Jovian: The platform for all your Data Science projects¶

Jovian is a platform for sharing and collaboraring on Jupyter notebooks and data science projects. jovian-py is an open-source Python package for uploading your data science code, Jupyter notebooks, ML models, hyperparameters, metrics etc. to your Jovian account.

Installation¶

The jovian python library can be installed using the pip package manager. To install jovian via terminal or command line, run:

pip install jovian --upgrade

You can also install the jovian library directly within a Jupyter Notebook, by running the following command in a code cell:

!pip install jovian --upgrade

Caution

If you get a Permission denied error, try installing with sudo permission (on Linux/Mac).

$ sudo pip install jovian --upgrade

Another alternative is to try installing with the --user flag, but you’ll need to ensure that the target directory is added to your system PATH.

$ pip install jovian --upgrade --user

Once the installation is complete, you can start uploading Jupyter notebooks to Jovian.

Configuration (for Jovian Pro users only)

If you are a Jovian Pro user, run the following commands on the terminal (or command line) to connect the jovian library with your company’s internal Jovian Pro site:

jovian configure

You can also do this directly within a Jupyter notebook, by executing the following inside a code cell:

import jovian
jovian.configure()

The above command prompts for the following information:

Organization ID: The Organization ID provided by your company for authentication. E.g. if you are accessing Jovian Pro at https://mycompany.jovian.ai , your organization ID is mycompany.
API key: You’ll get the API key when you’re logged in to your organization’s Jovian Pro site. By clicking on the API key button, the key will be copied to clipboard.

Note

You need to run jovian configure or jovian.configure() only once after installation. Your credentials are cached in the ~/.jovian directory on your computer. You can run jovian reset to clear this configuration.

You can learn more about Jovian Pro here, or start uploading Jupyter notebooks to Jovian in the next section.

Uploading Jupyter Notebooks to Jovian¶

Jovian allows you to upload and share Jupyter Notebook instantly with a single command, directly within Jupyter. Make sure you’ve completed the installation before reading further.

Uploading Notebooks¶

Step 1: Import jovian by running the following command within a Jupyter notebook.

import jovian

Step 2: After writing some code, running some experiments, training some models and plotting some charts, you can save and commit your Jupyter notebook.

jovian.commit()

When you run jovian.commit for the first time you’ll be asked to provide an API key, which you can get from your Jovian (or Jovian Pro) account.

Here’s what jovian.commit does:

It saves and uploads the Jupyter notebook to your Jovian (or Jovian Pro) 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.

For more features of jovian.commit and API reference visit Commit.

Attention

In certain environments like JupyterLab and password protected notebooks, jovian may not be able to detect the notebook filename automatically. In such cases, pass the notebook’s name as the nb_filename argument to jovian.commit.

Import notebooks from URL¶

Create a New Notebook imported from any hosted URL which points to a .ipynb file (Github/Gitlab/….) new notebook dropdown

Example: Retrieving raw URL from a notebook uploaded to Github. raw url from github

Benefits of Jovian¶

Easy sharing and collaboration: Just copy the link to share an uploaded notebook with your friends or colleages. Your notebooks are also visible on your profile page, unless you mark them Secret. You can also add collaborators and let others contribute to your project (learn more).

Cell-level comments and discussions: Jovian’s powerful commenting interface allows your team to discuss specific parts of a notebook with cell-level comment threads. Just hover over a cell and click the Comment button. You’ll receive an email when someone comments on your notebook, or replies to your comment.

End-to-end reproducibility: Jovian automatically captures Python libraries used in your notebook, so anyone (including you) can reproduce your work on any computer with a single command: jovian clone. You can also use the ‘Run’ dropdown on the Jovian notebook page to run your notebooks on free cloud GPU platforms like Google Colab, Kaggle Kernels and BinderHub.

This is just a small selection of features that Jovian offers. Continue reading by clicking the Next -> button to learn more, or use the sidebar to jump to a specific section.

Reproducing uploaded notebooks¶

An uploaded notebook on Jovian can be reproduced in any other machine. Following are the steps involved to reproduce a notebook.

Clone¶

Visit the link of the uploaded notebook.
Click on the Clone button, to copy the notebook’s clone command to the clipboard.
Paste the command in the terminal, in the directory where you want to clone the notebook project and then run the command.

The copied command will be of the the following format

jovian clone <username/project-title>

Install¶

Jovian captures the original python environment of the notebook, which make it easier to reproduce the notebook by installing all the required dependencies. The following commands uses Anaconda to install all the required packages, make sure that conda is installed.

Once the notebook is cloned, it would have created a folder with the name of the notebook project.

Move into that directory.

cd jovian-demo

Then run

jovian install

The above command prompts for a virtual environment name where it will install all the required packages. By default it will have the original environment name in the square brackets, just click enter key to retain the name else specify the environment name.

