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Scalable notebooks in my own AWS environment
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Excellent data science platform
Streamlining my Data Science Workflow
Seminal Development in Python
Saturn Cloud has opened an entirely new door of opportunities for both the average quantitative developer and the deep learning expert.
At my company, I have been able to explore multiple new avenues for alpha generation that would have been unfeasible prior to the scale and speed introduced by Saturn.
I look forward to seeing the array of new applications developed as Saturn fuses the rapid development speed of Python with the raw power of distributed GPU computing while trivializing DevOps in the process.
Seamless transition from local to cluster thanks to SaturnCloud
I used Saturn Cloud to run an NLP pipeline that I had started building locally (AWS t2.2xlarge) using Python (Jupyter notebooks), Dask, and SciSpacy, but which I was beginning to outgrow. Moving the code to SaturnCloud was quite painless -- all I had to do was to switch out the distributed Dask scheduler with the one provided by SaturnCloud, and re-point to S3 instead of local disk for my data. I would also like to thank the Saturn Cloud engineers, they are very professional and responsive, and without their timely help, my project would have taken much longer than it did. SaturnCloud also offers GPU machines for use with RAPIDS, and offers a Jupyter Lab environment as well. If you are using Dask and need to scale out, Saturn Cloud is a great way to do it without having to invest and set up your own cluster.
Great Product
In my opinion, this is the most best cloud hosted jupyter solution out there. The flexibility of scaling up and down as needed is great, as well as the seamless Dask integration. Not to mention the very responsive support team!
Easy way to run Dask and speed up model training
You get an integrated Jupyter Lab + Dask cluster management environment, which makes it straightforward to parallelize model training and get a big speedup. Collaboration is built-in as well.
When you absolutely, positively need to parallelize all the data
Dask is a very powerful library that allows for parallel execution of python code across essentially arbitrary compute resources. I've used dask previously on a smaller scale for things like out-of-memory processing of very large dataframes too big to fit into ram on a respectable workstation.
Dask can take almost any job and make it as much faster as you want, depending on the number of processing nodes and their network connections, and your ability to create, debug, and maintain a distributed dask cluster. The latter of these can be quite a painful challenge to overcome.
We are very happy with the service that Saturn provides as they solve both of these issues at once. Their distributed client can autoscale the number of nodes in its cluster using whatever ec2 instance type thats needed and it plays very nicely cuda, which can be quite tricky (frustrating) to properly configure.
Executing the same code across multiple nodes equipped with their own cpu/gpu/ram is what makes a supercomputer super. Saturn essentially makes it convenient to rent a python-based supercomputer with whatever desired specifications limited only by the hardware available on aws and your vpc quota.
Low maintenance, high performance
Before Saturn, I wasted a ton of time trying to manage my team's JupyterHub. What began as a fun little project quickly turned into a maintenance nightmare. Saturn eliminated all the hassle. The environment just works. Within minutes we can go from one small, basic instance to multiple 64-core servers crunching big data. What's even more exciting is Saturn keeps getting better. New and useful features keep showing up, making it easier for my team to do great work. I'm looking forward to working with Saturn for a long time to come!
Fast Setup, Easy to Use
I used Saturn Cloud for a Machine Learning project that trained a network intrusion classier using PCAP data. In a few minutes I was coding in a jupyter notebook without having to worry about data privacy, and collaboration was simple. The ease of setup and computational power available make this a great collaboration tool, and I will definitely be using again.