Footprint | eat impacts your carbon footprint | Frontend Framework library
kandi X-RAY | Footprint Summary
kandi X-RAY | Footprint Summary
Footprint, an app dedicated to assisting individuals in reducing their food consumption ecological footprint, will encourage behavioral change and a more sustainable lifestyle by providing personalized information for users to make the optimal decision. Instead of focusing on creating awareness, Footprint is a personalized tool for users to take action in saving the planet.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of Footprint
Footprint Key Features
Footprint Examples and Code Snippets
Lines of Code : 119
License : No License
Copypublic interface Potion {
void drink();
}
@Slf4j
public class HealingPotion implements Potion {
@Override
public void drink() {
LOGGER.info("You feel healed. (Potion={})", System.identityHashCode(this));
}
}
@Slf4j
public class HolyWate def vectorized_map(fn, elems, fallback_to_while_loop=True, warn=True):
"""Parallel map on the list of tensors unpacked from `elems` on dimension 0.
This method works similar to `tf.map_fn` but is optimized to run much faster,
possibly with a m def create_low_latency_conv_model(fingerprint_input, model_settings,
is_training):
"""Builds a convolutional model with low compute requirements.
This is roughly the network labeled as 'cnn-one-fstride4' in the
def create_conv_model(fingerprint_input, model_settings, is_training):
"""Builds a standard convolutional model.
This is roughly the network labeled as 'cnn-trad-fpool3' in the
'Convolutional Neural Networks for Small-footprint Keyword Spottin Community Discussions
Trending Discussions on Footprint
QUESTION
I was considering ways to reduce memory footprint, and it is constantly mentioned that a bool takes up more memory than it logically needs to, as a byproduct of processor design.
it is also sometimes mentioned that one could store several bool within an int.
I am wondering if this would actually be more memory efficient?
if we have a usecase where we can use a significant portion of 32 (or 64) bool. and we decide to store all of them in a single int. then on the surface we have saved
7 (bits) * 32 (size of int) = 224 (bits) or 28 (bytes)
but in order to get each of those bits from the int, we needed to use some method of masking such as:
- bit shifting the
intboth directions(int<>yhere we need to load and store x,y which are probably an int, but you could get them smaller depending on the use case - masking the
int:int & int2here we also store an additional int, which is stored and loaded
even if these aren't stored as variables, and they are defined statically within the code, it still ends up using additional memory, as it will increase the memory footprint of the instructions. as well as the instructions for the masking steps.
is there any way to do this that isn't actually worse for memory usage than just taking the hit on 7 wasted bits?
...ANSWER
Answered 2022-Apr-15 at 17:07You are describing a text book example of a trade-off.
Yes, several bools in one int is hugeley more memory efficient - in itself.
Yes, you need to spend code to use that.
Yes, for only a few bools (for different values of "few"), the code might take more space than you save.
However, you could look at the kind of memory which is used. In some environments, RAM (which is saved by your idea) is much more expensive than ROM (which has to be paid for your idea).
Also, the price to pay is mostly paid once for implementation and only paid a fraction for using, especially when the using code is reused, e.g. in loops.
Altogether, in case of many bools, you can save more than you pay.
The point of actually saving needs to be determined for the special case.
On the other hand, you have missed on "currency" on the price-tag for the idea. You not only pay in memory, you also pay in execution time. You focused your question on memory, so I won't elaborate here. But for anything time critical, you should take the longer execution time into conisderation. You might find that saving memory is quite achievable with your idea, but the whole thing gets unbearably slow.
Again from the other side, as Eric Postpischil points out in a comment, execution speed can also improve due to cache effects from better memory footprint.
QUESTION
I have source (src) image(s) I wish to align to a destination (dst) image using an Affine Transformation whilst retaining the full extent of both images during alignment (even the non-overlapping areas).
I am already able to calculate the Affine Transformation rotation and offset matrix, which I feed to scipy.ndimage.interpolate.affine_transform to recover the dst-aligned src image.
The problem is that, when the images are not fuly overlapping, the resultant image is cropped to only the common footprint of the two images. What I need is the full extent of both images, placed on the same pixel coordinate system. This question is almost a duplicate of this one - and the excellent answer and repository there provides this functionality for OpenCV transformations. I unfortunately need this for scipy's implementation.
Much too late, after repeatedly hitting a brick wall trying to translate the above question's answer to scipy, I came across this issue and subsequently followed to this question. The latter question did give some insight into the wonderful world of scipy's affine transformation, but I have as yet been unable to crack my particular needs.
The transformations from src to dst can have translations and rotation. I can get translations only working (an example is shown below) and I can get rotations only working (largely hacking around the below and taking inspiration from the use of the reshape argument in scipy.ndimage.interpolation.rotate). However, I am getting thoroughly lost combining the two. I have tried to calculate what should be the correct offset (see this question's answers again), but I can't get it working in all scenarios.
Translation-only working example of padded affine transformation, which follows largely this repo, explained in this answer:
...ANSWER
Answered 2022-Mar-22 at 16:44If you have two images that are similar (or the same) and you want to align them, you can do it using both functions rotate and shift :
QUESTION
I am working on a large Pandas DataFrame which needs to be converted into dictionaries before being processed by another API.
The required dictionaries can be generated by calling the .to_dict(orient='records') method. As stated in the docs, the returned value depends on the orient option:
Returns: dict, list or collections.abc.Mapping
Return a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the orient parameter.