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

Note

You have to own the notebook or have to be a collaborator to commit changes to the same notebook project. If not you can commit the cloned notebook with any changes to your Jovian profile as a new notebook project.

Pull¶

If there are any new versions uploaded after you have cloned the notebook by any of the collaborator. You can use pull to get all those changes.

Move to the cloned directory and run

jovian pull

Attention

Beware any uncommitted changes will be lost during the process of jovian pull. When you pull the notebook it will be a duplicate of the latest version of the notebook on Jovian.

Fork¶

A fork is a copy of a notebook. Forking a notebook allows you to freely experiment with changes without affecting the original notebook.

When you clone a notebook from Jovian, it creates a local copy of the notebook in your machine. You can fork a notebook instead, to create a copy in your Jovian profile.

Forking a notebook

This procedure assumes that the notebook version you’re trying to Fork is public, or shared with you if it is secret/private. See Collaboration section for more details.

Forking a notebook will save a copy of the Notebook in your Jovian profile.

Visit the notebook version page that you want to Fork.
Click on the Fork button.
This will create a copy of the notebook in your profile, and you will be redirected to the forked version of the Notebook.

Run notebooks online¶

Execute notebooks on your browser without the need of local setup. Run any notebook uploaded to Jovian with just a click.

Binder [CPU only instances]
Colab [GPU or TPU instances]
Kaggle [GPU or TPU instances]

Run on Binder¶

Click Run on Binder
You may have to wait few minutes while it installs initial dependencies.

Notebooks with conda dependency present will automatically install them to the instance. It may take few more minutes while installing project related dependencies.
Upon successful launch, you can expect similar notebook instance ready for execution.

Important

Please use the service judiciously, quit when execution is completed. These instances will shut down with inactivity, please use jovian.commit to save your progress.

Run on Colab¶

Run GPU/TPU instances on browsers with integration of Jovian and Google Drive.
Step 1: Create a jovian notebook or use a existing project (Required)
- To create a notebook click on New Notebook on your profile. You can also use the “Upload Notebook” option or “Duplicate” and existing notebook.
Step 2: Click Run on Colab
- Once you add new notebook or visit a existing notebook, you’ll be able to run it. Click Run on Colab
Step 3: Integrate Google Drive to Jovian
- A modal would pop up, choose Authorize and Run on Colab(only this process supports jovian.commit), this would take to you to a page when your integrate Google Drive with Jovian only for the first time. Authorize with the required Google Email ID.
- All the notebooks that you run will be saved on your drive under the email id that you authorize and the same file is picked up while committing back.
- After successful authorization you’ll be redirected to the Colab page which is private and only the authorized email id has the permissions to view/edit it.
Step 4: Executing cells on colab
- We have embedded first cell which has all the necessities required for jovian.commit to work including installation. Please retain this cell and run it.
- You can carry forward with other cells after this
Step 5: jovian.commit

jovian.commit(project="python-pandas") # project parameter is required(existing or new)

What to do if you already have Colab notebook and want to commit it ?¶

Currently we don’t have support for this, but there is a way to do it.
Step 1: Download .ipynb from Colab
Step 2: Upload it to Jovian from Webapp
- That's it. Once you have it on Jovian, the first flow can be used.

Why jovian.commit on Local Jupyter, Binder, Kaggle is simpler than Colab?¶

Colab uses their own version of notebook and have restrictions on file usage, so we need a integration with Google Drive to pick the file.
All Colab notebooks are files under your Google Drive

Is it okay to give file permissions to Jovian?¶

Yes, we only ask for permission to write new files and have access to edit the files that we create. So we don’t have access to any other files on your Google Drive.
All the files that we create are the notebooks that you do Run on Colab
We have a parent directory named Jovian you can delete this if you’re running out of space. Or delete more specifically inside this directly, you’ll find folders username/notebook-project

Run on Kaggle¶

Click Run on Kaggle and you will be redirected to Kaggle
Here you can choose if you want run a CPU only/GPU/TPU instance, then click on Create.
Make sure to you have internet toggle switched On and in required hardware accelerator. All these information is available on the right tab.

Important

Internet and GPU instance options are only available after a mobile number verification.

Notebook versioning and diffs¶

Version control¶

If you’re used to creating many duplicate versions of notebooks with slight modifications and long file names. Look no further, Jovian will be your version control for notebooks.

jovian.commit records all the versions under same notebook project. So, each change can be a version by author and collaborators which can be easily toggled in the website

Note

You have to own the notebook or have to be a collaborator to commit changes to the same project notebook. If not you can commit any changes made to your profile as a new notebook.

