Update README.md

dfdx · Oct 28, 2020 · 27a983f · 27a983f
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@@ -1,9 +1,9 @@
-# Lilith
+# Avalon
 
-[![Build Status](https://travis-ci.org/dfdx/Lilith.jl.svg?branch=master)](https://travis-ci.org/dfdx/Lilith.jl)
+[![Build Status](https://travis-ci.org/dfdx/Avalon.jl.svg?branch=master)](https://travis-ci.org/dfdx/Avalon.jl)
 
 
-**Lilith** is a deep learning library in Julia with focus on **high performance** and **interoperability with existing DL frameworks**. Its main features include:
+**Avalon** is a deep learning library in Julia with focus on **high performance** and **interoperability with existing DL frameworks**. Its main features include:
 
 * tracing autograd engine - models are just structs, transformations are just functions
 * optimizing code generator based on hackable computational graph
@@ -13,10 +13,10 @@
 
 ## Usage
 
-To get you a feeling of what Lilith is like, here's a definition of a small convolutional neural network:
+To get you a feeling of what Avalon is like, here's a definition of a small convolutional neural network:
 
 ```julia
-using Lilith
+using Avalon
 
 
 mutable struct Net
@@ -44,7 +44,7 @@ function (m::Net)(x::AbstractArray)
 end
 ```
 
-For detailed explanation of this and other models see [the tutorial](https://github.com/dfdx/Lilith.jl/tree/master/tutorial). Some predefined models are also available in [the zoo](https://github.com/dfdx/Lilith.jl/tree/master/zoo).
+For detailed explanation of this and other models see [the tutorial](https://github.com/dfdx/Avalon.jl/tree/master/tutorial). Some predefined models are also available in [the zoo](https://github.com/dfdx/Avalon.jl/tree/master/zoo).
 
 
 ## Performance
@@ -53,35 +53,35 @@ Performance comparison between different libraries is hard and benchmarks are ra
 
 ### Convolutional neural network
 
-Code available [here](https://github.com/dfdx/Lilith.jl/tree/master/benchmarks/cnn)
+Code available [here](https://github.com/dfdx/Avalon.jl/tree/master/benchmarks/cnn)
 
 |               | training 1 epoch | training total time* | prediction |
 | ------------- | ---------------- | -------------------- | ---------- |
-| Lilith (CPU)  |    170 s         |       1742 s         |   39 ms    |
+| Avalon (CPU)  |    170 s         |       1742 s         |   39 ms    |
 | Flux (CPU)    |    250 s         |       2515 s         |   42 ms    |
 | ------------- | ---------------- | -------------------- | ---------- |
-| Lilith (GPU)  |     10 s         |        164 s         |    5 ms    |
+| Avalon (GPU)  |     10 s         |        164 s         |    5 ms    |
 | Flux (GPU)    |     12 s         |        150 s         |    5 ms    |
 | PyTorch (GPU) |     12 s         |        120 s         |    2 ms    |
 
 `*` - total time includes 10 epochs + compilation time
 
-Note that in the test on GPU Lilith has longest compilation time and thus
+Note that in the test on GPU Avalon has longest compilation time and thus
 longest total training time _after 10 epochs_. However, time per epoch
-is the lowest, so Lilith is typically the fastest one in longer run.
+is the lowest, so Avalon is typically the fastest one in longer run.
 
 
 
 ### Variational Autoencoder
 
-Code available [here](https://github.com/dfdx/Lilith.jl/tree/master/benchmarks/vae)
+Code available [here](https://github.com/dfdx/Avalon.jl/tree/master/benchmarks/vae)
 
 |               | training 1 epoch | training total time  | prediction |
 | ------------- | ---------------- | -------------------- | ---------- |
-| Lilith (CPU)  |     50 s         |        535 s         |   395 μs   |
+| Avalon (CPU)  |     50 s         |        535 s         |   395 μs   |
 | Flux (CPU)    |    948 s         |        158 min       |    81 ms   |
 | ------------- | ---------------- | -------------------- | ---------- |
-| Lilith (GPU)  |      3 s         |         93 s         |   194 μs   |
+| Avalon (GPU)  |      3 s         |         93 s         |   194 μs   |
 | Flux (GPU)**  |     ---          |          ---         |     ---    |
 | PyTorch (GPU) |      7 s         |         66 s         |   501 µs   |
 
@@ -90,8 +90,8 @@ Code available [here](https://github.com/dfdx/Lilith.jl/tree/master/benchmarks/v
 
 ## API Stability
 
-One of the central ideas behind Lilith is the ability to reuse existing code instead of writing everything from scratch.
-To facilitate it, Lilith is committed to high, although not absolute backward compatibility. The following table
+One of the central ideas behind Avalon is the ability to reuse existing code instead of writing everything from scratch.
+To facilitate it, Avalon is committed to high, although not absolute backward compatibility. The following table
 outlines stability level you should expect from various components of the library.
 
 | Component       | API Stable? |
@@ -106,7 +106,7 @@ outlines stability level you should expect from various components of the librar
 | Device API      | Yes         |
 | Fitting API     | No**        |
 
-`*` - currently Lilith provides only basic implementations of vanilla RNN, LSTM and GRU; this implementation will be improved in future version and made more compatible with PyTorch version, but currently it cannot be considered stable
+`*` - currently Avalon provides only basic implementations of vanilla RNN, LSTM and GRU; this implementation will be improved in future version and made more compatible with PyTorch version, but currently it cannot be considered stable
 
 `**` - function `fit!()` provides a convenient shortcut for training supervised learning models, but in its current state it's too basic for most real use cases; for more durable code consider writing your own method for training using `fit!()` as a template