Add MlxDevice enum (Auto/Cpu/Gpu) that flows from the API through
PlaceInstance commands to instance creation and runner initialization.
The device is set via mx.set_default_device() in bootstrap.py before
the runner imports MLX, ensuring all subsequent operations use the
chosen device.
This enables Linux CPU-only machines to participate in inference and
allows forcing CPU mode for testing or when GPU has issues. Default
is Auto (current behavior) for full backward compatibility.
Closes#958
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
## Summary
- **Auto-open dashboard** in browser on first launch (uses
`~/.exo/.dashboard_opened` marker)
- **Welcome overlay** with "Choose a Model" CTA button when no model
instance is running
- **Tutorial progress messages** during model download → loading → ready
lifecycle stages
- **Fix conversation sidebar** text contrast — bumped to white text,
added active state background
- **Simplify technical jargon** — sharding/instance type/min nodes
hidden behind collapsible "Advanced Options" toggle; strategy display
hidden behind debug mode
- **Polished DMG installer** with drag-to-Applications layout, custom
branded background, and AppleScript-configured window positioning
## Test plan
- [ ] Launch exo for the first time (delete `~/.exo/.dashboard_opened`
to simulate) — browser should auto-open
- [ ] Verify welcome overlay appears on topology when no model is loaded
- [ ] Launch a model and verify download/loading/ready messages appear
in instance cards
- [ ] Check conversation sidebar text is readable (white on dark, yellow
when active)
- [ ] Verify "Advanced Options" toggle hides/shows sharding controls
- [ ] Build DMG with `packaging/dmg/create-dmg.sh` and verify
drag-to-Applications layout
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
## Motivation
There is an issue on Macs that means that an explicit synchronization is
necessary for memory to be updated from L1 cache. This means that GPU
locks can occur when a spin wait does not see the updated timestamp.
## Changes
Updated in my own personal fork.
## Why It Works
https://github.com/ARM-software/acle/releases
## Test Plan
### Manual Testing
Tested manually that no GPU locks occur (even with multiple simultaneous
instances running) and that the performance differential is negligible
(267 vs 269 tps on Llama 3.2 1B at an approx 10k context.)
------------------------------------------------------
I have seen a GPU lock, specifically when sending a particularly large
chat completion while the model was loading. However, I have since been
unable to reproduce and this may be something I did wrong. Please do
create an issue and tag me if any GPU locks do occur.
---------
Co-authored-by: Jake Hillion <jake@hillion.co.uk>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
## Motivation
Users currently manage instances directly, which means if a node
disconnects or connections break, the instance dies and nothing
recreates it. MetaInstance is a declarative primitive: "ensure an
instance matching these parameters always exists." The reconciler
watches for unhealthy or missing backing instances and re-places them
automatically.
## Changes
- **MetaInstance type** (`meta_instance.py`): declarative constraint
with `model_id`, `min_nodes`, optional `node_ids`, and `sharding`
- **Reconciler** (`reconcile.py`): `find_unsatisfied_meta_instances`
checks which MetaInstances lack a healthy backing instance,
`try_place_for_meta_instance` creates one
- **Master loop** (`main.py`): periodically reconciles unsatisfied
MetaInstances; immediate placement on `CreateMetaInstance` command
- **API** (`api.py`): `create_meta_instance` / `delete_meta_instance` /
`GET /meta_instances` endpoints; delete cascades to backing instances
with task cancellation
- **Binding via `meta_instance_id` on Instance** (`instances.py`): no
separate binding event or backing map — the instance carries its parent
MetaInstance ID directly, eliminating race conditions in the reconciler
- **Dashboard**: sidebar shows MetaInstances with their backing instance
status; orphan instances (created directly) still shown separately
- **Tests**: constraint matching, connection health, unsatisfied
detection, exclusive binding, cascade delete with task cancellation
### Recent improvements
- **fix: cancel active tasks on cascade delete** — `DeleteMetaInstance`
now emits `TaskStatusUpdated(Cancelled)` for any Pending/Running tasks
on backing instances before emitting `InstanceDeleted`. Previously,
cascade-deleting backing instances left orphaned task references in
state.
