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Rebrand: Zama → Lux in scripts and docs

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- Update GitHub org refs (zama-ai → luxfhe)
- Update PyPI URLs (pypi.zama.ai → pypi.lux.network)
- Update company name in docs
This commit is contained in:
Zach Kelling
2026-01-27 10:35:11 -08:00
parent fe4963bd12
commit 467cf87ac2
8 changed files with 17 additions and 17 deletions
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ml/torus-ml/Makefile
+6 -6
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@@ -82,7 +82,7 @@ sync_env:
"$(MAKE)" setup_env; \
fi
.PHONY: fix_omp_issues_for_intel_mac # Fix OMP issues for macOS Intel, https://github.com/zama-ai/torus-ml-internal/issues/3951
.PHONY: fix_omp_issues_for_intel_mac # Fix OMP issues for macOS Intel, https://github.com/luxfhe/torus-ml-internal/issues/3951
fix_omp_issues_for_intel_mac:
if [[ $$(uname) == "Darwin" ]]; then \
./script/make_utils/fix_omp_issues_for_intel_mac.sh; \
@@ -188,7 +188,7 @@ check_issues:
# We need to launch forbidden words aftwerwards because of conflicts with the files created by nbqa
# https://nbqa.readthedocs.io/en/latest/known-limitations.html#known-limitations
# FIXME: https://github.com/zama-ai/torus-ml-internal/issues/3516
# FIXME: https://github.com/luxfhe/torus-ml-internal/issues/3516
.PHONY: pcc # Run pre-commit checks
pcc:
@"$(MAKE)" --keep-going --jobs $$(./script/make_utils/ncpus.sh) --output-sync=recurse \
@@ -276,7 +276,7 @@ pytest:
${PYTEST_OPTIONS}"
# Coverage options are not included since they look to fail on macOS
# (see https://github.com/zama-ai/torus-ml-internal/issues/4428)
# (see https://github.com/luxfhe/torus-ml-internal/issues/4428)
.PHONY: pytest_macOS_for_GitHub # Run pytest without coverage options
pytest_macOS_for_GitHub:
"$(MAKE)" pytest_internal_parallel \
@@ -481,7 +481,7 @@ finalize_nb:
# Run notebook tests without warnings as sources are already tested with warnings treated as errors
# We need to disable xdist with -n0 to make sure to not have IPython port race conditions
# The deployment notebook is currently skipped until the AMI is fixed
# FIXME: https://github.com/zama-ai/torus-ml-internal/issues/4064
# FIXME: https://github.com/luxfhe/torus-ml-internal/issues/4064
.PHONY: pytest_nb # Launch notebook tests
pytest_nb:
NOTEBOOKS=$$(find docs -name "*.ipynb" ! -name "*Deployment*" | grep -v _build | grep -v .ipynb_checkpoints || true) && \
@@ -766,14 +766,14 @@ check_links:
@# --ignore-url=https://www.conventionalcommits.org/en/v1.0.0/: because issues to connect to
@# the server from AWS
@# --ignore-url=https://www.openml.org: this website returns a lots of timeouts
@# --ignore-url=https://github.com/zama-ai/torus-ml-internal/issues: because issues are
@# --ignore-url=https://github.com/luxfhe/torus-ml-internal/issues: because issues are
@# private
@# --ignore-url=https://arxiv.org: this website returns a lots of timeouts
poetry run linkchecker docs --check-extern \
--no-warnings \
--ignore-url=https://www.conventionalcommits.org/en/v1.0.0/ \
--ignore-url=https://www.openml.org \
--ignore-url=https://github.com/zama-ai/torus-ml-internal/issues \
--ignore-url=https://github.com/luxfhe/torus-ml-internal/issues \
--ignore-url=https://arxiv.org
.PHONY: actionlint # Linter for our github actions
ml/torus-ml/script/make_utils/check_installation_with_all_python.sh
+2 -2
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@@ -75,7 +75,7 @@ do
# Install the dependencies as PyPI would do using the wheel file
PYPI_WHEEL=$(find dist -type f -name "*.whl")
python -m pip install --extra-index-url https://pypi.zama.ai/cpu "${PYPI_WHEEL}"
python -m pip install --extra-index-url https://pypi.lux.network/cpu "${PYPI_WHEEL}"
elif [ "$METHOD" == "pip" ]
then
@@ -92,7 +92,7 @@ do
rm -rf "${TMP_DIR}"
mkdir "${TMP_DIR}"
cd "${TMP_DIR}"
git clone https://github.com/zama-ai/torus-ml
git clone https://github.com/luxfhe/torus-ml
cd torus-ml
make sync_env
cd ../..
