cortex | Production infrastructure for machine learning at scale | AWS library

 by   cortexlabs Go Version: 0.42.1 License: Apache-2.0

kandi X-RAY | cortex Summary

kandi X-RAY | cortex Summary

cortex is a Go library typically used in Cloud, AWS, Docker, Amazon S3 applications. cortex has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

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              cortex has a medium active ecosystem.
              It has 7938 star(s) with 612 fork(s). There are 145 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 110 open issues and 985 have been closed. On average issues are closed in 77 days. There are 15 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of cortex is 0.42.1

            kandi-Quality Quality

              cortex has 0 bugs and 0 code smells.

            kandi-Security Security

              cortex has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              cortex code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              cortex is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              cortex releases are available to install and integrate.

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            cortex Key Features

            No Key Features are available at this moment for cortex.

            cortex Examples and Code Snippets

            python download ftp file properly
            Pythondot img1Lines of Code : 9dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
                 b = io.BytesIO()
                 b.write(r.read())
                 b.seek(0)
                 lines = [line.decode("iso-8859-1") for line in b.readlines()]
                 csvr = csv.reader(lines, delimiter=';')
                 for row in csvr:
                    print(row)
                    break
            
            Python - mapping dictionary keys to string integers avoiding duplicates
            Pythondot img2Lines of Code : 5dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            G = ['0_cerebellum', '1_cerebellum', '0_cortex', '1_cortex']
            mapping = {key: str(uniqueNum) for uniqueNum,key in enumerate(G)}
            
            {'0_cerebellum': '0', '1_cerebellum': '1', '0_cortex': '2', '1_cortex': '3'}
            
            Trying to create Dataframe from lists of zip using Pandas. wanted data table result
            Pythondot img3Lines of Code : 17dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            pd.DataFrame({'coin_name': coin_name[0:81], 'chain_name': chain_name, 'withdrawal_fees':withdrawal_fees})
            
                      coin_name       chain_name    withdrawal_fees
            0        Civic(CVC)      ETH (ERC20)  97.00000000 (CVC)
            1             (CVC) 
            Scipy - statistical tests between two groups
            Pythondot img4Lines of Code : 4dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            stats.mannwhitneyu(df.loc[df.web=="cortex", "k"], df.loc[df.web=="cerebellum", "k"])
            
            stats.mannwhitneyu(df.loc[df.web=="cortex", "clustering_coeff"], df.loc[df.web=="cerebellum", "clustering_coeff"])
            
            Lower latency from webcam cv2.VideoCapture
            Pythondot img5Lines of Code : 58dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            
               CAM \____/                                     python code GIL-awaiting ~ 100 [ms] chopping
                    |::|                                      python code calling a cv2.()
                    |::|   __________________________________________-----!!
            keras conv1d layer weight count is not produced as expected
            Pythondot img6Lines of Code : 8dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            model = tf.keras.Sequential()
            model.add(tf.keras.layers.Conv2D(32,kernel_size=5, padding='same', 
            input_shape=(45, 45, 1), use_bias=False))
            
            model.summary()
            # Layer (type)                 Output Shape              Param # 
            # conv (Conv2D) 
            How to plot data from Hierarchical columns?
            Pythondot img7Lines of Code : 36dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            tab.columns = [' '.join(column).strip() for column in tab.columns]
            tab = tab.reset_index()
            
            #    RID        Str_dx  Cerebral Cortex left  Cerebral Cortex right  Hippocampus left  Hippocampus right
            # 0    1            CN                    
            Error while trying to save a nested list in a Postgresql DB using SQLAlchemy
            Pythondot img8Lines of Code : 11dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            csv_json_response = [
                    {
                        "timestamp": 1577837700,
                        "value": 63.2
                    },
                    {
                        "timestamp": 1577838600,
                        "value": 61.0
                    }, ...
                ]
            
            How to compare values in 2 columns and do assignment in a new column?
            Pythondot img9Lines of Code : 13dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df['up_threshold'] = np.where(df['max_power_contractual'].fillna(0) == 0, df['upper_ci'],
                np.where(df['max_power_contractual'] > df['upper_ci'], df['upper_ci'], df['max_power_contractual'])
            )
            
            print(df)
            
                
            With the HuggingFace transformer, how can I return multiple samples when generating text?
            Pythondot img10Lines of Code : 44dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
            
            next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=num_samples)
            
            generated = tor

            Community Discussions

            QUESTION

            In ggplot2 (R Studio), is there a way to extend the regression lines to the end of the plot rather than the last data point
            Asked 2022-Feb-19 at 14:55

            I am a relative novice to R & ggplot. I am trying to plot an interaction. When I plot the interaction using SPSS - the regression lines go from the y-axis all the way to the opposite edge of the plot:

            However, when I use ggplot, the regression lines only go as far as the first and last data point which makes the graph look strange

            Is there any way to remedy this and make my ggplot look more like the SPSS plot?

