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.. only:: html
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Click :ref:`here <sphx_glr_download_advanced_ddp_pipeline.py>`
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.. rst-class:: sphx-glr-example-title
.. _sphx_glr_advanced_ddp_pipeline.py:
Training Transformer models using Distributed Data Parallel and Pipeline Parallelism
====================================================================================
**Author**: `Pritam Damania <https://github.com/pritamdamania87>`_
This tutorial demonstrates how to train a large Transformer model across
multiple GPUs using `Distributed Data Parallel <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`__ and
`Pipeline Parallelism <https://pytorch.org/docs/stable/pipeline.html>`__. This tutorial is an extension of the
`Sequence-to-Sequence Modeling with nn.Transformer and TorchText <https://pytorch.org/tutorials/beginner/transformer_tutorial.html>`__ tutorial
and scales up the same model to demonstrate how Distributed Data Parallel and
Pipeline Parallelism can be used to train Transformer models.
Prerequisites:
* `Pipeline Parallelism <https://pytorch.org/docs/stable/pipeline.html>`__
* `Sequence-to-Sequence Modeling with nn.Transformer and TorchText <https://pytorch.org/tutorials/beginner/transformer_tutorial.html>`__
* `Getting Started with Distributed Data Parallel <https://pytorch.org/tutorials/intermediate/ddp_tutorial.html>`__
.. GENERATED FROM PYTHON SOURCE LINES 23-26
Define the model
----------------
.. GENERATED FROM PYTHON SOURCE LINES 28-33
``PositionalEncoding`` module injects some information about the
relative or absolute position of the tokens in the sequence. The
positional encodings have the same dimension as the embeddings so that
the two can be summed. Here, we use ``sine`` and ``cosine`` functions of
different frequencies.
.. GENERATED FROM PYTHON SOURCE LINES 33-62
.. code-block:: default
import sys
import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import tempfile
from torch.nn import TransformerEncoder, TransformerEncoderLayer
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.pe = nn.Parameter(pe, requires_grad=False)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
.. GENERATED FROM PYTHON SOURCE LINES 63-80
In this tutorial, we will split a Transformer model across two GPUs and use
pipeline parallelism to train the model. In addition to this, we use
`Distributed Data Parallel <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`__
to train two replicas of this pipeline. We have one process driving a pipe across
GPUs 0 and 1 and another process driving a pipe across GPUs 2 and 3. Both these
processes then use Distributed Data Parallel to train the two replicas. The
model is exactly the same model used in the `Sequence-to-Sequence Modeling with nn.Transformer and TorchText
<https://pytorch.org/tutorials/beginner/transformer_tutorial.html>`__ tutorial,
but is split into two stages. The largest number of parameters belong to the
`nn.TransformerEncoder <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html>`__ layer.
The `nn.TransformerEncoder <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html>`__
itself consists of ``nlayers`` of `nn.TransformerEncoderLayer <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoderLayer.html>`__.
As a result, our focus is on ``nn.TransformerEncoder`` and we split the model
such that half of the ``nn.TransformerEncoderLayer`` are on one GPU and the
other half are on another. To do this, we pull out the ``Encoder`` and
``Decoder`` sections into separate modules and then build an ``nn.Sequential``
representing the original Transformer module.
.. GENERATED FROM PYTHON SOURCE LINES 80-122
.. code-block:: default
if sys.platform == 'win32':
print('Windows platform is not supported for pipeline parallelism')
sys.exit(0)
if torch.cuda.device_count() < 4:
print('Need at least four GPU devices for this tutorial')
sys.exit(0)
class Encoder(nn.Module):
def __init__(self, ntoken, ninp, dropout=0.5):
super(Encoder, self).__init__()
self.pos_encoder = PositionalEncoding(ninp, dropout)
self.encoder = nn.Embedding(ntoken, ninp)
self.ninp = ninp
self.init_weights()
def init_weights(self):
initrange = 0.1
self.encoder.weight.data.uniform_(-initrange, initrange)
def forward(self, src):
# Need (S, N) format for encoder.
src = src.t()
src = self.encoder(src) * math.sqrt(self.ninp)
return self.pos_encoder(src)
class Decoder(nn.Module):
def __init__(self, ntoken, ninp):
super(Decoder, self).__init__()
self.decoder = nn.Linear(ninp, ntoken)
self.init_weights()
def init_weights(self):
initrange = 0.1
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, inp):
# Need batch dimension first for output of pipeline.
return self.decoder(inp).permute(1, 0, 2)
.. GENERATED FROM PYTHON SOURCE LINES 123-126
Start multiple processes for training
-------------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 129-131
We start two processes where each process drives its own pipeline across two
GPUs. ``run_worker`` is executed for each process.
