recall | Single-threaded off-heap object store | Key Value Database library
kandi X-RAY | recall Summary
kandi X-RAY | recall Summary
Recall is an off-heap, allocation-free object store for the JVM.
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Top functions reviewed by kandi - BETA
- Removes an entry from the index
- Move the last written entry to the index
- Copy bytes from source buffer to target buffer
- Loads the header information from the given file channel
- Read the header from the given buffer
- Get a version number from the given version number
- Store the given value
- Sets the length of the header to store
- Gets the underlying store
- Stores an entry
- Removes the given entry
- Marks the store asynchronously
- Writes a int to the buffer
- Loads an entry
- Get the storage value
- Writes the store to the given file channel
- Create an unsafe buffer from the given file channel
- Writes the header to the file
- Store the given length to the given file channel
- Generate a random hash
- Creates a new byte buffer from the given file channel
- Loads the given id into the buffer
- Generate a random hash value
- Write length to file
- Encodes the given value into the buffer
- Clears the index
recall Key Features
recall Examples and Code Snippets
private final OrderByteBufferTranscoder transcoder =
new OrderByteBufferTranscoder();
private final SingleTypeStore store =
new SingleTypeStore<>(
new BufferStore<>(MAX_RECORD_LENGTH, INITIAL_SIZE,
ByteBuffer::allocateDire
SingleTypeStore messageStore =
SbeMessageStoreFactory.forSbeMessage(new CarDecoder(),
MAX_RECORD_LENGTH, 100,
len -> new UnsafeBuffer(ByteBuffer.allocateDirect(len)),
new CarIdAccessor());
public void receiveCar(ReadableByteChannel
dependencies {
compile group: 'com.aitusoftware', name: 'recall-store', version: '0.2.0'
}
com.aitusoftware
recall-store
0.2.0
public class Order {
private long id;
private double quantity;
private double price;
// constr
Community Discussions
Trending Discussions on recall
QUESTION
Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/
This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?
I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:
...ANSWER
Answered 2021-Aug-19 at 03:49This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference
update to
QUESTION
Got this:
...ANSWER
Answered 2022-Mar-08 at 08:12Could not find symbol ''&Titlecase'' in ''GLOBAL::Lingua::EN''
The reason for the error is that you used Inline::Perl5
with perl module Lingua::En::Titlecase
which does not exist. You need a captial "N" in "EN":
QUESTION
For the SFINAE in the hypothetical call operator in this answer I need a type trait that "copies" reference category and const/volatile qualifications from one type to another:
...ANSWER
Answered 2022-Mar-09 at 15:54P1450 called this copy_cvref
and clone_cvref
(the former just applies the qualifiers to the 2nd parameter, the latter first removes the qualifiers from the 2nd parameter). The former is useful, I don't think I've ever personally had a need for the latter.
P0847 uses like_t
and forward_like
in a few contexts, like_t
there is basically P1450's copy_cvref
(the latter is... a significantly better name). forward_like
is separately proposed in P2445 (though without the other type trait helper).
QUESTION
I've written an import function that gets a single file from an aws s3-bucket.
That function itself is a wrapper around aws.s3::s3read_using()
which takes a reading function as its first argument.
Why do I wrap around aws.s3::s3read_using()
? Because I need to do some special error-handling and want the wrapping function to do some Recall()
up to a limit... but that's a different story.
Now that i've successfully build and tested my wrapping function i want to do another wrapping arround that:
I want to iterate n times over my wrapper to bind the downloaded files together. I now have the difficulty to hand the 'reading_function' to the FUN
argument of aws.s3::s3read_using()
.
I could do that by simply using ...
- BUT!
I want to make clear to the USER of my wrapping wrapper, that he needs to specify that argument.
So I've decided to use rlangs rlang::enexpr()
to capture the argument and to hand it over to my first wrapper via !!
- which in return captures that argument again with rlang::enexpr()
and hands it over - finally - to aws.s3::s3read_using()
via rlang::expr(aws.s3::s3read_using(FUN = !!reading_fn, object = s3_object))
That works perfectly fine and smooth. My Problem is with testing that function construct using testthat
and mockery
Here is some broadly simplyfied code:
...ANSWER
Answered 2021-Dec-09 at 20:11I think you're complicating things here, although maybe I'm not fully understanding your end goal. You can directly pass functions through arguments without any issue. Your example code above can be easily simplified to (keeping the loop just to match your test_that()
call):
QUESTION
My application consists of calling dozens of functions millions of times. In each of those functions, one or a few temporary std::vector
containers of POD (plain old data) types are initialized, used, and then destructed. By profiling my code, I find the allocations and deallocations lead to a huge overhead.
A lazy solution is to rewrite all the functions as functors containing those temporary buffer containers as class members. However this would blow up the memory consumption as the functions are many and the buffer sizes are not trivial.
A better way is to analyze the code, gather all the buffers, premeditate how to maximally reuse them, and feed a minimal set of shared buffer containers to the functions as arguments. But this can be too much work.
I want to solve this problem once for all my future development during which temporary POD buffers become necessary, without having to have much premeditation. My idea is to implement a container port, and take the reference to it as an argument for every function that may need temporary buffers. Inside those functions, one should be able to fetch containers of any POD type from the port, and the port should also auto-recall the containers before the functions return.