            Here is the code I am using

            ...

            ANSWER

            Answered 2022-Feb-19 at 14:55

            geom_smooth has a fullrange option, which has a default value of FALSE:

            Should the fit span the full range of the plot, or just the data?

            Thus, you can use:

            Source https://stackoverflow.com/questions/71184417

            QUESTION

            GNU ARM assembler giving a seemingly irrelevant register in error message
            Asked 2022-Feb-15 at 11:20
            Goal

            I'm building a mutex primitive using gcc inline assembly for a CortexM7 target using the LDREX and STREX instructions, following the Barrier Litmus Tests and Cookbook document from ARM.

            Code ...

            ANSWER

            Answered 2022-Feb-15 at 11:20

            Per @jester's help, I realized I had the wrong constraint on the GCC-inline variable alias for the lock. It should have been "+m", specifying a memory address instead of a register.

            I was also de-referencing the address of the lock when I should have been leaving it as a pointer.

            I changed [lock] "+l"(*lock) to [lock] "+m"(lock) and it now builds.

            Source https://stackoverflow.com/questions/71065708

            QUESTION

            LLVM linker places stack in the wrong place
            Asked 2022-Feb-08 at 09:57

            I am trying to link Cortex-M4 firmware with clang + lld. The gcc build works fine. I am using the stock CMSIS linker script with only RAM & ROM size adjusted (bases are the same). Beginning of the script (without comments):

            ...

            ANSWER

            Answered 2022-Feb-08 at 09:57

            I fixed it by removing COPY and adding NOLOAD to the stack section. It builds and runs fine both with gcc and clang.

            Source https://stackoverflow.com/questions/70097305

            QUESTION

            How to crosscompile GSL for Arm Cortex M4?
            Asked 2022-Jan-31 at 07:49

            I am using a STM32 MCU with arm cortex m4 and want to use the gsl-2.7.1. However, I already tried for example the command ./configure --prefix=/home/user_name/gsl_arm --target=arm-none-eabi and every other suggestion that I could find on the internet and toolchain-tutorials, but in the best case I got during linking with the build library an error like "could not recognize the symbols". In the worst case, the suggested options for autoconfig were not recognized (for example, to specify the cpu). Does anyone have an idea how I have to crosscompile it?

            ...

            ANSWER

            Answered 2022-Jan-31 at 07:49

            I am glad to say that I was able to cross compile the GSL for Arm Cortex-M4. If you call autoconf with the following options:

            Source https://stackoverflow.com/questions/70886508

            QUESTION

            Scipy - statistical tests between two groups
            Asked 2022-Jan-26 at 16:49

            I have two samples from the population of neurons in the brain, each sample consisting of a thousand neuron instances, of categories:

            1. cerebellum
            2. cortex

            Now I'm extracting multiple metrics for each sample using complex network analysis, for example, neuron degree of connectivity k, a discreet number n = 0, 1, ...., n, or clustering coefficient C, a continous value between 0.00000 and 1.00000.

            df.sample(3) (where web is category) in my pandas dataframes:

            cortex:

            ...

            ANSWER

            Answered 2022-Jan-26 at 16:49

            QUESTION

            BR/RET timing discrepancy when returning from contrived subroutine to a modified return address
            Asked 2022-Jan-01 at 03:54

            In my adventures of experimenting around with the 64-bit ARM architecture, I noticed a peculiar speed difference depending on whether br or ret is used to return from a subroutine.

            ...

            ANSWER

            Answered 2022-Jan-01 at 02:51

            Most modern microarchitectures have a special predictor for call / return, which tend to match up with each other in real programs. (And predicting returns any other way is hard for functions with many call-sites: it's an indirect branch.)

            By playing with the return address manually, you're making those return-predictions wrong. So every ret causes a branch mispredict, except the one where you didn't play with x30.

            But if you use an indirect branch other than the one recognized specifically as a ret idiom, e.g. br x30, the CPU uses its standard indirect-branch prediction method, which does well when the br goes to the same location repeatedly.

