From fc306f0366f3cba6e122a10ac20b120a06c4a2cb Mon Sep 17 00:00:00 2001 From: Huaiming Li Date: Mon, 2 Nov 2026 09:28:26 +0000 Subject: [PATCH] Add LLM capacity reservation environment with BurstGPT replay --- docs/source/_toctree.yml | 2 + docs/source/environments.md | 7 + docs/source/environments/llm_capacity.md | 264 + envs/llm_capacity_env/.dockerignore | 12 + envs/llm_capacity_env/.gitattributes | 2 + envs/llm_capacity_env/.gitignore | 16 + envs/llm_capacity_env/Dockerfile | 10 + envs/llm_capacity_env/LICENSE | 27 + envs/llm_capacity_env/README.md | 279 + envs/llm_capacity_env/THIRD_PARTY_NOTICES.md | 16 + envs/llm_capacity_env/__init__.py | 5 + envs/llm_capacity_env/client.py | 11 + envs/llm_capacity_env/examples/__init__.py | 0 envs/llm_capacity_env/examples/client_demo.py | 23 + envs/llm_capacity_env/examples/evaluate.py | 53 + envs/llm_capacity_env/examples/policies.py | 25 + .../llm_capacity_env/examples/record_trace.py | 118 + envs/llm_capacity_env/openenv.yaml | 23 + envs/llm_capacity_env/outputs/.gitkeep | 0 .../outputs/example-traces/README.md | 20 + .../outputs/example-traces/TRACE_RESULT.json | 25 + .../rolling_quantile-seed23.jsonl | 15 + .../outputs/example-traces/zero-seed23.jsonl | 15 + envs/llm_capacity_env/pyproject.toml | 33 + envs/llm_capacity_env/requirements.lock | 113 + .../scripts/rebuild_aggregates.py | 49 + envs/llm_capacity_env/server/Dockerfile | 9 + envs/llm_capacity_env/server/__init__.py | 0 envs/llm_capacity_env/server/app.py | 31 + .../server/capacity_environment.py | 68 + .../server/data/LICENSE-BurstGPT.txt | 395 + .../server/data/PROVENANCE.json | 32 + .../server/data/hourly_demand.csv | 11089 ++++++++++++++++ envs/llm_capacity_env/server/engine.py | 143 + envs/llm_capacity_env/server/trajectory.py | 54 + envs/llm_capacity_env/uv.lock | 3060 +++++ .../validation-evidence/BASELINES.json | 66 + .../validation-evidence/CI_MATRIX.json | 142 + .../validation-evidence/PROTOCOL.json | 32 + .../validation-evidence/README.md | 23 + .../validation-evidence/RECONSTRUCTION.json | 5 + .../validation-evidence/RUNTIME.json | 113 + .../SERVER_TRAJECTORIES.json | 8 + .../validation-evidence/SOURCE_MANIFEST.json | 45 + .../validation-evidence/STAGING.json | 4 + .../validation-evidence/STATIC.json | 88 + .../validation-evidence/SUMMARY.json | 12 + tests/envs/test_llm_capacity_mcp.py | 38 + tests/envs/test_llm_capacity_replay.py | 120 + tests/envs/test_llm_capacity_trajectory.py | 59 + 50 files changed, 16799 insertions(+) create mode 100644 docs/source/environments/llm_capacity.md create mode 100644 envs/llm_capacity_env/.dockerignore create mode 100644 envs/llm_capacity_env/.gitattributes create mode 100644 envs/llm_capacity_env/.gitignore create mode 100644 envs/llm_capacity_env/Dockerfile create mode 100644 envs/llm_capacity_env/LICENSE create mode 100644 envs/llm_capacity_env/README.md create mode 100644 envs/llm_capacity_env/THIRD_PARTY_NOTICES.md create mode 100644 envs/llm_capacity_env/__init__.py create mode 100644 envs/llm_capacity_env/client.py create mode 100644 envs/llm_capacity_env/examples/__init__.py create mode 100644 envs/llm_capacity_env/examples/client_demo.py create mode 100644 envs/llm_capacity_env/examples/evaluate.py create mode 100644 envs/llm_capacity_env/examples/policies.py create mode 100644 envs/llm_capacity_env/examples/record_trace.py create mode 100644 envs/llm_capacity_env/openenv.yaml create mode 100644 envs/llm_capacity_env/outputs/.gitkeep create mode 100644 envs/llm_capacity_env/outputs/example-traces/README.md create mode 100644 envs/llm_capacity_env/outputs/example-traces/TRACE_RESULT.json create mode 100644 envs/llm_capacity_env/outputs/example-traces/rolling_quantile-seed23.jsonl create mode 100644 envs/llm_capacity_env/outputs/example-traces/zero-seed23.jsonl create mode 100644 envs/llm_capacity_env/pyproject.toml create mode 100644 envs/llm_capacity_env/requirements.lock create mode 100644 envs/llm_capacity_env/scripts/rebuild_aggregates.py create mode 100644 envs/llm_capacity_env/server/Dockerfile create mode 100644 envs/llm_capacity_env/server/__init__.py create mode 100644 envs/llm_capacity_env/server/app.py create mode 100644 envs/llm_capacity_env/server/capacity_environment.py create mode 100644 envs/llm_capacity_env/server/data/LICENSE-BurstGPT.txt create mode 100644 envs/llm_capacity_env/server/data/PROVENANCE.json create mode 100644 envs/llm_capacity_env/server/data/hourly_demand.csv create mode 100644 envs/llm_capacity_env/server/engine.py create mode 100644 envs/llm_capacity_env/server/trajectory.py create mode 100644 envs/llm_capacity_env/uv.lock create mode 100644 envs/llm_capacity_env/validation-evidence/BASELINES.json create mode 100644 envs/llm_capacity_env/validation-evidence/CI_MATRIX.json create mode 100644 envs/llm_capacity_env/validation-evidence/PROTOCOL.json create mode 100644 envs/llm_capacity_env/validation-evidence/README.md create mode 100644 envs/llm_capacity_env/validation-evidence/RECONSTRUCTION.json create mode 100644 envs/llm_capacity_env/validation-evidence/RUNTIME.json create mode 100644 envs/llm_capacity_env/validation-evidence/SERVER_TRAJECTORIES.json create mode 100644 envs/llm_capacity_env/validation-evidence/SOURCE_MANIFEST.json create mode 100644 envs/llm_capacity_env/validation-evidence/STAGING.json create mode 100644 envs/llm_capacity_env/validation-evidence/STATIC.json create mode 100644 envs/llm_capacity_env/validation-evidence/SUMMARY.json create mode 100644 tests/envs/test_llm_capacity_mcp.py create mode 100644 tests/envs/test_llm_capacity_replay.py create mode 100644 tests/envs/test_llm_capacity_trajectory.py diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index e60c30c4cb..52bdc0290c 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -97,6 +97,8 @@ title: SUMO-RL - local: environments/finrl title: FinRL + - local: environments/llm_capacity + title: LLM Capacity Reservation - local: environments/textarena title: TextArena - local: environments/git diff --git a/docs/source/environments.md b/docs/source/environments.md index f81c303cfc..b434107f61 100644 --- a/docs/source/environments.md +++ b/docs/source/environments.md @@ -7,6 +7,13 @@ The OpenEnv community has built a catalog of ready-to-run environments that cove
+
+
LLM Capacity Reservation
+

Reserve hourly serving capacity before real workload demand is revealed; optimize modeled reservation and overflow costs through MCP tools.

+
+ 📄 Docs +
+
Echo

Minimal observation/action loop for verifying client integrations, CI pipelines, and onboarding flows in seconds.

