Hail
Deeplearning4j
Our great sponsors
Hail | Deeplearning4j | |
---|---|---|
5 | 13 | |
934 | 13,424 | |
1.4% | 0.5% | |
9.8 | 6.5 | |
about 11 hours ago | 7 days ago | |
Python | Java | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Hail
- We're wasting money by only supporting gzip for raw DNA files
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Software engineers: consider working on genomics
I don't have any funding to hire right now, but I'm always happy to chat about the industry and my experience building Hail (https://hail.is, https://github.com/hail-is/hail), a tool widely used by folks with large collections of human sequences.
The other posters are not wrong about compensation. Total compensation is off by a factor of two to three.
However, it is absolutely possible to work with a group of top-notch engineers on serious distributed systems & compilers in service of an excellent scientific-user experience. I know because I do. We are lucky to have a PI who respects and hires and diversity of expertise within his lab.
I enjoy being deeply embedded with our users. I do not have to guess what they need or want because I help them do it every day.
I also enjoy enmeshing engineering with statistics, mathematics, and biology. Work is more interesting when so many disciplines conspire towards the end of improved human health.
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AWS doesn't make sense for scientific computing
I think this post is identifying scientific computing with simulation studies and legacy workflows, to a fault. Scientific computing includes those things, but it also includes interactive analysis of very large datasets as well as workflows designed around cloud computing.
Interactive analysis of large datasets (e.g. genome & exome sequencing studies with 100s of 1000s of samples) is well suited to low-latency, server-less, & horizontally scalable systems (like Dremel/BigQuery, or Hail [1], which we build and is inspired by Dremel, among other systems). The load profile is unpredictable because after a scientist runs an analysis they need an unpredictable amount of time to think about their next step.
As for productionized workflows, if we redesign the tools used within these workflows to directly read and write data to cloud storage as well as to tolerate VM-preemption, then we can exploit the ~1/5 cost of preemptible/spot instances.
One last point: for the subset of scientific computing I highlighted above, speed is key. I want the scientist to stay in a flow state, receiving feedback from their experiments as fast as possible, ideally within 300 ms. The only way to achieve that on huge datasets is through rapid and substantial scale-out followed by equally rapid and substantial scale-in (to control cost).
[1] https://hail.is
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Ask HN: Who is hiring? (July 2021)
Broad Institute of MIT and Harvard | Cambridge, MA | Associate Software Engineer | Onsite
We are seeking an associate software engineer interested in contributing to an open-source data visualization library for analyzing the biological impact human genetic variation. You will contribute to projects like gnomAD (https://gnomad.broadinstitute.org), the world's largest catalogue of human genetic variation used by hundreds of thousands of researchers and help us scale towards millions of genomes in the coming years. We are also developing next-generation tools for enabling genetic analyses of large biobanks across richly phenotyped individuals (https://genebass.org). In this role you will gain experience developing data-intensive web applications with Typescript, React, Python, Terraform, Google Cloud Platform, and will make use of the scalable data analysis library Hail (https://hail.is). Key to our success is growing a strong team with a diverse membership who foster a culture of continual learning, and who support the growth and success of one another. Towards this end, we are committed to seeking applications from women and from underrepresented groups. We know that many excellent candidates choose not to apply despite their capabilities; please allow us to enthusiastically counter this tendency.
Please provide a CV and links previous work or projects, ideally with contributions visible on Github.
email: [email protected]
Deeplearning4j
- Deeplearning4j Suite Overview
- Java for ML?
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Best way to combine Python and Java?
Have you considered migrating off of Python to just using JVM ML libraries then? I hear good things about Deeplearning4j, but there's quite a few.
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Anybody here using Java for machine learning?
I've gone to the linux workflow as directed in the docs and reconstructed the maven command line:
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Data Science Competition
DL4J
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Java Matrix Benchmark is Updated! See how linear algebra libraries compare for speed
Hey folks, just letting you know we see this thread and I appreciate you guys running these benchmarks. I'm not seeing any of your posts on our forums. I think I saw a notification from our examples but we do not actually monitor that. Please use: https://community.konduit.ai/ or at least the main repo dl4j issues: https://github.com/eclipse/deeplearning4j/issues and you'll get a lot more visibility. Thanks!
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Does Java has similar project like this one in C#? (ml, data)
Also, the website is now redirected to: https://deeplearning4j.konduit.ai/
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If it gets better w age, will java become compatible for machine learning and data science?
On top of this several popular projects have been built. This includes tensorflow-java and our project eclipse deeplearning4j: https://github.com/eclipse/deeplearning4j
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Matrices multiplication benchmark: Apache math vs colt vs ejml vs la4j vs nd4j
Nd4j is actively developed. The latest commit was 6 hours ago. Nd4j is part of deeplearning4j which is now owned by eclipse (but the main contributors are from a company) https://github.com/eclipse/deeplearning4j/tree/master/nd4j
What are some alternatives?
GridScale - Scala library for accessing various file, batch systems, job schedulers and grid middlewares.
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
Vegas - The missing MatPlotLib for Scala + Spark
Weka
metorikku - A simplified, lightweight ETL Framework based on Apache Spark
tensorflow - An Open Source Machine Learning Framework for Everyone
Jupyter Scala - A Scala kernel for Jupyter
Smile - Statistical Machine Intelligence & Learning Engine
Scoozie - Scala DSL on top of Oozie XML
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Summingbird - Streaming MapReduce with Scalding and Storm
Apache Mahout - Mirror of Apache Mahout