View Differences¶

All the versions are comparable, you can view additions, deletions made among any 2 versions of the notebook and also hide/show common part of the code.

How to view the differences?

Commit different versions and visit Jovian .
Click on Version drop down on the right top corner.
Click on Compare Versions
Select any 2 versions with the use of check boxes and click on View Diff button.

There are more things to be compared, but first let’s add more content to the notebook to understand all the parameters that can be compared. Click on Next to follow through.

Attaching files and model outputs¶

As seen in the previous section by committing, source code and environment files are captured & uploaded. More files can be attached to the notebook such as files with helper code, output files/model checkpoints that the notebook is generating.

How to attach files?¶

jovian.commit(files=[], outputs=[])

What to include in the `files` argument?¶

The type of files which is required to run the notebook.

Helper code (.py)
Some input CSVs

What to include in the artifacts argument?¶

Any type of outputs that the notebook is generating.

Saved model or weights (.h5, .pkl, .pth)
Outputs, Submission CSVs
Images outputs

Where to search for the files after committing?¶

All the attached files are listed under Files Tab.

Files can be:

Renamed
Downloaded
Deleted
View Raw
Uploaded

Tracking Datasets, Hyperparameters and Metrics¶

Spreadsheets is one of the ways to track information & results of multiple ML experiments. However, using spreadsheets can be tiresome and non-intuitive without the context of the code.

Jovian makes its easy for anyone to track information about datasets, hyperparameters and metrics which are associated with each version of the your experiment in notebooks. Its also displays these information version-by-version of your notebook under single UI.

These information of a notebook are all added to Records Tab where you can toggle and view each version’s log.

Dataset¶

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

Hyperparameters¶

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

Metrics¶

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

The input to any of these can be a python dict . You can add custom parameters that are related to your experiment and have it record values manually, or automate it to record the values of a variable in a loop. Visit this page for these logging API reference.

We have callbacks for keras and fastai to automatically record hyperparams and metrics check it out.

Reset¶

If you’re not satisfied with some experiment and want to discard the current recorded logs before a commit. Use

jovian.reset()

Click Next to look at how to compare all of these information of all the versions.

Comparing and Analyzing experiments¶

Once you have more than one versions of a notebook, you will be able to use Compare Versions present in the Version dropdown on the top right corner.

Here you can observe all types of information about all of your versions.

Title
Time of Creation
Author
All the parameters logged under dataset.
All the parameters logged under hyperparameters.
All the parameters logged under metrics.
Notes (for author and collaborators add extra notes)

Sort¶

You can sort any column or a sub-column (For ex: accuracy or any other metric, date of creation etc.) by clicking on the column header.

Show, Hide and Reorder columns¶

You can create a custom view to analyse & compare your choice of parameters. Click on Configure button and then tick on the checkboxes to create a customized view. Click and drag the elements to reorder them based on your preference.

Add notes¶

You can add notes to summarize the experiment for reference or for collaborators to refer to.

View Diff between specific versions¶

Select any of the 2 versions by ticking the checkbox next to each version-row of the compare table which can be seen when you hover over any row. Click on View Diff button to view the additions and deletion made.

Archive/Delete versions¶

Select version/versions by ticking the checkbox of the row/rows. This enables both Archive and Delete ready for the respective actions.

Filter¶

By default all the archived versions are hidden, you can display them by enabling Show Archived in Filter dropdown.

Collaborating on Jovian projects¶

Jovian allows you to add collaborators to work with you on a ML Project.

How to add collaborators?¶

Click on Share button of the notebook and add them by their username or email id registered with Jovian (you can add a non jovian user email id as well to send an invite).

This will allow the contributors to be able to commit changes to the same notebook project. The experiments by all the collaborators will also show up in the compare table tab.

Comment on individual code cells¶

Users can comment on any code cells individually and maintain that thread to have specific discussion about a part of the source code with context.

Maintain public, secret and private notebooks¶

You can find the option to Public, Secret and Private in the settings for each notebook.

Public : These notebooks are visible on your public profile and accessible to all.
Secret : These notebooks are hidden from your public profile but anyone with the link can access the notebook.
Private : These notebooks are also hidden from your public profile and are only accessible to the owner and collaborators.

Note that Private and Secret notebooks will still be visible when you’re viewing your own profile. To hide a notebook from your own profile you can archive it.

Embed Jupyter Notebooks with Jovian¶

If you’re a blogger who takes screen snips from your Jupyter Notebooks to attach to your blog or want to showcase your wonderful notebooks on a website and wondering how to embed one. We have heard your worries and are here with this feature, any notebook on Jovian is embeddable.

We got you covered if you want to embed any of the following.

