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pin tf to ensure compatability with tensorflow-metal

This commit is contained in:
Levi Neuwirth 2026-04-16 15:26:55 -04:00
parent 8e1c8833f5
commit 01b9ed9475
10 changed files with 301 additions and 82 deletions
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1
.claude/scheduled_tasks.lock Normal file
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@ -0,0 +1 @@
{"sessionId":"b333c4dc-d866-4418-9a6e-f4cc30f904c1","pid":840357,"acquiredAt":1776367523862}

23
CHANGELOG.md
View File

@ -20,7 +20,7 @@ be split into per-release sections once tagging begins.
policy-enforcing `.gitignore`, pinned Python 3.11 (`.python-version`),
and `pyproject.toml` with full project metadata, classifiers, and
URL pointers. The runtime TensorFlow dependency is pinned to
`tensorflow>=2.16,<3.0` — see *Changed* below for the rationale.
`tensorflow>=2.16,<2.19` — see *Changed* below for the rationale.
`psutil>=5.9` is a runtime dependency used by the estimator's
always-on `PerformanceMetrics` collection to sample peak RSS.
- `[project.optional-dependencies].analysis` extra for fastdtw, scipy,
@ -381,17 +381,24 @@ be split into per-release sections once tagging begins.
`datetime.utcnow()`, addresses the "no-videos"-vs-exception-path
inconsistency (both now quarantine), and persists a structured
`error` string on every failure for grep-friendly diagnostics.
- **TensorFlow pin tightened to `tensorflow>=2.16,<3.0`.** The 2.16
- **TensorFlow pin set to `tensorflow>=2.16,<2.19`.** The 2.16
floor is the first release with native `darwin/arm64` wheels under
the `tensorflow` package name on PyPI, so a single dependency line
works across Linux x86_64, Linux arm64, and Apple Silicon macOS
without platform markers or a separate `tensorflow-macos` package.
Empirical verification: the pinned MeTRAbs SavedModel
(`metrabs_eff2l_y4_384px_800k_28ds`, serialized with TF 2.10)
loads and runs `detect_poses` end-to-end on TF 2.21 + Keras 3 with
no errors, and exposes only stock TensorFlow ops (zero MeTRAbs
custom kernels). Full test matrix and op inventory in
`RESEARCH.md`.
The `<2.19` ceiling is a `tensorflow-metal` compatibility constraint:
the latest Metal plugin (1.2.0, January 2025) advertises "TF 2.18+"
but in practice fails on 2.19 and 2.20 with symbol-not-found errors
and graph-execution `InvalidArgumentError`s
([tensorflow/tensorflow#84167](https://github.com/tensorflow/tensorflow/issues/84167)).
Cap is global rather than darwin-only so dependency resolution stays
identical across platforms. The MeTRAbs SavedModel itself
(`metrabs_eff2l_y4_384px_800k_28ds`, serialized with TF 2.10) was
separately verified to load and run `detect_poses` end-to-end on
TF 2.21 + Keras 3 with no errors and zero custom ops, so the cap is
purely an external-package constraint and can lift once Apple ships
a Metal plugin that tracks mainline TensorFlow again. Full probe
data and op inventory in `RESEARCH.md`.
- Operating-system classifiers in `pyproject.toml` extended from
Linux-only to `POSIX` + `POSIX :: Linux` + `MacOS`, reflecting the
Apple Silicon support that the TF 2.16 floor makes real.