- **Lifecycle logging** — added `logger.info`/`logger.warning` for:
`CreateMetaInstance` (model, min_nodes, sharding), `DeleteMetaInstance`
(with cascade count), reconciler placement success/failure, and retry
decisions with attempt counts in `InstanceHealthReconciler`.
- **GET `/meta_instances` endpoint** — lists all meta-instances without
needing to fetch full state.
- **2 regression tests** — `test_cascade_delete_cancels_active_tasks`
and `test_cascade_delete_skips_completed_tasks` verify the
cascade-delete event sequence.
## Why It Works
Putting `meta_instance_id` on `BaseInstance` makes binding inherent to
instance creation. When the reconciler creates an instance for a
MetaInstance, it tags it via `model_copy`. When the instance is deleted,
the binding disappears with it. This avoids the two bugs that a separate
binding mechanism would introduce:
1. Stale exclusion sets — the reconciler loop can't accidentally bind
two MetaInstances to the same instance
2. Delete ordering race — no window between deleting an instance and its
binding where the reconciler could re-place
## Test Plan
### Manual Testing
<!-- Hardware: (e.g., MacBook Pro M1 Max 32GB, Mac Mini M2 16GB,
connected via Thunderbolt 4) -->
- Created MetaInstance via dashboard, verified instance placed
- Verified delete cascades (deleting MetaInstance removes backing
instance)
- Verified orphan instances still work independently
### Automated Testing
- 30 tests in `test_meta_instance_edge_cases.py`: lifecycle, retry
logic, error handling, concurrent operations, cascade delete with task
cancellation
- 24 tests in `test_reconcile.py`: constraint matching, connection
health (single/multi-node, edge removal, IP changes), unsatisfied
detection, exclusive binding, idempotency
- All 261 tests pass
- basedpyright 0 errors, ruff clean, dashboard builds
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
## Motivation
Fixes#1370
When the macOS app stops exo, GPU/system memory isn't released. This
happens because:
1. The macOS app calls `process.terminate()` (SIGTERM) but the Python
process only registers a graceful shutdown handler for SIGINT, not
SIGTERM. SIGTERM's default Python behavior raises `SystemExit` which
bypasses the cleanup cascade (runner subprocess MLX cleanup via
`mx.clear_cache()`, channel closing, etc.).
2. The app doesn't wait for the process to actually finish cleanup — it
immediately nils out the process reference.
## Changes
**`src/exo/main.py`**: Register SIGTERM handler alongside SIGINT so the
graceful shutdown cascade (`Node.shutdown()` → cancel task group →
worker/runner cleanup → `mx.clear_cache()` + `gc.collect()`) runs
regardless of which signal is received.
**`app/EXO/EXO/ExoProcessController.swift`**: Replace immediate
`process.terminate()` with escalating shutdown per @Evanev7's
suggestion:
1. Send SIGINT via `process.interrupt()` — triggers the registered
Python handler for graceful cleanup
2. Wait up to 5 seconds for the process to exit
3. If still running, escalate to SIGTERM via `process.terminate()`
4. Wait up to 3 seconds
5. If still running, force kill via SIGKILL
The escalation runs in a detached `Task` so the UI updates immediately
(status → stopped) without blocking.
## Why It Works
The root cause is that SIGTERM wasn't triggering the graceful shutdown
path. By registering a SIGTERM handler in Python and sending SIGINT
first from the macOS app, the process gets a chance to run the full
cleanup cascade: cancelling the task group, shutting down runners (which
call `del model; mx.clear_cache(); gc.collect()`), closing channels, and
flushing logs. The escalation to SIGTERM and SIGKILL ensures the process
always terminates even if graceful shutdown hangs.
## Test Plan
### Manual Testing
<!-- Hardware: Mac Studio M4 Max 128GB -->
- Start exo via macOS app, load a model, run inference
- Stop via the toggle switch, verify memory is released without
requiring a system restart
- Test rapid stop/start (restart) to ensure no race conditions
### Automated Testing
- `uv run basedpyright` — 0 errors
- `uv run ruff check` — passes
- `nix fmt` — no changes
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Evan Quiney <evanev7@gmail.com>
2026-02-17 09:03:54 -08:00
8 changed files with 74 additions and 10 deletions
# Import main after setting global logger - this lets us just import logger from this module
try:
fromexo.worker.runner.runnerimportmain
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