merged master and fixed commits
This commit is contained in:
miguelgfierro
Коммит
24b52baee4
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@ -99,3 +99,9 @@ ENV/
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# mypy
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.mypy_cache/
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# Pycharm
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.idea/
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#################
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job.json
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@ -0,0 +1,3 @@
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FROM pytorch/pytorch:0.4_cuda9_cudnn7
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RUN pip install --no-cache-dir h5py scipy jupyter ipykernel numpy toolz pandas scikit-learn pillow
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@ -0,0 +1,11 @@
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DATA_DIR:=/mnt/imagenet
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PWD:=$(shell pwd)
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FAKE:='False'
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FAKE_DATA_LENGTH:=1281167
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name_prefix:=iliauk
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tag:=latest
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image-open:=$(name_prefix)/pytorch_gloo:$(tag)
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open-path:=$(PWD)/Docker
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script:=\$$AZ_BATCHAI_INPUT_SCRIPTS/imagenet_pytorch_gloo.py
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include ../include/build.mk
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@ -0,0 +1,283 @@
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import argparse
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import logging
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import os
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from os import path
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import numpy as np
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import pandas as pd
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import multiprocessing
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from toolz import pipe
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from timer import Timer
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from PIL import Image
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import torch
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import torch.nn as nn
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import torch.backends.cudnn as cudnn
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import torch.optim
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import torch.utils.data
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import torchvision.transforms as transforms
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from torch.utils.data import DataLoader, Dataset
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import torchvision.models as models
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import torch.distributed as dist
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import torch.utils.data.distributed
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print("PyTorch: ", torch.__version__)
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Distributed training settings
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parser = argparse.ArgumentParser(description='PyTorch ResNet Example')
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parser.add_argument('--world-size', default=1, type=int, help='number of distributed processes')
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parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str, help='url used to set up distributed training')
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parser.add_argument('--dist-backend', default='gloo', type=str, help='distributed backend')
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parser.add_argument('--rank', default=-1, type=int, help='rank of the worker')
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_WIDTH = 224
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_HEIGHT = 224
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_LR = 0.001
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_EPOCHS = 1
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_NUM_GPU = int(torch.cuda.device_count())
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_BATCHSIZE = 64*_NUM_GPU
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_RGB_MEAN = [0.485, 0.456, 0.406]
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_RGB_SD = [0.229, 0.224, 0.225]
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args = parser.parse_args()
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def _str_to_bool(in_str):
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if 't' in in_str.lower():
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return True
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else:
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return False
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_FAKE = _str_to_bool(os.getenv('FAKE', 'True'))
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_DATA_LENGTH = int(os.getenv('FAKE_DATA_LENGTH', 1281167)) # How much fake data to simulate, default to size of imagenet dataset
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#_DISTRIBUTED = _str_to_bool(os.getenv('DISTRIBUTED', 'False'))
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_DISTRIBUTED = True
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_CPU_COUNT = 8
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logger.info("Distributed mode: ", _DISTRIBUTED)
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logger.info("CPU Count: ", _CPU_COUNT)
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def _append_path_to(data_path, data_series):
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return data_series.apply(lambda x: path.join(data_path, x))
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def _load_training(data_dir):
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train_df = pd.read_csv(path.join(data_dir, 'train.csv'))
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return train_df.assign(filenames=_append_path_to(path.join(data_dir, 'train'),
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train_df.filenames))
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def _load_validation(data_dir):
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train_df = pd.read_csv(path.join(data_dir, 'validation.csv'))
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return train_df.assign(filenames=_append_path_to(path.join(data_dir, 'validation'),
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train_df.filenames))
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def _create_data_fn(train_path, test_path):
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logger.info('Reading training data info')
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train_df = _load_training(train_path)
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logger.info('Reading validation data info')
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validation_df = _load_validation(test_path)
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# File-path
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train_X = train_df['filenames'].values
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validation_X = validation_df['filenames'].values
