mirror of https://github.com/luxfhe/ml-sdk.git synced 2026-10-30 18:13:04 +00:00

Files

T

History

zeekay cb4a7af933 Merge remote-tracking branch 'origin/chore/purge-zama-brand-v2'

# Conflicts:
#	.github/workflows/benchmarks.yaml
#	.github/workflows/cifar_benchmark.yaml
#	.github/workflows/continuous-integration.yaml
#	.github/workflows/llama_finetuning_benchmark.yaml
#	.github/workflows/publish_aws_ami.yaml
#	.github/workflows/release.yaml
#	.github/workflows/resnet_hybrid_model.yaml
#	.github/workflows/sync_on_push.yaml
#	.github/workflows/weekly_tests.yaml
#	LICENSE
#	Makefile
#	README.md
#	UPGRADING.md
#	benchmarks/common.py
#	ci/aws_ami_build_component.yaml
#	conftest.py
#	docs/README.md
#	docs/SUMMARY.md
#	docs/advanced_examples/ClassifierComparison.ipynb
#	docs/advanced_examples/FullyConnectedNeuralNetwork.ipynb
#	docs/advanced_examples/FullyConnectedNeuralNetworkOnMNIST.ipynb
#	docs/advanced_examples/GLMComparison.ipynb
#	docs/advanced_examples/QuantizationAwareTraining.ipynb
#	docs/advanced_examples/RegressorComparison.ipynb
#	docs/advanced_examples/SVMClassifier.ipynb
#	docs/advanced_examples/utils/classifier_comparison_utils.py
#	docs/conventions.md
#	docs/deep-learning/fhe_assistant.md
#	docs/deep-learning/fhe_friendly_models.md
#	docs/developer/contributing.md
#	docs/developer/debug_support_submit_issues.md
#	docs/developer/project_setup.md
#	docs/explanations/compilation.md
#	docs/explanations/pruning.md
#	docs/getting-started/README.md
#	docs/getting-started/concepts.md
#	docs/getting-started/pip_installing.md
#	docs/guides/client_server.md
#	docs/references/api/concrete.ml.search_parameters.p_error_search.md
#	docs/references/api/concrete.ml.sklearn.base.md
#	docs/references/api/concrete.ml.sklearn.glm.md
#	docs/references/api/concrete.ml.sklearn.linear_model.md
#	docs/references/api/concrete.ml.sklearn.neighbors.md
#	docs/references/api/concrete.ml.sklearn.rf.md
#	docs/references/api/concrete.ml.sklearn.svm.md
#	docs/references/api/concrete.ml.sklearn.tree.md
#	docs/references/api/concrete.ml.sklearn.tree_to_numpy.md
#	docs/references/api/concrete.ml.sklearn.xgb.md
#	docs/tutorials/showcase.md
#	pyproject.toml
#	script/actions_utils/monitor.py
#	script/doc_utils/check_forbidden_words.py
#	script/make_utils/check_installation_with_all_python.sh
#	script/make_utils/check_issues.py
#	script/make_utils/fix_omp_issues_for_intel_mac.sh
#	script/make_utils/jupyter.sh
#	script/make_utils/local_link_check.py
#	src/concrete/ml/common/serialization/__init__.py
#	src/concrete/ml/common/serialization/encoder.py
#	src/concrete/ml/common/utils.py
#	src/concrete/ml/deployment/fhe_client_server.py
#	src/concrete/ml/onnx/convert.py
#	src/concrete/ml/onnx/onnx_model_manipulations.py
#	src/concrete/ml/onnx/onnx_utils.py
#	src/concrete/ml/onnx/ops_impl.py
#	src/concrete/ml/pandas/_development.py
#	src/concrete/ml/pandas/_operators.py
#	src/concrete/ml/pandas/_processing.py
#	src/concrete/ml/pandas/client_engine.py
#	src/concrete/ml/pandas/dataframe.py
#	src/concrete/ml/pytest/utils.py
#	src/concrete/ml/quantization/base_quantized_op.py
#	src/concrete/ml/quantization/linear_op_glwe_backend.py
#	src/concrete/ml/quantization/post_training.py
#	src/concrete/ml/quantization/quantized_module.py
#	src/concrete/ml/quantization/quantized_ops.py
#	src/concrete/ml/quantization/quantizers.py
#	src/concrete/ml/search_parameters/p_error_search.py
#	src/concrete/ml/sklearn/_fhe_training_utils.py
#	src/concrete/ml/sklearn/base.py
#	src/concrete/ml/sklearn/glm.py
#	src/concrete/ml/sklearn/linear_model.py
#	src/concrete/ml/sklearn/neighbors.py
#	src/concrete/ml/sklearn/qnn.py
#	src/concrete/ml/sklearn/tree_to_numpy.py
#	src/concrete/ml/sklearn/xgb.py
#	src/concrete/ml/torch/compile.py
#	src/concrete/ml/torch/hybrid_model.py
#	src/concrete/ml/torch/lora.py
#	tests/common/test_pbs_error_probability_settings.py
#	tests/common/test_serialization.py
#	tests/deployment/test_client_server.py
#	tests/onnx/test_onnx_utils.py
#	tests/pandas/test_pandas.py
#	tests/parameter_search/test_p_error_binary_search.py
#	tests/quantization/test_compilation.py
#	tests/quantization/test_quantized_module.py
#	tests/quantization/test_quantized_ops.py
#	tests/sklearn/test_dump_onnx.py
#	tests/sklearn/test_sklearn_models.py
#	tests/torch/test_brevitas_qat.py
#	tests/torch/test_compile_keras.py
#	tests/torch/test_compile_torch.py
#	tests/torch/test_torch_to_numpy.py
#	tests/torch/test_training.py
#	use_case_examples/cifar/README.md
#	use_case_examples/cifar/cifar_brevitas_finetuning/PerrorImpactOnFMNIST.ipynb
#	use_case_examples/cifar/cifar_brevitas_finetuning/README.md
#	use_case_examples/cifar/cifar_brevitas_finetuning/cifar_utils.py
#	use_case_examples/cifar/cifar_brevitas_training/evaluate_one_example_fhe.py
#	use_case_examples/cifar/cifar_brevitas_training/evaluate_torch_cml.py
#	use_case_examples/cifar/cifar_brevitas_training/trainer.py
#	use_case_examples/credit_card_approval_prediction/README.md
#	use_case_examples/deployment/sentiment_analysis/train.py
#	use_case_examples/deployment/server/deploy_to_docker.py
#	use_case_examples/disease_prediction/HealthCarePrediction.ipynb
#	use_case_examples/image_filtering/README.md
#	use_case_examples/llm/README.md
#	use_case_examples/llm/quant_framework.py
#	use_case_examples/lora_finetuning/README.md
#	use_case_examples/lora_finetuning/data_finetune/what_is_fhe.txt
#	use_case_examples/sentiment_analysis_with_transformer/SentimentClassification.ipynb
#	use_case_examples/titanic/KaggleTitanic.ipynb
#	use_case_examples/titanic/README.md
#	use_case_examples/white_paper_experiment/WhitePaperExperiments.ipynb

