Better training+hub

candle-book/src/training/README.md

@@ -6,12 +6,36 @@ start with the Hello world dataset of machine learning, MNIST.
 
 Let's start with downloading `MNIST` from [huggingface](https://huggingface.co/datasets/mnist).
 
-
-```rust
-use candle_datasets::from_hub;
+This requires `candle-datasets` with the `hub` feature.
+```bash
+cargo add candle-datasets --features hub
+cargo add hf-hub
+```
 
 
-let dataset = from_hub("mnist")?;
+```rust,ignore
+{{#include ../../../candle-examples/src/lib.rs:book_training_1}}
 ```
 
 This uses the standardized `parquet` files from the `refs/convert/parquet` branch on every dataset.
+`files` is now a `Vec` of [`parquet::file::serialized_reader::SerializedFileReader`].
+
+We can inspect the content of the files with:
+
+```rust,ignore
+{{#include ../../../candle-examples/src/lib.rs:book_training_2}}
+```
+
+You should see something like:
+
+```bash
+Column id 1, name label, value 6
+Column id 0, name image, value {bytes: [137, ....]
+Column id 1, name label, value 8
+Column id 0, name image, value {bytes: [137, ....]
+```
+
+So each row contains 2 columns (image, label) with image being saved as bytes.
+Let's put them into a useful struct.
+
+

candle-datasets/src/hub.rs

@@ -22,7 +22,7 @@ fn sibling_to_parquet(
     repo: &ApiRepo,
 ) -> Result<SerializedFileReader<File>, Error> {
     let local = repo.get(rfilename)?;
-    let file = File::open(&local)?;
+    let file = File::open(local)?;
     let reader = SerializedFileReader::new(file)?;
     Ok(reader)
 }

candle-examples/src/lib.rs

@@ -55,6 +55,8 @@ pub fn save_image<P: AsRef<std::path::Path>>(img: &Tensor, p: P) -> Result<()> {
 
 #[cfg(test)]
 mod tests {
+    use anyhow::Result;
+
     // NOTE: Waiting on https://github.com/rust-lang/mdBook/pull/1856
     #[rustfmt::skip]
     #[tokio::test]
@@ -150,4 +152,25 @@ let tp_tensor = Tensor::from_raw_buffer(&raw, dtype, &tp_shape, &Device::Cpu).un
         assert_eq!(view.shape(), &[768, 768]);
         assert_eq!(tp_tensor.dims(), &[192, 768]);
     }
+
+    #[rustfmt::skip]
+    #[test]
+    fn book_training_1() -> Result<()>{
+// ANCHOR: book_training_1
+use candle_datasets::hub::from_hub;
+use hf_hub::api::sync::Api;
+
+let api = Api::new()?;
+let files = from_hub(&api, "mnist".to_string())?;
+// ANCHOR_END: book_training_1
+// ANCHOR: book_training_2
+let rows = files.into_iter().flat_map(|r| r.into_iter()).flatten();
+for row in rows {
+    for (idx, (name, field)) in row.get_column_iter().enumerate() {
+        println!("Column id {idx}, name {name}, value {field}");
+    }
+}
+// ANCHOR_END: book_training_2
+Ok(())
+    }
 }