626_privacy/tensorflow_privacy
2
0
Fork 1
Code Issues Pull requests 1 Projects Releases Packages Wiki Activity Actions

Lira2021: fix readme install instructions

Browse source
This commit is contained in:
Akemi Izuko 2024-10-17 11:36:09 -06:00
parent 9c67e0abef
commit 9a0611fb37
Signed by: akemi
GPG key ID: 8DE0764E1809E9FC
3 changed files with 10 additions and 8 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

15
research/mi_lira_2021/README.md
View file

@ -8,14 +8,15 @@ by Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and F
### INSTALLING
You will need to install fairly standard dependencies
You will need to install fairly standard dependencies and python 3.11 minimum.
`pip install scipy, sklearn, numpy, matplotlib`
and also some machine learning framework to train models. We train our models
with JAX + ObJAX so you will need to follow build instructions for that
https://github.com/google/objax
https://objax.readthedocs.io/en/latest/installation_setup.html
```
pip install scipy scikit-learn numpy matplotlib tensorflow tensorflow_datasets objax
RELEASE_URL="https://storage.googleapis.com/jax-releases/jax_cuda_releases.html"
JAX_VERSION=`python3 -c 'import jax; print(jax.__version__)'`
pip uninstall -y jaxlib
pip install -f $RELEASE_URL jax[cuda]==$JAX_VERSION
```
### RUNNING THE CODE

2
research/mi_lira_2021/scripts/train_demo.sh
View file

@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
mkdir logs
mkdir -p logs
SECONDS=0

1
research/mi_lira_2021/scripts/train_demo_multigpu.sh
View file

@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
mkdir -p logs
CUDA_VISIBLE_DEVICES='0' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 0 --logdir exp/cifar10 &> logs/log_0 &
CUDA_VISIBLE_DEVICES='1' python3 -u train.py --dataset=cifar10 --epochs=100 --save_steps=20 --arch wrn28-2 --num_experiments 16 --expid 1 --logdir exp/cifar10 &> logs/log_1 &