21–30 of 93 posts
Earlier quoted context omitted.
Sounds like a straightforward time-space tradeoff: if you have the compressed layers sitting around when you need them, you can avoid the expense and time of compressing them.
I'm not sure about the fastest macbook disk access, but even with NVMe storage I've found lz4 to be faster than the disk. That is (it's hard to say this exactly correct) compressed content gets read/written FASTER than uncompressed content because fewer bytes need to transit the disk interface and the CPU is able to compress/decompress significantly faster than data is able to go through whatever disk bus you've got.
The article says to regularly run prune, how regularly? Currently I run the following once per day from cron: docker system prune -a -f docker volume prune -a -f
Ref: https://docs.docker.com/reference/cli/docker/system/prune/
Docker already fills up my dev machines yet they decided for this insane solution: > The containerd image store uses more disk space than the legacy storage drivers for the same images. This is because containerd stores images in both compressed and uncompressed formats, while the legacy drivers stored only the uncompressed layers. Why ?
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Earlier quoted context omitted.
> It is shameful for apple to hard solder their disks. There is no benefit to the user Actually, it is. The speed and latency difference does matter, that is how even an 8GB RAM MacBook feels snappier than many a 32GB Windows machine - it can use the disk as swap.
I had to work on a Mac M3 for a year, it sucked, it did not feel snappier than any Windows or Linux machine (including this one) that I've ever used and that is going back to the 1980's.
If you believe the latest M3 does not perform better than machines you’ve used in the 80s, I have no idea how to even start a reasonable discussion about this.
> This difference is particularly noticeable with multiple images sharing the same base layers. With legacy storage drivers, shared base layers were stored once locally, and reused images that depended on them. With containerd, each image stores its own compressed version of shared layers, even though the uncompressed layers are still de-duplicated through snapshotters. This seems like a really weird decision. If bas…
This is hell for a lot of ML containers, that have gigabytes of CUDA and PyTorch. Before at least you could keep your code contained to a layer. But if I understand this correctly every code revision duplicates gigabytes of the same damn bloated crap.
Earlier quoted context omitted.
This is hell for a lot of ML containers, that have gigabytes of CUDA and PyTorch. Before at least you could keep your code contained to a layer. But if I understand this correctly every code revision duplicates gigabytes of the same damn bloated crap.
If you have problems with 13 (I believe) GB of docker layers ... how do you deal with terabytes or petabytes of AI training data?
Inference, development cycles, any of the application domains of PyTorch that don't involve training frontier models... all of those are complicated by excessive container layers.
But mostly dev really sucks with writing out an extra 10GB for a small code change.
Earlier quoted context omitted.
This is hell for a lot of ML containers, that have gigabytes of CUDA and PyTorch. Before at least you could keep your code contained to a layer. But if I understand this correctly every code revision duplicates gigabytes of the same damn bloated crap.
If you have problems with 13 (I believe) GB of docker layers ... how do you deal with terabytes or petabytes of AI training data?
Docker already fills up my dev machines yet they decided for this insane solution: > The containerd image store uses more disk space than the legacy storage drivers for the same images. This is because containerd stores images in both compressed and uncompressed formats, while the legacy drivers stored only the uncompressed layers. Why ?
Just in case - I'm always amazed how many Docker users don't know about the prune command for cleaning up the caches and deleting unused container images and just slowly let their docker image cache eat their disk.
> This difference is particularly noticeable with multiple images sharing the same base layers. With legacy storage drivers, shared base layers were stored once locally, and reused images that depended on them. With containerd, each image stores its own compressed version of shared layers, even though the uncompressed layers are still de-duplicated through snapshotters. This seems like a really weird decision. If bas…