Welcome to Jovian!¶

Jovian is a platform that helps data scientists and ML engineers:

Track & reproduce data science projects
Collaborate easily with friends/colleagues, and
Automate repetitive tasks in their day-to-day workflow.

Getting Started¶

Learn more about installing Jovian python library and some of the core features of Jovian.

Run this command in your terminal:

pip install jovian -q --upgrade

Installation¶

You can do this from the terminal:

pip install jovian -q --upgrade

Or directly within a Jupyter notebook.

!pip install jovian -q --upgrade

Uploading your work to Jovian¶

It’s really easy to get started with Jovian!

Step 1: Install the `jovian` python library¶

You can do this from the terminal, or directly within a Jupyter notebook.

!pip install jovian -q

Step 2: Import the library¶

import jovian

Step 3: Run jovian.commit¶

After writing some code, running some experiments, training some models and plotting some charts, you can save and commit your Jupyter notebook.

jovian.commit()

Here’s what jovian.commit does:

It saves and uploads the Jupyter notebook to your Jovian account.
It captures and uploads the python virtual environment containing the list of libraries required to run your notebook.
It returns a link that you can use to view and share your notebook with friends or colleagues.

NOTE: When you run jovian.commit for the first time, you’ll be asked to provide an API, which you can find on your Jovian account.

Reproducing uploaded notebooks¶

Once a notebook is uploaded to Jovian, anyone (including you) can download the notebook and it’s Python dependencies by running jovian clone <notebook_id> command on the Linux/Mac terminal or Windows Command Prompt. Try clicking the ‘Clone’ button at the top of this page to copy the command (including notebook ID) to clipboard.

pip install jovian --upgrade
jovian clone 903a04b17036436b843d70443ef5d7ad

Once cloned, you can enter the directly and setup the virtual environment using jovian install.

cd jovian-demo
jovian install

Jovian uses conda internally, so make sure you have it installed before running the above commands. Once the libraries are installed, you can activate the environment and start Jupyter in the usual way:

conda activate jovian-demo
jupyter notebook

In this way, Jovian seamlessly ensures the end-to-end reproducibility of your Jupyter notebooks.

Updating existing notebooks¶

Updating existing notebooks is really easy too! Just run jovian.commit once again, and Jovian will automatically identify and update the current notebook on your Jovian account.

# Updating the notebook
jovian.commit()

Jovian keeps track of existing notebooks using a .jovianrc file next to your notebook. If you don’t want to update the current notebook, but create a new notebook instead, simply delete the .jovianrc file. Note that if you rename your notebook, Jovian will upload a new notebook when you commit, instead of updating the old one.

If you run into issues with updating a notebook, or want to replace a notebook in your account using a new/renamed notebook, you can provide the notebook_id argument to jovian.commit.

jovian.commit(notebook_id="903a04b17036436b843d70443ef5d7ad")

Getting new changes on cloned notebooks¶

Once a notebook has been updated, the new changes can be retrieved at any cloned location using the jovian pull command.

cd jovian-demo # Enter cloned directory
jovian pull    # Pull the latest changes

Development and Testing¶

To run the tests, run the following command in the project directory

python -m unittest discover

["-v" for verbose]

Coming Soon¶

Callbacks for Tensorflow, Keras, PyTorch and FastAI to record hyperparameters and metrics automatically
Full support for Windows, Python 2.7+, non-Anaconda environments and .py script files
Real time monitoring and email/Slack notifications for long running training jobs
Check out and reproduce tracked experiments on any machine with a single command

For feedback, suggestions and feature requests, drop us a line at hello@jvn.io or create a ticket in the issues tab .

Commit¶

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, capture the required dependencies from the python environment and uploads the notebook, env file, additional files like scripts, csv etc. to https: // www.jvn.io . Capturing the python environment ensures that the notebook can be reproduced and executed easily using the ** {links to reprodue notebooks}.**

Parameters

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.

Attention

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

Log Dataset, Hyperparams & Metrics¶

jovian.log_dataset(data, verbose=True)[source]¶

Record dataset details for the current experiment

Parameters

data (dict) – A python dict or a array of dicts to be recorded as Dataset.
verbose (bool, optional) – By default it prints the acknowledgement, you can remove this by setting the argument to False.

Example

import jovian

data = {
    'path': '/datasets/mnist',
    'description': '28x28 images of handwritten digits (in grayscale)'
}
jovian.log_dataset(data)

jovian.log_hyperparams(data, verbose=True)[source]¶

Record hyperparameters for the current experiment

Parameters

data (dict) – A python dict or a array of dicts to be recorded as hyperparmeters.
verbose (bool, optional) – By default it prints the acknowledgement, you can remove this by setting the argument to False.

