From 37fbcf1e5e9fe783b8f6858fa7c28d6dac5d23aa Mon Sep 17 00:00:00 2001 From: Francesco Cremonesi <francesco.cremonesi@inria.fr> Date: Thu, 8 Jun 2023 15:33:16 +0200 Subject: [PATCH] Start working on instructions --- _toc.yml | 21 ++------------------- fedbiomed-tutorial/aws-instructions.md | 22 ++++++++++++++++++++++ 2 files changed, 24 insertions(+), 19 deletions(-) create mode 100644 fedbiomed-tutorial/aws-instructions.md diff --git a/_toc.yml b/_toc.yml index f2fe7c1..eb23571 100644 --- a/_toc.yml +++ b/_toc.yml @@ -2,22 +2,5 @@ format: jb-book root: index.md title: Welcome parts: -- caption: Multivariate models for the analysis of heterogeneous information - chapters: - - file: heterogeneous_data/introduction.md - - file: heterogeneous_data/heterogeneous_data.ipynb - title: Multivariate association models for the analysis of heterogeneous data -- caption: Federated Learning - chapters: - - file: federated_learning/introduction.md - - file: federated_learning/FedAvg_FedProx_MNIST_iid_and_noniid.ipynb - title: FedAVG and FedProx - - file: federated_learning/federated_mcvae.ipynb - title: Federated VAEs - - file: federated_learning/mcvae_rotated_mnist.ipynb - title: Federated Multi-channel VAEs on MNIST - - file: federated_learning/federated_mcvae_adni.ipynb - title: Federated Multi-channel VAEs on biomedical data -- caption: About - chapters: - - file: contributors.md +- caption: Fed-BioMed tutorial + - file: fedbiomed-tutorial/aws-instructions.md diff --git a/fedbiomed-tutorial/aws-instructions.md b/fedbiomed-tutorial/aws-instructions.md new file mode 100644 index 0000000..2049251 --- /dev/null +++ b/fedbiomed-tutorial/aws-instructions.md @@ -0,0 +1,22 @@ +# Instructions for the tutorial + +Fed-BioMed is an open-source research and development initiative for translating federated learning into real-world medical applications. +The community of Fed-BioMed gathers experts in medical engineering, machine learning, communication, and security. +We all contribute to provide an open, user-friendly, and trusted framework for deploying the state-of-the-art of federated learning in sensitive environments, such as in hospitals and health data lakes. + +Check out our [fedbiomed.org](https://fedbiomed.org) for the latest documentation and news! + +## Using Fed-BioMed during the workshop + +We provide a ready-to-use Jupyterhub instance running on AWS for you. +To use it, check the table below and copy the url associated with your username in a browser. + +| user | address | +| --- | --- | +| francesco | http://0.0.0.0 | + +You may log in with the password provided to you by the workshop presenters. + +If you wish, you may also [install](https://fedbiomed.org/latest/tutorials/installation/0-basic-software-installation/) Fed-BioMed locally on your machine, however it is quite a long process and we can only provide limited support during today's workshop. + + -- GitLab