Markdown cell
Code cell with output
Code cell without output
Just the output cell
or Whole Notebook

Live Demo¶

Before seeing how to to embed, have a preview of the embed right here.Below we have embedded a Jupyter notebook in our docs page, using iframe. You can interact with the notebook like copy the source code of a cell, copy a image output, scroll each cells etc.

Customize options and preview¶

Go ahead if u need more customization like only output cell and preview the embed below.

Copy and Embed¶

Copy Embed Link for sites which support oEmbed¶

Medium and Reddit support oEmbed so if you’re writing a blog and want to add snippets with or without output charts or helping someone on reddit thread you can use this.

Just copy and paste the embed link, here is a example. Click on the image to visit the medium blog where snippets are added.

Copy iframe code and add it to a website¶

If you’re building a profile website to showcase your projects or academic institution/community/meetup website making a resources page this is right for you.

Just copy and paste the iframe code in the website’s code, here is a example. Click on the image to visit the a example Github Page where the whole notebook is embedded.

Slack Notifications¶

Get notifications from your training experiment and stay updated with all the milestones of your code. No more watching the progress bar of your fit function to keep track of your model training. Use the same integration to get notification about other activities on Jovian.

Connect to a Slack Workspace¶

Visit Jovian and click on the Connect Slack. You’ll be redirected to Slack Webpage.

Choose a workspace from the top right corner and a channel to integrate our Slack app. By clicking on Allow, integration will be completed and will get a acknowledgement on the selected channel, this is where you’ll be getting all your notifications.

Important

We suggest you to create your own Slack Workspace so that you won’t spam with notifications on a public workspace. Or you can choose your DM instead if you don’t want to create a new workspace.

Send Notifications from your script¶

This will be helpful to get updates on while training a model. You can send any python dict or string, it can be when some milestones are reached or information about the metrics(accuracy, loss …).

We have this integrated to our callbacks to get automated notifications about the metrics, check out Callbacks Section.

For API documentation check out Jovian Slack Notify

Integration Preferences¶

You can customize on what notifications you get to your Slack. To update the preferences visit Jovian Settings or you go to your Profile Dropdown on the top right corner and click on Settings.

Adding Topics to your Notebooks¶

Jovian allows you to add topics to your notebooks. Adding topics to your notebooks helps other people find and contribute to your projects easily. You can add topics related to your project’s intended purpose, subject area, frameworks/languages used, or other important qualities that you find useful.

About Topics¶

With topics, you can explore notebooks in a particular subject area, find projects to contribute to, discover new solutions to a specific problem, or simply explore the frameworks that you love in action. Topics appear on the main page of a notebook. You can click a topic name to see related list of other notebooks tagged with that topic.

To browse the most used topics, go to https://jovian.ai/explore.

How to add Topics in your notebooks?¶

1. Click on Add Topics button in the Notebook page.

Add Topics button in Notebook landing page

2. Type the topic you want to add to your notebook, then type a space. Only lowercase letters, digits and hyphens are allowed in a topic name.

3. After you’re done adding topics, click Save.

Important

Collaborators in your project will also be able to Add/Modify topics.

Explore Topics¶

Public, secret and private notebooks can have topics, although you will only see private/secret notebooks that you have access to in topic search results.

To explore more notebooks tagged with a topic, simply click on a topic badge that appears in the Notebook page.

You can search for notebooks that are associated with a particular topic. You can also search for a list of topics on Jovian. Change the URL on topics page with a comma separated list of topics.

Examples:

Topics Page

Featured Notebooks & Collections¶

Jovian provides you with the feature to highlight your Notebooks & Collections on profile overview. Pin your best projects to your profile, so that other people can view them easily. “Pinned Notebooks/Collections” always stays at the top among other projects.

Pin your Notebooks/Collections:¶

For each notebook and collection you have been provided with the option to Pin it.

Note

You cannot pin private/secret notebooks and private collections.

Note

You can pin maximum 6 Notebooks/Collections at a time.

View featured Notebooks/Collections:¶

Navigate to Overview section after you have pinned the notebook/collection.There you see your featured Notebooks and Collections.

Note

If no notebooks/collections are pinned, we’ll show 6 most recent notebooks/collections on your profile.

How to remove Notebooks/Collections from Featured Section?¶

If you want to remove a particular Notebook/Collection from feature , simply Unpin them like shown below.

Git Integration¶

jovian.commit automatically performs git commit if the current notebook/script is in a git repository, as git_commit is True by default and works only inside a git repository.

Use git_message parameter to give a different commit message to git, else it will take jovian’s commit message by default.

jovian.commit(message="jovian version commit message",
              git_message="git commit message")

Jovian also generates a link to the git commit associated to each jovian commit versions and is accessible with a button on the notebook linking to github/gitlab.

Important

Jovian does not perform git push, so if the asscociated link is not available then you’ll have push your repo.

