Update README with config for MinIO (#174)

* Update README with config for MinIO

* Update README.md with Support S3-Compatible Object Storage section

README.md

@@ -121,7 +121,7 @@ dataloader = StreamingDataLoader(dataset)
 # Key Features
 
 - [Multi-GPU / Multi-Node Support](#multi-gpu--multi-node-support)
-- [Subsample and split your datasets](#access-any-item)
+- [Subsample and split your datasets](#subsample-and-split-your-datasets)
 - [Access any item](#access-any-item)
 - [Use any data transforms](#use-any-data-transforms)
 - [The Map Operator](#the-map-operator)
@@ -131,6 +131,7 @@ dataloader = StreamingDataLoader(dataset)
 - [Reduce your memory footprint](#reduce-your-memory-footprint)
 - [Configure Cache Size Limit](#configure-cache-size-limit)
 - [On-Prem Optimizations](#on-prem-optimizations)
+- [Support S3-Compatible Object Storage](#support-s3-compatible-object-storage)
 
 
 ## Multi-GPU / Multi-Node Support
@@ -367,6 +368,35 @@ from litdata import StreamingDataset
 dataset = StreamingDataset(input_dir="local:/data/shared-drive/some-data")
 ```
 
+## Support S3-Compatible Object Storage
+
+Integrate S3-compatible object storage servers like [MinIO](https://min.io/) with litdata, ideal for on-premises infrastructure setups. Configure the endpoint and credentials using environment variables or configuration files. 
+
+Set up the environment variables to connect to MinIO:
+
+```bash
+export AWS_ACCESS_KEY_ID=access_key
+export AWS_SECRET_ACCESS_KEY=secret_key
+export AWS_ENDPOINT_URL=http://localhost:9000  # MinIO endpoint
+```
+
+Alternatively, configure credentials and endpoint in `~/.aws/{credentials,config}`:
+
+```bash
+mkdir -p ~/.aws && \
+cat <<EOL >> ~/.aws/credentials
+[default]
+aws_access_key_id = access_key
+aws_secret_access_key = secret_key
+EOL
+
+cat <<EOL >> ~/.aws/config
+[default]
+endpoint_url = http://localhost:9000  # MinIO endpoint
+EOL
+```
+Explore an example setup of litdata with MinIO in the [LitData with MinIO](https://github.com/bhimrazy/litdata-with-minio) repository for practical implementation details.
+
 # Benchmarks
 
 In order to measure the effectiveness of LitData, we used a commonly used dataset for benchmarks: [Imagenet-1.2M](https://www.image-net.org/) where the training set contains `1,281,167 images`.