From cc18768124214f57b5676d8b189066d93c77ae52 Mon Sep 17 00:00:00 2001 From: Julien Vignoud Date: Mon, 13 May 2024 15:28:16 +0200 Subject: [PATCH] Specify that users should clone the repo to deploy their own server --- DEV.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DEV.md b/DEV.md index 33ee38e41..7d7123c05 100644 --- a/DEV.md +++ b/DEV.md @@ -119,7 +119,7 @@ The web client should be running on `http://localhost:8081`, if not first restar There are multiple ways to use and interact with DISCO, depending on your objective: -- A non-technical user that wants to train models in a distributed manner without coding would want to use DISCO through the `webapp`. To do so, starting a local `server` instance is also needed as a backend to the `webapp`. Similarly, a contributor aiming to implement new UI features would certainly want to run the same setup. +- A non-technical user that wants to train models in a distributed manner without coding would want to use DISCO through the `webapp`. However, someone with expertise may still be needed if you want your own server (e.g., by cloning the repository). - A technical user may find it more flexible to use DISCO from a Node.js script, which gives users a finer control over the process. The `discojs-node` module is tailored to be used in Node.js scripts and allows to load data, helps starting a server and run distributed machine learning training tasks. - Finally, the `cli` (command line interface) can also be used to quickly start distributed model trainings. The `CLI` is more restricting than using `discojs-node` but allows to start training with multiple users in a single command. It is useful for benchmarking for example.