Hacktoberfest 2026: the issues maintainers tagged for October, open and beginner-friendly. Browse Hacktoberfest issues

Thrice as much memory for AlexNet on Caltech256 in Julia than in Python. Why?

Open
  • Is something wrong?
#261 3 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
35/100
Issue type
Bug
Clarity
Needs clarification
Activity status
Stale
Tech stack
julia
Domain
machine-learning

Research direction

Start by reproducing the Julia and Python variants from the linked alexnet.jl, main.jl, alexnet.py, and main.py files on a 12 GB GPU, using the reported batch sizes and logs. Compare their allocation behavior and identify what accounts for the Julia variant's higher memory use; done means the cause and a verified resolution or limitation are documented.

Written by the indexing model from the issue text.

Description

I am having a severe problem with training AlexNet (see alexnet.jl) in Julia (0.5.2) on my GPU (12gb mem).

I am training on Caltech256 dataset (see main.jl)

Julia variant: I am out of memory when starting. See the following log:

julia> include("main.jl")
[15:36:03] src/io/iter_image_recordio_2.cc:135: ImageRecordIOParser2: ../../../data/caltech256-train.rec, use 4 threads for decoding..
[15:36:03] src/io/iter_image_recordio_2.cc:135: ImageRecordIOParser2: ../../../data/caltech256-val.rec, use 4 threads for decoding..
INFO: Start training on MXNet.mx.Context[GPU0]
INFO: Initializing parameters...
INFO: Creating KVStore...
[15:36:09] src/operator/././cudnn_algoreg-inl.h:65: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
INFO: TempSpace: Total 1345 MB allocated on GPU0
INFO: Start training...
[15:36:12] /home/antholzer/mxnet/dmlc-core/include/dmlc/./logging.h:304: [15:36:12] src/storage/./pooled_storage_manager.h:84: cudaMalloc failed: out of memory

Now if I run the same Python variant (alexnet.py, main.py), I have no problems with memory. With a batch size of 128 I am running at 3gb memory and with batch size of 256 at around 4gb.

Note: At least I was able to train the Julia variant with a batch size of 16.

I wonder why the Julia variant blows up its memory... ❓ Anyone has an idea about this or experiences similar issues?

Dominant language
No language data
Stars
370
Forks
63
No merged PRs in 30d

Getting set up

This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from dmlc/MXNet.jl

All issues in dmlc/MXNet.jl

Similar issues

More Machine Learning issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.