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# One-hot encoded labels for torch
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train_labels = train_df[['num_id']].values.ravel()
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validation_labels = validation_df[['num_id']].values.ravel()
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# Index starts from 0
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train_labels -= 1
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validation_labels -= 1
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return train_X, train_labels, validation_X, validation_labels
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class ImageNet(Dataset):
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def __init__(self, img_locs, img_labels, transform=None):
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self.img_locs, self.labels = img_locs, img_labels
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self.transform = transform
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logger.info("Loaded {} labels and {} images".format(len(self.labels), len(self.img_locs)))
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def __getitem__(self, idx):
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im_file = self.img_locs[idx]
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label = self.labels[idx]
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with open(im_file, 'rb') as f:
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im_rgb = Image.open(f)
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# Make sure 3-channel (RGB)
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im_rgb = im_rgb.convert('RGB')
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if self.transform is not None:
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im_rgb = self.transform(im_rgb)
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return im_rgb, label
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def __len__(self):
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return len(self.img_locs)
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class FakeData(Dataset):
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def __init__(self,
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batch_size=32,
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num_batches=20,
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dim=(224, 224),
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n_channels=3,
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n_classes=10,
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length=_DATA_LENGTH,
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seed=42,
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data_transform=None):
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self.dim = dim
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self.n_channels = n_channels
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self.n_classes = n_classes
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self.num_batches = num_batches
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self._data = _create_data(batch_size, self.num_batches, self.dim, self.n_channels)
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self._labels = _create_labels(batch_size, self.num_batches, self.n_classes)
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self.translation_index = np.random.choice(len(self._labels), length)
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self._length=length
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self._data_transform = data_transform
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#logger = _get_logger()
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logger.info("Creating fake data {} labels and {} images".format(n_classes, len(self._data)))
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def __getitem__(self, idx):
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#logger = _get_logger()
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logger.debug('Retrieving samples')
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logger.debug(str(idx))
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tr_index_array = self.translation_index[idx]
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if self._data_transform is not None:
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data=self._data_transform(self._data[tr_index_array])
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else:
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data=self._data[tr_index_array]
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return data, self._labels[tr_index_array]
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def __len__(self):
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return self._length
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def _log_summary(data_length, duration):
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#logger = _get_logger()
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images_per_second = data_length / duration
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logger.info('Data length: {}'.format(data_length))
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logger.info('Total duration: {:.3f}'.format(duration))
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logger.info('Total images/sec: {:.3f}'.format(images_per_second))
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logger.info('Batch size: (Per GPU {}: Total {})'.format(int(_BATCHSIZE/_NUM_GPU), _BATCHSIZE))
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logger.info('Distributed: {}'.format('True' if _DISTRIBUTED else 'False'))
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logger.info('Num GPUs: {:.3f}'.format(_NUM_GPU)) # May need to pass in argument to get this
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logger.info('Dataset: {}'.format('Synthetic' if _FAKE else 'Imagenet'))
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def _create_data(batch_size, num_batches, dim, channels, seed=42):
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np.random.seed(seed)
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return np.random.rand(batch_size * num_batches,
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channels,
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dim[0],
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dim[1]).astype(np.float32)
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def _create_labels(batch_size, num_batches, n_classes):
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return np.random.choice(n_classes, batch_size * num_batches)
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def train(train_loader, model, criterion, optimizer, epoch):
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logger.info("Training ...")
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model.train()
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for i, (input, target) in enumerate(train_loader):
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input, target = input.cuda(non_blocking=True), target.cuda(non_blocking=True)
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# compute output
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output = model(input)
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loss = criterion(output, target)
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# compute gradient and do SGD step
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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def validate(val_loader, model, criterion):
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logger.info("Validating ...")
