jovian.commit(secret=False, nb_filename=None, files=[], capture_env=True, env_type='conda', notebook_id=None, create_new=None, artifacts=[])[source]

Commits a Jupyter Notebook with its environment to Jovian.

Saves the checkpoint of the notebook, captures the required dependencies from the python environment and uploads the notebook, env file, additional files like scripts, csv etc. to Jovian. Capturing the python environment ensures that the notebook can be reproduced.

  • secret (bool, optional) – Create a secret notebook on Jovian, which is only accessible via the link, and is not visible on the owner’s public profile. By default, committed notebooks are public and visible on the owner’s profile.

  • nb_filename (string, optional) – The filename of the jupyter notebook(including the .ipynb extension). This is detected automatically in most cases, but in certain environments like Jupyter Lab, the detection may fail and the filename needs to be provided using this argument.

  • files (array, optional) – Any additional scripts(.py files), CSVs that are required to run the notebook. These will be available in the files tab on Jovian .

  • capture_env (bool, optional) – If True, the Python environment(python version, libraries etc.) are captured and uploaded along with the notebook.

  • env_type (string, optional) – The type of environment to be captured. Allowed options are ‘conda’ and ‘pip’.

  • notebook_id (string, optional) – If you wish to update an existing notebook owned by you, you can use this argument to provide the base64 ID(present in the URL) of an notebook hosted on Jovian . In most cases, this argument is not required, and the library can automatically infer whether you are looking to update an existing notebook or create a new one.

  • create_new (bool, optional) – If set to True, doesn’t update the existing notebook on Jovian(if one is detected). Instead, it creates a new notebook when commit is called.

  • artifacts (array, optional) – Any outputs files or artifacts generated from the modeling processing. This can include model weights/checkpoints, generated CSVs, images etc.


Pass notebook’s name to nb_filename argument, in certain environments like Jupyter Lab and password protected notebooks sometimes it may fail to detect notebook automatically.