Submit assignments directly from jovian library¶

Commit and submit assignments for Courses hosted on Jovian directly from Jupyter notebook. All you need is an assignment code (available in the respective assignment pages) that you need pass to jovian.submit.

Example:

import jovian

jovian.submit(assignment="zero-to-pandas-a1")

Incase you just need to submit a notebook already uploaded to Jovian, you can pass the notebook_url to just make the submission without committing the current notebook.

Example:

import jovian

jovian.submit(assignment="zero-to-pandas-a1",
              notebook_url="https://jovian.ai/PrajwalPrashanth/assignment")

Submit from Kaggle Kernels¶

jovian.submit works well for Local Jupyter notebooks/Colab/Binder, but it is not completely supported for Kaggle Kernels. Where you would have to do jovian.commit to commit and then do jovian.submit with the URL from jovian.commit.

Example:

import jovian

jovian.commit(....) # returns and prints the URL of the committed notebook

jovian.submit(assignment="zero-to-pandas-a1",
              notebook_url=<notebook_url_taken_from_commit>)

Jovian Pro¶

Schedule a meeting for a Demo and discussion with Aakash NS (CEO, Jovian).

Please contact us at hello@jovian.ai

Commit¶

jovian.commit(message=None, files=[], outputs=[], environment='auto', privacy='auto', filename=None, project=None, new_project=None, git_commit=False, git_message='auto', **kwargs)[source]¶

Uploads the current file (Jupyter notebook or python script) 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.

Parameters

message (string, optional) – A short message to be used as the title for this version.
files (array, optional) – Any additional scripts(.py files), CSVs etc. that are required to run the notebook. These will be available in the files tab of the project page on Jovian
outputs (array, optional) – Any outputs files or artifacts generated from the modeling processing. This can include model weights/checkpoints, generated CSVs, output images etc.
environment (string, optional) – The type of Python environment to be captured. Allowed options are ‘conda’ , ‘pip’, ‘auto’ (for automatic detection) and None (to skip environment capture).
privacy (bool, optional) –
Privacy level of the project (if a new one is being created).
- ’auto’ - use account level settings. Defaults to ‘public’
- ’public’ - visible on profile and publicly accessible/searchable
- ’secret’ - not on profile only accessible via the direct link
- ’private’ - only for the accessible to owner and collaborators
This argument has no effect on existing project. Change the privacy settings of a existing notebook on the webapp.
filename (string, optional) – The filename of the current Jupyter notebook or Python script. This is detected automatically in most cases, but in certain environments like Jupyter Lab or password protected notebooks, the detection may fail and the filename needs to be provided using this argument.
project (string, optional) – Name of the Jovian project to which the current notebook/file should be committed. Format: ‘username/title’ e.g. ‘aakashns/jovian-example’ or ‘jovian-example’ (username of current user inferred automatically). If the project does not exist, a new one is created. If it exists, the current notebook is added as a new version to the existing project, if you are a owner/collaborator. If left empty, project name is picked up from the .jovianrc file in the current directory, or a new project is created using the filename as the project name.
new_project (bool, optional) – Whether to create a new project or update the existing one. Allowed option are False (use the existing project, if a .jovianrc file exists, if available), True (create a new project)
git_commit (bool, optional) – If True, also performs a Git commit and records the commit hash. This is applicable only when the notebook is inside a Git repository.
git_message (string, optional) – Commit message for git. If not provided, it uses the message argument

Attention

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

Log Dataset, Hyperparams & Metrics¶

jovian.log_dataset(data_dict=None, verbose=True, **data_args)[source]¶

Record dataset details for the current experiment

Parameters

data_dict (dict, optional) – A python dict to be recorded as dataset.
verbose (bool, optional) – By default it prints the acknowledgement, you can remove this by setting the argument to False.
**data_args (optional) – Instead of passing a dictionary, you can also pass each individual key-value pair as a argument (see example below)

Example

import jovian

path = 'data/mnist'
description = '28x28 images of handwritten digits (in grayscale)'

jovian.log_dataset(path=path, description=description)
# or
jovian.log_dataset({ 'path': path, 'description': description })

jovian.log_hyperparams(data_dict=None, verbose=True, **data_args)[source]¶

Record hyperparameters for the current experiment

Parameters

data_dict (dict, optional) – A python dict to be recorded as hyperparmeters.
verbose (bool, optional) – By default it prints the acknowledgement, you can remove this by setting the argument to False.
**data_args (optional) – Instead of passing a dictionary, you can also pass each individual key-value pair as a argument (see example below)