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model.eval()
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correct = 0
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total = 0
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with torch.no_grad():
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for i, (input, target) in enumerate(val_loader):
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target = target.cuda(non_blocking=True)
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# compute output
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output = model(input)
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_, predicted = torch.max(output.data, 1)
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total += target.size(0)
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correct += (predicted == target).sum().item()
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logger.info('Top-1 Accuracy: %.2f %%' % (100 * correct / total))
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def main():
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# Autotune
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cudnn.benchmark = True
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# Load symbol
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model = models.__dict__['resnet50'](pretrained=False)
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if _DISTRIBUTED:
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logger.info('Running in distributed mode')
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dist.init_process_group(
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backend=args.dist_backend,
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init_method=args.dist_url,
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world_size=args.world_size,
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rank=args.rank)
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model.cuda()
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model = torch.nn.parallel.DistributedDataParallel(model)
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else:
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model = torch.nn.DataParallel(model).cuda()
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# Optimisers
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criterion = nn.CrossEntropyLoss().cuda()
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optimizer = torch.optim.SGD(model.parameters(), lr=_LR)
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# Data-sets
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if _FAKE:
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logger.info("Setting up fake loaders")
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train_dataset = FakeData(n_classes=1000, data_transform=torch.FloatTensor)
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else:
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normalize = transforms.Normalize(_RGB_MEAN, _RGB_SD)
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train_X, train_y, valid_X, valid_y = _create_data_fn(os.getenv('AZ_BATCHAI_INPUT_TRAIN'),
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os.getenv('AZ_BATCHAI_INPUT_TEST'))
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train_dataset = ImageNet(
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train_X,
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train_y,
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transforms.Compose([
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transforms.RandomResizedCrop(_WIDTH),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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normalize]))
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if _DISTRIBUTED:
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train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
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else:
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train_sampler = None
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# Data-loaders
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train_loader = torch.utils.data.DataLoader(
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train_dataset, batch_size=_BATCHSIZE, shuffle=(train_sampler is None), num_workers=_CPU_COUNT, sampler=train_sampler)
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#val_loader = torch.utils.data.DataLoader(
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# ImageNet(
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# valid_X,
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# valid_y,
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# transforms.Compose([
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# transforms.Resize(256),
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# transforms.CenterCrop(_WIDTH),
