huawei-cloud-cloudrobo-train-metrics

Integrate the ocms (Octopus Metrics Summary) library into any PyTorch training script to report training metrics (loss, learning rate, timing, eval scores, grad norm, etc.) to the Huawei Octopus platform for visualization. Use this skill when the user mentions ocms, Octopus metrics, training metric reporting/upload/visualization, or wants to add metric logging to a training script. Supports custom training loops, HuggingFace Trainer, PyTorch Lightning, Accelerate, MMEngine, and shared base classes. Also triggers when the user asks to report/upload/log training metrics and the ocms package is installed in the conda env.

ocms Training Metric Integration

Overview

This skill integrates the ocms library into PyTorch training scripts so training metrics can be reported to the Huawei Octopus platform. It provides a framework-agnostic recorder module plus integration patterns for common training frameworks.

Workflow at a glance:

0. Check whether the ocms package is available (optional install, see "Step 0" — the integration works without it)
1. Discover where the training script logs metrics
2. Copy scripts/ocms_recorder.py into the project
3. Integrate per the training framework (see below)
4. Verify with python3 scripts/verify_ocms.py

Prerequisites

1. A PyTorch training script to integrate with (custom loop, HF Trainer, Lightning, Accelerate, MMEngine, or a shared base class).
2. Python 3.8+: run all commands with the training environment activated (e.g. conda activate <env>), so python3 is that environment's interpreter. The ocms package is optional: when it is missing, OcmsRecorder degrades gracefully without affecting training.
3. Training output directory: metrics fall back to <output_dir>/ocms_metrics outside the Octopus platform (on the platform, the METRICS env var is preferred).

All commands below run from this skill's directory. Project-specific paths are passed through shell variables that the agent sets from the user's answers (see "Parameter Confirmation"); no command contains a placeholder that must be edited by hand.

Workflow

Step 0: Check ocms (install only where available)

bash

python3 scripts/verify_ocms.py

The script reports whether ocms is installed and runs a self-test of the recorder in a temporary directory. Both outcomes are valid:

Result	Meaning	Next step
ocms: installed + OK: metrics.json and ... chart CSVs produced	metric reporting is enabled	continue with Step 1
ocms: not installed + OK: graceful degradation verified	the recorder disables itself; training is unaffected	continue with Step 1; install ocms only where it is available

ocms is a proprietary Octopus platform package: it is not on PyPI and is distributed only through a time-limited signed link in the Octopus course README. Environments that cannot obtain it (for example evaluation environments without that link) stay on the graceful-degradation path, which is the expected result there, not a failure. To install it where it is available, get the currently valid signed link from the official octopus-courses README (time-limited; re-fetch if expired), download the wheel to the current directory, then run:

bash

python3 -m pip install ./ocms-*.whl

After installing on the Octopus platform, re-run the check as python3 scripts/verify_ocms.py --require-ocms, which reports a missing package as an error instead of a degradation. Use that flag only where ocms is expected to be installed.

Step 1: Discover Metric-Logging Points

Search the training script for where metrics are built and logged. Common code patterns include:

python

# Code patterns to SEARCH FOR in the training script (not commands to run):
wandb.log(metrics, step=...)
logger.info(f"Step ..., Loss: {metrics}")
tensorboard_writer.add_scalar(...)
print(f"loss={loss.item()}")
metrics = {"loss": loss.item(), ...}

The function or method containing these calls is the integration point. Typical function names: _log_metrics, log_metrics, on_log, after_train_iter.

Also identify the available metric items — inspect the metrics dict keys:

• Loss: loss, train_loss, action_dit_loss, vlm_loss, etc.
• Learning rate: learning_rate, learning_rate/<group_name>, etc.
• Epoch: epoch
• Timing: timing/data, timing/model
• Eval: mse_score, accuracy, mIoU, etc.
• Gradient: grad_norm

Step 2: Copy the Recorder Module

Copy this skill's scripts/ocms_recorder.py into the target project's migrate_to_cloudrobo/ directory. It is a standalone, framework-agnostic module with no dependencies beyond ocms and the Python standard library.

bash

# Run from the skill directory. PROJECT_DIR is the training project root,
# set by the agent from the user's answer (see "Parameter Confirmation").
mkdir -p "$PROJECT_DIR/migrate_to_cloudrobo"
cp scripts/ocms_recorder.py "$PROJECT_DIR/migrate_to_cloudrobo/ocms_recorder.py"

The module exposes the following Python API. These are names used inside the training code, not shell commands:

python

from ocms_recorder import OcmsRecorder

recorder = OcmsRecorder.from_output_dir(output_dir)  # prefers $METRICS, else <output_dir>/ocms_metrics
# or: OcmsRecorder(log_dir=..., chart_groups=..., enabled=True)
recorder.log(step=global_step, metrics=metrics)       # log one metric snapshot (rank-0 only)
recorder.finish()                                     # cleanup (currently a no-op)

Step 3: Integrate Per Framework

The integration approach depends on the training framework. Read the corresponding section of references/integration_patterns.md for copy-paste-ready code snippets.