Example

import jovian
jovian.log_hyperparams(arch='cnn', lr=0.001)
# or
jovian.log_hyperparams({ 'arch': 'cnn', 'lr': 0.001 })

jovian.log_metrics(data_dict=None, verbose=True, **data_args)[source]¶

Record metrics for the current experiment

Parameters

data_dict (dict, optional) – A python dict to be recorded as metrics.
verbose (bool, optional) – By default it prints the acknowledgement, you can remove this by setting the argument to False.
**data_args (any, optional) – Instead of passing a dictionary, you can also pass each individual key-value pair as a argument (see example below)

Example

import jovian
jovian.log_metrics(epochs=1, train_loss=0.5,
                   val_loss=0.3, val_accuracy=0.9)
# or
jovian.log_metrics({ 'epochs': 1, 'train_loss': 0.5 })

jovian.reset(*record_types)[source]¶

Reset the tracked hyperparameters, metrics or dataset (for a fresh experiment)

Parameters: *record_types (strings, optional) – By default, resets all type of records. To reset specific type of records, pass arguments metrics, hyperparams, dataset

Example

import jovian
jovian.reset('hyperparams', 'metrics')

Send Notifications to Slack¶

jovian.notify(data, verbose=True, safe=False)[source]¶

Sends the data to the Slack workspace connected with your Jovian account.

Parameters

data (dict|string) – A dict or string to be pushed to Slack
verbose (bool, optional) – By default it prints the acknowledgement, you can remove this by setting the argument to False.
safe (bool, optional) – To avoid raising ApiError exception. Defaults to False.

Example

import jovian

data = "Hello from the Integration!"
jovian.notify(data)

Important

This feature requires for your Jovian account to be connected to a Slack workspace, visit Jovian Integrations to integrate them and to control the type of notifications.

CLI Commands¶

CLI - Command line interface, is a text-based interface to interact with your system. On Mac/Linux based OS you’ll have terminal application where these commands can be executed, on windows it’ll be named as command prompt or conda prompt can be used if Anaconda has been installed.

These commands requires installation of jovian library, if you’re working with pip/conda virtual environments make sure you activate the environment where the jovian library is installed.

These commands ensure that you can directly interact with Jovian from CLI.

clone¶

Clone a notebook hosted on Jovian:

$ jovian clone aakashns/jovian-tutorial

Or clone a specific version of notebook:

$ jovian clone aakashns/jovian-tutorial -v 10

jovian clone <username/title>

Options

-v, --version <version>¶: Version number

--no-outputs¶: Exclude output files

--overwrite¶: Overwrite existing project

Arguments

<username/title>¶: Required argument

install¶

Install packages from environment file:

$ jovian install

or, to specify environment name:

$ jovian install -n my_env

jovian install

Options

-n, --name <name>¶: Name of conda env

activate¶

Activate conda environment from environment file.

jovian activate

pull¶

Fetch the new version of notebook hosted on Jovian(inside a cloned directory):

$ jovian pull

Or fetch a specific version of a specific notebook:

$ jovian pull -n aakashns/jovian-tutorial -v 10

jovian pull

Options

-n, --notebook <notebook>¶: Notebook project (format: username/title)

-v, --version <version>¶: Version number

configure¶

Configure Jovian for Pro users.

jovian configure

set-project¶

Associate notebook (filename.ipynb) to Jovian project (username/title)

$ jovian set-project my_notebook.ipynb danb/keras-example

This will create or update the .jovianrc file in the current directory to ensure that commits inside the Jupyter notebook “my_notebook.ipynb” add new versions to the project danb/keras-example

jovian set-project NOTEBOOK PROJECT

Arguments

NOTEBOOK¶: Required argument

PROJECT¶: Required argument

commit¶

Create a new notebook on Jovian

$ jovian commit my_notebook.ipynb

jovian commit NOTEBOOK

Arguments

NOTEBOOK¶: Required argument

reset¶

Reset Jovian config.

jovian reset

add-slack¶

Connect slack to get updates.

jovian add-slack

enable-extension¶

Enable Jovian’s Jupyter notebook extension.

jovian enable-extension

disable-extension¶

Disable Jovian’s Jupyter notebook extension.

jovian disable-extension

version¶

Print installed Jovian library version.

jovian version

help¶

Print this help message.

jovian help

Fastai Callback¶

class jovian.callbacks.fastai.JovianFastaiCallback(learn: fastai.basic_train.Learner, arch_name=None, reset_tracking=True)[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 import JovianFastaiCallback

jvn_cb = JovianFastaiCallback(learn, 'res18')
learn.fit_one_cycle(5, 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.

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.

oEmbed Endpoint for embedding Jovian Notebooks¶

oEmbed is an open standard to easily embed content from oEmbed providers into your site. You can use the oEmbed standard for embedding Jovian notebooks into your website, have a look at oEmbed.com.