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# transforms.ToTensor(),
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# normalize])), batch_size=_BATCHSIZE, shuffle=False,
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# num_workers=_CPU_COUNT)
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# Main training-loop
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for epoch in range(_EPOCHS):
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if _DISTRIBUTED:
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train_sampler.set_epoch(epoch)
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# Train
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with Timer(output=logger.info, prefix="Training") as t:
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train(train_loader, model, criterion, optimizer, epoch)
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_log_summary(len(train_dataset), t.elapsed)
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# Validate
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#with Timer(output=logger.info, prefix="Testing"):
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# validate(val_loader, model, criterion)
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print("Finished")
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if __name__ == '__main__':
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print("Pytorch")
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main()
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@ -1,5 +1,5 @@
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# Variables for Batch AI - change as necessary
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ID:=disdl
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ID:=iliadl2
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LOCATION:=eastus
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GROUP_NAME:=batch${ID}rg
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STORAGE_ACCOUNT_NAME:=batch${ID}st
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@ -8,8 +8,8 @@ SELECTED_SUBSCRIPTION:="Team Danielle Internal"
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WORKSPACE:=workspace
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VM_SIZE:=Standard_NC24rs_v3
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NUM_NODES:=8
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CLUSTER_NAME:=msv100
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NUM_NODES:=2
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CLUSTER_NAME:=ikv100
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GPU_TYPE:=V100
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@ -89,7 +89,8 @@ def _fake_length_for(mpitype, fake_length, data):
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return ''
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def _prepare_command(mpitype, total_processes, processes_per_node, script, node_count, data=None, synthetic_length=1281167):
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def _prepare_command(mpitype, total_processes, processes_per_node, script, node_count, data=None,
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synthetic_length=1281167):
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command = cmd_choice_dict.get(mpitype, cmd_for_intel)
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return command.format(total_processes=total_processes,
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processes_per_node=processes_per_node,
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@ -141,6 +142,48 @@ def generate_job_dict(image_name,
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}
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def generate_job_dict_gloo(image_name,
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script,
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node_count=2):
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# Command is hard-coded for time-being
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# Not sure what world-size is?? Probably node_count but check
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return {
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"$schema": "https://raw.githubusercontent.com/Azure/BatchAI/master/schemas/2018-05-01/job.json",
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"properties": {
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"pyTorchSettings": {
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"pythonScriptFilePath": script,
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"commandLineArgs": "--world-size 2 --dist-backend $AZ_BATCHAI_PYTORCH_BACKEND --dist-url $AZ_BATCHAI_PYTORCH_INIT_METHOD --rank $AZ_BATCHAI_TASK_INDEX",
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"communicationBackend": "gloo"
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},
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"nodeCount": node_count,