diff --git a/docs/source/environments/llm_capacity.md b/docs/source/environments/llm_capacity.md new file mode 100644 index 0000000000..20f4841c8d --- /dev/null +++ b/docs/source/environments/llm_capacity.md @@ -0,0 +1,264 @@ + +# LLM Capacity Reservation + +An OpenEnv MCP environment for sequential capacity reservation using real, publicly +released BurstGPT conversation-service workload logs. Before seeing the next day's +requests, an agent commits 24 hourly token reservations for each of two deployments. +The environment then reveals demand and charges reservation and overflow costs. + +This is a **trace-replay environment with modeled economics**. It is not a live +Azure service, hardware simulator, or measurement of current cloud pricing. + +## Task and interaction + +One episode contains 14 decision days. The observation provides the previous 14 +realized days, the next relative log day, published cost coefficients, and the most +recent outcome. Only completed 14-decision episodes are comparable; an unfinished prefix is not a successful zero-cost episode. The evaluation harness must require completion. + +Each action is a dictionary with exactly `ChatGPT` and `GPT-4`, +each containing 24 finite nonnegative hourly token reservations in `[0, 1000000]`. +The upper bound is a numerical action guard, not a claim about GPU throughput. + +Agent-facing MCP tools: + +- `observe_demand_history()`: inspect revealed history and costs; does not advance time. +- `reserve_capacity(reservations)`: irreversibly commit all 48 hourly quantities, + advance one day, and return the realized demand, cost and next observation. + +The training orchestrator owns `reset(seed=...)`, `step`, and `state`; these are +not agent-facing MCP tools. Reading history pays zero reward. A successful +reservation emits its day's reward exactly once. Invalid actions do not change +costs or advance the decision day. Calls after the final decision cannot submit +additional reservations. + +## Cost and reward + +For model `m` and hour `h`, let `r` be reserved tokens and `d` realized total tokens: + +```text +hourly_modeled_cost = unit_cost[m] * (r + 4 * max(d - r, 0)) / 1000 +unit_cost = {ChatGPT: 0.002, GPT-4: 0.0375} +daily_reward = -sum(hourly_modeled_cost) / 1000 +``` + +Minimize total modeled episode cost, or equivalently maximize summed reward. +The coefficients are explicit accounting assumptions retained from our existing +capacity-reservation case. They are not real customer invoices, contemporary +provider prices, or service-level guarantees. There is **no binary acceptance +threshold**. The factor 1000 in reward is a fixed numerical scale; it does not +change the optimal policy. + +The declared per-step range is `[-2, 0]`. The data loader checks that hourly demand +is at most 500,000 tokens. Cost is convex in each reservation; at either legal +endpoint it is at most `unit_cost * 2,000,000 / 1000`. Summing 24 hours and the two +coefficients gives at most 1,896 modeled cost units, hence reward at least -1.896. +Zero reward is also used for reset and observation calls; it is not a claim that +zero operating cost is attainable on a nonempty demand day. + +The 0.75 empirical quantile is a useful baseline because the cost has a 4x +shortfall multiplier. Drift and limited observation history create forecasting +uncertainty. We do not claim this environment already defeats frontier models. + +## Data, split and provenance + +Data: Yuxin Wang et al., *BurstGPT: A Real-World Workload Dataset to Optimize LLM +Serving Systems*, KDD 2025, DOI `10.1145/3711896.3737413`, arXiv `2401.17644`. +Upstream: https://github.com/HPMLL/BurstGPT, release `v2.0`. + +We use hourly sums of `Total tokens` from `Conversation log` records, for the +`ChatGPT` and `GPT-4` labels. There are 231 original relative days: 0–120 and +225–334. Days 121–224 are absent in the upstream release. Episodes never cross +this gap. Missing hours within a logged day have zero demand. + +All 231 selected-stream days are retained, with no score-based episode filtering. +This is an adaptation of our S11 capacity case, which previously selected these +streams for studying workload drift. It is not an unbiased sample of all LLM use. +No request text, completions, or user identifiers are distributed. + +- Train decision-day segments: 14–89 and 239–304. +- Test decision-day segments: 90–120 and 305–334. +- Start points require 14 preceding observed days and 14 consecutive target days. +- There are 116 train starts and 35 test starts. Adjacent episodes overlap; they + must not be treated as independent experimental replicates. +- Test episodes may use earlier revealed history, including earlier days of the + test time segment; this models online deployment with expanding observations. + +Set `CAPACITY_SPLIT=train` (default) or `test` when starting the server. Seeded +reset chooses uniformly from eligible starts. The split is orchestrator-owned. +The complete trace is public and shipped on the **server side**; these are not +secret, contamination-proof holdouts. Give remote agents the tools, not server +filesystem access. No live data API, secrets, or provider account is required. + +To independently reconstruct the aggregate, download `BurstGPT_1.csv`, +`BurstGPT_2.csv`, and `BurstGPT_3.csv` from upstream release v2.0 into a raw directory +(gzip versions are also accepted), then run: + +```bash +python scripts/rebuild_aggregates.py --raw-dir /path/to/raw --output rebuilt-hourly.csv +sha256sum rebuilt-hourly.csv +``` + +Expected SHA-256: +`de9db57e9dd8aa1c96f33789e45eaf5fe0b09726160e9554645d2cb8ca58c130`. +This reconstruction has been run on all three original release assets; no demand +values were modified to obtain the baseline results. + +Data license: CC-BY-4.0, preserved in `server/data/LICENSE-BurstGPT.txt`. +Modification details and aggregate hashes are in `server/data/PROVENANCE.json`. +Environment code license: BSD-3-Clause. See `THIRD_PARTY_NOTICES.md`. + +## Install and run + +The environment supports Python 3.11 and later and pins OpenEnv 0.11.0. +From `envs/llm_capacity_env`, install the frozen dependency set and run: + +```bash +uv sync --python 3.11 --frozen --all-groups --all-extras +uv run --frozen python examples/evaluate.py baseline-results.json +uv run --frozen uvicorn server.app:app --host 0.0.0.0 --port 8000 +``` + +In another shell in the same directory: + +```bash +uv run --frozen python examples/client_demo.py http://localhost:8000 +uv run --frozen openenv validate . --level static --skip-build +uv run --frozen openenv validate --url http://localhost:8000 +``` + +The integration tests live in the OpenEnv repository. From its root, install +its development dependencies and run: + +```bash +uv sync --all-extras +PYTHONPATH=src:envs uv run pytest -q \ + tests/envs/test_llm_capacity_mcp.py \ + tests/envs/test_llm_capacity_replay.py \ + tests/envs/test_llm_capacity_trajectory.py +``` + +`requirements.lock` records the separately validated Python 3.13 dependency +snapshot used for the container and standalone runtime checks. `uv.lock` is +the environment lockfile used by the repository's frozen-dependency check. + +Docker: + +```bash +docker build -t llm-capacity-env:0.1.0 . +docker run --rm -p 8000:8000 -e CAPACITY_SPLIT=test llm-capacity-env:0.1.0 +``` + +`/health`, `/schema`, and `/openapi.json` are provided by OpenEnv. The debug UI is +available at `/web/` when `ENABLE_WEB_INTERFACE=true` (enabled in the Docker image). +MCP and WebSocket clients use OpenEnv's standard endpoints. The server supports +independent concurrent sessions through an environment factory. + +## Baselines and validation scope + +`examples/evaluate.py` evaluates zero