Switch main branch to Vitis and update environment for 1.0.0.

README.md

@@ -13,50 +13,18 @@ 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
-```
-
-## 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
+source /path/to/your/installtion/Xilinx/Vitis_HLS/202X.X/settings64.(c)sh
 ```
 
 ## Companion material

environment.yml

@@ -2,21 +2,20 @@ 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
+      - hls4ml[profiling]==1.0.0
       - conifer==0.2b0
       - pysr==0.16.3

part1_getting_started.ipynb

@@ -27,7 +27,7 @@
     "tf.random.set_seed(seed)\n",
     "import os\n",
     "\n",
-    "os.environ['PATH'] = os.environ['XILINX_VIVADO'] + '/bin:' + os.environ['PATH']"
+    "os.environ['PATH'] = os.environ['XILINX_VITIS'] + '/bin:' + os.environ['PATH']"
    ]
   },
   {
@@ -188,7 +188,7 @@
     "        X_train_val,\n",
     "        y_train_val,\n",
     "        batch_size=1024,\n",
-    "        epochs=30,\n",
+    "        epochs=10,\n",
     "        validation_split=0.25,\n",
     "        shuffle=True,\n",
     "        callbacks=callbacks.callbacks,\n",
@@ -224,14 +224,13 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Convert the model to FPGA firmware with hls4ml\n",
     "Now we will go through the steps to convert the model we trained to a low-latency optimized FPGA firmware with hls4ml.\n",
     "First, we will evaluate its classification performance to make sure we haven't lost accuracy using the fixed-point data types. \n",
-    "Then we will synthesize the model with Vivado HLS and check the metrics of latency and FPGA resource usage.\n",
+    "Then we will synthesize the model with Vitis HLS and check the metrics of latency and FPGA resource usage.\n",
     "\n",
     "### Make an hls4ml config & model\n",
     "The hls4ml Neural Network inference library is controlled through a configuration dictionary.\n",
@@ -246,13 +245,13 @@
    "source": [
     "import hls4ml\n",
     "\n",
-    "config = hls4ml.utils.config_from_keras_model(model, granularity='model')\n",
+    "config = hls4ml.utils.config_from_keras_model(model, granularity='model', backend='Vitis')\n",
     "print(\"-----------------------------------\")\n",
     "print(\"Configuration\")\n",
     "plotting.print_dict(config)\n",
     "print(\"-----------------------------------\")\n",
     "hls_model = hls4ml.converters.convert_from_keras_model(\n",
-    "    model, hls_config=config, output_dir='model_1/hls4ml_prj', part='xcu250-figd2104-2L-e'\n",
+    "    model, hls_config=config, backend='Vitis', output_dir='model_1/hls4ml_prj', part='xcu250-figd2104-2L-e'\n",
     ")"
    ]
   },
@@ -327,21 +326,23 @@
    "metadata": {},
    "source": [
     "## Synthesize\n",
-    "Now we'll actually use Vivado HLS to synthesize the model. We can run the build using a method of our `hls_model` object.\n",
+    "Now we'll actually use Vitis HLS to synthesize the model. We can run the build using a method of our `hls_model` object.\n",
     "After running this step, we can integrate the generated IP into a workflow to compile for a specific FPGA board.\n",
-    "In this case, we'll just review the reports that Vivado HLS generates, checking the latency and resource usage.\n",
+    "In this case, we'll just review the reports that Vitis HLS generates, checking the latency and resource usage.\n",
     "\n",
     "**This can take several minutes.**\n",
     "\n",
     "While the C-Synthesis is running, we can monitor the progress looking at the log file by opening a terminal from the notebook home, and executing:\n",
     "\n",
-    "`tail -f model_1/hls4ml_prj/vivado_hls.log`"
+    "`tail -f model_1/hls4ml_prj/vitis_hls.log`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "hls_model.build(csim=False)"
@@ -352,7 +353,7 @@
    "metadata": {},
    "source": [
     "## Check the reports\n",
-    "Print out the reports generated by Vivado HLS. Pay attention to the Latency and the 'Utilization Estimates' sections"
+    "Print out the reports generated by Vitis HLS. Pay attention to the Latency and the 'Utilization Estimates' sections"
    ]
   },
   {
@@ -373,11 +374,18 @@
     "Calculate how many multiplications are performed for the inference of this network...\n",
     "(We'll discuss the outcome)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -391,7 +399,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,