This tutorial is designed to run on `lxplus` using a Python environment from CMSSW. In addition we install some extra Python packages, and we use Xilinx's Vivado HLS software from a container on /cvmfs. This tutorial is designed to get you working on the notebooks with minimal setup. For real ML@L1T development work we recommend using a high-memory, high single-core-performance PC with the latest Xilinx tools.
This tutorial is designed to run on `lxplus8` using a Python environment from CMSSW. In addition we install some extra Python packages, and we use Xilinx's Vivado HLS software from a container on /cvmfs. This tutorial is designed to get you working on the notebooks with minimal setup. For real ML@L1T development work we recommend using a high-memory, high single-core-performance PC with the latest Xilinx tools.
**Note** with the recent switch to `lxplus9` as the alias for `lxplus`, you need to specify `lxplus8` to run this tutorial!
To set up the environment and launch the jupyter notebooks:
To set up the environment and launch the jupyter notebooks:
From your laptop, open a terminal and use your CERN computing account to `ssh` to `lxplus`, e.g.:
From your laptop, open a terminal and use your CERN computing account to `ssh` to `lxplus8`, e.g.:
```
```
ssh <user>@lxplus.cern.ch
ssh <user>@lxplus8.cern.ch
```
```
Note which `lxplus` node your are connected to, e.g. `lxplus123.cern.ch`, we will need to refer to the exact server in a later step.
Note which `lxplus8` node your are connected to, e.g. `lxplus812.cern.ch`, we will need to refer to the exact server in a later step.
From the terminal on `lxplus`, first clone this repository
From the terminal on `lxplus8`, first clone this repository