fk-visual-search | Flipkart 's visual search and recommendation system | Network Attached Storage library

 by   flipkart-incubator Python Version: Current License: Apache-2.0

kandi X-RAY | fk-visual-search Summary

kandi X-RAY | fk-visual-search Summary

fk-visual-search is a Python library typically used in Storage, Network Attached Storage, Pytorch applications. fk-visual-search has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However fk-visual-search build file is not available. You can download it from GitHub.

Flipkart's visual search and recommendation system
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            kandi-support Support

              fk-visual-search has a low active ecosystem.
              It has 151 star(s) with 78 fork(s). There are 19 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 8 open issues and 3 have been closed. On average issues are closed in 2 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of fk-visual-search is current.

            kandi-Quality Quality

              fk-visual-search has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              fk-visual-search 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

              fk-visual-search releases are not available. You will need to build from source code and install.
              fk-visual-search has no build file. You will be need to create the build yourself to build the component from source.
              fk-visual-search saves you 201 person hours of effort in developing the same functionality from scratch.
              It has 495 lines of code, 16 functions and 10 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed fk-visual-search and discovered the below as its top functions. This is intended to give you an instant insight into fk-visual-search implemented functionality, and help decide if they suit your requirements.
            • Download an image
            • Return True if the given URL is a valid URL
            • Index a batch of features
            • Write fv_dict to lmdb file
            • Compute the recall for a given vertical image
            • Download images from urlObjects
            Get all kandi verified functions for this library.

            fk-visual-search Key Features

            No Key Features are available at this moment for fk-visual-search.

            fk-visual-search Examples and Code Snippets

            No Code Snippets are available at this moment for fk-visual-search.

            Community Discussions

            QUESTION

            How can I read/write data from/to network attached storage with kedro?
            Asked 2020-May-14 at 09:24

            In the API docs about kedro.io and kedro.contrib.io I could not find info about how to read/write data from/to network attached storage such as e.g. FritzBox NAS.

            ...

            ANSWER

            Answered 2020-May-14 at 09:24

            So I'm a little rusty on network attached storage, but:

            1. If you can mount your network attached storage onto your OS and access it like a regular folder, then it's just a matter of providing the right filepath when writing the config for a given catalog entry. See for example: Using Python, how can I access a shared folder on windows network?

            2. Otherwise, if accessing the network attached storage requires anything special, you might want to create a custom dataset that uses a Python library for interfacing with your network attached storage. Something like pysmb comes to mind.

            The custom dataset could borrow heavily from the logic in existing kedro.io or kedro.extras.datasets datasets, but you replace the filepath/fsspec handling code with pysmb instead.

            Source https://stackoverflow.com/questions/61791713

            QUESTION

            About NAS and SAN(protocols, architecture, etc..)
            Asked 2020-Apr-07 at 09:44

            I am currently kind of having trouble to understand between NAS and SAN.

            As far as I figured out, NAS and SAN are kind of defined as below.

            NAS(Network attached storage)
            - Usually used as file storage and use Ethernet Infrastructure to communicate
            - As file storage, support protocols like NFS, CIFS, SMB, HTTP(S)

            SAN(Storage Area Network)
            - Network Protocol to communicate with block storage for data access.
            - Configured with separated network system
            - Commonly based on Fibre Channel(FC) technology.
            - Could use iSCSI(in small and medium sized business) or FCoE for less expensive alternative to FC

            So, below is my questions.
            1. Is File Storage and Block Storage are the solutions? I researched and found that NAS is File Storage Solution and SAN Storage is Block Storage Solution.
            - In that case, are their base infrastructure(storage device) same? Only different with protocols, network devices, may be storage os something that controls underline device and way of usage?

            2. I found there are NAS Solutions that support iSCSI. But I found that iSCSI is SCSI Protocol that use TCP/IP Network system and SCSI is for block level storage communication protocols.
            - And Now I am confused. NAS is a file storage solution and how could that support iSCSI Protocol?

            3. Are AWS root disk and EBS storage SAN Storage?
            - I read that SAN Storage configuration could be expensive so iSCSI or FCoE are less expensive way to configure.
            - With what technology AWS storage Infrastructure is configured??

            I am kind of newly studying of these storage part computer science and got some questions.
            Is there anyone can explain those questions clearly?
            Thank you.

            ...

            ANSWER

            Answered 2020-Apr-07 at 09:44

            It depends on what you call a "Solution". The basic infrastructure is the same it's a some kind of a "storage server" (storage system) with physical disk(s), but it very much dependent of technologies, vendors and various options. Typically, a storage system provides access to its physical disks with different protocols of 2 main groups: block-level protocols like SCSI or rarely ATA on one hand, or file-level protocols like NFS, CIFS, etc on the other. It doesn't mean, a storage system can't work in both, block and file modes.

            Storage network - SAN can be build over FC, FCoE, converged infrastructure, pure TCP/IP for iSCSI, Infiniband or any other infrastructure. Typically, when people say "SAN" they mean Block storage devices and FC protocol, but it doesn't mean, that a file storage - NAS can't be connected with SAN and vice verse.

            Source https://stackoverflow.com/questions/60768967

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

            Vulnerabilities

            No vulnerabilities reported

            Install fk-visual-search

            You can download it from GitHub.
            You can use fk-visual-search like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            https://github.com/flipkart-incubator/fk-visual-search.git

          • CLI

            gh repo clone flipkart-incubator/fk-visual-search

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            git@github.com:flipkart-incubator/fk-visual-search.git

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