.. GENERATED FROM PYTHON SOURCE LINES 131-135
.. code-block:: default
def run_worker(rank, world_size):
.. GENERATED FROM PYTHON SOURCE LINES 136-139
Load and batch data
-------------------
.. GENERATED FROM PYTHON SOURCE LINES 142-170
The training process uses Wikitext-2 dataset from ``torchtext``.
To access torchtext datasets, please install torchdata following instructions at https://github.com/pytorch/data.
The vocab object is built based on the train dataset and is used to numericalize
tokens into tensors. Starting from sequential data, the ``batchify()``
function arranges the dataset into columns, trimming off any tokens remaining
after the data has been divided into batches of size ``batch_size``.
For instance, with the alphabet as the sequence (total length of 26)
and a batch size of 4, we would divide the alphabet into 4 sequences of
length 6:
.. math::
\begin{bmatrix}
\text{A} & \text{B} & \text{C} & \ldots & \text{X} & \text{Y} & \text{Z}
\end{bmatrix}
\Rightarrow
\begin{bmatrix}
\begin{bmatrix}\text{A} \\ \text{B} \\ \text{C} \\ \text{D} \\ \text{E} \\ \text{F}\end{bmatrix} &
\begin{bmatrix}\text{G} \\ \text{H} \\ \text{I} \\ \text{J} \\ \text{K} \\ \text{L}\end{bmatrix} &
\begin{bmatrix}\text{M} \\ \text{N} \\ \text{O} \\ \text{P} \\ \text{Q} \\ \text{R}\end{bmatrix} &
\begin{bmatrix}\text{S} \\ \text{T} \\ \text{U} \\ \text{V} \\ \text{W} \\ \text{X}\end{bmatrix}
\end{bmatrix}
These columns are treated as independent by the model, which means that
the dependence of ``G`` and ``F`` can not be learned, but allows more
efficient batch processing.
.. GENERATED FROM PYTHON SOURCE LINES 170-215
.. code-block:: default
# In 'run_worker'
def print_with_rank(msg):
print('[RANK {}]: {}'.format(rank, msg))
from torchtext.datasets import WikiText2
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
train_iter = WikiText2(split='train')
tokenizer = get_tokenizer('basic_english')
vocab = build_vocab_from_iterator(map(tokenizer, train_iter), specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])
def data_process(raw_text_iter):
data = [torch.tensor(vocab(tokenizer(item)), dtype=torch.long) for item in raw_text_iter]
return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))
train_iter, val_iter, test_iter = WikiText2()
train_data = data_process(train_iter)
val_data = data_process(val_iter)
test_data = data_process(test_iter)
device = torch.device(2 * rank)
def batchify(data, bsz, rank, world_size, is_train=False):
# Divide the dataset into ``bsz`` parts.
nbatch = data.size(0) // bsz
# Trim off any extra elements that wouldn't cleanly fit (remainders).
data = data.narrow(0, 0, nbatch * bsz)
# Evenly divide the data across the ``bsz`` batches.
data = data.view(bsz, -1).t().contiguous()
# Divide the data across the ranks only for training data.
if is_train:
data_per_rank = data.size(0) // world_size
data = data[rank * data_per_rank : (rank + 1) * data_per_rank]
return data.to(device)
batch_size = 20
eval_batch_size = 10
train_data = batchify(train_data, batch_size, rank, world_size, True)
val_data = batchify(val_data, eval_batch_size, rank, world_size)
test_data = batchify(test_data, eval_batch_size, rank, world_size)
.. GENERATED FROM PYTHON SOURCE LINES 216-219
Functions to generate input and target sequence
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. GENERATED FROM PYTHON SOURCE LINES 222-234
``get_batch()`` function generates the input and target sequence for
the transformer model. It subdivides the source data into chunks of
length ``bptt``. For the language modeling task, the model needs the
following words as ``Target``. For example, with a ``bptt`` value of 2,
we’d get the following two Variables for ``i`` = 0:
.. image:: ../_static/img/transformer_input_target.png
It should be noted that the chunks are along dimension 0, consistent
with the ``S`` dimension in the Transformer model. The batch dimension
``N`` is along dimension 1.
