DeepSpeed training
Training
Bing chat に教えてもらったDeepSpeedでのTraing 方法。DeepSpeedChat にはtrain.py という簡単な学習用のサンプルコードが入っているので、Chat用ならそちらを使う方が早い。勉強用にゼロから書くなら、下記を参考に。
```python
import torch
from deepspeed.ops.adam import FusedAdam
from deepspeed.ops.lamb import FusedLamb
from deepspeed.ops.adam import DeepSpeedCPUAdam
from deepspeed.ops.adam import DeepSpeedCPUAdamBuilder
from deepspeed.ops.adam import DeepSpeedCPUAdamBuilderConfig
# Define your model here
model = ...
# Define your optimizer here
optimizer = FusedAdam(model.parameters(), lr=0.0001)
# Define your loss function here
loss_fn = ...
# Define your data loader here
train_loader = ...
# Train your model with DeepSpeed
from deepspeed import DeepSpeedEngine
engine, _, _, _ = DeepSpeedEngine.setup(
model=model,
optimizer=optimizer,
loss_scale=0,
loss_fn=loss_fn,
training_data=train_loader,
lr_scheduler=None,
mpu=None,
dist_init_required=False,
collate_fn=None,
config=None,
)
engine.train()
# Save your model
torch.save(model.state_dict(), 'model.pt')
# Load your model
model.load_state_dict(torch.load('model.pt'))
```
(1) GitHub - microsoft/DeepSpeed: DeepSpeed is a deep learning .... https://github.com/microsoft/DeepSpeed
(2) Getting Started - DeepSpeed. https://www.deepspeed.ai/getting-started/
(3) deepspeed · PyPI. https://pypi.org/project/deepspeed/ アクセス
An example of how to create a train loader with DeepSpeed:
```python
import torch
from torch.utils.data import DataLoader
# Define your dataset here
dataset = ...
# Define your batch size here
batch_size = ...
# Create your train loader with DeepSpeed
from deepspeed.ops.adam import DeepSpeedCPUAdamBuilderConfig
train_loader = DataLoader(
dataset=dataset,
batch_size=batch_size,
num_workers=0,
pin_memory=True,
shuffle=True,
drop_last=True,
)
```
This code imports the necessary libraries and creates a train loader with DeepSpeed. You can replace the placeholders with your own dataset and batch size.
GitHub - microsoft/DeepSpeed: DeepSpeed is a deep learning .... https://github.com/microsoft/DeepSpeed
(2) GitHub - microsoft/DeepSpeedExamples: Example models .... https://github.com/microsoft/DeepSpeedExamples
(3) microsoft/Megatron-DeepSpeed - Github. https://github.com/microsoft/Megatron-DeepSpeed
You can use PyTorch's built-in datasets or create your own custom dataset. Here's an example of how to create a custom dataset:
```python
import torch
from torch.utils.data import Dataset
class CustomDataset(Dataset):
def __init__(self):
# Define your data here
self.data = ...
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
```
This code defines a custom dataset that you can use with DeepSpeed. You can replace the placeholders with your own data.
deepspeed · PyPI. https://pypi.org/project/deepspeed/
(2) GitHub - microsoft/DeepSpeedExamples: Example models .... https://github.com/microsoft/DeepSpeedExamples
(3) GitHub - microsoft/DeepSpeed: DeepSpeed is a deep learning .... https://github.com/microsoft/DeepSpeed
The `__getitem__()` method should return a tuple of `(input, target)`. Here's an example:
```python
import torch
from torch.utils.data import Dataset
class CustomDataset(Dataset):
def __init__(self):
# Define your data here
self.data = ...
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
input = self.data[idx][0]
target = self.data[idx][1]
return input, target
```
This code defines a custom dataset that returns a tuple of `(input, target)` in the `__getitem__()` method.