reservation, previous-day demand, a fixed +initial-history 0.75 quantile, and an adaptive rolling 0.75 quantile over **every** +eligible start. It also computes a perfect-information cost lower bound offline. +That diagnostic reads future demand and is explicitly not a causal policy or +agent tool. Baselines are descriptive controls, not a required pass score. + +Full example baseline trajectories are included under `outputs/example-traces/`. +Each records observations, MCP actions, rewards, terminal flags and next +observations for 14 decisions. Exact replay was checked independently. Reproduce: + +```bash +python examples/record_trace.py outputs/traces +``` + +These are deterministic heuristic-policy traces, not LLM reasoning traces or +evidence of frontier-agent difficulty. Recording here is explicit through the +example runner. The API server additionally records each reset and MCP transition +(action, observation, reward, terminal flag and step count) as a separate JSONL +file under `outputs/trajectories/`. Set `CAPACITY_TRACE_DIR` to choose another +server-owned output directory. Trace files are not exposed through agent tools +and generated session files are excluded from the release package. Direct +Python use records only when `trace_dir` or `CAPACITY_TRACE_DIR` is set. +This instrumentation is not a claim of full hub telemetry certification. + +Tests cover cost arithmetic, data hashes, gap handling, split boundaries, causal +observations, invalid actions, deterministic replay, terminal behavior, isolation, +MCP tool declarations, and reward accounting. A compact validation record is included in `validation-evidence/`; full raw +cluster/container logs remain in the accompanying submission preparation. +The documented static/API validation does not constitute full semantic-validator +certification, upstream approval, or a frontier-model difficulty result. + +## Publish + +After validating, the contributor can publish to their own Hugging Face Space: + +```bash +hf auth login +openenv push --repo-id YOUR_HF_USERNAME/llm_capacity_env +``` + +Publishing a new personal Space does not automatically add it to an official +OpenEnv collection or merge it into the GitHub monorepo. Ask maintainers about +listing/review using the prepared contribution description and validation record. + +## Contributor reference-policy study + +A separate 2026-11-02 study generated three independent policies from each of +`claude-opus-5-5` and `gemini-4-argon` gateway aliases, with three train-feedback +submissions per repeat. All six policies froze before the 35-start test; all +210 test trajectories (2,940 decisions) were exactly replayed. Mean test cost +over the three programs was 1173.798785 for Opus and 1180.904867 for Gemini, +compared with the causal rolling baseline at 1240.313545. Costs are modeled +accounting units and lower is better. This was contributor-side program synthesis, +not an official hosted evaluation or LLM weight training. The evidence archive +`reference-model-evidence-20261102.tar.gz` is supplied separately for review, +including requests, responses, policies, feedback, hashes and replay instructions. + +A separate 72-setting-per-deployment causal policy parameter fit selected the +original baseline and showed no test improvement. These results demonstrate +policy differences and measured baseline improvement, without establishing a +hard frontier-model benchmark, ordered model capability tiers, an optimal causal +ceiling or successful RL training. Test episodes overlap; they are not independent +statistical samples. + +## Additional difficulty and trainability evidence (2026-11-02) + +A completed extension trains a 32-hidden-unit neural quantile policy with Adam +on the exact environment cost. Across three seeds per initialization, cold-start +cost drops 45.20%; training a residual on +the existing rolling baseline drops cost 3.93% +(conditional 95% interval [0.94%, 6.84%]). +These statistics aggregate 61 unique test dates and use seven-day time blocks, +not independent-sample inference over overlapping episodes. This demonstrates +held-out improvement within these public recorded segments, including improvement +from a useful baseline rather than only a weak initialization. + +There are 24 fitting configurations (two arms, three seeds, two learning rates, +two regularizers), 80 epochs each, 82 fit days and 60 validation days. There are 2,480 minibatch updates per configuration (59,520 total); +training-label collection covers 116 overlapping 14-decision episodes, 1,624 +replay decisions and 142 unique label days. Only +validation selects configuration/checkpoint; all six selections are frozen before +testing all 35 starts. Test records were used in earlier studies; they are not +claimed to be previously unseen by all authors or contamination-proof. Training +uses labels revealed after legal training actions. It is supervised neural policy +fitting, not LLM weight fine-tuning or policy-gradient RL. + +A new controlled program-synthesis comparison uses Haiku 4.5 and Opus 5.5 gateway +aliases, three independent programs each, three train-feedback submissions each, +the same prompt/train starts/sandbox and 32,768 output tokens with 8,192 thinking +tokens per response. All six programs freeze before test. Opus/Haiku three-program mean 14-day costs were 1169.929902/1274.433480. The separate unique-day analysis gives 6.47% cost reduction for Opus, conditional 95% interval [0.79%, 12.79%]. +The full interval and per-program results, including unfavorable results, are +retained. API aliases/returned IDs are checked, not independently vendor-attested. + +The new evidence contains 280 initial/trained test episodes, 12 fixed-policy +repeat episodes and 210 matched-model test episodes. All **502 episodes / 7,028 +decisions** pass independent exact replay, source/data/hash and API-budget checks; +frozen neural weights also reproduce actions. Fixed-policy repeats have zero +observed cost range, which checks deterministic replay rather than distributional +robustness. Learning curves, all 192 checkpoints, model requests/responses, +feedback and selection records are archived in `quality-evidence-20261102.tar.gz`. + +The earlier small-grid fitting negative result remains valid for that narrower +policy family. These results establish measured learnability and characterize +policy/model differences on the recorded data; they do not establish a universal +frontier-hardness level, causal optimum, or transfer from training to general LLM +capability. The hindsight bound reads future demand and is only a diagnostic. +The reward/data/environment implementation is unchanged by this extension. diff --git a/envs/llm_capacity_env/.dockerignore b/envs/llm_capacity_env/.dockerignore new file mode 100644 index 0000000000..fe81d7b3ad --- /dev/null +++ b/envs/llm_capacity_env/.dockerignore @@ -0,0 +1,12 @@ +.git +.venv +__pycache__ +*.pyc +.pytest_cache +.ruff_cache +validation + +build/ +*.egg-info/ + +outputs/trajectories/ diff --git a/envs/llm_capacity_env/.gitattributes b/envs/llm_capacity_env/.gitattributes new file mode 100644 index 0000000000..1b9f494d98 --- /dev/null +++ b/envs/llm_capacity_env/.gitattributes @@ -0,0 +1,2 @@ +# Preserve exact provenance-verified CSV bytes when entering an LF-normalized monorepo. +server/data/hourly_demand.csv binary diff --git a/envs/llm_capacity_env/.gitignore b/envs/llm_capacity_env/.gitignore new file mode 100644 index 0000000000..58509d582d --- /dev/null +++ b/envs/llm_capacity_env/.gitignore @@ -0,0 +1,16 @@ +.venv/ +__pycache__/ +*.pyc +.pytest_cache/ +.ruff_cache/ + +build/ +*.egg-info/ + +outputs/trajectories/ + +# Ship only the reviewed example trajectories; generated