2026-05-29 14:00:32 -07:00

breast_cancer

Merge remote-tracking branch 'origin/chore/purge-zama-brand-v2'

2026-05-29 14:00:32 -07:00

cifar

Merge remote-tracking branch 'origin/chore/purge-zama-brand-v2'

2026-05-29 14:00:32 -07:00

sentiment_analysis

Merge remote-tracking branch 'origin/chore/purge-zama-brand-v2'

2026-05-29 14:00:32 -07:00

server

Merge remote-tracking branch 'origin/chore/purge-zama-brand-v2'

2026-05-29 14:00:32 -07:00

README.md

chore(brand): purge Zama refs across docs, README, configs — phase 1

2026-05-29 13:53:42 -07:00

README.md

Deployment Examples

This folder contains examples of how to deploy TorusML models using Fully Homomorphic Encryption (FHE). These examples demonstrate the process of training, compiling, and deploying models for various use cases.

Overview

The deployment process generally follows these steps:

Train the model (optional, depending on the use case)
Compile the model to an FHE circuit
Deploy the model using Docker
Run inference using a client (locally or in Docker)

Available Examples

We provide three different use cases to demonstrate the deployment process:

Getting Started

Each example folder contains its own README with specific instructions. However, the general process is similar:

Train or compile the model using the provided scripts
Deploy the model using deploy_to_docker.py from the server folder
Build the client Docker image
Run the client to interact with the deployed model

For detailed instructions, please refer to the README in each example folder.

Requirements

Docker
Python 3.8 or later
TorusML library installed

README.md

Deployment Examples

Overview

Available Examples

Getting Started

Requirements

Additional Resources