.. GENERATED FROM PYTHON SOURCE LINES 234-244
.. code-block:: default
# In 'run_worker'
bptt = 35
def get_batch(source, i):
seq_len = min(bptt, len(source) - 1 - i)
data = source[i:i+seq_len]
target = source[i+1:i+1+seq_len].view(-1)
# Need batch dimension first for pipeline parallelism.
return data.t(), target
.. GENERATED FROM PYTHON SOURCE LINES 245-248
Model scale and Pipe initialization
-----------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 251-266
To demonstrate training large Transformer models using pipeline parallelism,
we scale up the Transformer layers appropriately. We use an embedding
dimension of 4096, hidden size of 4096, 16 attention heads and 8 total
transformer layers (``nn.TransformerEncoderLayer``). This creates a model with
**~1 billion** parameters.
We need to initialize the `RPC Framework <https://pytorch.org/docs/stable/rpc.html>`__
since Pipe depends on the RPC framework via `RRef <https://pytorch.org/docs/stable/rpc.html#rref>`__
which allows for future expansion to cross host pipelining. We need to
initialize the RPC framework with only a single worker since we're using a
single process to drive multiple GPUs.
The pipeline is then initialized with 8 transformer layers on one GPU and 8
transformer layers on the other GPU. One pipe is setup across GPUs 0 and 1 and
another across GPUs 2 and 3. Both pipes are then replicated using ``DistributedDataParallel``.
.. GENERATED FROM PYTHON SOURCE LINES 266-336
.. code-block:: default
# In 'run_worker'
ntokens = len(vocab) # the size of vocabulary
emsize = 4096 # embedding dimension
nhid = 4096 # the dimension of the feedforward network model in ``nn.TransformerEncoder``
nlayers = 8 # the number of ``nn.TransformerEncoderLayer`` in ``nn.TransformerEncoder``
nhead = 16 # the number of heads in the Multihead Attention models
dropout = 0.2 # the dropout value
from torch.distributed import rpc
tmpfile = tempfile.NamedTemporaryFile()
rpc.init_rpc(
name="worker",
rank=0,
world_size=1,
rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
init_method="file://{}".format(tmpfile.name),
# Specifying _transports and _channels is a workaround and we no longer
# will have to specify _transports and _channels for PyTorch
# versions >= 1.8.1
_transports=["ibv", "uv"],
_channels=["cuda_ipc", "cuda_basic"],
)
)
# Number of GPUs for model parallelism.
num_gpus = 2
partition_len = ((nlayers - 1) // num_gpus) + 1
# Add encoder in the beginning.
tmp_list = [Encoder(ntokens, emsize, dropout).cuda(2 * rank)]
module_list = []
# Add all the necessary transformer blocks.
for i in range(nlayers):
transformer_block = TransformerEncoderLayer(emsize, nhead, nhid, dropout)
if i != 0 and i % (partition_len) == 0:
module_list.append(nn.Sequential(*tmp_list))
tmp_list = []
device = i // (partition_len)
tmp_list.append(transformer_block.to(2 * rank + device))
# Add decoder in the end.
tmp_list.append(Decoder(ntokens, emsize).cuda(2 * rank + num_gpus - 1))
module_list.append(nn.Sequential(*tmp_list))
# Need to use 'checkpoint=never' since as of PyTorch 1.8, Pipe checkpointing
# doesn't work with DDP.
from torch.distributed.pipeline.sync import Pipe
chunks = 8
model = Pipe(torch.nn.Sequential(
*module_list), chunks = chunks, checkpoint="never")
# Initialize process group and wrap model in DDP.
from torch.nn.parallel import DistributedDataParallel
import torch.distributed as dist
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
dist.init_process_group(
backend="nccl", rank=rank, world_size=world_size)
model = DistributedDataParallel(model)
def get_total_params(module: torch.nn.Module):
total_params = 0
for param in module.parameters():
total_params += param.numel()
return total_params
print_with_rank('Total parameters in model: {:,}'.format(get_total_params(model)))
.. GENERATED FROM PYTHON SOURCE LINES 337-340
Run the model
-------------
.. GENERATED FROM PYTHON SOURCE LINES 343-353
`CrossEntropyLoss <https://pytorch.org/docs/master/nn.html?highlight=crossentropyloss#torch.nn.CrossEntropyLoss>`__
is applied to track the loss and
`SGD <https://pytorch.org/docs/master/optim.html?highlight=sgd#torch.optim.SGD>`__
implements stochastic gradient descent method as the optimizer. The initial
learning rate is set to 5.0. `StepLR <https://pytorch.org/docs/master/optim.html?highlight=steplr#torch.optim.lr_scheduler.StepLR>`__ is
applied to adjust the learn rate through epochs. During the
training, we use
`nn.utils.clip_grad_norm\_ <https://pytorch.org/docs/master/nn.html?highlight=nn%20utils%20clip_grad_norm#torch.nn.utils.clip_grad_norm_>`__
function to scale all the gradient together to prevent exploding.