28
RESEARCH.md
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@ -356,7 +356,7 @@ that crosses the Keras-3 cutover at TF 2.16.
### Decision
Pin `tensorflow>=2.16,<3.0`. Reasoning:
Pin `tensorflow>=2.16,<2.19`. Reasoning:
1. **2.16 is the Apple Silicon floor that matters.** TF 2.16 is the
first release with native `darwin/arm64` wheels published on PyPI
@ -387,6 +387,26 @@ Pin `tensorflow>=2.16,<3.0`. Reasoning:
`docs/getting-started.md`, and users are expected to spot-check
`poses3d` output against the CPU path before trusting it for any
clinical measurement.
4. **`tensorflow-metal` forces a TF upper bound.** `tensorflow-metal`
1.2.0 (released January 2025, the latest version as of 2026-04) is
advertised as supporting "TF 2.18+" but in practice fails on
2.19 and 2.20 with symbol-not-found errors and graph-execution
`InvalidArgumentError`s. See
[tensorflow/tensorflow#84167](https://github.com/tensorflow/tensorflow/issues/84167)
and the Apple Developer forum threads at
[developer.apple.com/forums/thread/772147](https://developer.apple.com/forums/thread/772147)
and [developer.apple.com/forums/thread/803658](https://developer.apple.com/forums/thread/803658).
2.18.x is the last version confirmed to work cleanly on Apple
Silicon GPU. Even though the Metal path is opt-in, dependency
resolution is shared — if uv resolves `tensorflow` to 2.21 on a
Linux developer's machine and 2.18 on the Mac, lockfile churn
and "works on my box" become permanent. Cap is therefore applied
globally rather than via a darwin/arm64 marker split. Cost on
Linux is zero: nothing in the pipeline depends on TF 2.19+
features, and the SavedModel ran fine on TF 2.21 in the probe
above, so the cap is purely an external-package constraint. Lift
it once Apple ships a Metal plugin that tracks mainline
TensorFlow again.
### What is **not** yet verified
@ -422,9 +442,11 @@ Pin `tensorflow>=2.16,<3.0`. Reasoning:
detector head), how do we want the loader to fail? Currently the
`_REQUIRED_MODEL_ATTRS` interface check would still pass; the failure
would surface at first `detect_poses` call, which is late.
3. Does it make sense to upper-bound the pin more tightly than `<3.0`
3. ~~Does it make sense to upper-bound the pin more tightly than `<3.0`
(e.g. `<2.22` to bound to tested versions), or is the SemVer guard
sufficient given the all-stock-ops result?
sufficient given the all-stock-ops result?~~ **Resolved 2026-04-16.**
Tightened to `<2.19` for `tensorflow-metal` compatibility. See
reasoning point 4 in the Decision section above.
### Next steps

48
benchmarks/README.md
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@ -49,32 +49,42 @@ a tracked extension inside `benchmarks/videos/` because only
## Rsyncing to the research Mac
The directory layout is designed so one `rsync` path covers both
code and videos:
The repo ships a wrapper script, [`scripts/sync_benchmarks.sh`](../scripts/sync_benchmarks.sh),
that pushes the whole `benchmarks/` directory to the research Mac
with a predictable command:
```console
$ rsync -av --delete \
--exclude='.venv/' \
--exclude='site/' \
--exclude='.git/' \
~/Repos/research/brown/shu/neuropose/ \
mac.local:~/Repos/research/brown/shu/neuropose/
$ scripts/sync_benchmarks.sh # copy files, no deletions
$ scripts/sync_benchmarks.sh --dry-run # preview what would transfer
$ scripts/sync_benchmarks.sh --delete # make remote exactly mirror local
```
Defaults are baked in for the Shu Lab research Mac
(`Levi@100.64.15.110:/Users/levi/Repos/neuropose/benchmarks/`). Point
the script at a different destination via environment variables:
```console
$ REMOTE_HOST=me@other-host REMOTE_PATH=/tmp/benchmarks/ \
scripts/sync_benchmarks.sh
```
After the sync, the videos in `benchmarks/videos/` on the Mac are
identical to the ones on Linux, so a benchmark run on the Mac can
reference the same filename the Linux report does — makes cross-
machine comparisons trivial.
identical to the ones on the source machine, so a benchmark run on
the Mac can reference the same filename the local report does —
makes cross-machine comparisons trivial.
Tips:
Notes:
- Add `--exclude='benchmarks/videos/*.json'` if you want to keep
per-machine benchmark results isolated.
- `--delete` makes the target exactly mirror the source. Without
it, old files on the target persist — safer but surprising.
- For one-off pushes, `scp benchmarks/videos/clip.mp4
mac.local:~/Repos/research/brown/shu/neuropose/benchmarks/videos/`
works without touching the rest of the repo.
- **`--delete` is opt-in.** The default is purely additive so the
remote can hold per-machine benchmark result JSONs that the source
machine does not have. Pass `--delete` only when you want the
remote to become an exact mirror.
- **`--partial` is always on.** An interrupted transfer of a
multi-gigabyte video can resume on the next invocation without
starting from zero.
- **One-off pushes** of a single file still work fine with plain
`scp` (or rsync directly) — the wrapper script is for the
"everything in `benchmarks/`" case.
## Bulk intake via `neuropose ingest`

12
docs/getting-started.md
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@ -16,10 +16,14 @@ before.
- Internet access on first run (for the ~2 GB MeTRAbs model download)
!!! note "Apple Silicon"
NeuroPose pins `tensorflow>=2.16`, which is the first TensorFlow
release with native `darwin/arm64` wheels on PyPI. Mac users get a
working CPU install from the same command Linux users run — no
`tensorflow-macos`, no platform markers, no extra configuration.
NeuroPose pins `tensorflow>=2.16,<2.19`. The 2.16 floor is the first
TensorFlow release with native `darwin/arm64` wheels on PyPI; the
`<2.19` ceiling tracks the highest TF version that Apple's
`tensorflow-metal` plugin works cleanly with (see the *Metal GPU
acceleration* note below and `RESEARCH.md` for the full reasoning).
Mac users get a working CPU install from the same command Linux
users run — no `tensorflow-macos`, no platform markers, no extra
configuration.
Metal GPU acceleration is available as an **opt-in extra** for
users who need it:

16
pyproject.toml
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@ -45,8 +45,18 @@ classifiers = [
# split to ``tensorflow-macos`` on Apple Silicon. The MeTRAbs SavedModel was
# serialized with TF 2.10 and uses only stock ops (no custom kernels), so
# any TF >= 2.10 loads it; 2.16 is chosen for cross-platform install
# uniformity, not for any MeTRAbs-side requirement. End-to-end verification
# against TF 2.21 is logged in RESEARCH.md.
# uniformity, not for any MeTRAbs-side requirement.
#
# The ``<2.19`` ceiling is a tensorflow-metal compatibility constraint, not
# a MeTRAbs one. Apple's ``tensorflow-metal`` 1.2.0 (the latest release,
# Jan 2025) advertises "TF 2.18+" support, but community reports on the
# Apple Developer forums and tensorflow/tensorflow#84167 show it actually
# breaks on 2.19+ with symbol-not-found errors and ``InvalidArgumentError``
# at graph execution time. 2.18.x is the last version confirmed to work
# cleanly on Apple Silicon GPU. Cap applied globally rather than with a
# darwin/arm64 marker split to keep dependency resolution identical across
# platforms; revisit once Apple ships a Metal plugin that tracks mainline
# TensorFlow again.
dependencies = [
"typer>=0.12",
"pydantic>=2.6",
@ -61,7 +71,7 @@ dependencies = [
# small C extension; brought in at runtime because metrics are always
# collected, not an optional feature.
"psutil>=5.9",
"tensorflow>=2.16,<3.0",
"tensorflow>=2.16,<2.19",
"tensorflow-hub>=0.16",
]

119
scripts/sync_benchmarks.sh Executable file
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@ -0,0 +1,119 @@
#!/usr/bin/env bash
# Sync the benchmarks/ directory from this repo checkout to a remote
# machine — typically the research Mac where inference actually runs.
#
# Defaults match the Shu Lab research Mac (a Tailscale address):
#
# Levi@100.64.15.110:/Users/levi/Repos/neuropose/benchmarks/
#
# Override either side via environment variables:
#
# REMOTE_HOST=user@host # SSH target
# REMOTE_PATH=/absolute/path/ # Remote destination (trailing slash
# # recommended — see rsync(1))
#
# Flags:
#
# -n, --dry-run Show what would be transferred, change nothing.
# --delete Make the remote exactly mirror the local source,
# deleting any files on the remote that are not in
# the local tree. OFF by default because the remote
# may hold per-machine benchmark result JSONs you
# do not want to lose. Pass --delete explicitly
# when you *want* a clean mirror.
# -h, --help Print this header.
#
# What gets transferred:
#
# The entire benchmarks/ directory, including videos/*.mp4 and any
# *.json result files. The directory's .gitignore status is
# irrelevant to rsync — rsync copies what is in the working tree,
# not what is tracked by git — so the videos you drop into
# benchmarks/videos/ travel to the remote exactly as they sit on
# disk.
#
# Safety:
#
# - The script resolves its source directory relative to the repo
# root via `git rev-parse --show-toplevel`, so it works from any
# subdirectory of the checkout and never accidentally syncs some
# other `benchmarks/` that happens to live in $PWD.
# - --partial keeps half-transferred files on the remote so an
# interrupted run can resume on the next invocation.
# - Without --delete, the script is purely additive: it will create
# and update files on the remote but never remove them.
#
# Examples:
#
# scripts/sync_benchmarks.sh
# scripts/sync_benchmarks.sh --dry-run
# scripts/sync_benchmarks.sh --delete
# REMOTE_HOST=me@other-host scripts/sync_benchmarks.sh
set -euo pipefail
REMOTE_HOST="${REMOTE_HOST:-Levi@100.64.15.110}"
REMOTE_PATH="${REMOTE_PATH:-/Users/levi/Repos/neuropose/benchmarks/}"
dry_run=0
do_delete=0
while [ $# -gt 0 ]; do
case "$1" in
-n|--dry-run)
dry_run=1
;;
--delete)
do_delete=1
;;
-h|--help)
# Echo the leading comment block as the help text so the
# in-script documentation is the single source of truth.
sed -n '2,60p' "$0" | sed 's/^# \{0,1\}//'
exit 0
;;
*)
echo "error: unknown argument: $1" >&2
echo "run with --help for usage" >&2
exit 2
;;
esac
shift
done
# Resolve the repo root so this script works from any cwd inside the
# checkout. $(dirname "$0") is the scripts/ directory; the parent is
# the repo root.
script_dir="$(cd "$(dirname "$0")" && pwd)"
repo_root="$(git -C "$script_dir" rev-parse --show-toplevel)"
source_dir="$repo_root/benchmarks/"
if [ ! -d "$source_dir" ]; then
echo "error: source directory does not exist: $source_dir" >&2
exit 1
fi
# rsync flags:
# -a archive mode (recurse + preserve metadata)
# -h human-readable sizes
# --partial resume interrupted transfers on the next run
# --info=progress2 single running progress line (rsync >= 3.1.0
# on the local side; the remote's rsync version
# is irrelevant because progress is client-side)
rsync_flags=(-ah --partial --info=progress2)
if [ $dry_run -eq 1 ]; then
rsync_flags+=(--dry-run)
echo "[dry-run] no files will actually be transferred"
fi
if [ $do_delete -eq 1 ]; then
rsync_flags+=(--delete)
echo "[delete] remote will be pruned to exactly mirror local"
fi
echo "source: $source_dir"
echo "target: $REMOTE_HOST:$REMOTE_PATH"
echo
exec rsync "${rsync_flags[@]}" "$source_dir" "$REMOTE_HOST:$REMOTE_PATH"

