Tab-separated table
Use one header row. CSV is accepted for prediction, but TSV remains the workflow default.
[ai/ml online workbench]
Configure.Train.Predict.
A Matrix-native interface for FNN regression workflows, parameter search, data validation, and ONNX prediction.
01
[ABOUT THE PROJECT]
What this workbench does
This project turns the Python regression workflow into an online AI/ML tool for configuring experiments, running predictions, and explaining the model pipeline.
Project explanation
[gnnv3 online]GNNv3 Online connects a Next.js interface to a FastAPI backend that serves the saved FNN model pipeline. The backend loads the trained artifacts, applies the same scaler and categorical encoder used during training, runs the ONNX model, and returns prediction results in a browser-readable table.
Training is used when you need a new or better model. The website prepares the FNN command and explains the settings, but it does not launch heavy runs from the browser. Long Ax or grid experiments should be run manually on a machine sized for training.
Controlled execution
The browser prepares FNN commands and runs prediction. Heavy training is kept outside the website because the VPS is expected to return to 2 vCPUs.
Reusable artifacts
A successful training run produces files that can be reused later: model weights or ONNX export, scaler, encoder, final parameters, and losses.
Search strategy
Grid search tests a fixed list of combinations. Ax search proposes new combinations based on earlier results to find lower validation loss faster.
02
[PARAMETER INDEX]
Every control and why it exists
Use this index when preparing a run command. Most fields map directly to Python CLI arguments or model training settings.
FNN Run Setup
[surface]- Model
- Fixed to FNN. The website is focused on the neural network regression path only.
- Mode
- Controls the operation: train, test, or predict.
- Ax Search
- Switches FNN training from manual grid combinations to Ax-guided hyperparameter search. Large Ax runs should be executed manually, not launched from the website.
- Feature file
- Path or uploaded file containing the input table.
- Output folder
- Folder where training artifacts, logs, parameters, and losses are stored.
- Target
- The supervised label column used for training and evaluation, usually ddGexp.
Data Columns
[schema]- Numeric features
- Comma-separated numeric columns used directly after scaling.
- Categorical features
- Comma-separated categorical columns converted with the selected encoder.
- Row names
- Optional identifier columns kept for tracking rows through prediction output.
- Scaler
- Normalizes numeric feature ranges so training is more stable.
- Encoding
- Converts categorical values into model-readable numeric inputs.
Training Control
[fit]- Ax trials
- Maximum number of Ax-suggested training attempts. Keep this small for command preparation and run large experiments manually.
- Seed
- Random seed for reproducible splits and model initialization.
- Folds
- Number of k-fold validation splits used to estimate model performance.
- Test size
- Holdout fraction used when a train/test split is required.
- Batch sizes
- Candidate mini-batch sizes used during neural network training.
- Epochs
- Maximum training passes through the dataset for each candidate model.
FNN Hyperparameters
[network]- Learning rates
- Candidate optimizer step sizes. Smaller values are steadier, larger values move faster.
- Hidden layers
- Candidate number of internal network layers.
- Nodes
- Candidate number of neurons per hidden layer.
- Dropout
- Regularization rate that randomly disables units during training.
- Activations
- Nonlinear functions tested inside the network, such as Tanh or LeakyReLU.
- Loss
- Training objective used to score model error.
- Optimizer
- Algorithm that updates model weights, such as Adam.
- Shape
- Controls whether layer widths stay rectangular, grow, or shrink.
03
[CONFIGURE TRAINING]
Training parameters
Select the FNN run mode, feature columns, search strategy, and neural network hyperparameters. The terminal command updates as you work and should be launched manually for training.
FNN run surface
[AX]command.preview[live]
python gml.py \ --method FNN \ --mode train \ --use_Ax \ --ax_trials 300 \ --feature_file data/features.tsv \ --scaler PowerTransformer \ --encoding onehot \ --reproduce 5 \ --foldername runs/Trial_1_AX_300 \ --test_size 0.2 \ --n_folds 5 \ --batch_sizes 16,64,256 \ --learning_rates 0.0001,0.001,0.01 \ --n_epochs 500 \ --hidden_layers 1,2,3,5 \ --num_nodes 16,24,32 \ --dropout_rates 0.0,0.1 \ --activation_functions Sigmoid,Tanh,LeakyReLU \ --loss_function SmoothL1Loss \ --optimizers Adam \ --net_shape rectangle
FNN hyperparameters
[matrix]04
[PROCESS FLOW]
How the model workflow is used
Use the online workbench in two paths: prepare train or test commands for manual execution, and run prediction online when a saved model is ready.
01.input
Prepare the table
Start with a TSV or CSV file that contains the selected numeric and categorical feature columns. Training also needs the target column.
02.configure
Configure the run
Choose train, test, or predict, then set the FNN feature names, scaler, encoding, seed, and output folder.
03.train
Train when you need artifacts
Training creates artifacts, but the website only prepares the command. Large runs should be launched manually from a terminal on suitable hardware.
04.predict
Predict with saved artifacts
Prediction loads the saved model pipeline, validates the uploaded file, transforms the data, and appends the PredY output column.
Mode meaning
[operator guide]trainPrepares the command for fitting a new FNN model from labeled data and writing model artifacts.
testPrepares the command for evaluating a saved run folder against known target values.
predictUses an existing saved model to generate predictions for new feature rows.
05
[DATA SCHEMA]
Prepare compatible input
Prediction and training depend on exact column names. Keep the file tab-separated, keep headers stable, and include the selected feature columns.
Training columns
Include numeric features, categorical features, and the target column selected in the console.
Prediction columns
Include the feature columns only. Target values are optional. The API appends PredY.
Exact names
Comma-separated parameter lists cannot contain spaces. Column names must match the file header.
06
[PREDICTION PIPELINE]
Upload data and predict
Send a feature file to the FastAPI backend. The UI keeps the workflow visible while the model transforms, encodes, and predicts.
Input file
[empty]runtime.log[live]
> api.loading
> model.none
> features.pending
> search=ax trials=300
> seed=5 folds=5
> training.manual=standby
> prediction.empty
07
[RUN STATUS]
Results and status
Prediction results render as a compact table. Training status reflects command preparation only, not a hosted training job.
System state
[attention]Backendloading
Trainingmanual command only
Predictionempty
ComparisonAx/Grid comparison needs manually loaded run metrics
Prediction preview
[empty][empty]
No prediction output yet. Upload a compatible feature file in step 06.