yea, but the "other" stuff you do will be more involved (all the aws cli stuff) https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html#cli-quick-configuration
is it 1 TB or many?
because for many TB, you will probably want to do it on a cluster. And for that @igorpisarev stuff might be perfect
Otherwise, if it is 1TB it might still be possible from Fiji/BDV locally
Awesome that you have done the work for the Bdv! It is 8 TB if you naively load it into RAM, but since there are many black pixels, lossless compressed it is 2.5 TB. @tpietzsch would your code also support export to and reading from n5-aws-s3 or only to the standard file based n5?
I have a hard time understanding the link between the
SharedQueue beeing passed when using VolatileViews.wrapAsVolatile(final RandomAccessibleInterval< T > rai,
final SharedQueue queue,
final CacheHints hints ) and the CacheControl that I see a bit everywhere when Volatile type are involved. My main issue is to make a class that makes a VolatileSource from a non-volatile source (see here), but I don't know how to deal properly with the cache.
@hanslovsky @NicoKiaru @bogovicj Does any of you maybe have an idea about this issue? tischi/bdv-utils#2
Hi, http://fly.mpi-cbg.de:8081 do not seem to be alive. Is it temporary ? Is there another bigdataserver that can be used for testing ?
I've just pull a PR in
bigdataviewer-vistools which allows for some bdv.viewer.Sourceto be wrapped as Volatile. (bigdataviewer/bigdataviewer-vistools#38) Probably not optimal, but I think it exposes a current limitation of BdvFunctions.show(Souce src): it does wrap sources that could be wrapped as volatile. I put an example of such limitation being overcomed in this small video : https://www.youtube.com/watch?v=SdngXeEatqA. Maybe it's the wrong way to solve the limitation (exposing the SourceAndConverter is probably more general), but here it is if you want to have a look.
Oh wait, I am talking about a different server I think. The 3D Viewer docs.
@turekg Do you have power/influence over the
fly.mpi-cbg.de server?
(oh, and because there might still be some useful stuff, is it possible to get a copy of my home directory there?)
...probably asking too late...
Hi @tischi! I'm back and can help with setting up n5-s3 if you have any questions. As @hanslovsky and @bogovicj suggested, you can use n5-spark to generate multiscale exports using an instance of
N5AmazonS3Writer. The best examples would probably be the tests in n5-spark and n5-aws-s3 repositories
n5-spark can run either locally or on a cluster, including Amazon's EMR platform (elastic map-reduce) that supports Spark. There is a wiki page in https://github.com/saalfeldlab/n5-spark/wiki that shows how to run a Spark application on AWS
@tischi the n5 downsampling code is in n5-spark, but you can run it on your machine. Take a look at n5-spark repo, there are scripts for building the shaded jar and running it on a local machine. When you run locally, Spark emulates the cluster environment by using threads, and it comes as a self-contained maven dependency so you don't need to install anything extra. You can also run it in your IDE by supplying the following JVM variable:
-Dspark.master=local[1] that sets the number of threads to use, or -Dspark.master=local[*] for using all cores
An
Or is using
imglib2-roi question: is it possible to sample (random) points on a sphere by taking some GeomMasks.closedSphere(...).xor(GeomMasks.openSphere(…))? And/or to get a full list of points representing the sphere surface by some Bresenham-like algorithm?Or is using
imglib2-roi for this a bad idea, and I should rather take a math function of a sphere and sample points “manually” ?
(I was thinking in the direction of ImgLib2 Example 4a, but taking a sphere surface instead of the full volume…)
I am due to retire this machine, after moving some stuff over to bds.mpi-cbg.de and archiving the rest. Tell me where I can upload your stuff and I can get a tarball of your dir to you
@igorpisarev May I ask couple of questions related to n5-s3?
- Are you using BigDataViewer to view the n5-s3 data?
- Do you have some experience what the optimal block size is for streaming n5-s3 data through the internet into some viewer such as Bdv?
- Is it possible to work with compressed n5-s3 blocks and decompress on the client side? Would this give you a better performance because you need to send less data through the web?
Hi @tischi, for browsing I created N5 Viewer which is a BDV-based plugin that supports AWS and Google cloud: https://github.com/saalfeldlab/n5-viewer
2 - I've only used streaming through cloud platforms for smaller test projects, but I assume that the same guidelines for choosing the block size would apply there, and something like 64x64x64 would be good. I think both S3 and Google Cloud Storage charge not only for the data throughput, but also for the number of I/O requests, so you would probably want to avoid having too many tiny block files.
3 - if you use any N5 compression scheme such as GzipCompression, the blocks are always stored and sent in the compressed form
2 - I've only used streaming through cloud platforms for smaller test projects, but I assume that the same guidelines for choosing the block size would apply there, and something like 64x64x64 would be good. I think both S3 and Google Cloud Storage charge not only for the data throughput, but also for the number of I/O requests, so you would probably want to avoid having too many tiny block files.
3 - if you use any N5 compression scheme such as GzipCompression, the blocks are always stored and sent in the compressed form
@NicoKiaru it should also be super easy to start a BigDataServer locally. See https://imagej.net/BigDataServer
Basically, just download the bigdataviewer.jar
and
java -jar bigdataserver.jar myName myDataset.xml
@NicoKiaru regarding
CacheControl and SharedQueue
CacheControl has only one method: prepareNextFrame()
This is called by BDV whenever the timepoint is changed, the viewer transformation is changed, etc
It is a hint to the asynchronous loading infrastructure in the background: Data that might be enqueued for loading is not immediately needed anymore, and new requests should be prioritized. For example when moving to timepoint 2, then image tiles that were required to render timepoint 1 and are still enqueued for loading can be discarded (or bumped to a lower priority)
In general nothing bad happens if this hint is simply ignored.
SharedQueue is a CacheControl