API Endpoint URL¶

You can use our API endpoint to request the embed code for public Notebooks, all responses are in json format. Replace {notebook-url} by your Jovian Notebook URL, or Jovian Viewer URL, or any raw .ipynb file URL:

[GET]https://jovian.ai/api/oembed.json/?url={notebook-url}&maxwidth={max-width}

Parameters:

url (String, required): The URL of the notebook or Jovian Viewer URL.
cellId (Integer, optional): Index of the cell of the notebook for cell-level embeds. If no cellId id present, whole notebook gets embedded.
maxwidth (Integer, optional): The maximum width of the embedded resource (optional). Note that the maxheight parameter is not supported. This is because the embed code is responsive and its height varies depending on its width.

Example URLs:

Jovian Notebook URLs
- Jovian Notebook
- Jovian Notebook (Version specified)
Jovian Viewer URLs
- https://jovian.ai/viewer?url={notebook-url}
Raw .ipynb file URL

Response:

{
  "title": "Jovian Viewer",
  "provider_name": "Jovian",
  "provider_url": "https://jovian.ai",
  "author_name": "Jovian ML",
  "version": "1.0",
  "type": "rich",
  "author_url": "https://github.com/JovianML",
  "height": 800,
  "width": 800,
  "html": "<iframe src='https://jovian.ai/embed?url=https%3A//jvn.storage.googleapis.com/gists/aakashns/5bc23520933b4cc187cfe18e5dd7e2ed/raw/901a9d2508bd441dbf06954c5f46bf58/movielens-fastai.ipynb' title='Jovian Viewer' height=800 width=800 frameborder=0 allowfullscreen></iframe>"
}

Jovian: Libraries and Integrations¶

Jovian integrates seamlessly with your favorite tools and libraries. Automate your workflow and boost your productivity.

Jupyter Notebook Extension¶

Now you can commit your Jupyter Notebook to Jovian with just One Click. Make sure you’ve completed the Installation before reading further.

Using Jovian Jupyter Extension¶

Once you have successfully installed jovian, a new button Commit will appear on the tool bar. When using Commit button for first time you’ll be asked to provide an API key.

You can get the API key at Jovian. Once you log in, just click on API key button, and the key will be copied to the clipboard.

Valid API key¶

If the key is valid you will be notified with the following alert.

Error with API key¶

If the entered API key is invalid you will get following error.

Successful Commit¶

Once the API key has been validated, you can start committing to Jovian by clicking Commit button. Once the Notebook has been committed successfully you will get the confirmation message with the link where the Jupyter Notebook has been uploaded to, you can use the copy button to get the link to the share the notebook.

Commit with more options¶

This makes use of jovian.commit’s parameters to enable the user to commit with preferences like private notebook, new notebook project, to add outputs and files …..

Step 1: click the dropdown menu

Step 2: choose commit with options

Note: By default the parameters are derived from jovian.commit, changes to any parameter persists after commit.

Step 3: Click on Commit to commit the notebook with following options.

Enable or Disable the extension¶

By default, the Jovian Jupyter Notebook Extension is enabled to the environment where jovian is installed.

You can also disable the extension by running the following command.

$ jovian disable-extension

To Enable the Notebook Extension, when you have manually disabled it.

$ jovian enable-extension

Jupyter Lab Extension¶

Now you can commit your Jupyter Notebook to Jovian with just One Click. Make sure you’ve completed the Installation before reading further.

Using Jovian Jupyter Lab Extension¶

Once you have successfully installed jovian, a new button Commit will appear on the tool bar. When using Commit button for first time you’ll be asked to provide an API key.

You can get the API key at Jovian. Once you log in, just click on API key button, and the key will be copied to the clipboard.

Valid API key¶

If the key is valid you will be notified with the following alert.

Error with API key¶

If the entered API key is invalid you will get following error.

Successful Commit¶

Once the API key has been validated, you can start committing to Jovian by clicking Commit button. Once the Notebook has been committed successfully you will get the confirmation message with the link where the Jupyter Notebook has been uploaded to, you can click the link to your Notebook in Jovian.

Commit with more options¶

This makes use of jovian.commit’s parameters to enable the user to commit with preferences like private notebook, new notebook project, to add outputs and files …..

Step 1: click the dropdown menu

Step 2: choose commit with options

Note: By default the parameters are derived from jovian.commit, changes to any parameter persists after commit.

Step 3: Click on Commit to commit the notebook with following options.

Enable or Disable the extension from CLI¶

You can also disable the extension by running the following command.

$ jupyter labextension disable jovian-jupyterlab

To Enable the Notebook Extension, when you have manually disabled it.

$ jupyter labextension enable jovian-jupyterlab

Github Integration¶

jovian.commit automatically performs git commit if the current notebook/script is in a git repository, as git_commit is True by default and works only inside a git repository.

