jpsync 1.1.0

A git-like CLI to sync local folders with a remote JupyterHub via the Contents API.

jpsync

A git-like CLI to safely sync local folders with a remote JupyterHub — zero dependencies, pure Python.

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jp keeps a local folder in sync with a directory on a JupyterHub server — the way git keeps you in sync with a remote. You edit notebooks and scripts on your laptop, jp push to send them up, run your training on the server's GPUs, and jp pull the results back down.

It talks to the JupyterHub REST API directly, has zero third-party dependencies (pure Python standard library), and runs anywhere Python 3.9+ runs — macOS, Windows, Linux.

Why jp?

• Git-like workflow — jp clone, jp status, jp push, jp pull. Same muscle memory.
• Safe by default — on a shared research machine, jp never deletes remote files unless you explicitly turn that on, and even then it asks you file-by-file. Conflicts are never silently overwritten.
• Zero dependencies — one install, no dependency hell; ships as a wheel, a single .pyz, or a standalone binary.
• Cross-platform — macOS, Windows, Linux; Python 3.9 → 3.13.

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Installation

Recommended — pipx:

pipx install jpsync

Or with uv:

uv tool install jpsync

Both install the jp command in an isolated environment and put it on your PATH. Then check it works:

jp --version

If jp: command not found, run pipx ensurepath (or uv tool update-shell) and reopen your terminal.

Other ways to install

No Python required — standalone binary (macOS / Linux):

curl -fsSL https://raw.githubusercontent.com/pehqge/jpsync/main/scripts/install.sh | sh

No Python required — standalone binary (Windows, PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://raw.githubusercontent.com/pehqge/jpsync/main/scripts/install.ps1 | iex"

Single file — grab jp.pyz from the latest release and run it with any Python 3.9+:

python jp.pyz --help

From source (latest main):

pipx install "git+https://github.com/pehqge/jpsync"

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Getting started

1. Get your JupyterHub API token

jp authenticates with a personal API token from your JupyterHub.

1. Open your JupyterHub in a browser and log in (e.g. https://jupyter.example.com).
2. Go to the Token page — usually the Token link in the top bar, or visit https://<your-hub>/hub/token directly.
3. Type a note (e.g. jp), leave the scopes blank (full access to what you can already do), and click Request new API token.
4. Copy the token now — JupyterHub shows it only once.

Security: the token is like a password for your account. jp stores only the path to a token file, never the token value, and never logs or commits it.

2. Log in with jp login

Run jp login and follow the prompts. It asks for a short name, walks you through getting the token, then you paste it (your input stays hidden). Credentials are saved globally by default (usable from anywhere); pass --local to keep it only in the current workspace:

$ jp login
Name this server/credential (e.g. myserver): myserver
To get a JupyterHub API token:
  1. Open your JupyterHub in a browser and log in.
  2. Go to the Token page (the 'Token' link, or <your-hub>/hub/token).
  3. Click 'Request new API token' and copy it (it is shown only once).

Paste your API token (input hidden):
✓ saved global credential 'myserver'

Everything stays on your machine. jp writes the token to a private file (permissions 600) under ~/.config/jp/ (or the workspace's .jp/ for a local credential) and records only the credential's name in config — never the token value. The token is never printed, logged, committed, or sent anywhere except as the Authorization header to your own hub.

Run jp login again any time to add another server — keep as many credentials as you like and pick one when you clone. See Credentials.

3. Make sure your server is running

jp talks to your single-user server, so it must be started: open JupyterHub and, if needed, click Start My Server. (jp doctor will tell you if it's stopped.)

4. Clone your folder

Copy the URL of the folder from your browser's address bar — the lab/tree/... URL works directly:

jp clone https://jupyter.example.com/user/<you>/lab/tree/your-folder
cd your-folder

That creates a your-folder/ folder with a .jp/ workspace inside (like .git/) and downloads the remote tree. If you saved more than one credential, jp asks which one to use; with a single one it just uses it. The choice is remembered in the workspace (jp clone … --credential <name> to skip the prompt).

5. Work like git

jp status          # what changed, locally vs the server
jp push            # send local changes up
jp pull            # bring remote changes (e.g. training output) down

That's it. From any subdirectory of the workspace, jp finds its root automatically (it walks up looking for .jp/, stopping at your home folder).

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Command reference

Command	What it does
jp clone <url> [dir]	Clone a remote Jupyter folder into a new local directory. Accepts a lab/tree URL or --base-url/--prefix.
jp init <url>	Turn the current folder into a jp workspace (no download).
jp login	Save a named API-token credential (name the server, paste the token; defaults to global; use --local for workspace-only).
jp status	Show local vs. remote differences. Read-only.
jp push	Upload local changes. Additive by default.
jp pull	Download remote changes. Additive by default.
jp diff [path]	Show file-level differences.
jp ls [remote-path]	List a remote directory (no local writes).
jp config	Interactive settings editor (see below). Also config get/set/list.
jp ignore [pattern]	Manage .jpignore patterns.
jp rm <path>	Delete on the remote — gated, dry-run + typed confirmation. The only deleter.
jp kernel	Set up a VS Code remote kernel to run notebooks in the right directory (guide).
jp terminal	Open the remote machine's shell in your terminal, in the workspace folder. Creates/deletes only an ephemeral terminal session; touches no files.
jp doctor	Diagnose token, connectivity, server status.
jp update	Update jp to the latest version.
jp version	Print the version (also jp --version).

Global flags: -q/--quiet, --no-color. Every command has --help.

jp config — interactive settings

Run jp config with no arguments in a terminal for a settings screen:

  Mirror mode (allow deletes)              false
> Dotfile policy                           skip
  Colored output                           auto
  Network timeout (s)                      30.0

Up/Down move · Space change · i info · / search · Enter save · Esc cancel

• ↑/↓ move · Space cycle the value · i show help for the selected setting · / search · Enter save · Esc cancel.

