v0.24.0 (#402)

.github/workflows/build-wheels.yml

@@ -82,3 +82,22 @@ jobs:
               asset_path: ${{ env.wheel_name }}
               asset_name: ${{env.wheel_file_name}}
               asset_content_type: application/octet-stream
+  publish-to-pypi:
+    name: >-
+      Publish Python 🐍 distribution 📦 to PyPI
+    needs:
+    - build
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/ttnn-visualizer
+    permissions:
+      id-token: write  # IMPORTANT: mandatory for trusted publishing
+    steps:
+    - name: Download all the dists
+      uses: actions/download-artifact@v4
+      with:
+        name: python-wheel
+        path: dist/
+    - name: Publish distribution 📦 to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1

.npmrc

@@ -0,0 +1 @@
+node-linker=hoisted

LICENSE_understanding.txt

@@ -0,0 +1,3 @@
+For the avoidance of doubt, this software assists in programming Tenstorrent products.
+
+However, making, using, or selling hardware, models, or IP may require the license of rights (such as patent rights) from Tenstorrent or others.

README.md

@@ -5,8 +5,8 @@
 
 <div align="center">
 <picture>
-  <source media="(prefers-color-scheme: dark)" srcset="./src/assets/tt-logo-dark.svg">
-  <img alt="" src="./src/assets/tt-logo.svg">
+  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/tenstorrent/ttnn-visualizer/refs/heads/main/src/assets/tt-logo-dark.svg">
+  <img alt="" src="https://raw.githubusercontent.com/tenstorrent/ttnn-visualizer/refs/heads/main/src/assets/tt-logo.svg">
 </picture>
 
 A tool for visualizing the Tenstorrent Neural Network model (TT-NN)
@@ -21,6 +21,19 @@ A tool for visualizing the Tenstorrent Neural Network model (TT-NN)
 
 </div>
 
+## Quick Start
+
+TT-NN Visualizer can be installed from PyPI:
+
+`pip install ttnn-visualizer`
+
+After installation run `ttnn-visualizer` to start the application.
+
+It is recommended to do this within a virtual environment. The minimum Python version is 3.12.
+
+Please see the [getting started](https://github.com/tenstorrent/ttnn-visualizer/blob/main/docs/getting-started.md) guide
+for further information on getting up and running with TT-NN Visualizer.
+
 ## Features
 
 For the latest updates and features, please see [releases](https://github.com/tenstorrent/ttnn-visualizer/releases).
@@ -73,13 +86,9 @@ https://github.com/user-attachments/assets/d00a2629-0bd1-4ee1-bb12-bd796d85221d
 |-----------------------------------------------|------------------------------------------|
 | <img width="400" alt="NPE" src="https://github.com/user-attachments/assets/1f4441c6-9d9e-4834-9e71-edec1955243c" /> | <img width="400" alt="NPE" src="https://github.com/user-attachments/assets/7159992e-7691-41cf-a152-8bc6a3606ade" /> |
 
-## Getting started
-
-How to [get started](./docs/getting-started.md) with TT-NN Visualizer.
-
 ## Remote Querying
 
-Use [remote querying](./docs/remote-querying.md) instead of syncing the report data to your local file system.
+Use [remote querying](https://github.com/tenstorrent/ttnn-visualizer/blob/main/docs/remote-querying.md) instead of syncing the report data to your local file system.
 
 ## Sample reports
 
@@ -104,4 +113,4 @@ Unzip the files into their own directories and select them with the local folder
 
 ## Contributing
 
-How to run [TT-NN Visualizer](./docs/contributing.md) from source.
+How to run [TT-NN Visualizer](https://github.com/tenstorrent/ttnn-visualizer/blob/main/docs/contributing.md) from source.

backend/ttnn_visualizer/csv_queries.py

@@ -567,6 +567,7 @@ class OpsPerformanceReportQueries:
         "output_subblock_h",
         "output_subblock_w",
         "advice",
+        "raw_op_code"
     ]
 
     DEFAULT_SIGNPOST = None
@@ -590,6 +591,8 @@ def generate_report(cls, session):
             csv_output_file,
             cls.DEFAULT_NO_ADVICE,
             cls.DEFAULT_TRACING_MODE,
+            True,
+            True,
         )
 
         report = []

backend/ttnn_visualizer/requirements.txt

@@ -1,3 +1,4 @@
+Flask==3.1.0
 gunicorn~=22.0.0
 uvicorn==0.30.1
 paramiko~=3.4.0
@@ -11,12 +12,12 @@ flask-sqlalchemy
 flask-socketio
 gevent==24.10.2
 flask-session
-pandas
+pandas==2.2.3
 wheel
 build
 PyYAML==6.0.2
 python-dotenv==1.0.1
-tt-perf-report==1.0.3
+tt-perf-report==1.0.6
 
 # Dev dependencies
 mypy

docs/getting-started.md

@@ -51,6 +51,42 @@ Network-on-chip performance estimator data can be loaded separately on the `/npe
 
 To generate this data for your model, refer to the [tt-npe documentation](https://github.com/tenstorrent/tt-npe).
 
+## Installing from PyPI
+
+TTNN-Visualizer can be installed from PyPI:
+
+`pip install ttnn-visualizer`
+
+After installation run `ttnn-visualizer` to start the application.
+
+It is recommended to do this within a virtual environment. The minimum Python version is 3.12. Pyenv can be used
+to ensure the application runs with a supported version of Python:
+
+```bash
+mkdir ttnn-visualizer
+cd ttnn-visualizer
+pyenv local 3.12 # Optional step if using pyenv to manage versions
+python -m venv .env
+source .env/bin/activate
+pip install ttnn-visualizer
+ttnn-visualizer 
+```
+
+For pyenv installation instructions, see: 
+
+https://github.com/pyenv/pyenv
+
+If you have `pipx` installed on your system, it can be used to simplify the above steps:
+
+```bash
+pipx install ttnn-visualizer
+ttnn-visualizer
+```
+
+For help with setting up `pipx`, see:
+
+https://pipx.pypa.io/stable/installation/
+
 ## Installing as a Python Wheel
 
 The application is designed to run on user local system and has python requirement of `3.12.3`.

package.json

@@ -1,7 +1,7 @@
 {
   "name": "ttnn-visualizer",
   "private": true,
-  "version": "0.23.0",
+  "version": "0.24.0",
   "type": "module",
   "scripts": {
     "dev": "vite",