Switch to Apache-v2/MIT licensing, fixes #13.

Cargo.toml

@@ -1,22 +1,22 @@
 [package]
 name = "tch"
-version = "0.0.6"
+version = "0.0.7"
 authors = ["Laurent Mazare <[email protected]>"]
 edition = "2018"
 
 description = "Rust wrappers for the PyTorch C++ api (libtorch)."
 repository = "https://github.com/LaurentMazare/tch-rs"
 keywords = ["pytorch", "deep-learning", "machine-learning"]
 categories = ["science"]
-license = "Apache-2.0"
+license = "MIT/Apache-2.0"
 readme = "README.md"
 
 [dependencies]
 failure = "0.1"
 lazy_static = "1.3.0"
 libc = "0.2.0"
 rand = "0.6.5"
-torch-sys = { version = "0.0.5", path = "torch-sys" }
+torch-sys = { version = "0.0.6", path = "torch-sys" }
 zip = "0.5"
 
 cpython = { version = "0.2.0", optional = true }

LICENSE-MIT

@@ -0,0 +1,23 @@
+Permission is hereby granted, free of charge, to any
+person obtaining a copy of this software and associated
+documentation files (the "Software"), to deal in the
+Software without restriction, including without
+limitation the rights to use, copy, modify, merge,
+publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software
+is furnished to do so, subject to the following
+conditions:
+
+The above copyright notice and this permission notice
+shall be included in all copies or substantial portions
+of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
+ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
+TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.

README.md

@@ -122,3 +122,10 @@ Further examples include:
   example as well as an A2C implementation that can run on Atari games.
 * A [Transfer Learning Tutorial](https://github.com/LaurentMazare/tch-rs/blob/master/examples/transfer-learning)
   shows how to finetune a pre-trained ResNet model on a very small dataset.
+
+## License
+`tch-rs` is distributed under the terms of both the MIT license
+and the Apache license (version 2.0), at your option.
+
+See [LICENSE-APACHE](LICENSE-APACHE), [LICENSE-MIT](LICENSE-MIT) for more
+details.

torch-sys/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "torch-sys"
-version = "0.0.5"
+version = "0.0.6"
 authors = ["Laurent Mazare <[email protected]>"]
 edition = "2018"
 build = "build.rs"
@@ -10,7 +10,7 @@ description = "Low-level FFI bindings for the PyTorch C++ api (libtorch)."
 repository = "https://github.com/LaurentMazare/tch-rs"
 keywords = ["pytorch", "ffi", "deep-learning", "machine-learning"]
 categories = ["external-ffi-bindings", "science"]
-license = "Apache-2.0"
+license = "MIT/Apache-2.0"
 
 [dependencies]
 libc = "0.2.0"