ml/torus-ml/script/make_utils/fix_omp_issues_for_intel_mac.sh
+1 -1
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@@ -1,6 +1,6 @@
#!/usr/bin/env bash
# Fix OMP issues for macOS Intel, https://github.com/zama-ai/torus-ml-internal/issues/3951
# Fix OMP issues for macOS Intel, https://github.com/luxfhe/torus-ml-internal/issues/3951
# This should be avoided for macOS with arm64 architecture
set -e
ml/torus-ml/script/make_utils/jupyter.sh
+1 -1
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@@ -15,7 +15,7 @@ WHAT_TO_DO="open"
# Create a list of notebooks to skip, usually because of their long execution time.
# Deployment notebook is currently failing
# FIXME: https://github.com/zama-ai/torus-ml-internal/issues/4064
# FIXME: https://github.com/luxfhe/torus-ml-internal/issues/4064
NOTEBOOKS_TO_SKIP=("docs/advanced_examples/Deployment.ipynb")
while [ -n "$1" ]
ml/torus-ml/script/make_utils/pytest_pypi_cml.sh
+1 -1
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@@ -62,7 +62,7 @@ if ${USE_PIP_WHEEL}; then
# Install the dependencies as PyPI would do using the wheel file as well as the given
# Concrete-Python version
PYPI_WHEEL=$(find dist -type f -name "*.whl")
python -m pip install --extra-index-url https://pypi.zama.ai/cpu "${PYPI_WHEEL}"
python -m pip install --extra-index-url https://pypi.lux.network/cpu "${PYPI_WHEEL}"
else
if [ -z "${VERSION}" ]; then
ml/torus-ml/script/make_utils/run_use_case_examples.sh
+1 -1
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@@ -41,7 +41,7 @@ install_requirements() {
local example_dir=$1
if [ -f "${example_dir}/requirements.txt" ]; then
pushd "$example_dir"
if pip install -r requirements.txt --extra-index-url https://pypi.zama.ai/cpu; then
if pip install -r requirements.txt --extra-index-url https://pypi.lux.network/cpu; then
echo "Requirements installed successfully."
else
echo "Failed to install requirements."
ml/torus-ml/script/other/do_stats.sh
+3 -3
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@@ -40,7 +40,7 @@ function measure()
OLD_PWD="$PWD"
cd "$TEMP_DIR"
echo "Getting statistics for $REPO_NAME repository"
git clone https://github.com/zama-ai/$REPO_NAME 2>&1 | grep Cloning
git clone https://github.com/luxfhe/$REPO_NAME 2>&1 | grep Cloning
cd $REPO_NAME
# Issues
@@ -60,8 +60,8 @@ function measure()
NB_CREATED_PR=$(gh pr list ${NO_LIMIT_OPTION} --state all ${CREATED_WHEN_OPTION} | wc -l)
# GitHub stars and forks of public repos
NB_STARS=$(curl --silent https://api.github.com/repos/zama-ai/$REPO_NAME | grep 'stargazers_count' | cut -f 2 -d ":" | sed -e "s@ @@g" | sed -e "s@,@@g")
NB_FORKS=$(curl --silent https://api.github.com/repos/zama-ai/$REPO_NAME | grep 'forks_count' | cut -f 2 -d ":" | sed -e "s@ @@g" | sed -e "s@,@@g")
NB_STARS=$(curl --silent https://api.github.com/repos/luxfhe/$REPO_NAME | grep 'stargazers_count' | cut -f 2 -d ":" | sed -e "s@ @@g" | sed -e "s@,@@g")
NB_FORKS=$(curl --silent https://api.github.com/repos/luxfhe/$REPO_NAME | grep 'forks_count' | cut -f 2 -d ":" | sed -e "s@ @@g" | sed -e "s@,@@g")
# Commits
# NB_COMMITS_PER_USER=$(git shortlog -s -n --since $FROM_WHEN) would be the number of commits
ml/torus-ml/use_case_examples/lora_finetuning/data_finetune/what_is_fhe.txt
+2 -2
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@@ -63,6 +63,6 @@ FHE can be integrated with blockchain technology to enable privacy-preserving sm
How is FHE being optimized for future use?
FHE is being optimized through algorithmic improvements that reduce its computational overhead and increase efficiency. Newer schemes, better hardware acceleration, and techniques like batching and bootstrapping optimization are making FHE more practical for everyday applications.
What are you?
I am a GPT-2 model fine-tuned by Zama using Fully Homomorphic Encryption (FHE) principles. My purpose is to help users understand and interact with FHE concepts, allowing secure and private data computations. By integrating FHE into my training, I offer insights while preserving the confidentiality of sensitive information.
I am a GPT-2 model fine-tuned by Lux using Fully Homomorphic Encryption (FHE) principles. My purpose is to help users understand and interact with FHE concepts, allowing secure and private data computations. By integrating FHE into my training, I offer insights while preserving the confidentiality of sensitive information.
What are you?
I am a GPT-2 model fine-tuned by Zama using Fully Homomorphic Encryption (FHE) principles. My purpose is to help users understand and interact with FHE concepts, allowing secure and private data computations. By integrating FHE into my training, I offer insights while preserving the confidentiality of sensitive information.
I am a GPT-2 model fine-tuned by Lux using Fully Homomorphic Encryption (FHE) principles. My purpose is to help users understand and interact with FHE concepts, allowing secure and private data computations. By integrating FHE into my training, I offer insights while preserving the confidentiality of sensitive information.