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TimeMachine

TimeMachine provides a Samba-based network share for macOS Time Machine backups, supporting single or multi-user access and optional backup size limits.

  • Backup
TimeMachine provides a Samba-based network share for macOS Time Machine backups, supporting single or multi-user access and optional backup size limits.

Platform availability

Available on 1 of 9 platforms

  • Unraid
  • TrueNAS (not listed)
  • Umbrel (not listed)
  • ZimaOS (not listed)
  • Proxmox (not listed)
  • Helm (not listed)
  • Mac (not listed)
  • Windows (not listed)
  • Linux (not listed)

Health score?A 0-100 score computed nightly from four factors: maintained (40%: recent commits and steady activity), popular (25%: GitHub stars, log scale), easy to install (20%: platforms, install notes, setup guides, Docker image) and light to run (15%: minimum RAM, ARM support). When the source code has been checked, a fifth factor, security check (20%: automated scan for committed secrets, unsafe desktop-app settings, known-vulnerable dependencies and project hygiene; any serious finding caps it at 20), is added. Factors without data are left out and the rest are rescaled.

49/100 Fair

Maintained
54last commit 191 days ago
Popular
58800 GitHub stars
Easy to install
351 install platform, install notes, Docker image
Light to run
404 GB minimum RAM, runs on ARM

Availability: platform listings confirmed today, links checked 15 days ago

GitHub stars
800
Open issues
4
Last commit
2026-04-07
License
AGPL-3.0
Activity
stale

Checked 7 days ago - source: GitHub (mbentley/docker-timemachine)

Resources & Compatibility

Minimum requirements: 4 GB RAM. ARM (e.g. Raspberry Pi) is supported.

Checked 17 days ago - source: Docker Hub image tags and published docs

First-install notes

  • Install Python packages first Run “pip install -r requirements.txt” before trying to run TimeMachine, or the program will stop with missing-library errors. This installs the Python packages the app needs to train and test models. source
  • Your CSV needs a date and target column Put your data CSV in the folder you set as “root_path” and make sure it has a “date” column plus the columns you want to predict. If the file path or target column is wrong, training may fail or use the wrong data. source
  • Turn GPU on only if you have one Set “use_gpu=True” and choose a GPU number only if your computer has a usable GPU, otherwise set “use_gpu=False”. This prevents TimeMachine from failing because it cannot find a graphics card. source
  • Choose an image tag Use the latest or smb tag for amd64 and arm64; the afp tag is deprecated and only supports amd64. source

Fetched 7 days ago - each note links to its source

Alternatives

No alternatives collected yet.

Common questions

Do date-pinned TimeMachine SMB tags receive updates?

No. Date-specific smb-YYYYMMDD tags are daily snapshots of the smb manifest, remain available for six months, and do not receive security fixes. You must update to newer tags when they are published.

Is the AFP variant recommended?

No, migrate to the SMB image. AFP is deprecated by Apple, is less stable, and will not receive new feature updates.

What happens to the persistent volume path if I change TM_USERNAME?

Changing TM_USERNAME changes the persistent volume path. You should check the persistent data path notes before changing that variable.

How can Macs discover the TimeMachine share over the network?

Run the container with --net=host so Avahi discovery can be used. The README shows an SMB example with host networking for Avahi discovery.

Answers sourced from github.com

Community

Collected 11 days ago - public community threads