):
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log.info('Training data loader called.')
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return self.__dataloader(train=True)
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def val_dataloader(self):
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log.info('Validation data loader called.')
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return self.__dataloader(train=False)
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def test_dataloader(self):
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log.info('Test data loader called.')
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return self.__dataloader(train=False)
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def test_step(self, batch, batch_idx):
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"""
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Lightning calls this during testing, similar to `validation_step`,
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with the data from the test dataloader passed in as `batch`.
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"""
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output = self.validation_step(batch, batch_idx)
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# Rename output keys
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output['test_loss'] = output.pop('val_loss')
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output['test_acc'] = output.pop('val_acc')
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return output
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def test_epoch_end(self, outputs):
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"""
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Called at the end of test to aggregate outputs, similar to `validation_epoch_end`.
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:param outputs: list of individual outputs of each test step
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"""
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results = self.validation_step_end(outputs)
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# rename some keys
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results['progress_bar'].update({
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'test_loss': results['progress_bar'].pop('val_loss'),
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'test_acc': results['progress_bar'].pop('val_acc'),
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})
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results['log'] = results['progress_bar']
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results['test_loss'] = results.pop('val_loss')
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return results
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@staticmethod
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def add_model_specific_args(parent_parser, root_dir): # pragma: no-cover
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"""
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Parameters you define here will be available to your model through `self.hparams`.
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"""
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parser = ArgumentParser(parents=[parent_parser])
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# param overwrites
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# parser.set_defaults(gradient_clip_val=5.0)
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# network params
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parser.add_argument('--in_features', default=28 * 28, type=int)
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parser.add_argument('--out_features', default=10, type=int)
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# use 500 for CPU, 50000 for GPU to see speed difference
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parser.add_argument('--hidden_dim', default=50000, type=int)
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parser.add_argument('--drop_prob', default=0.2, type=float)
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parser.add_argument('--learning_rate', default=0.001, type=float)
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# data
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parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
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# training params (opt)
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parser.add_argument('--epochs', default=20, type=int)
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parser.add_argument('--optimizer_name', default='adam', type=str)
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parser.add_argument('--batch_size', default=64, type=int)
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return parser
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@@ -0,0 +1,51 @@
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"""
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Multi-node example (GPU)
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"""
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import os
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from argparse import ArgumentParser
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import numpy as np
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import torch
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import pytorch_lightning as pl
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from pl_examples.models.lightning_template import LightningTemplateModel
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SEED = 2334
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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def main(hparams):
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"""Main training routine specific for this project."""
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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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model = LightningTemplateModel(hparams)
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# ------------------------
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# 2 INIT TRAINER
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# ------------------------
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trainer = pl.Trainer(
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gpus=2,
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num_nodes=2,
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distributed_backend='ddp2'
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)
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# ------------------------
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# 3 START TRAINING
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# ------------------------
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trainer.fit(model)
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if __name__ == '__main__':
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root_dir = os.path.dirname(os.path.realpath(__file__))
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parent_parser = ArgumentParser(add_help=False)
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# each LightningModule defines arguments relevant to it
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parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
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hyperparams = parser.parse_args()
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# ---------------------
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# RUN TRAINING
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# ---------------------
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main(hyperparams)
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@@ -0,0 +1,51 @@
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"""
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Multi-node example (GPU)
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"""
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import os
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from argparse import ArgumentParser
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import numpy as np
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import torch
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import pytorch_lightning as pl
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from pl_examples.models.lightning_template import LightningTemplateModel
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SEED = 2334
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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def main(hparams):
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"""Main training routine specific for this project."""
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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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model = LightningTemplateModel(hparams)
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# ------------------------
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# 2 INIT TRAINER
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# ------------------------
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trainer = pl.Trainer(
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gpus=2,
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num_nodes=2,
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distributed_backend='ddp'
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)
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# ------------------------
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# 3 START TRAINING
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# ------------------------
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trainer.fit(model)
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if __name__ == '__main__':
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root_dir = os.path.dirname(os.path.realpath(__file__))
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parent_parser = ArgumentParser(add_help=False)
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# each LightningModule defines arguments relevant to it
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parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
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hyperparams = parser.parse_args()
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# ---------------------
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# RUN TRAINING
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# ---------------------
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main(hyperparams)
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+27
@@ -0,0 +1,27 @@
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#!/bin/bash -l
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# SLURM SUBMIT SCRIPT
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#SBATCH --nodes=2
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#SBATCH --gres=gpu:2
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#SBATCH --ntasks-per-node=1
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#SBATCH --mem=0
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#SBATCH --time=0-02:00:00
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# activate conda env
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source activate $1
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# -------------------------
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# debugging flags (optional)
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export NCCL_DEBUG=INFO
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export PYTHONFAULTHANDLER=1
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# on your cluster you might need these:
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# set the network interface
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# export NCCL_SOCKET_IFNAME=^docker0,lo
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# might need the latest cuda
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# module load NCCL/2.4.7-1-cuda.10.0
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# -------------------------
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# run script from above
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srun python3 multi_node_ddp2_demo.py
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Executable
+27
@@ -0,0 +1,27 @@
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#!/bin/bash -l
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# SLURM SUBMIT SCRIPT
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#SBATCH --nodes=2
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#SBATCH --gres=gpu:2
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#SBATCH --ntasks-per-node=2
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#SBATCH --mem=0
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#SBATCH --time=0-02:00:00
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# activate conda env
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source activate $1
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# -------------------------
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# debugging flags (optional)
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export NCCL_DEBUG=INFO
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export PYTHONFAULTHANDLER=1
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# on your cluster you might need these:
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# set the network interface
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# export NCCL_SOCKET_IFNAME=^docker0,lo
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# might need the latest cuda
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# module load NCCL/2.4.7-1-cuda.10.0
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# -------------------------
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# run script from above
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srun python3 multi_node_ddp_demo.py
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