From c4c2e3cdc7a7475e77374d044564d2ff87134f15 Mon Sep 17 00:00:00 2001 From: Vruddhi Shah Date: Sun, 7 Sep 2025 23:59:46 +0530 Subject: [PATCH 1/2] Fix typo --- SETUP.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SETUP.md b/SETUP.md index 520513dbb..8bb0d05a6 100644 --- a/SETUP.md +++ b/SETUP.md @@ -105,7 +105,7 @@ $ssh -L local_port:remote_address:remote_port @ ``` For example, if I want to run `jupyter notebook --port 8888` on my VM and I -wish to run the Jupyter notebooks on my local broswer on `localhost:9999`, I +wish to run the Jupyter notebooks on my local browser on `localhost:9999`, I would ssh into my VM using the following command: ``` From 6ec93b73b998525c84e3f8b44aa7a0ecb1075225 Mon Sep 17 00:00:00 2001 From: Vruddhi Shah Date: Tue, 9 Sep 2025 22:33:39 +0530 Subject: [PATCH 2/2] Fixed some grammar errors --- SETUP.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/SETUP.md b/SETUP.md index 8bb0d05a6..2431aa1f9 100644 --- a/SETUP.md +++ b/SETUP.md @@ -1,6 +1,6 @@ # Setup Guide -This document describes how to setup all the dependencies, and optionally create a virtual machine, +This document describes how to set up all the dependencies and optionally create a virtual machine, to run the notebooks in this repository. @@ -13,7 +13,7 @@ to run the notebooks in this repository. ## Installation -To install the repository and its dependencies follow these simple steps: +To install the repository and its dependencies follow these steps: 1. (optional) Install Anaconda with Python >= 3.6. [Miniconda](https://conda.io/miniconda.html). This step can be skipped if working on a Data Science Virtual Machine (see the compute environment section). @@ -70,7 +70,7 @@ If you don't have CUDA Toolkit or don't have the right version, please download ## Compute Environments -Many computer visions scenarios are extremely computationally heavy. Training a model often requires a machine that has a strong GPU, and would otherwise be too slow. +Many computer vision scenarios are extremely computationally intensive. Training a model often requires a machine that has a strong GPU, and would otherwise be too slow. The easiest way to get started is to use the [Azure Data Science Virtual Machine (DSVM)](https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/). This VM will come installed with all the system requirements that are needed to run the notebooks in this repository. If you choose this option, you can skip the [System Requirements](#system-requirements) step in this guide as those requirements come pre-installed on the DSVM.