Comments (3)
If you have built the project using cargo then then you can find dream_go
in the directory target/release
. In the README I shortened the path to just ./dream_go
to keep it simple. The full serie of commands, including building the binary if you have not done so already, is:
cargo build --release
./target/release/dream_go --extract kgs_bal.sgf > kgs_big.bin
...
You can of course also copy it to the current directory for future easy access:
cargo build --release && cp target/release/dream_go ./dream_go
./dream_go --extract kgs_bal.sgf > kgs_big.bin
...
from dream-go.
thanks this help!
When i build it gets me error below:
`error[E0554]: #![feature] may not be used on the stable release channel
--> /Users/.cargo/registry/src/github.com-1ecc6299db9ec823/lazy_static-1.0.0/src/lib.rs:104:32
|
104 | #![cfg_attr(feature="nightly", feature(unreachable))]
| ^^^^^^^^^^^^^^^^^^^^^
error: aborting due to previous error
Build failed, waiting for other jobs to finish...
error: Could not compile lazy_static
.
To learn more, run the command again with --verbose.`
from dream-go.
Dream Go requires the nightly branch of rust for various reasons mostly related to performance. You will need to "upgrade" your current stable branch to nightly to compile. It seems like you are using rustup
so the following commands should work:
rustup toolchain install nightly
rustup default nightly
If this worked then you should see a version similar to the the one below when running cargo --version
:
$ cargo --version
cargo 0.26.0-nightly (1d6dfea44 2018-01-26)
Once you are done compiling Dream Go and you want to switch back to stable you can do so by typing:
rustup default stable
PS: Here are some binaries you can use if you are running Linux (linked against CUDA 9.1 and cuDNN 7). Unfortunately I do not have access to a Mac or Windows machine, so if you are using that then we'll have to see if we can make the compilation work.
from dream-go.
Related Issues (20)
- Re-balance search tree size vs neural network size HOT 2
- Scoring and `kgs-genmove_cleanup` improvements
- About MCTSnet HOT 2
- Introduce a new self-play mode
- Poor GPU utilization observed during play HOT 2
- Re-factor MCTS code to use asynchronous framework
- Shape of the convolution in the policy head
- Monte-Carlo tree search as regularized policy optimization HOT 3
- Investigate MCTS parallelism degradation HOT 7
- Prune nodes from the search tree that are obviously bad HOT 1
- Re-implement `INT8x32_CONFIG` support during inference
- Investigate SWISH as activation function in cuDNN
- GPU vs CPU matrix multiplication HOT 1
- Sparse Quantized Model
- MLP-Mixer: An all-MLP Architecture for Vision HOT 7
- NNUE (ƎUИИ Efficiently Updatable Neural Network) for Go HOT 5
- Triton: Open-Source GPU Programming for Neural Networks
- Long startup times due to `cudnnBuildRNNDynamic`
- 2022 TCGA Computer Go Tournament is coming! HOT 1
- Unsound uninitialized array
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from dream-go.