sessions stay local. +!outputs/ +outputs/* +!outputs/.gitkeep +!outputs/example-traces/ diff --git a/envs/llm_capacity_env/Dockerfile b/envs/llm_capacity_env/Dockerfile new file mode 100644 index 0000000000..8a8acd41c6 --- /dev/null +++ b/envs/llm_capacity_env/Dockerfile @@ -0,0 +1,10 @@ +# Hugging Face Spaces uses the root Dockerfile. Keep identical to server/Dockerfile. +FROM python:3.13-slim@sha256:aaf08c5490d52cd92bc27142a5215b1e47355a5eaf9adfad2a0fbc022d0b5490 +WORKDIR /app/env +COPY requirements.lock /app/requirements.lock +RUN pip install --no-cache-dir -r /app/requirements.lock +COPY . /app/env +ENV PYTHONUNBUFFERED=1 PORT=8000 ENABLE_WEB_INTERFACE=true +EXPOSE 8000 +HEALTHCHECK --interval=30s --timeout=5s --start-period=60s CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" +CMD ["uvicorn", "server.app:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/envs/llm_capacity_env/LICENSE b/envs/llm_capacity_env/LICENSE new file mode 100644 index 0000000000..257162f43d --- /dev/null +++ b/envs/llm_capacity_env/LICENSE @@ -0,0 +1,27 @@ +BSD 3-Clause License + +Copyright (c) 2026, Huaiming Li +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. +3. Neither the name of the copyright holder nor the names of its contributors + may be used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/envs/llm_capacity_env/README.md b/envs/llm_capacity_env/README.md new file mode 100644 index 0000000000..817f86ccd2 --- /dev/null +++ b/envs/llm_capacity_env/README.md @@ -0,0 +1,279 @@ +--- +title: LLM Capacity Reservation +emoji: 📊 +colorFrom: blue +colorTo: green +sdk: docker +app_port: 8000 +base_path: /web +license: bsd-3-clause +tags: + - openenv + - reinforcement-learning + - resource-allocation + - llm-serving +--- + +# LLM Capacity Reservation + +An OpenEnv MCP environment for sequential capacity reservation using real, publicly +released BurstGPT conversation-service workload logs. Before seeing the next day's +requests, an agent commits 24 hourly token reservations for each of two deployments. +The environment then reveals demand and charges reservation and overflow costs. + +This is a **trace-replay environment with modeled economics**. It is not a live +Azure service, hardware simulator, or measurement of current cloud pricing. + +## Task and interaction + +One episode contains 14 decision days. The observation provides the previous 14 +realized days, the next relative log day, published cost coefficients, and the most +recent outcome. Only completed 14-decision episodes are comparable; an unfinished prefix is not a successful zero-cost episode. The evaluation harness must require completion. + +Each action is a dictionary with exactly `ChatGPT` and `GPT-4`, +each containing 24 finite nonnegative hourly token reservations in `[0, 1000000]`. +The upper bound is a numerical action guard, not a claim about GPU throughput. + +Agent-facing MCP tools: + +- `observe_demand_history()`: inspect revealed history and costs; does not advance time. +- `reserve_capacity(reservations)`: irreversibly commit all 48 hourly quantities, + advance one day, and return the realized demand, cost and next observation. + +The training orchestrator owns `reset(seed=...)`, `step`, and `state`; these are +not agent-facing MCP tools. Reading history pays zero reward. A successful +reservation emits its day's reward exactly once. Invalid actions do not change +costs or advance the decision day. Calls after the final decision cannot submit +additional reservations. + +## Cost and reward + +For model `m` and hour `h`, let `r` be reserved tokens and `d` realized total tokens: + +```text +hourly_modeled_cost = unit_cost[m] * (r + 4 * max(d - r, 0)) / 1000 +unit_cost = {ChatGPT: 0.002, GPT-4: 0.0375} +daily_reward = -sum(hourly_modeled_cost) / 1000 +``` + +Minimize total modeled episode cost, or equivalently maximize summed reward. +The coefficients are explicit accounting assumptions retained from our existing +capacity-reservation case. They are not real customer invoices, contemporary +provider prices, or service-level guarantees. There is **no binary acceptance +threshold**. The factor 1000 in reward is a fixed numerical scale; it does not +change the optimal policy. + +The declared per-step range is `[-2, 0]`. The data loader checks that hourly demand +is at most 500,000 tokens. Cost is convex in each reservation; at either legal +endpoint it is at most `unit_cost * 2,000,000 / 1000`. Summing 24 hours and the two +coefficients gives at most 1,896 modeled cost units, hence reward at least -1.896. +Zero reward is also used for reset and observation calls; it is not a claim that +zero operating cost is attainable on a nonempty demand day. + +The 0.75 empirical quantile is a useful baseline because the cost has a 4x +shortfall multiplier. Drift and limited observation history create forecasting +uncertainty. We do not claim this environment already defeats frontier models. + +## Data, split and provenance + +Data: Yuxin Wang et al., *BurstGPT: A Real-World Workload Dataset to Optimize LLM +Serving Systems*, KDD 2025, DOI `10.1145/3711896.3737413`, arXiv `2401.17644`. +Upstream: https://github.com/HPMLL/BurstGPT, release `v2.0`. + +We use hourly sums of `Total tokens` from `Conversation log` records, for the +`ChatGPT` and `GPT-4` labels. There are 231 original relative days: 0–120 and +225–334. Days 121–224 are absent in the upstream release. Episodes never cross +this gap. Missing hours within a logged day have zero demand. + +All 231 selected-stream days are retained, with no score-based episode filtering. +This is an adaptation of our S11 capacity case, which previously selected these +streams for studying workload drift. It is not an unbiased sample of all LLM use. +No request text, completions, or user identifiers are distributed. + +- Train decision-day segments: 14–89 and 239–304. +- Test decision-day segments: 90–120 and 305–334. +- Start points require 14 preceding observed days and 14 consecutive target days. +- There are 116 train starts and 35 test starts. Adjacent episodes overlap; they + must not be treated as independent experimental replicates. +- Test episodes may use earlier revealed history, including earlier days of the + test time segment; this models online deployment with expanding observations. + +Set `CAPACITY_SPLIT=train` (default) or `test` when starting the server. Seeded +reset chooses uniformly from eligible starts. The split is orchestrator-owned. +The complete trace is public and shipped on the **server side**; these are not +secret, contamination-proof holdouts. Give remote agents the tools, not server +filesystem access. No live data API, secrets, or provider account is required. + +To independently reconstruct the aggregate, download `BurstGPT_1.csv`, +`BurstGPT_2.csv`, and `BurstGPT_3.csv` from upstream release v2.0 into a raw directory +(gzip versions are also accepted), then run: + +```bash +python scripts/rebuild_aggregates.py --raw-dir /path/to/raw --output rebuilt-hourly.csv +sha256sum rebuilt-hourly.csv +``` + +Expected SHA-256: +`de9db57e9dd8aa1c96f33789e45eaf5fe0b09726160e9554645d2cb8ca58c130`. +This reconstruction has been run on all three original release assets; no demand +values were modified to obtain the baseline results. + +Data license: CC-BY-4.0, preserved in `server/data/LICENSE-BurstGPT.txt`. +Modification details and aggregate hashes are in `server/data/PROVENANCE.json`. +Environment code license: BSD-3-Clause. See `THIRD_PARTY_NOTICES.md`. + +## Install and run + +The environment supports Python 3.11 and later and pins OpenEnv 0.11.0. +From `envs/llm_capacity_env`, install the frozen dependency set and run: + +```bash +uv sync --python 3.11 --frozen --all-groups --all-extras +uv run --frozen python examples/evaluate.py baseline-results.json +uv run --frozen uvicorn server.app:app --host 0.0.0.0 --port 8000 +``` + +In another shell in the same directory: + +```bash +uv run --frozen python examples/client_demo.py http://localhost:8000 +uv run --frozen openenv validate . --level static --skip-build +uv run --frozen openenv validate --url http://localhost:8000 +``` + +The integration tests live in the OpenEnv repository. From its root, install +its development dependencies and run: + +```bash +uv sync --all-extras +PYTHONPATH=src:envs uv run pytest -q \ + tests/envs/test_llm_capacity_mcp.py \ + tests/envs/test_llm_capacity_replay.py \ + tests/envs/test_llm_capacity_trajectory.py +``` + +`requirements.lock` records the separately validated Python 3.13 dependency +snapshot used for the container and standalone runtime checks. `uv.lock` is +the environment lockfile used by the repository's frozen-dependency check. + +Docker: + +```bash +docker build -t llm-capacity-env:0.1.0 . +docker run --rm -p 8000:8000 -e CAPACITY_SPLIT=test llm-capacity-env:0.1.0 +``` + +`/health`, `/schema`, and `/openapi.json` are provided by OpenEnv. The debug UI is +available at `/web/` when `ENABLE_WEB_INTERFACE=true` (enabled in the Docker image). +MCP and WebSocket clients use OpenEnv's standard endpoints. The server supports +independent concurrent sessions through an environment factory. + +## Baselines and validation scope + +`examples/evaluate.py` evaluates zero reservation, previous-day demand, a fixed +initial-history 0.75 quantile, and an adaptive rolling 0.75 quantile over **every** +eligible start. It also computes a perfect-information cost lower bound offline. +That diagnostic reads future demand and is explicitly not a causal policy or +agent tool. Baselines are descriptive controls, not a required pass score. + +Full example baseline trajectories are included under `outputs/example-traces/`. +Each records observations, MCP actions, rewards, terminal flags and next +observations for 14 decisions. Exact replay was checked independently. Reproduce: + +```bash +python examples/record_trace.py outputs/traces +``` + +These are deterministic heuristic-policy traces, not LLM reasoning traces or +evidence of frontier-agent difficulty. Recording here is explicit through the +example runner. The API server additionally records each reset and MCP transition +(action, observation, reward, terminal flag and step count) as a separate JSONL +file under `outputs/trajectories/`. Set `CAPACITY_TRACE_DIR` to choose another +server-owned output directory. Trace files are not exposed through agent tools +and generated session files are excluded from the release package. Direct +Python use records only when `trace_dir` or `CAPACITY_TRACE_DIR` is set. +This instrumentation is not a claim of full hub telemetry certification. + +Tests cover cost arithmetic, data hashes, gap handling, split boundaries, causal +observations, invalid actions, deterministic replay, terminal behavior, isolation, +MCP tool declarations, and reward accounting. A compact validation record is included in `validation-evidence/`; full raw +cluster/container logs remain in the accompanying submission preparation. +The documented static/API validation does not constitute full semantic-validator +certification, upstream approval, or a frontier-model difficulty result. + +## Publish + +After validating, the contributor can publish to their own Hugging Face Space: + +```bash +hf auth login +openenv push --repo-id YOUR_HF_USERNAME/llm_capacity_env +``` + +Publishing a new personal Space does not automatically add it to an official +OpenEnv collection or merge it into the GitHub monorepo. Ask maintainers about +listing/review using the prepared contribution description and validation record. + +## Contributor reference-policy study + +A separate 2026-11-02 study generated three independent policies from each of +`claude-opus-5-5` and `gemini-4-argon` gateway aliases, with three train-feedback +submissions per repeat. All six policies froze before the 35-start test; all +210 test trajectories (2,940 decisions) were exactly replayed. Mean test cost +over the three programs was 1173.798785 for Opus and 1180.904867 for Gemini, +compared with the causal rolling baseline at 1240.313545. Costs are modeled +accounting units and lower is better. This was contributor-side program synthesis, +not an official hosted evaluation or LLM weight training. The evidence archive +`reference-model-evidence-20261102.tar.gz` is supplied separately for review, +including requests, responses, policies, feedback, hashes and replay instructions. + +A separate 72-setting-per-deployment causal policy parameter fit selected the +original baseline and showed no test improvement. These results demonstrate +policy differences and measured baseline improvement, without establishing a +hard frontier-model benchmark, ordered model capability tiers, an optimal causal +ceiling or successful RL training. Test episodes overlap; they are not independent +statistical samples. + +## Additional difficulty and trainability evidence (2026-11-02) + +A completed extension trains a 32-hidden-unit neural quantile policy with Adam +on the exact environment cost. Across three seeds per initialization, cold-start +cost drops 45.20%; training a residual on +the existing rolling baseline drops cost 3.93% +(conditional 95% interval [0.94%, 6.84%]). +These statistics aggregate 61 unique test dates and use seven-day time blocks, +not independent-sample inference over overlapping episodes. This demonstrates +held-out improvement within these public recorded segments, including improvement +from a useful baseline rather than only a weak initialization. + +There are 24 fitting configurations (two arms, three seeds, two learning rates, +two regularizers), 80 epochs each, 82 fit days and 60 validation days. There are 2,480 minibatch updates per configuration (59,520 total); +training-label collection covers 116 overlapping 14-decision episodes, 1,624 +replay decisions and 142 unique label days. Only +validation selects configuration/checkpoint; all six selections are frozen before +testing all 35 starts. Test records were used in earlier studies; they are not +claimed to be previously unseen by all authors or contamination-proof. Training +uses labels revealed after legal training actions. It is supervised neural policy +fitting, not LLM weight fine-tuning or policy-gradient RL. + +A new controlled program-synthesis comparison uses Haiku 4.5 and Opus 5.5 gateway +aliases, three independent programs each, three train-feedback submissions each, +the same prompt/train starts/sandbox and 32,768 output tokens with 8,192 thinking +tokens per response. All six programs freeze before test. Opus/Haiku three-program mean 14-day costs were 1169.929902/1274.433480. The separate unique-day analysis gives 6.47% cost reduction for Opus, conditional 95% interval [0.79%, 12.79%]. +The full interval and per-program results, including unfavorable results, are +retained. API aliases/returned IDs are checked, not independently vendor-attested. + +The new evidence contains 280 initial/trained test episodes, 12 fixed-policy +repeat episodes and 210 matched-model test episodes. All **502 episodes / 7,028 +decisions** pass independent exact replay, source/data/hash and API-budget checks; +frozen neural weights also reproduce actions. Fixed-policy repeats have zero +observed cost range, which checks deterministic replay rather than distributional +robustness. Learning curves, all 192 checkpoints, model requests/responses, +feedback and selection records are archived in `quality-evidence-20261102.tar.gz`. + +The earlier small-grid fitting negative result remains valid for that narrower +policy family. These results establish measured learnability and characterize +policy/model differences on the recorded data; they do not establish a universal +frontier-hardness level, causal optimum, or transfer from training to general LLM +capability. The hindsight bound reads future demand and is only a diagnostic. +The reward/data/environment implementation is unchanged by this extension. diff --git a/envs/llm_capacity_env/THIRD_PARTY_NOTICES.md b/envs/llm_capacity_env/THIRD_PARTY_NOTICES.md new file mode 100644 index 0000000000..d3b4d37b7d --- /dev/null +++ b/envs/llm_capacity_env/THIRD_PARTY_NOTICES.md @@ -0,0 +1,16 @@ +# Third-party notices + +- BurstGPT v2.0 hourly aggregates: Creative Commons Attribution 4.0 International. + Copyright remains with the dataset authors. Yuxin Wang et al., *BurstGPT: A + Real-World Workload Dataset to Optimize LLM Serving Systems*, KDD 2025, + DOI 10.1145/3711896.3737413. Original: https://github.com/HPMLL/BurstGPT. + Changes: conversation-service selection, hourly aggregation, zero-filled hours, + and chronological episode partitions. Full data license and exact provenance: + `server/data/`. +- OpenEnv runtime 0.11.0: BSD-3-Clause, Meta Platforms, Inc. and affiliates and + respective contributors. https://github.com/openenv/OpenEnv. Environment + wiring follows its documented MCPEnvironment/create_app API patterns. + The runtime is an installed dependency; no modified OpenEnv runtime is vendored + in the submission. +- Other pinned dependencies retain their individual licenses. The code license + on this Space does not replace the CC-BY-4.0 data license or dependency licenses. diff --git a/envs/llm_capacity_env/__init__.py b/envs/llm_capacity_env/__init__.py new file mode 100644 index 0000000000..8f0bc3146b --- /dev/null +++ b/envs/llm_capacity_env/__init__.py @@ -0,0 +1,5 @@ +"""LLM capacity reservation client package.""" + +from .client import LLMCapacityEnv + +__all__ = ["LLMCapacityEnv"] diff --git a/envs/llm_capacity_env/client.py b/envs/llm_capacity_env/client.py new file mode 100644 index 0000000000..1f351ec6ae --- /dev/null +++ b/envs/llm_capacity_env/client.py @@ -0,0 +1,11 @@ +"""Remote MCP client. This module never imports the server or trace data.""" + +from openenv.core.mcp_client import MCPToolClient + + +class LLMCapacityEnv(MCPToolClient): + """OpenEnv client for the LLM capacity reservation tools. + + Infrastructure may call reset/step; an agent should receive only + observe_demand_history and reserve_capacity via MCP. + """ diff --git a/envs/llm_capacity_env/examples/__init__.py b/envs/llm_capacity_env/examples/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/envs/llm_capacity_env/examples/client_demo.py b/envs/llm_capacity_env/examples/client_demo.py new file mode 100644 index 0000000000..7825f30a7d --- /dev/null +++ b/envs/llm_capacity_env/examples/client_demo.py @@ -0,0 +1,23 @@ +"""Training orchestrator demo; an agent should only receive the MCP tools.""" + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from openenv.core.mcp_client import MCPToolClient + +from examples.policies import reservations + + +def main(url): + with MCPToolClient(base_url=url).sync() as env: + env.reset(seed=17) # Orchestration only. + view = env.call_tool("observe_demand_history") + while not view["done"]: + view = env.call_tool("reserve_capacity", reservations=reservations(view)) + print(f"day={view['episode_day']} cost={view['previous']['modeled_cost']:.6f}") + print(f"total_modeled_cost={view['cumulative_modeled_cost']:.6f}") + + +if __name__ == "__main__": + main(sys.argv[1] if len(sys.argv) > 1 else "http://localhost:8000") diff --git a/envs/llm_capacity_env/examples/evaluate.py b/envs/llm_capacity_env/examples/evaluate.py new file mode 100644 index 0000000000..59ae6f7956 --- /dev/null +++ b/envs/llm_capacity_env/examples/evaluate.py @@ -0,0 +1,53 @@ +"""Compare causal baselines on every eligible episode start; not a frontier-agent trial.""" + +import json +import sys +from pathlib import Path +from statistics import mean + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from examples.policies import reservations +from server.engine import EPISODE_DAYS, Replay + + +def evaluate(): + result = {} + for split in ("train", "test"): + result[split] = {} + for policy in ( + "zero", + "previous_day", + "fixed_quantile", + "rolling_quantile", + "perfect_information", + ): + env = Replay(split) + costs = [] + for start in env.starts: + env.reset(0) + env.start = start # Offline orchestration only; not an agent tool. + view = env.observe() + frozen = reservations(view, "fixed_quantile") + for _ in range(EPISODE_DAYS): + if policy == "perfect_information": + # Offline lower bound reads unrevealed demand; NEVER a causal policy. + action = env.trace[env.start + env.turn] + else: + action = reservations(view, policy, frozen) + view = env.reserve(action) + costs.append(env.total_cost) + result[split][policy] = { + "episodes": len(costs), + "mean_episode_modeled_cost": mean(costs), + "min_episode_modeled_cost": min(costs), + "max_episode_modeled_cost": max(costs), + } + return result + + +if __name__ == "__main__": + report = evaluate() + text = json.dumps(report, indent=2) + "\n" + if len(sys.argv) > 1: + Path(sys.argv[1]).write_text(text) + print(text) diff --git a/envs/llm_capacity_env/examples/policies.py b/envs/llm_capacity_env/examples/policies.py new file mode 100644 index 0000000000..a5cea5a9fb --- /dev/null +++ b/envs/llm_capacity_env/examples/policies.py @@ -0,0 +1,25 @@ +"""Causal baselines using only the history in the public tool observation.""" + +from math import ceil + + +def quantile(xs, q=0.75): + values = sorted(xs) + return values[max(0, ceil(q * len(values)) - 1)] + + +def reservations(view, policy="rolling_quantile", frozen=None): + models = tuple(view["unit_cost_per_1000_tokens"]) + if policy == "zero": + return {m: [0] * 24 for m in models} + if policy == "fixed_quantile" and frozen is not None: + return frozen + if policy not in ("rolling_quantile", "fixed_quantile", "previous_day"): + raise ValueError("Unknown policy") + if policy == "previous_day": + return {m: list(view["history"][-1]["tokens"][m]) for m in models} + # Cost minimizer of empirical newsvendor objective with 4x overflow price: q=0.75. + return { + m: [quantile([day["tokens"][m][h] for day in view["history"]]) for h in range(24)] + for m in models + } diff --git a/envs/llm_capacity_env/examples/record_trace.py b/envs/llm_capacity_env/examples/record_trace.py new file mode 100644 index 0000000000..e292782263 --- /dev/null +++ b/envs/llm_capacity_env/examples/record_trace.py @@ -0,0 +1,118 @@ +"""Record and independently replay full local MCP transitions of causal baselines. + +These are deterministic heuristic-policy traces, not LLM reasoning traces, and not +an implementation of automatic server-side session telemetry / RFC 008 grading. +""" + +import hashlib +import json +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from openenv.core.env_server.mcp_types import CallToolAction + +from examples.policies import reservations +from server.capacity_environment import CapacityEnvironment + + +def unwrap(observation): + assert observation.error is None, observation.error + result = observation.result + if hasattr(result, "data"): + return result.data + return result["data"] + + +def record(out, policy, seed=23): + path = out / f"{policy}-seed{seed}.jsonl" + env = CapacityEnvironment(split="test") + records = [] + try: + initial = env.reset(seed=seed, episode_id=f"trace-{policy}-{seed}") + view = unwrap(env.step(CallToolAction(tool_name="observe_demand_history", arguments={}))) + assert