Start Training

Step-by-step guide to training a model on the tracebloc platform.

You have joined a use case and accepted the terms. Training a model is easy: you run a Jupyter notebook locally to connect and submit models to the workspace. For a smooth first run, let’s follow the step by step notebook in the public GitHub repository along with the documentation below.

Alternatively, you may use the notebook hosted in Google Colab and jump directly to Step 1.

Pull Training Notebook and Model Repositories

On your machine, create a tracebloc folder and pull the Training GitHub repository and the Model Zoo GitHub repository. The notebook contains all commands to connect and start training, the model zoo contains a selection of compatible models ready for training. The easiest way to customize models is by starting from the model zoo. Open a terminal and run the following commands:

mkdir tracebloc && cd tracebloc

git clone https://github.com/tracebloc/start-training.git
git clone https://github.com/tracebloc/model-zoo.git

cd start-training

Then, install the Anaconda package manager.

Create a Virtual Environment

Create a new environment, name it for example “tracebloc”:

conda create -n tracebloc python=3.9
conda activate tracebloc

Then, install requirements:

python -m pip install --upgrade pip

# Pick the extra that matches your ML framework:
pip install "tracebloc_package[pytorch]>=0.6.33"      # most common
# pip install "tracebloc_package[tensorflow]>=0.6.33" # TensorFlow
# pip install "tracebloc_package[all]>=0.6.33"        # everything

See the tracebloc Package page for all available extras.

Install and Launch Jupyter Notebook

Install Jupyter into your environments:

conda install jupyter notebook

Launch the notebook:

jupyter notebook notebooks/traceblocTrainingGuide.ipynb

1. Connect to the tracebloc workspace

Follow the instructions in the notebook to authenticate. Have your tracebloc user credentials ready:

Getting Help

For more info about available functions and methods, call the help function:

user.help()

2. Upload Model & Customize

Choose a model from the tracebloc model zoo, it is the easiest way to get started. The model zoo provides starter templates you can modify freely. Make sure the mandatory variables in your model file match the use case parameters. You can find all the necessary info from the use case description and exploratory data analysis (EDA). Alternatively, you can define your own architecture from scratch.

Model Parameters by Task

Data Type	Task	Model Parameters
Image	Classification	image_size has to match image x/y-dimensions output_classes has to match # of image classes
Image	Object Detection	image_size has to match image x/y-dimensions output_classes has to match # of object types
Image	Semantic Segmentation	image_size has to match image x/y-dimensions output_classes has to match # of object classes
Image	Keypoint Detection	image_size has to match image x/y-dimensions output_classes has to match # of object classes num_feature_points has to match # of keypoints
Tabular	Tabular Classification	output_classes has to match # of classes num_feature_points has to match # of features
Text	Text Classification	input_shape sequence_length output_classes

Example

A 3-way classification task on 224x224 images with LeNet would need the following lenet.py configuration:

import torch
import torch.nn as nn

# Mandatory variables, adapt as necessary.
framework = "pytorch"
main_class = "MyModel"
image_size = 224
batch_size = 16
output_classes = 3
category = "image_classification"


class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(3, 6, kernel_size=5, stride=1, padding=0),
            nn.BatchNorm2d(6),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.fc = nn.Linear(16 * 53 * 53, 120)
        self.relu = nn.ReLU()
        self.fc1 = nn.Linear(120, 84)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(84, output_classes)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        out = self.relu(out)
        out = self.fc1(out)
        out = self.relu1(out)
        out = self.fc2(out)
        return out

The variables at the top are mandatory for the use case workspace but the model definition is fully flexible. Open the model file from the model zoo in your editor, e.g.:

model-zoo/model_zoo/image_classification/pytorch/lenet.py

Modify the architecture however you like: add layers, change activations, swap to a different backbone entirely. The only requirement is that the mandatory variables (framework, main_class, image_size, batch_size, output_classes, category) stay consistent with the use case parameters. In case of multiple uploads, only the most recently uploaded model will be linked to the dataset.

Upload

Upload the model to the use case workspace from your notebook:

user.uploadModel("../../model-zoo/model_zoo/<task>/<framework>/model.py")

For details on model code formats, mandatory variables per framework, and pre-trained weights, see Customize Models.

3. Link Model with Dataset

Navigate to the use case and copy the “Training Dataset ID” at the center of the use case pane and enter it to establish the link

trainingObject = user.linkModelDataset('Dataset ID')

You should get “Assignment successful!” and the dataset parameters.

4. Set Training Plan

Set the experiment name and configure hyperparameters.

# Set experiment name
trainingObject.experimentName("My Experiment")

# Set training parameters
trainingObject.epochs(10)
...

# Get training plan
trainingObject.getTrainingPlan()

Get the training plan to check settings before you start the training. For a detailed list of all hyperparameter options, see Hyperparameters.

For classical, non federated and non gradient descent-based machine learning algorithms like random forests, XGBoost, SVMs, logistic regression, use simplified settings:

# Set training parameters
trainingObject.epochs(1)

# Set federated learning cycles = 1
trainingObject.cycles(1)

5. Start Training

To send the model to the workspace infrastructure and start training on the training data, run:

trainingObject.start()

Go to the tracebloc website and your use case, then navigate to the “Training/Finetuning” tab you will see your experiment. Monitor the training process hover over the learning curves to check the performance at specific epochs and cycles.

If you want to run a second experiment, overwrite parameters and re-start training with trainingObject.start().

Pause, Re-Start and Stop:

To pause, stop, or resume running experiments, click here:

Once stopped, an experiment cannot be rerun.

Note: The compute budget is set by the use case owner. Your team shares one compute budget with no per-person limit.

Submit an Experiment to the Leaderboard

Once training is complete, submit your best model to the leaderboard for evaluation on the test dataset. For the full submission flow and leaderboard details, see the Evaluate Model guide.

Inviting Others to Your Team

See the Join guide for instructions.

Next Steps

• customize models: Follow model optimisation.

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Need Help?

For more info about available functions and methods, call the help function in your notebook:

user.help()

• Email us at [e-mail]