Quickstart using CLI
Prior steps are logging in, creating a project and a dataset instance.
Install
leapcli using the following command:pip install leapcli
Once installed successfully, the
leap command shall be available.Once the
leap CLI is installed, you can initialize your project to be synced with the Tensorleap platform. In the target project path, run the following command to initialize Tensorleap within the project:
leap init PROJECT_NAME DATASET_NAME
Replace the arguments with the corresponding value:
Args | Text |
|---|---|
PROJECT_NAME | The name of the project |
DATASET_NAME |
All the parameters are stored at the
.tensorleap/config.toml file and could be changed if needed.In order to access the platform, we must run the
leap login command within the initialized project path, as such:leap login [API_ID] [API_KEY] [ORIGIN]
The 
button within the Resources Management view.
API_ID , API_KEY and the ORIGIN, along with the full command, can easily be found by clicking the For Tensorleap to read the data and fetch it to the model for training or evaluation, we must provide a Dataset Script. This script defines the preprocessing function, input/ground_truth encoders and metadata functions.
The script should be set at the
.tensorleap/dataset.py file.Important to note that this script should be contained and not use external project dependencies!
The model integration script is located at
.tensorleap/model.py, an example script below:Tensorflow
PyTorch
from pathlib import Path
from myproject.model import build_model # import from the parent project
def leap_save_model(target_file_path: Path):
# Load your model
model = build_model()
# Save it to the path supplied as an arugment (has a .h5 suffix)
model.save(target_file_path)
Coming Soon
The
leap_save_model is automatically called by the CLI when pushing a model into the Tensorleap platform. Its purpose is to prepare and store the model in the provided target_file_path.You can validate the dataset and model scripts locally by using the following command:
leap check --all
This command will validate the scripts together with its synchronization with the Tensorleap platform.
Once everything is validated, you can push the dataset script and model to the Tensorleap platform for further evaluation/training/analysis.
To push the dataset script, use the following command from the project path:
leap push --dataset
To push the model to the project, use the following command:
leap push --model
You can also set the
branch-name, description and model-name as such:leap push --model [--branch-name=<BRANCH_NAME> [--description=<DESCRIPTION>]
[--model-name=<MODEL_NAME>]
You can now log in into the Tensorleap Platform within the UI, and find the imported model within the project, and the dataset within the Resources Management view.
You can follow the next steps to prepare the model for analysis:
- Find the imported model on the Versions view, hover your cursor over the view and clickon the right to Open Commit.
- Back on the Network view, set the Dataset Block to point to the
DATASET_NAMEDataset Instance, and connect it to the first layer. - Add the ground truth, loss and optimizer blocks, and connect them to the end of the network. More info at Dataset Block Setup and Loss and Optimizer.
- Save the version model by clicking thebutton and set the
Revision Name. This adds the new version to the Versions view. More info at Save a Version. - The model is ready for training or evaluation:Already trained model - clickfrom the top bar to inference the data and collect metrics. Re-train / train from scratch in the Tensorleap platform - click
from the top bar to train the model. More info at Evaluate / Train Model.
Last modified 8mo ago