.. GENERATED FROM PYTHON SOURCE LINES 353-414
.. code-block:: default
# In 'run_worker'
criterion = nn.CrossEntropyLoss()
lr = 5.0 # learning rate
optimizer = torch.optim.SGD(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
import time
def train():
model.train() # Turn on the train mode
total_loss = 0.
start_time = time.time()
ntokens = len(vocab)
# Train only for 50 batches to keep script execution time low.
nbatches = min(50 * bptt, train_data.size(0) - 1)
for batch, i in enumerate(range(0, nbatches, bptt)):
data, targets = get_batch(train_data, i)
optimizer.zero_grad()
# Since the Pipe is only within a single host and process the ``RRef``
# returned by forward method is local to this node and can simply
# retrieved via ``RRef.local_value()``.
output = model(data).local_value()
# Need to move targets to the device where the output of the
# pipeline resides.
loss = criterion(output.view(-1, ntokens), targets.cuda(2 * rank + 1))
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
optimizer.step()
total_loss += loss.item()
log_interval = 10
if batch % log_interval == 0 and batch > 0:
cur_loss = total_loss / log_interval
elapsed = time.time() - start_time
print_with_rank('| epoch {:3d} | {:5d}/{:5d} batches | '
'lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch, nbatches // bptt, scheduler.get_last_lr()[0],
elapsed * 1000 / log_interval,
cur_loss, math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
def evaluate(eval_model, data_source):
eval_model.eval() # Turn on the evaluation mode
total_loss = 0.
ntokens = len(vocab)
# Evaluate only for 50 batches to keep script execution time low.
nbatches = min(50 * bptt, data_source.size(0) - 1)
with torch.no_grad():
for i in range(0, nbatches, bptt):
data, targets = get_batch(data_source, i)
output = eval_model(data).local_value()
output_flat = output.view(-1, ntokens)
# Need to move targets to the device where the output of the
# pipeline resides.
total_loss += len(data) * criterion(output_flat, targets.cuda(2 * rank + 1)).item()
return total_loss / (len(data_source) - 1)
.. GENERATED FROM PYTHON SOURCE LINES 415-417
Loop over epochs. Save the model if the validation loss is the best
we've seen so far. Adjust the learning rate after each epoch.
.. GENERATED FROM PYTHON SOURCE LINES 417-440
.. code-block:: default
# In 'run_worker'
best_val_loss = float("inf")
epochs = 3 # The number of epochs
best_model = None
for epoch in range(1, epochs + 1):
epoch_start_time = time.time()
train()
val_loss = evaluate(model, val_data)
print_with_rank('-' * 89)
print_with_rank('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
print_with_rank('-' * 89)
if val_loss < best_val_loss:
best_val_loss = val_loss
best_model = model
scheduler.step()
.. GENERATED FROM PYTHON SOURCE LINES 441-445
Evaluate the model with the test dataset
-------------------------------------
Apply the best model to check the result with the test dataset.
.. GENERATED FROM PYTHON SOURCE LINES 445-459
.. code-block:: default
# In 'run_worker'
test_loss = evaluate(best_model, test_data)
print_with_rank('=' * 89)
print_with_rank('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
test_loss, math.exp(test_loss)))
print_with_rank('=' * 89)
# Main execution
import torch.multiprocessing as mp
if __name__=="__main__":
world_size = 2
mp.spawn(run_worker, args=(world_size, ), nprocs=world_size, join=True)
.. GENERATED FROM PYTHON SOURCE LINES 460-463
Output
------
.. GENERATED FROM PYTHON SOURCE LINES 466-517
.. code-block:: py
[RANK 0]: | epoch 1 | 10/ 50 batches | lr 5.00 | ms/batch 778.97 | loss 43.31 | ppl 6432469059895903232.00
[RANK 1]: | epoch 1 | 10/ 50 batches | lr 5.00 | ms/batch 778.90 | loss 44.50 | ppl 21245447128217366528.00
[RANK 0]: | epoch 1 | 20/ 50 batches | lr 5.00 | ms/batch 699.89 | loss 44.50 | ppl 21176949187407757312.00
[RANK 1]: | epoch 1 | 20/ 50 batches | lr 5.00 | ms/batch 699.87 | loss 44.62 | ppl 23975861229620961280.00
[RANK 0]: | epoch 1 | 30/ 50 batches | lr 5.00 | ms/batch 698.86 | loss 41.62 | ppl 1193312915629888256.00
[RANK 1]: | epoch 1 | 30/ 50 batches | lr 5.00 | ms/batch 698.87 | loss 40.69 | ppl 471605759847546240.00
[RANK 0]: | epoch 1 | 40/ 50 batches | lr 5.00 | ms/batch 698.34 | loss 45.20 | ppl 42812308420836458496.00
[RANK 1]: | epoch 1 | 40/ 50 batches | lr 5.00 | ms/batch 698.33 | loss 45.68 | ppl 68839569686012223488.00
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 1]: | end of epoch 1 | time: 40.08s | valid loss 0.80 | valid ppl 2.22
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 0]: | end of epoch 1 | time: 40.09s | valid loss 0.80 | valid ppl 2.22
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 0]: | epoch 2 | 10/ 50 batches | lr 4.75 | ms/batch 768.51 | loss 36.34 | ppl 6063529544668166.00
[RANK 1]: | epoch 2 | 10/ 50 batches | lr 4.75 | ms/batch 769.23 | loss 37.41 | ppl 17651211266236086.00
[RANK 0]: | epoch 2 | 20/ 50 batches | lr 4.75 | ms/batch 699.57 | loss 28.97 | ppl 3798441739584.11
[RANK 1]: | epoch 2 | 20/ 50 batches | lr 4.75 | ms/batch 699.56 | loss 29.28 | ppl 5203636967575.47
[RANK 0]: | epoch 2 | 30/ 50 batches | lr 4.75 | ms/batch 699.04 | loss 28.43 | ppl 2212498693571.25
[RANK 1]: | epoch 2 | 30/ 50 batches | lr 4.75 | ms/batch 699.05 | loss 28.33 | ppl 2015144761281.48
[RANK 0]: | epoch 2 | 40/ 50 batches | lr 4.75 | ms/batch 699.10 | loss 23.30 | ppl 13121380184.92
[RANK 1]: | epoch 2 | 40/ 50 batches | lr 4.75 | ms/batch 699.09 | loss 23.41 | ppl 14653799192.87
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 0]: | end of epoch 2 | time: 39.97s | valid loss 0.24 | valid ppl 1.27
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 1]: | end of epoch 2 | time: 39.98s | valid loss 0.24 | valid ppl 1.27
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 0]: | epoch 3 | 10/ 50 batches | lr 4.51 | ms/batch 769.36 | loss 12.80 | ppl 361681.11
[RANK 1]: | epoch 3 | 10/ 50 batches | lr 4.51 | ms/batch 768.97 | loss 12.57 | ppl 287876.61
[RANK 0]: | epoch 3 | 20/ 50 batches | lr 4.51 | ms/batch 698.27 | loss 12.01 | ppl 164364.60
[RANK 1]: | epoch 3 | 20/ 50 batches | lr 4.51 | ms/batch 698.30 | loss 11.98 | ppl 159095.89
[RANK 0]: | epoch 3 | 30/ 50 batches | lr 4.51 | ms/batch 697.75 | loss 10.90 | ppl 54261.91
[RANK 1]: | epoch 3 | 30/ 50 batches | lr 4.51 | ms/batch 697.72 | loss 10.89 | ppl 53372.39
[RANK 0]: | epoch 3 | 40/ 50 batches | lr 4.51 | ms/batch 699.49 | loss 10.78 | ppl 47948.35
[RANK 1]: | epoch 3 | 40/ 50 batches | lr 4.51 | ms/batch 699.50 | loss 10.79 | ppl 48664.42
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 0]: | end of epoch 3 | time: 39.96s | valid loss 0.38 | valid ppl 1.46
[RANK 0]: -----------------------------------------------------------------------------------------
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 1]: | end of epoch 3 | time: 39.96s | valid loss 0.38 | valid ppl 1.46
[RANK 1]: -----------------------------------------------------------------------------------------
[RANK 0]: =========================================================================================
[RANK 0]: | End of training | test loss 0.33 | test ppl 1.39
[RANK 0]: =========================================================================================
[RANK 1]: =========================================================================================
[RANK 1]: | End of training | test loss 0.33 | test ppl 1.39
[RANK 1]: =========================================================================================
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.000 seconds)
.. _sphx_glr_download_advanced_ddp_pipeline.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: ddp_pipeline.py <ddp_pipeline.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: ddp_pipeline.ipynb <ddp_pipeline.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_