Frequently asked questions¶
- General FAQ
- What are notebooks and why would I use them?
- How do I access Anaconda Notebooks?
- What do I have access to?
- Is Anaconda Notebooks different from Jupyter notebooks?
- Where can I get support?
- What packages are preconfigured on Anaconda Notebooks?
- Can I share my notebooks?
- How do I upload a notebook to the service?
- How do I save a notebook?
- What kind of storage does Anaconda Notebooks come with?
- Can I add more storage?
- What are the memory limits of this service?
- Can I use packages from the Professional repository in Anaconda Notebooks?
- Can I install new packages or create custom environments in Anaconda Notebooks?
- Can I use Anaconda Notebooks for work?
- I have an organization in Anaconda Nucleus. How can my team leverage Anaconda Notebooks?
- Can I control access to Anaconda Notebooks?
- I have a site license. How do I give my members access?
Jupyter Notebooks provide a web-based interface for creating and sharing computational documents. You can seamlessly mix executable code, documentation, and instructions in one portable document. Notebooks are not only a great portable learning tool, but also a highly capable vehicle for prototyping and producing data science work.
Anaconda Notebooks lets you skip setup and installation and get straight to learning and writing code.
You can access and use Anaconda Notebooks from any modern web browser and anywhere you have an internet connection.
After you have logged into your account on Anaconda Nucleus, go directly to nb.anaconda.cloud or click on “Notebooks” from the top navigation bar of Anaconda Nucleus.
With Anaconda Notebooks, you get all of the following running on our resilient and supported cloud platform, so you can use it anywhere on any device!
- A dedicated JupyterLab notebook interface
- 5GB of fast, backed-up, SSD storage
- Conda environments with the most popular python packages and the ability to create and upload your own custom environments
- Example notebooks
Anaconda Notebooks is a hosted JupyterLab service that enables you to run JupyterLab notebooks reliably online. Your dedicated JupyterLab instance comes preconfigured with persistent cloud storage, hundreds of data science packages, and a managed infrastructure.
You can get community support on our community forums. You can also contact user care via the Support Center on Anaconda Nucleus or submit a support request directly.
All packages available from the Anaconda installer are preloaded and ready to code through Anaconda Notebooks. More specifically, the service will include environments based on the most recent installers. For example,
anaconda-2022.05-py39 is the latest release of Anaconda Distribution and is the default environment within Anaconda Notebooks. As new installers are released, new environments will be available.
To see a list view of all preloaded packages, launch Anaconda Notebooks and select the
anaconda-2022.05-py39 kernel. Once the kernel is activated, enter
conda list into any notebook file.
In the Anaconda Notebooks JupyterLab interface, click Upload files in the File Browser to browse for a local
.ipynb file. Then, click Open. The notebook will appear in the left-hand menu.
You can also drag and drop a notebook from a folder on your system to the file browser to upload it.
Like most IDEs or editors, JupyterLab has the standard “Save” and “Save As…” functions that will save a notebook in your directory on our platform. You can also download a notebook file from the File menu to save it locally.
The storage provided through the notebook service is persistent Elastic Block Store (EBS) storage. EBS storage is fast, backed-up, SSD storage and supports common data science and machine learning workloads. EBS storage is generally faster and more reliable than most cloud-hosted options.
Not yet, but soon! If you’re running out of storage space, we suggest that you remove any unused notebook assets, such as extra file directories, notebook files, and custom conda environments.
On this service, each process is limited to 6GB of memory. If you exceed that, your process will be killed and you will need to restart your kernel. If you need to run much larger processes, please contact us at firstname.lastname@example.org.
Packages available from Anaconda Notebooks are a subset of packages available from the free and public repo.anaconda.com repository. Installing packages from the Professional repository via tokenized access is not currently supported.
Customers can create their own conda environments using any packages that conda can install from repo.anaconda.com. Note: Custom environments will be stored using the user’s dedicated, persistent Anaconda Notebooks storage. This ensures the custom environment will be available after the current session.
Customers accessing Anaconda Notebooks with subscription tiers Pro and above are permitted to use all Anaconda products for commercial use. However, Anaconda Notebooks alone does not provide commercial compliance to its users. Anaconda Notebooks is intended only for individual and educational purposes.
Registered customers who are part of organizations on Anaconda Nucleus can independently access Anaconda Notebooks. Access to Anaconda Notebooks is granted upon member role designation and registration.
All registered customers can access Anaconda Notebooks. Organization-level features, including user access controls, are coming soon. Stay tuned!
If you are a customer but have not yet registered your organization on Anaconda Nucleus, please refer to this documentaion on how to set up your organization and invite members.
The most common cause of errors is a lack of required package(s) installed in your environment. The default environment we provide, based on the Anaconda distribution, contains hundreds of the most common python packages for data science, but it doesn’t include everything. You may need to create a custom environment to install the package you need.
Here are a couple of steps to help resolve this:
Make sure you have the right kernel/environment selected
anaconda-<YEAR>.<MONTH>-py<PYTHON_VERSION> environments have a broad selection of packages, but you may have created a custom environment for your notebook. Separate environments are represented as “kernels” in JupyterLab. You can view and switch between available kernels by clicking the kernel name in the upper-right corner of the content pane.
List the packages available in an environment
You can see which packages are available in each environment in the terminal by running
conda env list to list available environments and then
conda list -n <ENV_NAME> to see packages in that environment.
You can run those commands directly in a code cell within your notebook just by adding a “!” to the front of the command (e.g.
!conda env list).
Create a custom environment
If none of your existing environments have the right package(s), either install the package into one of your custom environments with
conda install <PACKAGE> or create a new custom conda environment with the right packages. You can add new environments via the terminal by running
conda create --name ENV_NAME.
Once an environment is created, it will be available as a kernel for running your notebook.
You may have exceeded your CPU usage limit for the day. Our notebook instances have a limit for the maximum number of seconds fully utilizing the CPU. Once an instance hits that limit, it is not shut down, but instead given lower CPU priority and a limit to the amount of compute resources available. This limit is reset every day, so full compute access will be restored the next day.
To see current progress towards your daily quota, reference the widget in the upper right of the interface that shows current CPU usage vs. the daily limit.
To better manage your CPU usage, regularly check the Running Terminals and Kernels widget in the left sidebar to kill unnecessary kernels when you no longer need them.
You can check the status of your disk usage via the widget in the top right of the screen, which shows current usage as a percentage of the total space available.
If you are running out of space, the ability to expand your storage and upgrade to a larger tier is coming soon; for now, you will need to delete some items from your drive:
Do you have any extra notebooks or directories you can remove?
You can view and delete files from the File Browser in the upper left, or on the command line by launching a terminal.
Do you have any custom conda environments?
conda env listand see if there are any environments NOT in
- If there are, you can remove those that you don’t need any more by running
conda env remove -n <ENV_NAME>.
- Further, running
conda clean --allwill clean up the cache and some other artifacts.