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• AWS Lambda : "Unzipped size must be smaller than 106534017 bytes" after adding single file

  17 septembre 2023, par leon

  When trying to deploy my lambdas using AWS through the serverless framework I had no problems until I tried adding the ffmpeg binary.

  


  Now the ffmpeg binaries I have tried to add have ranged from 26 mb to 50 mb. Whichever I add, I get the following error :

  


  UPDATE_FAILED: WhatsappDocumentHandlerLambdaFunction (AWS::Lambda::Function)
Resource handler returned message: "Unzipped size must be smaller than 106534017 bytes (Service: Lambda, Status Code: 400, Request ID: ...)" (RequestToken: ..., HandlerErrorCode: InvalidRequest)


  


  The problem is that I did not add the file to this function. I added it to a completely different one.

  


  I have tried the following things :

  


  

  • Creating an "empty" function that only contains the ffmpeg binary and a function handler

  


  • Creating a layer that only contains the ffmpeg binary

  


  • Deleting the ffmpeg binary (the error goes away and deployment succeeds

  


  • Varying sizes of ffmpeg binaries between 26 and 50mb

  


  • Getting the ffmpeg-lambda-layer (https://github.com/serverlesspub/ffmpeg-aws-lambda-layer ; https://serverlessrepo.aws.amazon.com/applications/us-east-1/145266761615/ffmpeg-lambda-layer) and deploying it myself

  


  


  When trying every single one of these options I get the UPDATE_FAILED error in a different function that surely is not too big.

  


  I know I can deploy using a docker image but why complicate things with docker images when it should work ?

  


  I am very thankful for any ideas.

  


• FFmpeg transcode GIF into Mp4 and Mp4 to AVI using GPU

  9 octobre 2023, par Cristian

  I'm trying to convert GIF animated to mp4 and mp4 to AVI with FFmpeg.

  


  I started to use just the CPU, but I have to process millions of GIFs/mp4 content pieces. So, I started to have a lot of errors processing them, and it ended as a bottleneck. Therefore, I'm trying to use GPU to process the videos.

  


  Converting GIF to mp4 with CPU, I run the following command :

  


  ffmpeg -i animated.gif -movflags faststart -pix_fmt yuv420p -vf "scale=trunc(iw/2)*2:trunc(ih/2)*2" video.mp4


  


  Using the GPU I'm trying the following :

  


  ffmpeg
  -y
  -hwaccel nvdec
  -hwaccel_output_format cuda
  -i gifInputPath
  -threads 1
  -filter_threads 1
  -c:v h264_nvenc
  -vf hwupload_cuda,scale_cuda=-2:320:240:format=yuv420p
  -gpu 0
   mp4VideoPath


  


  The above command generates an exit status 1.

  


  The following is the dmesg command log

  


  Converting mp4 videos to AVI videos I'm running the following command

  


  ffmpeg
-i videoInputPath
-vcodec rawvideo
-pix_fmt yuv420p
-acodec pcm_s16le
-ar 44100
-ac 2
-s 320x240
-r 4
-f avi
aviOutputVideoPath


  


  For GPU I tried :

  


  ffmpeg
 -y
 -hwaccel cuda
 -hwaccel_output_format cuda
 -i videoInputPath
 -threads 1
 -filter_threads 1
 -c:a pcm_s16le
 -ac 2
 -ar 44100
 -c:v h264_nvenc
 -vf hwupload_cudascale_cuda=-2:320:240:format=yuv420p
 -r 4
 -f avi
 -gpu 0
 aviOutputVideoPath


  


  The following is the dmseg output is log

  


  


  1. What should be the best command for converting the GIF into Mp4 and Mp4 into AVI based on CPU configuration using the GPU(Amazon Nvidia t4) for best performance, low CPU, and moderated GPU consumption ?

     


  


  2. What are the best suggestions to Process these content pieces concurrently using GPU ?

     


  


  


  Note : I'm using Golang to execute the FFmpeg commands.

  


• ffmpeg extract multiple frames from single input

  10 octobre 2023, par Andrew Still

  I'm trying to dynamically extract multiple different frames from single video input. So the command I'm calling looking like this

  


  ffmpeg -loglevel debug -hide_banner -t 13.269541 -y -ss 0 -i "input-s3-url" -ss 13.269541 -i "same-input-s3-url" -map 0:v -vframes 1 /tmp/ca4cd7a3159743938c5362c171ea2cae.0.png -map 1:v -vframes 1 /tmp/ca4cd7a3159743938c5362c171ea2cae.13.269541.png


  


  It works and everything is good, until I deploy it to lambda. Even though I'm using 10gb of RAM it still failing with error. Locally it works like a charm but not on lambda. I'm not sure what the problem here but i'm regularly (not always) getting SIGSEGV

  


  

  


  Double-checked memory usage and it's doesn't look like a reason, but I'm not sure how correct this number

  


  

  


  I'm think maybe it's because it's making requests for each input, at least that's what I saw in debug mode, but still have no idea what's the problem here, would appreciate any suggestions/optimizations/help. Thanks

  


  ffmpeg added on lambda using this layer - https://serverlessrepo.aws.amazon.com/applications/us-east-1/145266761615/ffmpeg-lambda-layer