> For the complete documentation index, see [llms.txt](https://docs.zama.org/concrete-ml/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/concrete-ml/1.2/readme.md). # What is Concrete ML? [⭐️ Star the repo on Github](https://github.com/zama-ai/concrete-ml) | [🗣 Community support forum](https://community.zama.ai/c/concrete-ml/8) | [📁 Contribute to the project](/concrete-ml/1.2/developer-guide/workflow/contributing.md) ![](https://2993873596-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FH2LBykNhGsJzuzlkPZtC%2Fuploads%2Fgit-blob-bd13f146460dd3096ed0021391be3e4790c18bef%2F3.png?alt=media) Concrete ML is an open source, privacy-preserving, machine learning inference framework based on Fully Homomorphic Encryption (FHE). It enables data scientists without any prior knowledge of cryptography to automatically turn machine learning models into their FHE equivalent, using familiar APIs from scikit-learn and PyTorch (see how it looks for [linear models](/concrete-ml/1.2/built-in-models/linear.md), [tree-based models](/concrete-ml/1.2/built-in-models/tree.md), and [neural networks](/concrete-ml/1.2/built-in-models/neural-networks.md)). Fully Homomorphic Encryption is an encryption technique that allows computing directly on encrypted data, without needing to decrypt it. With FHE, you can build private-by-design applications without compromising on features. You can learn more about FHE in [this introduction](https://www.zama.ai/post/tfhe-deep-dive-part-1) or by joining the [FHE.org](https://fhe.org) community. ## Example usage Here is a simple example of classification on encrypted data using logistic regression. More examples can be found [here](/concrete-ml/1.2/built-in-models/ml_examples.md). ```python from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from concrete.ml.sklearn import LogisticRegression # Lets create a synthetic data-set x, y = make_classification(n_samples=100, class_sep=2, n_features=30, random_state=42) # Split the data-set into a train and test set X_train, X_test, y_train, y_test = train_test_split( x, y, test_size=0.2, random_state=42 ) # Now we train in the clear and quantize the weights model = LogisticRegression(n_bits=8) model.fit(X_train, y_train) # We can simulate the predictions in the clear y_pred_clear = model.predict(X_test) # We then compile on a representative set model.compile(X_train) # Finally we run the inference on encrypted inputs y_pred_fhe = model.predict(X_test, fhe="execute") print(f"In clear : {y_pred_clear}") print(f"In FHE : {y_pred_fhe}") print(f"Similarity: {(y_pred_fhe == y_pred_clear).mean():.1%}") # Output: # In clear : [0 0 0 0 1 0 1 0 1 1 0 0 1 0 0 1 1 1 0 0] # In FHE : [0 0 0 0 1 0 1 0 1 1 0 0 1 0 0 1 1 1 0 0] # Similarity: 100.0% ``` It is also possible to call encryption, model prediction, and decryption functions separately as follows. Executing these steps separately is equivalent to calling `predict_proba` on the model instance. ```python # Predict probability for a single example y_proba_fhe = model.predict_proba(X_test[[0]], fhe="execute") # Quantize an original float input q_input = model.quantize_input(X_test[[0]]) # Encrypt the input q_input_enc = model.fhe_circuit.encrypt(q_input) # Execute the linear product in FHE q_y_enc = model.fhe_circuit.run(q_input_enc) # Decrypt the result (integer) q_y = model.fhe_circuit.decrypt(q_y_enc) # De-quantize and post-process the result y0 = model.post_processing(model.dequantize_output(q_y)) print("Probability with `predict_proba`: ", y_proba_fhe) print("Probability with encrypt/run/decrypt calls: ", y0) ``` This example shows the typical flow of a Concrete ML model: * The model is trained on unencrypted (plaintext) data using scikit-learn. As FHE operates over integers, Concrete ML quantizes the model to use only integers during inference. * The quantized model is compiled to an FHE equivalent. Under the hood, the model is first converted to a Concrete Python program, then compiled. * Inference can then be done on encrypted data. The above example shows encrypted inference in the model-development phase. Alternatively, during [deployment](/concrete-ml/1.2/getting-started/cloud.md) in a client/server setting, the data is encrypted by the client, processed securely by the server, and then decrypted by the client. ## Current limitations To make a model work with FHE, the only constraint is to make it run within the supported precision limitations of Concrete ML (currently 16-bit integers). Thus, machine learning models must be quantized, which sometimes leads to a loss of accuracy versus the original model, which operates on plaintext. Additionally, Concrete ML currently only supports FHE *inference*. Training has to be done on unencrypted data, producing a model which is then converted to an FHE equivalent that can perform encrypted inference (i.e., prediction over encrypted data). Finally, there is currently no support for pre-processing model inputs and post-processing model outputs. These processing stages may involve text-to-numerical feature transformation, dimensionality reduction, KNN or clustering, featurization, normalization, and the mixing of results of ensemble models. These issues are currently being addressed, and significant improvements are expected to be released in the coming months. ## Concrete stack Concrete ML is built on top of Zama's [Concrete](https://github.com/zama-ai/concrete). ## Online demos and tutorials Various tutorials are available for [built-in models](/concrete-ml/1.2/built-in-models/ml_examples.md) and [deep learning](/concrete-ml/1.2/deep-learning/examples.md). Several stand-alone demos for use cases can be found in the [Demos and Tutorials](/concrete-ml/1.2/getting-started/showcase.md) section. If you have built awesome projects using Concrete ML, feel free to let us know and we'll link to your work! ## Additional resources * [Dedicated Concrete ML community support](https://community.zama.ai/c/concrete-ml/8) * [Zama's blog](https://www.zama.ai/blog) * [FHE.org community](https://fhe.org) ## Support * Support forum: (we answer in less than 24 hours). * Live discussion on the FHE.org Discord server: (inside the #**concrete** channel). * Do you have a question about Zama? Write us on [Twitter](https://twitter.com/zama_fhe) or send us an email at: **** --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.zama.org/concrete-ml/1.2/readme.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.