6
tests/unit/test_benchmark.py
View File

@ -246,7 +246,7 @@ class TestFormatBenchmarkReport:
peak_rss_mb=512.0,
active_device="/CPU:0",
tensorflow_metal_active=False,
tensorflow_version="2.21.0",
tensorflow_version="2.18.0",
)
def test_report_mentions_key_fields(
@ -276,7 +276,7 @@ class TestFormatBenchmarkReport:
peak_rss_mb=100.0,
active_device="/GPU:0",
tensorflow_metal_active=True,
tensorflow_version="2.21.0",
tensorflow_version="2.18.0",
)
agg = BenchmarkAggregate(
repeats_measured=1,
@ -290,7 +290,7 @@ class TestFormatBenchmarkReport:
peak_rss_mb_max=100.0,
active_device="/GPU:0",
tensorflow_metal_active=True,
tensorflow_version="2.21.0",
tensorflow_version="2.18.0",
)
result = BenchmarkResult(
video_name="test.mp4",

8
tests/unit/test_io.py
View File

@ -223,7 +223,7 @@ def _make_metrics(
peak_rss_mb=peak_rss_mb,
active_device=active_device,
tensorflow_metal_active=metal_active,
tensorflow_version="2.21.0",
tensorflow_version="2.18.0",
)
@ -240,7 +240,7 @@ def _make_aggregate() -> BenchmarkAggregate:
peak_rss_mb_max=512.0,
active_device="/CPU:0",
tensorflow_metal_active=False,
tensorflow_version="2.21.0",
tensorflow_version="2.18.0",
)
@ -256,7 +256,7 @@ class TestPerformanceMetricsModel:
total_seconds=-1.0,
peak_rss_mb=0.0,
active_device="/CPU:0",
tensorflow_version="2.21.0",
tensorflow_version="2.18.0",
)
def test_rejects_negative_peak_rss(self) -> None:
@ -265,7 +265,7 @@ class TestPerformanceMetricsModel:
total_seconds=1.0,
peak_rss_mb=-5.0,
active_device="/CPU:0",
tensorflow_version="2.21.0",
tensorflow_version="2.18.0",
)
def test_model_load_seconds_optional(self) -> None:

122
uv.lock
View File

@ -719,7 +719,7 @@ requires-dist = [
{ name = "scikit-learn", marker = "extra == 'analysis'", specifier = ">=1.4" },
{ name = "scipy", marker = "extra == 'analysis'", specifier = ">=1.12" },
{ name = "sktime", marker = "extra == 'analysis'", specifier = ">=0.28" },
{ name = "tensorflow", specifier = ">=2.16,<3.0" },
{ name = "tensorflow", specifier = ">=2.16,<2.19" },
{ name = "tensorflow-hub", specifier = ">=0.16" },
{ name = "tensorflow-metal", marker = "platform_machine == 'arm64' and sys_platform == 'darwin' and extra == 'metal'", specifier = ">=1.2,<2" },
{ name = "typer", specifier = ">=0.12" },
@ -750,28 +750,20 @@ wheels = [
[[package]]
name = "numpy"
version = "2.3.5"
version = "2.0.2"
source = { registry = "https://pypi.org/simple" }
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