Quick reference — the integration points below are Python classes/methods to edit in the training code, not commands to execute:

Framework	Integration point (Python)	Pattern
Custom PyTorch loop	_log_metrics or equivalent	direct recorder.log()
HuggingFace Trainer	TrainerCallback.on_log	callback class
PyTorch Lightning	Callback.on_train_batch_end	callback class
Accelerate	training loop (rank-0 guard)	direct recorder.log()
MMEngine	Hook.after_train_iter	Hook class
Shared base class	base _init_ocms + _ocms_log	inherited methods

General rule: find where metrics are currently logged (wandb, tensorboard, logger), then add recorder.log(step=step, metrics=metrics) right after it. Run on rank-0 only to avoid file conflicts.

Step 4: Verify

After integration, run a short training, then check the metric files of that run (OUTPUT_DIR is the training output directory, set by the agent from the user's answer):

bash

python3 scripts/verify_ocms.py --output-dir "$OUTPUT_DIR"

• ocms installed: checks metrics.json and the chart CSVs (for example loss.csv, learning_rate.csv, epoch.csv, timing.csv) in $METRICS or $OUTPUT_DIR/ocms_metrics, and fails if they are missing.
• ocms not installed: no metric files are expected, so the check passes on the graceful-degradation path.
• An empty OUTPUT_DIR falls back to the self-test from Step 0.

Each CSV file's row format is [step, val1, val2, ...] with no header row. The metrics.json file describes the chart structure for the Octopus UI.

How the Recorder Works

Chart Groups

Metrics are grouped by predicate functions into at most 6 charts (ocms hard limit). Each group is a (chart_name, predicate) tuple; the predicate receives a metric key name and returns True when that metric belongs to this chart:

python

DEFAULT_CHART_GROUPS = [
    ("loss",         lambda k: k == "loss" or k.endswith("_loss")),
    ("learning_rate", lambda k: k == "learning_rate" or k.startswith("learning_rate/")),
    ("epoch",        lambda k: k == "epoch"),
    ("timing",       lambda k: k.startswith("timing/")),
    ("eval_score",   lambda k: k == "mse_score" or k == "accuracy" or k == "mIoU"),
    ("grad_norm",    lambda k: k == "grad_norm"),
]

Custom Chart Groups

If the default groups do not cover your metrics, pass custom groups:

python

recorder = OcmsRecorder(
    log_dir="...",
    chart_groups=[
        ("loss", lambda k: "loss" in k),
        ("lr", lambda k: "lr" in k or "learning_rate" in k),
        ("reward", lambda k: "reward" in k),
        # ... up to 6 groups
    ],
)

Key Name Sanitization

ocms only allows [a-zA-Z0-9_\-] for y-axis names. Metric keys like learning_rate/vision_tower or timing/data are automatically sanitized to lr_vision_tower and data.

Log Directory

Prefers the METRICS env var (set by the Octopus platform), falling back to <output_dir>/ocms_metrics for non-platform runs. This means the same code works both on the Octopus platform and locally.

Core Commands

Real, placeholder-free commands, run from this skill's directory with the training environment activated:

bash

# Check ocms and self-test the recorder (passes with or without ocms)
python3 scripts/verify_ocms.py

# Check the metric files of a training run (empty OUTPUT_DIR = self-test)
python3 scripts/verify_ocms.py --output-dir "$OUTPUT_DIR"

On the Octopus platform only, add --require-ocms to the first command to treat a missing ocms package as an error.

Integration itself is a code edit (Steps 2–3). OcmsRecorder, recorder.log(), and the framework callbacks are Python APIs, not commands.

Parameter Confirmation

Confirm these with the user before Step 2. The agent exports the paths as shell variables used by the commands above:

Parameter	Description
Training environment	The Python/conda environment with the training stack; activate it before running the commands so python3 is its interpreter
PROJECT_DIR	Training project root; the recorder is copied to $PROJECT_DIR/migrate_to_cloudrobo/
OUTPUT_DIR	Training output directory; metrics fall back to $OUTPUT_DIR/ocms_metrics
METRICS env var	Set by the Octopus platform; preferred metric directory when present
chart_groups	(chart_name, predicate) tuples, up to 6 charts (ocms hard limit)
step / metrics	Args to recorder.log(): training step (X axis) and flat metric dict
rank-0 only	Report from rank-0 only to avoid file conflicts

Reference Documents

• scripts/ocms_recorder.py — standalone recorder module (copy into the project to use)
• scripts/verify_ocms.py — ocms check + recorder self-test, and metric-file check for a training run (works with or without ocms)
• references/iam-policies.md — IAM permissions (none required)
• references/ocms_api.md — detailed ocms library API reference
• references/integration_patterns.md — integration code snippets per framework