Example

import jovian

hyperparams = {
    'arch_name': 'cnn_1',
    'lr': .001
}
jovian.log_hyperparams(hyperparams)

jovian.log_metrics(data, verbose=True)[source]¶

Record metrics for the current experiment

Parameters

data (dict) – A python dict or a array of dicts to be recorded as metrics.
verbose (bool, optional) – By default it prints the acknowledgement, you can remove this by setting the argument to False.

Example

import jovian

metrics = {
    'epoch': 1,
    'train_loss': .5,
    'val_loss': .3,
    'acc': .94
}
jovian.log_metrics(metrics)

Command Line Commands¶

Initialize¶

Requests for a API Key for a new user, can find the key at Jovian. By clicking on API key button, key will be copied to the clipboard.

$ jovian init

Clone a Notebook¶

Clone a notebook form Jovian, by clicking on the Clone button of a notebook repo the whole clone command will be copied to the clipboard.

$ jovian clone {notebook_id}

Pull the latest Notebook¶

Pull the latest version of the notebook, use the command in a cloned repository or from a repository where you have committed to jovian.

$ jovian pull

Caution

Make sure the changes are committed if needed, pull overwrites the current notebook.

Install the required dependencies¶

Install all the dependencies required to the the cloned notebook, use the command in a cloned repository.

$ jovian install

Important

The above command prompts ` Please provide a name for the conda environment [{env_name}]: `

Press enter to install the dependencies to env_name (base env if the content of the square brackets is empty) else provide the env name in the prompt.

Version¶

Displays the current installed version of jovian library.

$ jovian version

Enable or Disable Jupyter Notebook Extension¶

By default, the jovian jupyter extension is enabled.

$ jovian enable-ext

$ jovian disable-ext

Note

The changes are observed when the webpage of the notebook is refreshed.

Fastai Callback¶

class jovian.callbacks.fastai.JovianFastaiCallback(learn: fastai.basic_train.Learner, arch_name: str)[source]¶

Fastai callback to automatically log hyperparameters and metrics.

Parameters

learn (Learner) – A learner object reference of your current model.
arch_name (string) – A name for the model you’re training.

Example

from jovian.callbacks.fastai_callback import FastaiCallback

jvn_cb = FastaiCallback(learn, 'res18')
learn.fit_one_cycle(5, callbacks = jvn_cb)

Tutorial

Visit this for a detailed example on using the keras callback, also visit the Records tab to see all the logs of that notebook logged by the callback.

Keras Callback¶

class jovian.callbacks.keras.JovianKerasCallback(reset_tracking=True, arch_name='', every_epoch=False, notify=False)[source]¶

Keras Callback to log hyperparameters and metrics during model training.

Parameters

reset_tracking (string, optional) – Will clear previously tracked hyperparameters & metrics, and start a fresh recording. Defaults to True.
arch_name (string, optional) – A name for the model you’re training.
every_epoch (bool, optional) – Whether to record losses & metrics for every epoch or just the final loss & metric. Defaults to False.
notify (bool, optional) – Whether to send notification on slack when the training ends. Defaults to False.

Example

from jovian.callbacks.keras import JovianKerasCallback

# To record logs of every epoch and to notify on slack
jvn_cb = JovianKerasCallback(arch_name='resnet18', every_epoch=True, notify=True)
model.fit(x_train, y_train, ...., callbacks=[jvn_cb])

Tutorial

Visit this for a detailed example on using the fastai callback, also visit the Records tab to see all the logs of that notebook logged by the callback.

Welcome to Jovian!¶

Getting Started¶

Installation¶

Uploading your work to Jovian¶

Step 1: Install the `jovian` python library¶

Step 2: Import the library¶

Step 3: Run jovian.commit¶

Reproducing uploaded notebooks¶

Updating existing notebooks¶

Getting new changes on cloned notebooks¶

Development and Testing¶

Coming Soon¶

Commit¶

Log Dataset, Hyperparams & Metrics¶

Command Line Commands¶

Initialize¶

Clone a Notebook¶

Pull the latest Notebook¶

Install the required dependencies¶

Version¶

Enable or Disable Jupyter Notebook Extension¶

Fastai Callback¶

Keras Callback¶

Use Extension to Commit¶

Enable or Disable¶

Welcome to Jovian!¶

Getting Started¶

Installation¶

Uploading your work to Jovian¶

Step 1: Install the jovian python library¶

Step 2: Import the library¶

Step 3: Run jovian.commit¶

Reproducing uploaded notebooks¶

Updating existing notebooks¶

Getting new changes on cloned notebooks¶

Development and Testing¶

Coming Soon¶

Commit¶

Log Dataset, Hyperparams & Metrics¶

Command Line Commands¶

Initialize¶

Clone a Notebook¶

Pull the latest Notebook¶

Install the required dependencies¶

Version¶

Enable or Disable Jupyter Notebook Extension¶

Fastai Callback¶

Keras Callback¶

Use Extension to Commit¶

Enable or Disable¶

Step 1: Install the `jovian` python library¶