For my case, passing orient='records', a list of dictionaries is returned. When dealing with lists, the complete memory required to store the list items, is reserved/allocated. As my dataframe can get rather large, this might lead to memory issues especially as the code might be executed on lower spec target systems.
I could certainly circumvent this issue by processing the dataframe chunk-wise and generate the list of dictionaries for each chunk which is then passed to the API. Furthermore, calling iter(df.to_dict(orient='records')) would return the desired generator, but would not reduce the required memory footprint as the list is created intermediately.
Is there a way to directly return a generator expression from df.to_dict(orient='records') instead of a list in order to reduce the memory footprint?
ANSWER
Answered 2022-Feb-25 at 22:32There is not a way to get a generator directly from to_dict(orient='records'). However, it is possible to modify the to_dict source code to be a generator instead of returning a list comprehension:
QUESTION
I am working on submitting an R package to CRAN. Right now I am trying to reduce the memory footprint of the package. Because this package deals with spatial data that has a very particular format, I want to include a properly formatted shapefile as an example. If I include the full-size original shapefile, there are no warnings (other than file size) in the R CMD checks. However, if I crop the file and include the cropped version in the package (in "inst/extdata") I get this warning:
...ANSWER
Answered 2022-Feb-11 at 23:59This is a known issue[1] where file will mis-identify DBF files with last-update date in the year 2022. Easiest fix is to not use a 2022 update date when saving the file. Alternatively you can simply change the second byte of the file after the fact, e.g.:
QUESTION
The code calculates the minimum value at each row and picks the next minimum by scanning the nearby element on the same and the next row. Instead, I want the code to start with minimum value of the first row and then progress by scanning the nearby elements. I don't want it to calculate the minimum value for each row. The outputs are attached.
...ANSWER
Answered 2022-Feb-07 at 21:01You can solve this using a simple while loop: for a given current location, each step of the loop iterates over the neighborhood to find the smallest value amongst all the valid next locations and then update/write it.
Since this can be pretty inefficient in pure Numpy, you can use Numba so the code can be executed efficiently. Here is the implementation:
QUESTION
I am facing an issue with plotting points in a time series since I cannot identify the y-axis value. I have 2 datasets: one NetCDF file with satellite data (sea surface temperature), and another CSV file with storm track data (time, longitude, latitude, wind speed, etc.). I can plot the desired temperature time series for all storm track locations located in the ocean. However, I want to indicate the time of the storm footprint occurrence within each time series line. So, one line represents one location and the changing temperature over time, but I also want to show WHEN the storm occurred at that location.
This is my code so far (it works):
...ANSWER
Answered 2022-Jan-10 at 14:00I have found the way to do this:
QUESTION
In this programming problem, the input is an n×m integer matrix. Typically, n≈ 105 and m ≈ 10. The official solution (1606D, Tutorial) is quite imperative: it involves some matrix manipulation, precomputation and aggregation. For fun, I took it as an STUArray implementation exercise.
I have managed to implement it using STUArray, but still the program takes way more memory than permitted (256MB). Even when run locally, the maximum resident set size is >400 MB. On profiling, reading from stdin seems to be dominating the memory footprint:
Functions readv and readv.readInt, responsible for parsing integers and saving them into a 2D list, are taking around 50-70 MB, as opposed to around 16 MB = (106 integers) × (8 bytes per integer + 8 bytes per link).
Is there a hope I can get the total memory below 256 MB? I'm already using Text package for input. Maybe I should avoid lists altogether and directly read integers from stdin to the array. How can we do that? Or, is the issue elsewhere?
ANSWER
Answered 2021-Dec-05 at 11:40Contrary to common belief Haskell is quite friendly with respect to problems like that. The real issue is that the array library that comes with GHC is total garbage. Another big problem is that everyone is taught in Haskell to use lists where arrays should be used instead, which is usually one of the major sources of slow code and memory bloated programs. So, it is not surprising that GC takes a long time, it is because there is way too much stuff being allocation. Here is a run on the supplied input for the solution provided below:
QUESTION
ANSWER
Answered 2021-Nov-24 at 06:14Have you set log in loggingInterceptor or restadapter ?
if yes then try to set it NONE.
QUESTION
I have a struct defined that is used for messages sent across two different interfaces. One of them requires 32-bit alignment, but I need to minimize the space they take. Essentially I'm trying to byte-pack the structs, i.e. #pragma pack(1) but ensure that the resulting struct is a multiple of 32-bits long. I'm using a gcc arm cross-compiler for a 32-bit M3 processor. What I think I want to do is something like this:
ANSWER
Answered 2021-Nov-17 at 22:08As you use gcc you need to use one of the attributes.
Example + demo.
QUESTION
I have a model that trains just fine on a single GPU. But I'm getting CUDA memory errors when I switch to Pytorch distributed data parallel (DDP). Specifically, the DDP model takes up twice the memory footprint compared to the model with no parallelism. Here is a minimal reproducible example:
...ANSWER
Answered 2021-Sep-03 at 17:46I'm adding here the solution of @ptrblck written in the PyTorch discussion forum.
Here're two quotes.
The statement:
[...] the allocated memory get doubled when
torch.distributed.Reduceris instantiated in the constructor ofDistributedDataParallel
And the answer:
[...] the
Reducerwill create gradient buckets for each parameter, so that the memory usage after wrapping the model intoDDPwill be 2xmodel_parameter_size. Note that the parameter size of a model is often much smaller than the activation size so that this memory increase might or might not be significant
So, from here we can see the reason why the memory footprint sometimes doubles.
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