Merge branch 'main' into pre-commit-ci-update-config

Author: jmitrevs

.github/workflows/deploy.yml

@@ -25,7 +25,7 @@ jobs:
         channels: conda-forge
         activate-environment: hls4ml-tutorial
         environment-file: environment.yml
-        python-version: 3.10.10
+        python-version: 3.10.16
         auto-activate-base: false
 
     # Check dependencies

.github/workflows/docker-publish.yml

@@ -13,51 +13,21 @@ There are several ways to run the tutorial notebooks:
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastmachinelearning/hls4ml-tutorial/HEAD)
 
 ## Conda
+Running the tutorials requires AMD Vitis HLS to be installed, see [here](https://www.xilinx.com/support/download/index.html/content/xilinx/en/downloadNav/vitis.html).
+After the installation, the necessary environmental variables can be set using
+```
+source /path/to/your/installtion/Xilinx/Vitis_HLS/202X.X/settings64.(c)sh
+```
+
 The Python environment used for the tutorials is specified in the `environment.yml` file.
 It can be setup like:
 ```bash
 conda env create -f environment.yml
 conda activate hls4ml-tutorial
+source /path/to/your/installtion/Xilinx/Vitis_HLS/202X.X/settings64.(c)sh
 ```
 
-## Docker without Vivado
-Pull the prebuilt image from the GitHub Container Registry:
-```bash
-docker pull ghcr.io/fastmachinelearning/hls4ml-tutorial/hls4ml-0.8.0:latest
-```
-
-Follow these steps to build a Docker image that can be used locally, or on a JupyterHub instance.
-You can build the image (without Vivado):
-```bash
-docker build https://github.com/fastmachinelearning/hls4ml-tutorial -f docker/Dockerfile
-```
-Alternatively, you can clone the repository and build locally:
-```bash
-git clone https://github.com/fastmachinelearning/hls4ml-tutorial
-cd hls4ml-tutorial
-docker build -f docker/Dockerfile -t ghcr.io/fastmachinelearning/hls4ml-tutorial/hls4ml-0.8.0:latest .
-```
-Then to start the container:
-```bash
-docker run -p 8888:8888 ghcr.io/fastmachinelearning/hls4ml-tutorial/hls4ml-0.8.0:latest
-```
-When the container starts, the Jupyter notebook server is started, and the link to open it in your browser is printed.
-You can clone the repository inside the container and run the notebooks.
-
-## Docker with Vivado
-Pull the prebuilt image from the GitHub Container Registry:
-```bash
-docker pull ghcr.io/fastmachinelearning/hls4ml-tutorial/hls4ml-0.8.0-vivado-2019.2:latest
-```
-
-To build the image with Vivado, run (Warning: takes a long time and requires a lot of disk space):
-```bash
-docker build -f docker/Dockerfile.vivado -t ghcr.io/fastmachinelearning/hls4ml-tutorial/hls4ml-0.8.0-vivado-2019.2:latest .
-```
-Then to start the container:
-```bash
-docker run -p 8888:8888 ghcr.io/fastmachinelearning/hls4ml-tutorial/hls4ml-0.8.0-vivado-2019.2:latest
-```
+Note that part 7 of the tutorial makes use of the `VivadoAccelator` backend of hls4ml for which no Vitis equivalent is available yet. For this part of the tutorial it is therefore necesary to install and source Vivado HLS version 2019.2 or 2020.1, which can be obtained [here](https://www.xilinx.com/support/download/index.html/content/xilinx/en/downloadNav/vivado-design-tools/archive.html).
 
 ## Companion material
 We have prepared a set of slides with some introduction and more details on each of the exercises.

README.md

@@ -2,21 +2,21 @@ name: hls4ml-tutorial
 channels:
   - conda-forge
 dependencies:
-  - python=3.10.10
-  - jupyter_contrib_nbextensions==0.7.0
-  - jupyterhub==3.1.1
-  - jupyter-book==0.15.1
+  - python=3.10.16
+  - jupyter_contrib_nbextensions
+  - jupyterhub
+  - jupyter-book
   - jsonschema-with-format-nongpl
   - pydot==1.4.2
   - graphviz==7.1.0
   - scikit-learn==1.2.2
-  - tensorflow==2.11.1
+  - tensorflow==2.14.0
   - tensorflow-datasets==4.8.3
   - webcolors
   - widgetsnbextension==3.6.0
   - pip==23.0.1
   - pip:
-      - hls4ml[profiling]==0.8.0
-      - qkeras==0.9.0
-      - conifer==0.2b0
+      - hls4ml[profiling,optimization,sr,HGQ]==1.0.0
+      - conifer==1.5
       - pysr==0.16.3
+      - xgboost==1.7.5