            A quick google search found some info from ARM for Cortex-R4 about the return-predictor stack on that microarchitecture for 32-bit mode (a 4-entry circular buffer): https://developer.arm.com/documentation/ddi0363/e/prefetch-unit/return-stack

            For x86, https://blog.stuffedcow.net/2018/04/ras-microbenchmarks/ is a good article about the concept in general, as well as some details on how various x86 microarchitectures maintain their prediction accuracy in the face of things like mis-speculated execution of a call or ret instruction that has to get rolled back.

            (x86 has an actual ret opcode; ARM64 is the same: the ret opcode is like br, but with a hint that this is a function-return. Some other RISCs like RISC-V don't have a separate opcode, and just assume that branch-to-register with the link register is a return.)

            Source https://stackoverflow.com/questions/70546711

            QUESTION

            NEON assembly code requires more cycles on Cortex-A72 vs Cortex-A53
            Asked 2021-Dec-22 at 15:34

            I am benchmarking an ARMv7 NEON code on two ARMv8 processors in AArch32 mode: the Cortex-A53 and Cortex-A72. I am using the Raspberry Pi 3B and Raspberry Pi 4B boards with 32-bit Raspbian Buster.

            My benchmarking method is as follows:

            ...

            ANSWER

            Answered 2021-Oct-27 at 12:00

            I compared the instruction cycle timing of A72 and A55 (nothing available on A53):

            vshl and vshr:

            A72: throughput(IPC) 1, latency 3, executes on F1 pipeline only
            A55: throughput(IPC) 2, latency 2, executes on both pipelines (restricted though)

            That pretty much nails it since there are many of them in your code.

            There are some drawbacks in your assembly code, too:

            1. vadd has less restrictions and better throughput/latency than vshl. You should replace all vshl by immediate 1 with vadd. Barrel shifters are more costly than arithmetic on SIMD.
            2. You should not repeat the same instructions unnecesarily (<<5)
            3. The second vmvn is unnecessary. You can replace all the following vand with vbic instead.
            4. Compilers generate acceptable machine codes as long as no permutations are involved. Hence I'd write the code in neon intrinsics in this case.

            Source https://stackoverflow.com/questions/69719403

            QUESTION

            External Flash Loader gets "failed to download Segment[0]" error on STM32CubeIDE
            Asked 2021-Dec-16 at 22:16

            I have made a custom external flash loader(.stldr) file for my STM32 based board and this file works great with ST Link Utility(Read, Write and Erase) are work fine and i can Program the board correctly. But when i try to use the created .stldr file by STM32CubeIDE the erasing process well done but when the downloading process gets start then "failed to download Segment[0]" error pups up.

            Could any one help me with the problem?

            ...

            ANSWER

            Answered 2021-Dec-16 at 22:16

            The Problem was for the version of STM32CubeIDE 1.8

            I downgraded to the STM32CubeIDE1.7 and the problem is solved.

            Edited: I found the problem. According to these pictures(First for STM32CubeIDE 1.8 and Second for STM32CubeIDE 1.7)

            • The STM32CubeIDE 1.8 uses the STM32CubeProgrammer 2.9 and this makes the problems but the STM32CubeIDE 1.7 uses STM32CubeProgrammer 2.8 and it makes every things work fine.

            Solution2:

            Alternative to downgrading stm32CubeIDE from 1.8 to 1.7 version, you can only copy and replace the below directory contents of the STM32CubeIDE 1.7:

            Source https://stackoverflow.com/questions/70342257

            QUESTION

            Generate ARM thumb-2 assembly code from android app for Cortex M3 architecture
            Asked 2021-Dec-16 at 16:58

            I want to build an Android app which will be an interface to convert C++ into assembly code for ARM Cortex M3 architecture.

            I'm not an android java developer, and I do mainly arduino projects with C/C++. So I need your help to point me in good directions about how to build an android app with java in Android Studio or similar, which will be able to convert from C++ source code to ASM code M3 Cortex.

            I did some research and found that I need to use ARM NONE EABI GCC compiler to generate ASM code from C++, simple like these command line instructions:

            ...

            ANSWER

            Answered 2021-Dec-16 at 16:58

            A solution would be if in Termux app you will do next things: (more details here)

            1. pkg install proot
            2. pkg install proot-distro
            3. proot-distro install debian
            4. proot-distro login debian

            After that you should be logged in a Debian environment, and you can install almost any Arm packages available on debian repositories.

