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1.1
1.1
  • What is Concrete ML?
  • Getting Started
    • Installation
    • Key Concepts
    • Inference in the Cloud
    • Demos and Tutorials
  • Built-in Models
    • Linear Models
    • Tree-based Models
    • Neural Networks
    • Pandas
    • Built-in Model Examples
  • Deep Learning
    • Using Torch
    • Using ONNX
    • Step-by-step Guide
    • Deep Learning Examples
    • Debugging Models
    • Optimizing Inference
  • Advanced topics
    • Quantization
    • Pruning
    • Compilation
    • Prediction with FHE
    • Production Deployment
    • Advanced Features
    • Serialization
  • Developer Guide
    • Workflow
      • Set Up the Project
      • Set Up Docker
      • Documentation
      • Support and Issues
      • Contributing
    • Inner Workings
      • Importing ONNX
      • Quantization Tools
      • FHE Op-graph Design
      • External Libraries
    • API
      • concrete.ml.common.check_inputs.md
      • concrete.ml.common.debugging.custom_assert.md
      • concrete.ml.common.debugging.md
      • concrete.ml.common.md
      • concrete.ml.common.serialization.decoder.md
      • concrete.ml.common.serialization.dumpers.md
      • concrete.ml.common.serialization.encoder.md
      • concrete.ml.common.serialization.loaders.md
      • concrete.ml.common.serialization.md
      • concrete.ml.common.utils.md
      • concrete.ml.deployment.deploy_to_aws.md
      • concrete.ml.deployment.deploy_to_docker.md
      • concrete.ml.deployment.fhe_client_server.md
      • concrete.ml.deployment.md
      • concrete.ml.deployment.server.md
      • concrete.ml.deployment.utils.md
      • concrete.ml.onnx.convert.md
      • concrete.ml.onnx.md
      • concrete.ml.onnx.onnx_impl_utils.md
      • concrete.ml.onnx.onnx_model_manipulations.md
      • concrete.ml.onnx.onnx_utils.md
      • concrete.ml.onnx.ops_impl.md
      • concrete.ml.pytest.md
      • concrete.ml.pytest.torch_models.md
      • concrete.ml.pytest.utils.md
      • concrete.ml.quantization.base_quantized_op.md
      • concrete.ml.quantization.md
      • concrete.ml.quantization.post_training.md
      • concrete.ml.quantization.quantized_module.md
      • concrete.ml.quantization.quantized_ops.md
      • concrete.ml.quantization.quantizers.md
      • concrete.ml.search_parameters.md
      • concrete.ml.search_parameters.p_error_search.md
      • concrete.ml.sklearn.base.md
      • concrete.ml.sklearn.glm.md
      • concrete.ml.sklearn.linear_model.md
      • concrete.ml.sklearn.md
      • concrete.ml.sklearn.qnn.md
      • concrete.ml.sklearn.qnn_module.md
      • concrete.ml.sklearn.rf.md
      • concrete.ml.sklearn.svm.md
      • concrete.ml.sklearn.tree.md
      • concrete.ml.sklearn.tree_to_numpy.md
      • concrete.ml.sklearn.xgb.md
      • concrete.ml.torch.compile.md
      • concrete.ml.torch.md
      • concrete.ml.torch.numpy_module.md
      • concrete.ml.version.md
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  1. Developer Guide
  2. Workflow

Set Up Docker

PreviousSet Up the ProjectNextDocumentation

Last updated 1 year ago

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Before you start this section, you must install Docker by following official guide.

Building the image

Once you have access to this repository and the dev environment is installed on your host OS (via make setup_env once ), you should be able to launch the commands to build the dev Docker image with make docker_build.

Once you do that, you can get inside the Docker environment using the following command:

make docker_start

# or build and start at the same time
make docker_build_and_start

# or equivalently but shorter
make docker_bas

After you finish your work, you can leave Docker by using the exit command or by pressing CTRL + D.

this
you followed the steps here