Cheaper Sagemaker Hosting – When it comes to scientific experimentation and collaboration, people want the same thing: an easy-to-use interface to hack and improve their algorithms, a data processing system, and support for their programming language. A natural solution to these problems came in 2011 with the release of Jupyter, a versatile web application that allows you to create a notebook file that serves as a code interface, data visualization tool, and markup editor. .
There are many ways to share a static Jupyter notebook with others, such as posting it on GitHub or sharing a link with nbviewer. However, recipients can only interact with the notebook file if they have a Jupyter Notebook environment. What if you want to share a Jupyter notebook that doesn’t require any installation? Or do you want to create your own Jupyter notebook without installing anything on your local machine?
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The realization that Jupyter Notebooks have become the standard gateway for data scientists to machine learning modeling and analysis has spurred a proliferation of software products marketed as “Jupyter Notebooks in the Cloud (plus new features!”)”. In addition to memory, up to this description A few companies that offer and launch products that are either directly or partially compatible are: Kaggle Kernels, Google Colab, AWS SageMaker, Google Cloud Datalab, Domino Data Lab, DataBrick Notebooks, Azure Notebooks… In my conversations with my data science colleagues Based on this, the two most popular Jpyter cloud tools seem to be Google Colab and Amazon SageMaker.
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Google Colab is great for everything from improving your Python coding skills to working with deep learning libraries like PyTorch, Keras, TensorFlow, and OpenCV. You can create notebooks in Colab, upload notebooks, save notebooks, share notebooks, upload them to Google Drive and use what you save there, download as many directories as you like, upload Jupyter notebooks, etc. You can upload notebooks, Kaggle files directly from GitHub. , download notebooks and do anything else you want.
Visually, the Colab interface is similar to the Jupyter interface. However, working in Colab is very different from working in Jupyter Notebook:
Much has been written about troubleshooting Google Colab, so without going down the rabbit hole, here are a few things that were less than ideal. Because some items are missing from the Colab menu bar and the toolkit is kept very simple, some actions can only be performed using keyboard keys. You cannot download the brochure in other useful formats, such as an HTML page or Markdown file (although you can download it as a Python script). You can load a database for use in Colab Notebook, but it is automatically deleted when you end your session.
As for public sharing, if you choose to make your notebook public and share a link, anyone can access it without creating a Google account, and anyone with a Google account can copy it to their account. You can also allow Colab to store a copy of your notebook on GitHub or Gist and then share it there.
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As for collaboration options, you can make your notebook private, but invite specific people to view or edit it (using Google’s familiar sharing interface). You and your colleagues can edit notebooks and see each other’s changes, as well as add comments to each other (similar to Google Docs). However, your edits are not visible to collaborators in real time (there is a delay of up to 30 seconds), and if many people are editing the notebook at the same time, there is a chance that your edits will be lost. Also, you don’t share your environment with your partners (ie no coordination of which code is managed), which greatly limits the benefits of collaboration functionality.
Colab gives you access to GPU or TPU. Otherwise, Google will not provide any information about their environment. If you connect Colab to Google Drive, it gives you up to 15 GB of disk space to store your data. Sessions are closed after 60 minutes of inactivity, but they can last up to 12 hours.
Colab’s main strength is that it’s easy to get started because most people already have a Google account, and it’s easy to share notebooks because the functionality is similar to Google Docs. However, laborious keyboard keys and difficulties in working with databases are significant disadvantages. The ability to collaborate on the same notebook is useful; but less useful because you don’t share the environment and you can’t collaborate in real time.
Amazon SageMaker is a fully managed machine learning service that helps data scientists and developers quickly and easily build and train models, then deploy them directly into a production-ready environment. It provides a built-in Jupyter notebook instance for easy database access for learning/analysis, so you don’t have to manage servers. It also provides standard ML algorithms designed to work efficiently with large amounts of data in a distributed environment. With native support for your own algorithms and frameworks, Amazon SageMaker offers flexible distributed learning capabilities that fit your unique workflow.
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” contains the Jupyter Notebook itself, all notebooks, supporting scripts, and other files. There is no need to link to this instance (you really can’t, even if you wanted to) or configure it in any way. Everything is already prepared for you to create a new workbook that you can use to collect and organize data, define a model, and start the learning process. All processing, display of calculation examples, data movement, etc. is literally invoked in a single function call. This elegant approach describes a unique way of defining models and processing data.
SageMaker is built on top of other AWS services. Laptops, training machines, and deployment machines are common examples of EC2 running Amazon Machine Images (AMI). And data (as well as results, checkpoints, logs, etc.) is stored in S3 object storage. If you work with images, videos or large amounts of data, this can be a problem. The trick is to upload all the data to S3. When you configure training, you tell SageMaker where to find your data. SageMaker then automatically downloads data from S3 for each training instance before starting the training. Every time. FYI, it takes about 20 minutes to download a 100GB video. This means that you should wait at least 25 minutes before the start of the training. Good luck with your styling! On the other hand, when all the initial testing is done elsewhere and your model is already polished, the training experience is much easier. Easily upload your data to S3 and get temporary results as well.
Another factor to consider is price. Sample notebooks can be very inexpensive, especially when there is no need to transfer data. On the other hand, tutorials can easily burn a hole in your pocket. Check here for all pricing, as well as a list of regions where SageMaker is already launched.
Saturn Cloud is the new kid on the block that caters to data scientists who aren’t interested in setting up infrastructure but are interested in doing data science with ease. In particular, the platform helps manage Python environments in the cloud.
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You can start with the free level after signing up for an account. In the control panel, you can create a Jupyter Notebook for the project by selecting the disk space and size of your machine. Processing meets the requirements of many data science projects. You can also set an automatic deadline for your project, which prevents your project from closing due to inactivity.
Sharing notebooks through Saturn Cloud is easy. I did a previous project that explored the Instacart Market Basket Analysis problem and you can see the public notebook here: https://www.saturncloud.io/yourpub/khanhnamle1994/instacart-notebooks/notebooks/Association-Rule-Mining. ipnb. I especially liked how the block code and visualizations were rendered without any fuss, as we’ve seen in Google Colab notebooks. This seems to be the intended report. I also like the “Run on Saturn” option where users can go and click to run this code without having to enter the code.
Overall, using Saturn Cloud makes it easy to share laptops with other friends without the hassle of making sure they have all the right libraries installed. This sharing power surpasses Google Colab.
Also, for those who run out of memory and run out of laptops, it allows you to transfer virtual machines to memory and RAM, you only pay for what you use. . This cost association is a huge advantage compared to Amazon SageMaker.
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