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
Create a Virtual Environment
Create a new environment, name it for example “tracebloc”:
conda create -n tracebloc python=3.9
conda activate tracebloc
python -m pip install --upgrade pip
pip install tracebloc_package
Install and Launch Jupyter Notebook
Install Jupyter into your environments:
conda install jupyter 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:
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
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")
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')
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()
# 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:
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
Need Help?
For more info about available functions and methods, call the help function in your notebook:
