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fix: models

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bernard-ng
2025-10-05 21:54:25 +02:00
parent 9dd4f759b3
commit 137dea7fe5
15 changed files with 376 additions and 197 deletions
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@@ -91,11 +91,6 @@ uv run ners research train --name="random_forest" --type="baseline" --env="produ
uv run ners research train --name="random_forest_native" --type="baseline" --env="production"
uv run ners research train --name="random_forest_surname" --type="baseline" --env="production"
# svm
uv run ners research train --name="svm" --type="baseline" --env="production"
uv run ners research train --name="svm_native" --type="baseline" --env="production"
uv run ners research train --name="svm_surname" --type="baseline" --env="production"
# naive bayes
uv run ners research train --name="naive_bayes" --type="baseline" --env="production"
uv run ners research train --name="naive_bayes_native" --type="baseline" --env="production"
@@ -112,46 +107,6 @@ uv run ners research train --name="xgboost_native" --type="baseline" --env="prod
uv run ners research train --name="xgboost_surname" --type="baseline" --env="production"
```
## TensorFlow on macOS (Intel) with uv
TensorFlow no longer publishes wheels for macOS Intel. To keep using uv and run TF reliably, use a Linux container with TF preinstalled and install project code with minimal extras inside the container.
### One-time build
```bash
docker compose -f docker/compose.tf.yml build
If you see a message like `tensorflow/tensorflow:<tag>: not found`, update `docker/Dockerfile.tf-cpu` to a tag that exists (e.g., `2.17.0`) and rebuild:
```bash
sed -n '1,20p' docker/Dockerfile.tf-cpu # verify the FROM line
docker pull tensorflow/tensorflow:2.17.0 # quick availability check
docker compose -f docker/compose.tf.yml build
```
```
### Start a shell with uv and TF available
```bash
docker compose -f docker/compose.tf.yml run --rm tf bash
```
Inside the container:
```bash
# Install project in editable mode without pulling full deps
uv pip install -e . --no-deps
# Install only what research needs alongside TensorFlow
uv pip install typer pandas scikit-learn seaborn plotly
# Sanity check
uv run python -c "import tensorflow as tf; print(tf.__version__)"
# Run an experiment
uv run ners research train --name="lstm" --type="baseline" --env="production"
```
## Web Interface
This project includes a user-friendly web interface built with Streamlit, allowing non-technical users to run