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"stdOutErrPathPrefix": "$AZ_BATCHAI_MOUNT_ROOT/extfs",
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"inputDirectories": [{
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"id": "SCRIPTS",
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"path": "$AZ_BATCHAI_MOUNT_ROOT/extfs/scripts"
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},
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{
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"id": "TRAIN",
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"path": "$AZ_BATCHAI_MOUNT_ROOT/nfs/imagenet",
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},
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{
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"id": "TEST",
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"path": "$AZ_BATCHAI_MOUNT_ROOT/nfs/imagenet",
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},
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],
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"outputDirectories": [{
|
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"id": "MODEL",
|
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"pathPrefix": "$AZ_BATCHAI_MOUNT_ROOT/extfs",
|
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"pathSuffix": "Models"
|
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}],
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"containerSettings": {
|
||||
"imageSourceRegistry": {
|
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"image": image_name
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
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||||
|
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def generate_job_dict_cntk(image_name,
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command,
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||||
node_count=2,
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|
|
@ -203,6 +246,11 @@ def synthetic_data_job(image_name,
|
|||
filename, image_name))
|
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total_processes = processes_per_node * \
|
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node_count if total_processes is None else total_processes
|
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if mpitype == "gloo":
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job_template = generate_job_dict_gloo(image_name,
|
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script,
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node_count=node_count)
|
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else:
|
||||
command = _prepare_command(mpitype,
|
||||
total_processes,
|
||||
processes_per_node,
|
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|
|
@ -212,6 +260,7 @@ def synthetic_data_job(image_name,
|
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job_template = generate_job_dict(image_name,
|
||||
command,
|
||||
node_count=node_count)
|
||||
|
||||
write_json_to_file(job_template, filename)
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||||
logger.info('Done')
|
||||
|
||||
|
|
@ -228,6 +277,7 @@ def imagenet_data_job(image_name,
|
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filename, image_name))
|
||||
total_processes = processes_per_node * \
|
||||
node_count if total_processes is None else total_processes
|
||||
# non-synthetic gloo to add
|
||||
command = _prepare_command(mpitype,
|
||||
total_processes,
|
||||
processes_per_node,
|
||||
|
|
|
|||
|
|
@ -45,6 +45,11 @@ define submit_pytorch_local
|
|||
$(call submit_job, $(2))
|
||||
endef
|
||||
|
||||
define submit_pytorch_gloo
|
||||
$(call generate_job_gloo,iliauk/pytorch_gloo,\$$AZ_BATCHAI_INPUT_SCRIPTS/imagenet_pytorch_gloo.py,$(1),$(2), --synthetic_length ${FAKE_DATA_LENGTH})
|
||||
$(call submit_job, $(3))
|
||||
endef
|
||||
|
||||
define submit_cntk
|
||||
$(call generate_job_openmpi,hoaphumanoid/cntk:distributed,\$$AZ_BATCHAI_INPUT_SCRIPTS/imagenet_cntk.py,$(1),$(2), --synthetic_length ${FAKE_DATA_LENGTH})
|
||||
$(call submit_job, $(3))
|
||||
|
|
@ -75,6 +80,7 @@ create-cluster: upload-nodeprep-scripts
|
|||
submit-all: submit-keras-intel32 submit-keras-intel16 submit-keras-intel8 submit-keras-intel4 \
|
||||
submit-tf-intel32 submit-tf-intel16 submit-tf-intel8 submit-tf-intel4 \
|
||||
submit-pytorch32 submit-pytorch16 submit-pytorch8 submit-pytorch4 \
|
||||
submit-pytorch_gloo32 submit-pytorch_gloo16 submit-pytorch_gloo8 submit-pytorch_gloo4 \
|
||||
submit-cntk32 submit-cntk16 submit-cntk8 submit-cntk4 \
|
||||
submit-keras-local submit-tf-local submit-pytorch-local submit_cntk_local
|
||||
|
||||
|
|
@ -140,3 +146,16 @@ submit-cntk4:
|
|||
|
||||
submit-cntk-local:
|
||||
$(call submit_cntk_local,1,cntk-local)
|
||||
|
||||
|
||||
submit-pytorch_gloo32:
|
||||
$(call submit_pytorch_gloo,8,$(PROCESSES_PER_NODE),pytorch_gloo-32)