...ANSWER
Answered 2022-Jan-20 at 17:21Let me frame this by saying I don't think there's an "authoritative" answer to this question. That said, you've provided enough constraints that a suggested path is at least worthwhile. Let's review the requirements:
- Solution must use
std::vector
. This is in my opinion the most unfortunate requirement for reasons I won't get into here. - Solution must be standards compliant and not resort to rule violations, like the strict aliasing rule.
- Solution must either reduce the number of allocations performed, or reduce the overhead of allocations to the point of being negligible.
In my opinion this is definitely a job for a custom allocator. There are a couple of off-the-shelf options that come close to doing what you want, for example the Boost Pool Allocators. The one you're most interested in is boost::pool_allocator. This allocator will create a singleton "pool" for each distinct object size (note: not object type), which grows as needed, but never shrinks until you explicitly purge it.
The main difference between this and your solution is that you'll have distinct pools of memory for objects of different sizes, which means it will use more memory than your posted solution, but in my opinion this is a reasonable trade-off. To be maximally efficient, you could simply start a batch of operations by creating vectors of each needed type with an appropriate size. All subsequent vector operations which use these allocators will do trivial O(1) allocations and deallocations. Roughly in pseudo-code:
QUESTION
So I'm currently trying to deploy a router smart contract. I've been building it through erdpy contract build
, which has been successful (I'm on rust nightly tool chain as the Smart contract needs it). And I am now trying to deploy it, but I can't manage to do it. I keep having a 400 BadRequest
from https://devnet-api.elrond.com/transaction/send
.
Here are the logs from the deployment:
...ANSWER
Answered 2022-Jan-05 at 10:47I have you tried to deploy with the argument --verbose
?
That should be something like that (not sure of the syntax because I am on phone)
erdpy --verbose contract deploy
QUESTION
I am trying to understand how to use torchmetrics with PyTorch Lightning.
But, I got a same output with Accuracy, F1-score, Precision, etc.
This is the code.
...ANSWER
Answered 2022-Jan-11 at 21:26I guess that you are looking for this simple example of how to use TorchMetrics for logging your training progress with PytorchLightning. Otherwise, could you more elaborate on your use-case, ideally add your application example?
QUESTION
I can't recall if I have ever tinkered with the settings of Android Emulator, but I've been testing my app on an Android Emulator using Android Studio, and every time I take a screenshot, it crashes.
I tried deleting, and wiping, and creating a new Emulator. None of it works. I tried also to take a screenshot without running my app, with a fresh emulator, and the same problem occurs. It just crashes whenever I try to take a picture.
Android Studio reports this error:
Blockquote WARNING | unexpected system image feature string, emulator might not function correctly, please try updating the emulator. WARNING | cannot add library /Users/sbenati/Library/Android/sdk/emulator/qemu/darwin-x86_64/lib64/vulkan/libvulkan.dylib: failed INFO | configAndStartRenderer: setting vsync to 60 hz INFO | added library /Users/sbenati/Library/Android/sdk/emulator/lib64/vulkan/libvulkan.dylib WARNING | cannot add library /Users/sbenati/Library/Android/sdk/emulator/qemu/darwin-x86_64/lib64/vulkan/libMoltenVK.dylib: failed INFO | added library /Users/sbenati/Library/Android/sdk/emulator/lib64/vulkan/libMoltenVK.dylib INFO | Started GRPC server at 127.0.0.1:8554, security: Local INFO | Advertising in: /Users/sbenati/Library/Caches/TemporaryItems/avd/running/pid_935.ini
My machine is a Mac with 32GB of RAM and i7 CPU, so I can't imaging this an issue with system performance.
If no one has any suggestions, I will have to just reinstall everything. Thanks for the tips everyone.
Edit:
I ran this on a new Mac mini I recently acquired, and got this really helpful message. I traced it down to a suggested solution about switching off Vulcan, but it did not work for me.
...ANSWER
Answered 2021-Dec-04 at 02:28I've been having the same problem (I'm on macOS Monterey), each time I try to take a screenshot the emulator crashes.
Sadly I haven't found a direct solution to this problem, that is a solution fixing the issue in the simulator. But I have learned that it is possible to take screenshots of the app from inside Android Studio, using Logcat.
Essentially, when you're running your app, if you go to the Logcat tab, there is a screenshot option which does seem to work without crashing. I've added a link to developer.android.com which explains how to do it.
Even thought this doesn't exactly fix the problem I hope it helps!
Take a screenshot (through android studio)
Edit:
I am happy to report that after a recent update for the emulator released by the developers, the issue no longer exists for me! The screenshot button has now started working again.
So if someone has the issue, I believe it can now be fixed by just updating your emulator to the latest version available.
QUESTION
I have defined a callback that runs on the epoch end and calculate the metrics. It is working fine in terms of calculating the desired metrics. Below is the function for reference
callback to find metrics at epoch end ...ANSWER
Answered 2021-Nov-28 at 21:09Make sure that the metric callback is listed before the modelcheckpoint callback.
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image
...ANSWER
Answered 2021-Nov-24 at 13:26What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true
and y_pred
.
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Vulnerabilities
No vulnerabilities reported
Install recall
You can use recall like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the recall component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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