Use git_message parameter to give a different commit message to git, else it will take jovian’s commit message by default.

jovian.commit(message="jovian version commit message",
              git_message="git commit message")

Jovian also generates a link to the git commit associated to each jovian commit versions and is accessible with a button on the notebook linking to github/gitlab.

Important

Jovian does not perform git push, so if the asscociated link is not available then you’ll have push your repo.

Keras Integration¶

Step 1 Import

import jovian
from jovian.callbacks.keras import JovianKerasCallback

Step 2 Pass the callback to the fit method.

# 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])

For more details visit Keras callback API reference

Step 3 Perform jovian commit

jovian.commit(message="keras callback")

Step 4 View and compare experiment logs

View all the log of a certain version is the Records Tab

Compare the results of many expriments that you have performed. For more usage of compare details visit Compare

Fastai Integration¶

Step 1 Import

import jovian
from jovian.callbacks.fastai import JovianFastaiCallback

Step 2 Pass the callback to the fit method.

learn = cnn_learner(data, resnet34)
jvn_cb = JovianFastaiCallback(learn, 'res18')
learn.fit_one_cycle(5, callbacks = jvn_cb)

For more details visit Fastai callback API reference

Step 3 Perform jovian commit

jovian.commit(message="fastai callback")

Step 4 View and compare experiment logs

View all the log of a certain version is the Records Tab

Compare the results of many expriments that you have performed.For more usage of compare details visit Compare

VS Code Integration¶

jovian.commit works for VS Code Notebooks too.

jovian.commit(message="example commit", filename="lesson1-pets")

Visit commit api reference for more commit options

PyCharm Integration¶

jovian.commit works for PyCharm notebooks too.

jovian.commit(message="pycharm commit")

Visit commit api reference for more commit options

Important

Jupyter support is provided in PyCharm’s Professional version.

Tensorflow Integration¶

Page under Construction

PyTorch Integration¶

Page under Construction

Telegram Integration¶

Page under Construction

Scikit Learn Integration¶

Page under Construction

Xgboost Integration¶

Page under Construction

SciPy Integration¶

Page under Construction

OpenCV Integration¶

Page under Construction

Apache Spark Integration¶

Page under Construction

Anaconda Integration¶

Page under Construction

Jovian: The platform for all your Data Science projects¶

Installation¶

Uploading Jupyter Notebooks to Jovian¶

Uploading Notebooks¶

Import notebooks from URL¶

Benefits of Jovian¶

Reproducing uploaded notebooks¶

Clone¶

Install¶

Pull¶

Fork¶

Forking a notebook

Run notebooks online¶

Run on Binder¶

Run on Colab¶

What to do if you already have Colab notebook and want to commit it ?¶

Why jovian.commit on Local Jupyter, Binder, Kaggle is simpler than Colab?¶

Is it okay to give file permissions to Jovian?¶

Run on Kaggle¶

Notebook versioning and diffs¶

Version control¶

View Differences¶

Attaching files and model outputs¶

How to attach files?¶

What to include in the files argument?¶

What to include in the artifacts argument?¶

Where to search for the files after committing?¶

Tracking Datasets, Hyperparameters and Metrics¶

Dataset¶

Hyperparameters¶

Metrics¶

Reset¶

Comparing and Analyzing experiments¶

Sort¶

Show, Hide and Reorder columns¶

Add notes¶

View Diff between specific versions¶

Archive/Delete versions¶

Filter¶

Collaborating on Jovian projects¶

How to add collaborators?¶

Comment on individual code cells¶

Maintain public, secret and private notebooks¶

Embed Jupyter Notebooks with Jovian¶

Live Demo¶

Open up Embed modal¶

Click on Embed button¶

Click on Embed icon on any cell¶

Customize options and preview¶

Copy and Embed¶

Copy Embed Link for sites which support oEmbed¶

Copy iframe code and add it to a website¶

Slack Notifications¶

Connect to a Slack Workspace¶

Send Notifications from your script¶

Integration Preferences¶

Adding Topics to your Notebooks¶

About Topics¶

How to add Topics in your notebooks?¶

Explore Topics¶

Topics Page

Featured Notebooks & Collections¶

Pin your Notebooks/Collections:¶

View featured Notebooks/Collections:¶

How to remove Notebooks/Collections from Featured Section?¶

Git Integration¶

Submit assignments directly from jovian library¶

Submit from Kaggle Kernels¶

Jovian Pro¶

Commit¶

Log Dataset, Hyperparams & Metrics¶

Send Notifications to Slack¶

CLI Commands¶

clone¶

install¶

activate¶

pull¶

configure¶

set-project¶

commit¶

What to include in the `files` argument?¶