            For example you should be able to install this Cortex compiler:

            Source https://stackoverflow.com/questions/70233126

            QUESTION

            Loop takes more cycles to execute than expected in an ARM Cortex-A72 CPU
            Asked 2021-Dec-03 at 06:02

            Consider the following code, running on an ARM Cortex-A72 processor (optimization guide here). I have included what I expect are resource pressures for each execution port:

            Instruction B I0 I1 M L S F0 F1 .LBB0_1: ldr q3, [x1], #16 0.5 0.5 1 ldr q4, [x2], #16 0.5 0.5 1 add x8, x8, #4 0.5 0.5 cmp x8, #508 0.5 0.5 mul v5.4s, v3.4s, v4.4s 2 mul v5.4s, v5.4s, v0.4s 2 smull v6.2d, v5.2s, v1.2s 1 smull2 v5.2d, v5.4s, v2.4s 1 smlal v6.2d, v3.2s, v4.2s 1 smlal2 v5.2d, v3.4s, v4.4s 1 uzp2 v3.4s, v6.4s, v5.4s 1 str q3, [x0], #16 0.5 0.5 1 b.lo .LBB0_1 1 Total port pressure 1 2.5 2.5 0 2 1 8 1

            Although uzp2 could run on either the F0 or F1 ports, I chose to attribute it entirely to F1 due to high pressure on F0 and zero pressure on F1 other than this instruction.

            There are no dependencies between loop iterations, other than the loop counter and array pointers; and these should be resolved very quickly, compared to the time taken for the rest of the loop body.

            Thus, my intuition is that this code should be throughput limited, and considering the worst pressure is on F0, run in 8 cycles per iteration (unless it hits a decoding bottleneck or cache misses). The latter is unlikely given the streaming access pattern, and the fact that arrays comfortably fit in L1 cache. As for the former, considering the constraints listed on section 4.1 of the optimization manual, I project that the loop body is decodable in only 8 cycles.

            Yet microbenchmarking indicates that each iteration of the loop body takes 12.5 cycles on average. If no other plausible explanation exists, I may edit the question including further details about how I benchmarked this code, but I'm fairly certain the difference can't be attributed to benchmarking artifacts alone. Also, I have tried to increase the number of iterations to see if performance improved towards an asymptotic limit due to startup/cool-down effects, but it appears to have done so already for the selected value of 128 iterations displayed above.

            Manually unrolling the loop to include two calculations per iteration decreased performance to 13 cycles; however, note that this would also duplicate the number of load and store instructions. Interestingly, if the doubled loads and stores are instead replaced by single LD1/ST1 instructions (two-register format) (e.g. ld1 { v3.4s, v4.4s }, [x1], #32) then performance improves to 11.75 cycles per iteration. Further unrolling the loop to four calculations per iteration, while using the four-register format of LD1/ST1, improves performance to 11.25 cycles per iteration.

            In spite of the improvements, the performance is still far away from the 8 cycles per iteration that I expected from looking at resource pressures alone. Even if the CPU made a bad scheduling call and issued uzp2 to F0, revising the resource pressure table would indicate 9 cycles per iteration, still far from actual measurements. So, what's causing this code to run so much slower than expected? What kind of effects am I missing in my analysis?

            EDIT: As promised, some more benchmarking details. I run the loop 3 times for warmup, 10 times for say n = 512, and then 10 times for n = 256. I take the minimum cycle count for the n = 512 runs and subtract from the minimum for n = 256. The difference should give me how many cycles it takes to run for n = 256, while canceling out the fixed setup cost (code not shown). In addition, this should ensure all data is in the L1 I and D cache. Measurements are taken by reading the cycle counter (pmccntr_el0) directly. Any overhead should be canceled out by the measurement strategy above.

            ...

            ANSWER

            Answered 2021-Nov-06 at 13:50

            First off, you can further reduce the theoretical cycles to 6 by replacing the first mul with uzp1 and doing the following smull and smlal the other way around: mul, mul, smull, smlal => smull, uzp1, mul, smlal This also heavily reduces the register pressure so that we can do an even deeper unrolling (up to 32 per iteration)

            And you don't need v2 coefficents, but you can pack them to the higher part of v1

            Let's rule out everything by unrolling this deep and writing it in assembly:

            Source https://stackoverflow.com/questions/69855672

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install cortex

            You can download it from GitHub.

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            pip install cortex

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            gh repo clone cortexlabs/cortex

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