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If you're frustrated with Docker's slow pace of development are are looking for a good alternative, have a look at Singularity containers. They're interoperable

If you're frustrated with Docker's slow pace of development are are looking for a good alternative, have a look at Singularity containers. They're interoperable with Docker Hub and OCI compatible images, and offer a much better experience for HPC, machine learning and big data environments. Their GPU support is top notch. They don't run on as many platforms as Docker, but are aiming to grow into cross-platform. Sylabs is a small team in Austin, TX that does a lot of the development work, but they are quickly gaining adoption in academia. It's available on Cedar and Graham in Compute Canada (a large supercomputing cluster that serves most of Canada). We use Singularity on all our lab machines as it is much easier for IT admins to manage, and doesn't require sudo access. Their GPU support is top notch. https://www.sylabs.io/ https://www.sylabs.io/

deleted 8y ago
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btown 8y ago
Is there any overhead due to the virtualization layer, relative to bare metal? Scientific computing usually has much more stringent requirements for CPU/GPU performance than the average web startup. If the performance is good enough, containerization would be a great match for the domain, as you’re often shipping code to government-funded clusters you have little control over.
thanatropism 8y ago
Your repetition repetition of "their gpu support is top notch" reminded me of Florida man Marco Rubio in those 2015 presidential debates.
- bmc7505 8y ago
  The topmost of notches. Very reproducible and user-friendly. https://devblogs.nvidia.com/docker-compatibility-singularity-hpc/ https://devblogs.nvidia.com/docker-compatibility-singularity...
Arkanosis 8y ago
It's important to mention that Singularity does not cover the same functional scope as Docker, and therefore isn't a good alternative for all use cases. It's much better for some, close to useless for others. To make it short: - huge pro: it relies on user rights and doesn't require root, so it's easier to trust for an admin, and to use for a non-admin; - huge con: it provides no isolation (it's not that it does a bad job at doing it: it doesn't even try, by design). There are probably many other differences, but that's what has stroke me the most. Don't get me wrong: I've only had positive feedback about it; it just isn't a one-to-one replacement for Docker.

deleted 8y ago

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btown 8y ago

Is there any overhead due to the virtualization layer, relative to bare metal? Scientific computing usually has much more stringent requirements for CPU/GPU performance than the average web startup. If the performance is good enough, containerization would be a great match for the domain, as you’re often shipping code to government-funded clusters you have little control over.

thanatropism 8y ago

Your repetition repetition of "their gpu support is top notch" reminded me of Florida man Marco Rubio in those 2015 presidential debates.

bmc7505 8y ago
The topmost of notches. Very reproducible and user-friendly. https://devblogs.nvidia.com/docker-compatibility-singularity-hpc/ https://devblogs.nvidia.com/docker-compatibility-singularity...

Arkanosis 8y ago

It's important to mention that Singularity does not cover the same functional scope as Docker, and therefore isn't a good alternative for all use cases. It's much better for some, close to useless for others. To make it short: - huge pro: it relies on user rights and doesn't require root, so it's easier to trust for an admin, and to use for a non-admin; - huge con: it provides no isolation (it's not that it does a bad job at doing it: it doesn't even try, by design). There are probably many other differences, but that's what has stroke me the most. Don't get me wrong: I've only had positive feedback about it; it just isn't a one-to-one replacement for Docker.