Merge pull request #150 from LaurentMazare/add_cheatsheet

Adding cheatsheet + expand on other ML frameworks.

README.md

@@ -27,6 +27,21 @@ let c = a.matmul(&b)?;
 
 ## How to use ?
 
+Cheatsheet:
+
+|            | Using PyTorch                            | Using Candle                                                     |
+|------------|------------------------------------------|------------------------------------------------------------------|
+| Creation   | `torch.Tensor([[1, 2], [3, 4]])`         | `Tensor::new(&[[1f32, 2.]], [3., 4.]], &Device::Cpu)?`           |
+| Indexing   | `tensor[:, :4]`                          | `tensor.i((.., ..4))?`                                           |
+| Operations | `tensor.view((2, 2))`                    | `tensor.reshape((2, 2))?`                                        |
+| Operations | `a.matmul(b)`                            | `a.matmul(&b)?`                                                  |
+| Arithmetic | `a + b`                                  | `&a + &b`                                                        |
+| Device     | `tensor.to(device="cuda")`               | `tensor.to_device(&Device::Cuda(0))?`                            |
+| Dtype      | `tensor.to(dtype=torch.float16)`         | `tensor.to_dtype(&DType::F16)?`                                  |
+| Saving     | `torch.save({"A": A}, "model.bin")`      | `tensor.save_safetensors("A", "model.safetensors")?`             |
+| Loading    | `weights = torch.load("model.bin")`      | TODO (see the examples for now)                                  |
+
+
 Check out our [examples](./candle-examples/examples/):
 
 - [Whisper](./candle-examples/examples/whisper/)
@@ -38,16 +53,34 @@ Check out our [examples](./candle-examples/examples/):
 
 ## FAQ
 
-- Why Candle?
+### Why Candle?
 
 Candle stems from the need to reduce binary size in order to *enable serverless*
-possible by making the whole engine smaller than PyTorch very large library volume
+possible by making the whole engine smaller than PyTorch very large library volume.
+This enables creating runtimes on a cluster much faster.
 
 And simply *removing Python* from production workloads.
 Python can really add overhead in more complex workflows and the [GIL](https://www.backblaze.com/blog/the-python-gil-past-present-and-future/) is a notorious source of headaches.
 
 Rust is cool, and a lot of the HF ecosystem already has Rust crates [safetensors](https://github.com/huggingface/safetensors) and [tokenizers](https://github.com/huggingface/tokenizers).
 
+
+### Other ML frameworks
+
+- [dfdx](https://github.com/coreylowman/dfdx) is a formidable crate, with shapes being included
+  in types preventing a lot of headaches by getting compiler to complain about shape mismatch right off the bat
+  However we found that some features still require nightly and writing code can be a bit dauting for non rust experts.
+
+  We're leveraging and contributing to other core crates for the runtime so hopefully both crates can benefit from each
+  other
+
+- [burn](https://github.com/burn-rs/burn) is a general crate that can leverage multiple backends so you can choose the best
+  engine for your workload
+
+- [tch-rs](https://github.com/LaurentMazare/tch-rs.git) Bindings to the torch library in Rust. Extremely versatile, but they 
+  do bring in the entire torch library into the runtime. The main contributor of `tch-rs` is also involved in the development
+  of `candle`.
+
 ### Missing symbols when compiling with the mkl feature.
 
 If you get some missing symbols when compiling binaries/tests using the mkl