Skip to content

Remove DataLoader example in README #162

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jun 12, 2024
Merged

Remove DataLoader example in README #162

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 5 additions & 7 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -101,8 +101,7 @@ Here is how to upload the optimized dataset using the [AWS CLI](https://aws.amaz
Then, the Streaming Dataset can read the data directly from [AWS S3](https://aws.amazon.com/s3/).

```python
from litdata import StreamingDataset
from torch.utils.data import DataLoader
from litdata import StreamingDataset, StreamingDataLoader

# Remote path where full dataset is stored
input_dir = 's3://my-bucket/my_optimized_dataset'
Expand All @@ -115,8 +114,8 @@ sample = dataset[50]
img = sample['image']
cls = sample['class']

# Create PyTorch DataLoader and iterate over it to train your AI models.
dataloader = DataLoader(dataset)
# Create dataLoader and iterate over it to train your AI models.
dataloader = StreamingDataLoader(dataset)
```

# Key Features
Expand Down Expand Up @@ -210,11 +209,10 @@ Easily experiment with dataset mixtures using the `CombinedStreamingDataset` cla
As an example, this mixture of [Slimpajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B) & [StarCoder](https://huggingface.co/datasets/bigcode/starcoderdata) was used in the [TinyLLAMA](https://github.com/jzhang38/TinyLlama) project to pretrain a 1.1B Llama model on 3 trillion tokens.

```python
from litdata import StreamingDataset, CombinedStreamingDataset
from litdata import StreamingDataset, CombinedStreamingDataset, StreamingDataLoader
from litdata.streaming.item_loader import TokensLoader
from tqdm import tqdm
import os
from torch.utils.data import DataLoader

train_datasets = [
StreamingDataset(
Expand All @@ -235,7 +233,7 @@ train_datasets = [
weights = (0.693584, 0.306416)
combined_dataset = CombinedStreamingDataset(datasets=train_datasets, seed=42, weights=weights)

train_dataloader = DataLoader(combined_dataset, batch_size=8, pin_memory=True, num_workers=os.cpu_count())
train_dataloader = StreamingDataLoader(combined_dataset, batch_size=8, pin_memory=True, num_workers=os.cpu_count())

# Iterate over the combined datasets
for batch in tqdm(train_dataloader):
Expand Down
Loading