For scripts, the classic forms still work: jp config list, jp config get <key>, jp config set <key> <value>.

Mirror mode (deleting files to match the other side)

By default jp push/jp pull are additive — they never delete. If you want true mirroring (delete on the remote when you delete locally, and vice-versa), turn on mirror mode:

jp config set mirror true      # persist it, or use --mirror for one run
jp push --mirror               # one-off

With mirror on, after the normal sync jp finds files that exist on one side but not the other and — always, before deleting anything — shows you the list and lets you choose, with the arrow keys, which to keep and which to delete:

Mirror mode: 2 file(s) exist on remote but not on the other side.
Choose which to DELETE on remote. Default is KEEP.

> [keep]   old_experiment.py
  [keep]   scratch.ipynb

Up/Down move · Space toggle · a delete-all · n keep-all · Enter confirm · Esc cancel

Nothing is deleted unless you mark it. In a non-interactive shell, mirror deletions are refused unless you pass --yes. Conflicts (both sides changed) are never deleted or overwritten.

Credentials & jp login

jp login is how you give jp your JupyterHub API token. It is fully interactive and everything happens locally — the token never leaves your machine and is never printed:

• You give the credential a name (usually the server, e.g. myserver).
• It shows you how to get a token, then prompts you to paste it with the input hidden (no echo).
• The scope defaults to global (stored in ~/.config/jp/, usable from any directory). Pass --local to store the credential only in the current workspace's .jp/.
• The token value goes into a private 600 file; only its name is recorded in the workspace config (credential key).
• A local credential lives in the workspace's .jp/, and jp drops a .jp/.gitignore (*) so that — even if the workspace is also a git repo — git ignores the whole .jp/ directory and the token can never be committed.

Save as many as you like — run jp login once per server:

jp login                                      # interactive: name, paste token; saves globally
jp login --name myserver --global             # scriptable form
jp login --token-stdin --name lab-gpu --local < token.txt

When you jp clone / jp init, jp reads the credentials available globally and locally: with one it's used automatically, with several you pick which server to use (or pass --credential <name>). The choice is saved in the workspace so later push/pull just work.

At sync time the token is resolved, in order: $JP_TOKEN (a value, for CI) → $JP_TOKEN_FILE (a path) → the workspace's saved credential → legacy token_path / ~/.config/jp/token. jp warns if any token file is readable by other users.

Keeping jp up to date

jp update           # detects pipx / uv / pip and upgrades in place
jp update --check   # just check; don't install

For a standalone binary install, jp update prints the one-line reinstall command for your OS.

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Configuration

Each workspace stores its settings in .jp/config.json (JSON, never the token value). Keys: base_url, prefix, credential, token_path, mirror, dotfiles, color, timeout. See docs/commands.md and docs/architecture.md.

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Security

jp is built for a shared machine where a mistake can destroy someone else's research. The guarantees:

• push/pull never delete unless you opt into mirror mode — and even then jp asks you, file by file, defaulting to keep.
• Conflicts are never silently overwritten. If both sides changed since the last sync, jp aborts that file and tells you.
• Path-jailing. Every remote operation is confined to your workspace's prefix. The server root and shared spaces (compartilhado, lapix, shared, …) are refused outright.
• Untrusted server on download. File names from the server are sanitized before anything is written locally (anti path-traversal / Zip-Slip), and writes are atomic and never follow a symlink.
• Your token never leaks — stored by path only, sent in the Authorization header (never a URL), redacted from all output, never committed.

Found a vulnerability? See SECURITY.md — please don't open a public issue.

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FAQ

Is jp related to git? No — it borrows git's workflow, not its internals. There's no remote version history on a JupyterHub.

Does it need Jupyter installed locally? No. Just Python 3.9+; it talks to the Hub over HTTPS.

Why won't my .gitignore (or any dotfile) upload? Most JupyterHub servers run with allow_hidden=False, which rejects creating hidden files (names starting with .). jp detects this and skips dotfiles on push, reporting them instead of failing — your .git/, .gitignore, .env etc. simply stay local (which is usually what you want). A nice side effect: secrets in dotfiles never get pushed by accident.

Will it overwrite my work? Never silently. A conflict aborts that file; remote deletes are opt-in (mirror mode) and confirmed file-by-file.

Can I edit notebooks locally in VS Code but run them on the remote GPUs? Yes — that's a core workflow. Sync with jp, then connect VS Code to your remote kernel. One catch: a remote kernel starts in the server's home, not your notebook's folder, so relative paths fail. Run jp kernel once to fix it. The full walkthrough (connecting the kernel + the cwd fix) is in docs/vscode-remote-cwd.md.

Troubleshooting

• jp: command not found — run pipx ensurepath / uv tool update-shell, reopen the terminal.
• your JupyterHub server appears to be stopped — open the Hub UI and click Start My Server.
• authentication failed / HTTP 403 — your token expired; create a new one and jp login again (or update the token file).
• A big upload times out — raise the timeout: jp config set timeout 120.
• FileNotFoundError / relative paths fail in VS Code with a remote kernel — the kernel starts in the server's home, not your notebook's folder. Run jp kernel (see the VS Code remote-kernel guide).

Run jp doctor for a guided check.

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Contributing

Contributions welcome — see CONTRIBUTING.md and the Code of Conduct. The project is standard-library only; please keep it dependency-free.

Support

jpsync is free and open source. If it saves you time on your JupyterHub workflow, you can support its development — thank you! ☕

  

License

MIT © Pedro Gimenez