|
||||
|
||||
submit-pytorch_gloo16:
|
||||
$(call submit_pytorch_gloo,4,$(PROCESSES_PER_NODE),pytorch_gloo-16)
|
||||
|
||||
submit-pytorch_gloo8:
|
||||
$(call submit_pytorch_gloo,2,$(PROCESSES_PER_NODE),pytorch_gloo-8)
|
||||
|
||||
submit-pytorch_gloo4:
|
||||
$(call submit_pytorch_gloo,1,$(PROCESSES_PER_NODE),pytorch_gloo-4)
|
||||
|
|
|
|||
|
|
@ -51,6 +51,16 @@ define generate_job_local
|
|||
endef
|
||||
|
||||
|
||||
define generate_job_gloo
|
||||
python ../generate_job_spec.py $(1) gloo \
|
||||
$(2) \
|
||||
--filename job.json \
|
||||
--node_count $(3) \
|
||||
--ppn $(4) \
|
||||
$(5)
|
||||
endef
|
||||
|
||||
|
||||
define stream_stdout
|
||||
az batchai job file stream -w $(WORKSPACE) -e $(EXPERIMENT) \
|
||||
--j $(1) --output-directory-id stdouterr -f stdout.txt
|
||||
|
|
@ -113,6 +123,7 @@ upload-scripts: set-storage
|
|||
$(call upload_script, ../../HorovodPytorch/src/imagenet_pytorch_horovod.py)
|
||||
$(call upload_script, ../../CNTK/src/imagenet_cntk.py)
|
||||
$(call upload_script, ../../CNTK/src/resnet_models.py)
|
||||
$(call upload_script, ../../Pytorch/src/imagenet_pytorch_gloo.py)
|
||||
$(call upload_script, ../../common/timer.py)
|
||||
|
||||
upload-nodeprep-scripts: set-storage
|
||||
|
|
@ -160,7 +171,7 @@ delete: delete-cluster
|
|||
az group delete --name ${GROUP_NAME} -y
|
||||
|
||||
|
||||
setup: select-subscription create-resource-group create-workspace create-storage set-storage set-az-defaults create-fileshare create-cluster list-clusters
|
||||
setup: select-subscription create-resource-group create-workspace create-storage set-storage set-az-defaults create-fileshare create-directory upload-scripts create-cluster list-clusters create-experiment
|
||||
@echo "Cluster created"
|
||||
|
||||
#
|
||||
|
|
@ -169,6 +180,7 @@ setup: select-subscription create-resource-group create-workspace create-storage
|
|||
submit-all: submit-keras-intel32 submit-keras-intel16 submit-keras-intel8 submit-keras-intel4 \
|
||||
submit-tf-intel32 submit-tf-intel16 submit-tf-intel8 submit-tf-intel4 \
|
||||
submit-pytorch32 submit-pytorch16 submit-pytorch8 submit-pytorch4 \
|
||||
submit-pytorch_gloo32 submit-pytorch_gloo16 submit-pytorch_gloo8 submit-pytorch_gloo4 \
|
||||
submit-cntk32 submit-cntk16 submit-cntk8 submit-cntk4 \
|
||||
submit-keras-local submit-tf-local submit-pytorch-local submit_cntk_local
|
||||
|
||||
|
|
@ -191,6 +203,11 @@ clean-jobs:
|
|||
$(call delete_job, pytorch-16)
|
||||
$(call delete_job, pytorch-32)
|
||||
|
||||
$(call delete_job, pytorch_gloo-4)
|
||||
$(call delete_job, pytorch_gloo-8)
|
||||
$(call delete_job, pytorch_gloo-16)
|
||||
$(call delete_job, pytorch_gloo-32)
|
||||
|
||||
$(call delete_job, cntk-local)
|
||||
$(call delete_job, cntk-4)
|
||||
$(call delete_job, cntk-8)
|
||||
|
|
@ -198,6 +215,7 @@ clean-jobs:
|
|||
$(call delete_job, cntk-32)
|
||||
|
||||
####### Gather Results ######
|
||||
# TODO for PyTorch_Gloo
|
||||
|
||||
gather-results:results.json
|
||||
@echo "All results gathered"
|
||||
|
|
@ -205,6 +223,9 @@ gather-results:results.json
|
|||
results.json: pytorch_1gpulocal_$(GPU_TYPE)_local.results pytorch_4gpuopen_$(GPU_TYPE)_open.results \
|
||||
pytorch_8gpuopen_$(GPU_TYPE)_open.results pytorch_16gpuopen_$(GPU_TYPE)_open.results \
|
||||
pytorch_32gpuopen_$(GPU_TYPE)_open.results \
|
||||
pytorch_gloo_1gpulocal_$(GPU_TYPE)_local.results pytorch_gloo_4gpuopen_$(GPU_TYPE)_open.results \
|
||||
pytorch_gloo_8gpuopen_$(GPU_TYPE)_open.results pytorch_gloo_16gpuopen_$(GPU_TYPE)_open.results \
|
||||
pytorch_gloo_32gpuopen_$(GPU_TYPE)_open.results \
|
||||
tf_1gpulocal_$(GPU_TYPE)_local.results tf_4gpuintel_$(GPU_TYPE)_intel.results \
|
||||
tf_8gpuintel_$(GPU_TYPE)_intel.results tf_16gpuintel_$(GPU_TYPE)_intel.results \
|
||||
tf_32gpuintel_$(GPU_TYPE)_intel.results \
|
||||
|
|
@ -233,7 +254,20 @@ pytorch_32gpuopen_$(GPU_TYPE)_open.results:
|
|||
$(call stream_stdout, pytorch-32)>pytorch_32gpuopen_$(GPU_TYPE)_open.results
|
||||
|
||||
|
||||
pytorch_gloo_1gpulocal_$(GPU_TYPE)_local.results:
|
||||
$(call stream_stdout, pytorch_gloo-local)>pytorch_gloo_1gpulocal_$(GPU_TYPE)_local.results
|
||||
|
||||
pytorch_gloo_4gpuopen_$(GPU_TYPE)_open.results:
|
||||
$(call stream_stdout, pytorch_gloo-4)>pytorch_gloo_4gpuopen_$(GPU_TYPE)_open.results
|
||||
|
||||
pytorch_gloo_8gpuopen_$(GPU_TYPE)_open.results:
|
||||
$(call stream_stdout, pytorch_gloo-8)>pytorch_gloo_8gpuopen_$(GPU_TYPE)_open.results
|
||||
|
||||
pytorch_gloo_16gpuopen_$(GPU_TYPE)_open.results:
|
||||
$(call stream_stdout, pytorch_gloo-16)>pytorch_gloo_16gpuopen_$(GPU_TYPE)_open.results
|
||||
|
||||
pytorch_gloo_32gpuopen_$(GPU_TYPE)_open.results:
|
||||
$(call stream_stdout, pytorch_gloo-32)>pytorch_gloo_32gpuopen_$(GPU_TYPE)_open.results
|
||||
|
||||
tf_1gpulocal_$(GPU_TYPE)_local.results:
|
||||
$(call stream_stdout, tf-local)>tf_1gpulocal_$(GPU_TYPE)_local.results
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче