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.