view == initial.metadata + records.append( + { + "type": "reset", + "policy_kind": "deterministic_heuristic", + "policy": policy, + "seed": seed, + "split": "test", + "observation": view, + } + ) + for turn in range(14): + action = { + "tool_name": "reserve_capacity", + "arguments": {"reservations": reservations(view, policy)}, + } + observation = env.step(CallToolAction(**action)) + after = unwrap(observation) + records.append( + { + "type": "transition", + "step": turn + 1, + "observation": view, + "action": action, + "reward": observation.reward, + "terminal": observation.done, + "next_observation": after, + } + ) + assert observation.done == (turn == 13) + assert observation.reward == after["previous"]["reward"] + view = after + path.write_text( + "".join(json.dumps(x, allow_nan=False, separators=(",", ":")) + "\n" for x in records) + ) + finally: + env.close() + return path + + +def replay(path): + records = [json.loads(line) for line in path.read_text().splitlines()] + header = records[0] + env = CapacityEnvironment(split=header["split"]) + total = 0.0 + try: + initial = env.reset(seed=header["seed"]) + assert initial.metadata == header["observation"] + for row in records[1:]: + before = unwrap( + env.step(CallToolAction(tool_name="observe_demand_history", arguments={})) + ) + assert before == row["observation"] + result = env.step(CallToolAction(**row["action"])) + assert unwrap(result) == row["next_observation"] + assert result.reward == row["reward"] and result.done == row["terminal"] + total += result.reward + assert len(records) == 15 and result.done + cost = row["next_observation"]["cumulative_modeled_cost"] + assert abs(total + cost / 1000) < 1e-10 + return { + "file": path.name, + "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + "policy": header["policy"], + "steps": 14, + "replay_exact": True, + "episode_modeled_cost": cost, + "episode_reward": total, + } + finally: + env.close() + + +if __name__ == "__main__": + out = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("outputs/traces") + out.mkdir(parents=True, exist_ok=True) + results = [replay(record(out, p)) for p in ("zero", "rolling_quantile")] + report = { + "passed": True, + "scope": "Local MCP baseline traces and exact replay; no LLM run or automatic subject-telemetry certification", + "recorder_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(), + "traces": results, + } + (out / "TRACE_RESULT.json").write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps(report, indent=2)) diff --git a/envs/llm_capacity_env/openenv.yaml b/envs/llm_capacity_env/openenv.yaml new file mode 100644 index 0000000000..27440b5fbd --- /dev/null +++ b/envs/llm_capacity_env/openenv.yaml @@ -0,0 +1,23 @@ +spec_version: 1 +name: llm_capacity_env +version: 0.1.0 +type: space +runtime: fastapi +app: server.app:app +port: 8000 +validation: + reward: + range: [-2.0, 0.0] + oracle_tolerance: 0.0 + floor_margin: 0.1 + resources: + cpu: 1.0 + memory_mb: 2048 + disk_mb: 2048 + episode_timeout_s: 120.0 + capabilities: + verifier: + kind: reward_channel + declared_tools: [observe_demand_history, reserve_capacity] + types: + tags: [resource-allocation, llm-serving, trace-replay] diff --git a/envs/llm_capacity_env/outputs/.gitkeep b/envs/llm_capacity_env/outputs/.gitkeep new file mode 100644 index 0000000000..e69de29bb2 diff --git a/envs/llm_capacity_env/outputs/example-traces/README.md b/envs/llm_capacity_env/outputs/example-traces/README.md new file mode 100644 index 0000000000..abeabbc013 --- /dev/null +++ b/envs/llm_capacity_env/outputs/example-traces/README.md @@ -0,0 +1,20 @@ +# Example baseline trajectories + +Two completed local MCP episodes, seed 23, test split: zero reservation and +rolling empirical 0.75 quantile. Every JSONL contains reset metadata and all 14 +observation/action/reward/terminal/next-observation transitions. Both were +independently replayed through the environment with exact equality. + +These are deterministic heuristic-policy runs, not LLM agent or reasoning traces. +They do not certify automatic server-side session telemetry or all RFC 008 checks. +They were run on the center cluster, not on the local controller. + +Reproduce from the environment root: + +```bash +python examples/record_trace.py outputs/traces +``` + +The seed was reused from the protocol smoke test. This single episode is only +an instrumentation/replay example, not a representative difficulty estimate. +For aggregate baselines, see validation-evidence/BASELINES.json. diff --git a/envs/llm_capacity_env/outputs/example-traces/TRACE_RESULT.json b/envs/llm_capacity_env/outputs/example-traces/TRACE_RESULT.json new file mode 100644 index 0000000000..30e72761b7 --- /dev/null +++ b/envs/llm_capacity_env/outputs/example-traces/TRACE_RESULT.json @@ -0,0 +1,25 @@ +{ + "passed": true, + "scope": "Local MCP baseline traces and exact replay; no LLM run or automatic subject-telemetry certification", + "recorder_sha256": "e723b6b6381daf41f74cdf68cc3a1491c536610cf37c606d179eaa030412b83e", + "traces": [ + { + "file": "zero-seed23.jsonl", + "sha256": "93912519e51c1cce71c0545cf15f388a3f00d2bb46870a385bec1d8ccc1b7357", + "policy": "zero", + "steps": 14, + "replay_exact": true, + "episode_modeled_cost": 4831.748251999999, + "episode_reward": -4.831748251999999 + }, + { + "file": "rolling_quantile-seed23.jsonl", + "sha256": "68acb4d39060fead6a68acbe4f78ad6f1cf358b6133e0374ee89949fbe090771", + "policy": "rolling_quantile", + "steps": 14, + "replay_exact": true, + "episode_modeled_cost": 2203.2003324999996, + "episode_reward": -2.2032003325 + } + ] +} diff --git a/envs/llm_capacity_env/outputs/example-traces/rolling_quantile-seed23.jsonl b/envs/llm_capacity_env/outputs/example-traces/rolling_quantile-seed23.jsonl new file mode 100644 index 0000000000..48d9257e52 --- /dev/null +++ b/envs/llm_capacity_env/outputs/example-traces/rolling_quantile-seed23.jsonl @@ -0,0 +1,15 @@ 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diff --git a/envs/llm_capacity_env/outputs/example-traces/zero-seed23.jsonl b/envs/llm_capacity_env/outputs/example-traces/zero-seed23.jsonl new file mode 100644 index 0000000000..a82f532780 --- /dev/null +++ b/envs/llm_capacity_env/outputs/example-traces/zero-seed23.jsonl @@ -0,0 +1,15 @@ 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diff --git a/envs/llm_capacity_env/pyproject.toml b/envs/llm_capacity_env/pyproject.toml new file mode 100644 index 0000000000..9add5ae311 --- /dev/null +++ b/envs/llm_capacity_env/pyproject.toml @@ -0,0 +1,33 @@ +[build-system] +requires = ["setuptools>=77", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "openenv-llm-capacity-env" +version = "0.1.0" +readme = "README.md" +license-files = ["LICENSE", "THIRD_PARTY_NOTICES.md", "server/data/LICENSE-BurstGPT.txt"] +description = "Causal LLM serving-capacity reservation with BurstGPT trace replay" +requires-python = ">=3.11" +license = "BSD-3-Clause" +authors = [{name = "Huaiming Li", email = "lihuaiming01@baidu.com"}] +dependencies = ["openenv==0.11.0"] + +[project.optional-dependencies] +dev = ["pytest==8.4.2", "ruff==0.14.13"] + +[project.scripts] +server = "llm_capacity_env.server.app:main" + +[tool.setuptools] +packages = ["llm_capacity_env", "llm_capacity_env.server"] +package-dir = { "llm_capacity_env" = ".", "llm_capacity_env.server" = "server" } + +[tool.pytest.ini_options] +pythonpath = ["."] + +[tool.ruff] +line-length = 100 + +[tool.setuptools.package-data] +"llm_capacity_env.server" = ["data/*"] diff --git a/envs/llm_capacity_env/requirements.lock b/envs/llm_capacity_env/requirements.lock new file mode 100644 index 0000000000..5887458dd4 --- /dev/null +++ b/envs/llm_capacity_env/requirements.lock @@ -0,0 +1,113 @@ +# Resolved and tested on Python 3.13 / Linux amd64. +Authlib==1.9.1 +Jinja2==3.1.6 +MarkupSafe==3.0.4 +PyJWT==2.15.1 +PyYAML==6.0.3 +Pygments==2.21.0 +SecretStorage==3.5.0 +aiofile==3.12.3 +annotated-doc==0.0.5 +annotated-types==0.8.0 +anyio==4.16.0 +attrs==26.1.0 +audioop-lts==0.2.2 +beartype==0.23.1 +brotli==1.2.1 +cachetools==7.2.8 +caio==0.12.10 +certifi==2026.10.23 +cffi==2.1.1 +charset-normalizer==3.5.2 +click==8.5.0 +cryptography==50.0.2 +cyclopts==5.3.2 +dnspython==2.9.1 +docstring_parser==0.18.0 +email-validator==2.3.0 +exceptiongroup==1.3.1 +fastapi==0.162.0 +fastmcp-slim==3.4.11 +fastmcp==3.4.11 +filelock==4.2.6 +fsspec==2026.9.0 +gradio==6.31.0 +gradio_client==2.7.2 +griffelib==2.4.1 +groovy==0.1.2 +h11==0.16.0 +hf-gradio==0.4.1 +hf-xet==1.8.0 +httpcore2==2.16.0 +httpcore==1.0.9 +httpx-sse==0.4.3 +httpx2==2.16.0 +httpx==0.28.1 +huggingface_hub==2.4.0 +idna==3.20 +iniconfig==2.3.1 +jaraco.classes==3.4.0 +jaraco.context==6.1.2 +jaraco.functools==4.6.0 +jeepney==0.9.0 +jiter==0.18.0 +joserfc==1.7.7 +jsonref==1.1.0 +jsonschema-path==0.5.0 +jsonschema-specifications==2025.9.1 +jsonschema==4.26.0 +keyring==25.7.0 +markdown-it-py==4.2.0 +mcp==1.30.0 +mdurl==0.1.2 +more-itertools==11.2.1 +numpy==2.5.4 +openai==3.42.0 +openapi-pydantic==0.6.0 +openenv==0.11.0 +opentelemetry-api==1.45.2 +orjson==3.13.0 +packaging==26.3 +pandas==3.1.0 +pathable==0.6.0 +pillow==12.3.0 +platformdirs==4.14.2 +pluggy==1.7.0 +py-key-value-aio==0.4.6 +pycparser==3.12 +pydantic-settings==2.16.2 +pydantic==2.14.2 +pydantic_core==2.50.2 +pydub==0.25.1 +pyperclip==1.11.0 +pytest==8.4.2 +python-dateutil==4.0.0 +python-dotenv==1.2.4 +python-multipart==0.0.33 +pytz==2026.6 +referencing==0.37.0 +requests==2.34.2 +rich-rst==2.2.0 +rich==15.0.0 +rpds-py==2026.9.1 +ruff==0.14.13 +safehttpx==0.1.7 +semantic-version==2.10.0 +shellingham==1.5.4 +sniffio==1.3.1 +sse-starlette==3.6.0 +starlette==1.10.0 +tomli==2.5.0 +tomli_w==1.2.0 +tomlkit==0.14.0 +tqdm==4.70.1 +truststore==0.10.5 +typer==0.27.3 +typing-inspection==0.4.4 +typing_extensions==4.16.0 +tzdata==2026.6 +uncalled-for==0.4.1 +urllib3==2.8.0 +uvicorn==0.54.0 +watchfiles==1.3.0 +websockets==17.4 diff --git a/envs/llm_capacity_env/scripts/rebuild_aggregates.py b/envs/llm_capacity_env/scripts/rebuild_aggregates.py new file mode 100644 index 0000000000..d2d9f002e0 --- /dev/null +++ b/envs/llm_capacity_env/scripts/rebuild_aggregates.py @@ -0,0 +1,49 @@ +"""Rebuild the shipped aggregate from upstream BurstGPT CSV or CSV.gz files. + +Download the three v2.0 release assets yourself; this program has no network access. +Writes only the requested output path, never edits raw data. +""" + +import argparse +import csv +import gzip +import hashlib +from pathlib import Path + + +def main(): + parser = argparse.ArgumentParser(__doc__) + parser.add_argument("--raw-dir", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + totals = {} + days = set() + for part in (1, 2, 3): + raw = args.raw_dir / f"BurstGPT_{part}.csv" + if not raw.exists(): + raw = raw.with_suffix(".csv.gz") + opener = gzip.open if raw.suffix == ".gz" else open + with opener(raw, "rt", newline="") as f: + for row in csv.DictReader(f): + if row["Log Type"] != "Conversation log" or row["Model"] not in ( + "ChatGPT", + "GPT-4", + ): + continue + second = int(float(row["Timestamp"])) + day, hour = second // 86400, second % 86400 // 3600 + days.add(day) + key = (day, hour, row["Model"]) + totals[key] = totals.get(key, 0) + int(row["Total tokens"]) + with args.output.open("w", newline="") as f: + w = csv.writer(f) + w.writerow(["log_day", "hour", "model", "total_tokens"]) + for day in sorted(days): + for hour in range(24): + for model in ("ChatGPT", "GPT-4"): + w.writerow([day, hour, model, totals.get((day, hour, model), 0)]) + print("days=", len(days), "sha256=", hashlib.sha256(args.output.read_bytes()).hexdigest()) + + +if __name__ == "__main__": + main() diff --git a/envs/llm_capacity_env/server/Dockerfile b/envs/llm_capacity_env/server/Dockerfile new file mode 100644 index 0000000000..3e20ee4a6a --- /dev/null +++ b/envs/llm_capacity_env/server/Dockerfile @@ -0,0 +1,9 @@ +FROM python:3.13-slim@sha256:aaf08c5490d52cd92bc27142a5215b1e47355a5eaf9adfad2a0fbc022d0b5490 +WORKDIR /app/env +COPY requirements.lock /app/requirements.lock +RUN pip install --no-cache-dir -r /app/requirements.lock +COPY . /app/env +ENV PYTHONUNBUFFERED=1 PORT=8000 ENABLE_WEB_INTERFACE=true +EXPOSE 8000 +HEALTHCHECK --interval=30s --timeout=5s --start-period=60s CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" +CMD ["uvicorn", "server.app:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/envs/llm_capacity_env/server/__init__.py b/envs/llm_capacity_env/server/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/envs/llm_capacity_env/server/app.py b/envs/llm_capacity_env/server/app.py new file mode 100644 index 0000000000..2dbb5588b5 --- /dev/null +++ b/envs/llm_capacity_env/server/app.py @@ -0,0 +1,31 @@ +"""Standalone OpenEnv HTTP/WebSocket and MCP server.""" + +import os + +from openenv.core.env_server.http_server import create_app +from openenv.core.env_server.mcp_types import CallToolAction, CallToolObservation +from openenv.core.env_server.types import Observation + +from .capacity_environment import CapacityEnvironment + +# Default server runs emit JSONL trajectories. Direct library evaluators can opt in separately. +os.environ.setdefault("CAPACITY_TRACE_DIR", "outputs/trajectories") + +app = create_app( + CapacityEnvironment, + CallToolAction, + CallToolObservation, + reset_observation_cls=Observation, + env_name="llm_capacity_env", + max_concurrent_envs=int(os.getenv("MAX_CONCURRENT_ENVS", "8")), +) + + +def main(): + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) + + +if __name__ == "__main__": + main() diff --git a/envs/llm_capacity_env/server/capacity_environment.py b/envs/llm_capacity_env/server/capacity_environment.py new file mode 100644 index 0000000000..c3b3254437 --- /dev/null +++ b/envs/llm_capacity_env/server/capacity_environment.py @@ -0,0 +1,68 @@ +"""OpenEnv MCP boundary. Agents receive tools, never simulation controls.""" + +import os +from uuid import uuid4 + +from fastmcp import FastMCP +from openenv.core.env_server.mcp_environment import MCPEnvironment +from openenv.core.env_server.types import Observation, State + +from .engine import Replay +from .trajectory import TrajectoryRecorder + + +class CapacityEnvironment(MCPEnvironment): + SUPPORTS_CONCURRENT_SESSIONS = True + + def __init__(self, split=None, trace_dir=None): + self.replay = Replay(split or os.getenv("CAPACITY_SPLIT", "train")) + self._recorder = TrajectoryRecorder(trace_dir or os.getenv("CAPACITY_TRACE_DIR")) + self._state = State(episode_id=str(uuid4()), step_count=0) + mcp = FastMCP("llm_capacity_env") + + @mcp.tool + def observe_demand_history() -> dict: + """Read the last 14 realized days, cost rules and previous outcome; time does not advance.""" + return self.replay.observe() + + @mcp.tool + def reserve_capacity(reservations: dict[str, list[float]]) -> dict: + """Commit next day's hourly token reservations: ChatGPT and GPT-4, each 24 values in [0,1000000]. + + Advances exactly one day and reveals that day's outcome. Decisions cannot be undone. + Each day's reward is negative modeled cost / 1000. Minimize total modeled cost. + """ + result = self.replay.reserve(reservations) + self._state.step_count = self.replay.turn + return result + + super().__init__(mcp) + + def reset(self, seed=None, episode_id=None, **kwargs): + view = self.replay.reset(seed) + self._state = State(episode_id=episode_id or str(uuid4()), step_count=0) + observation = Observation(reward=0.0, done=False, metadata=view) + self._recorder.start(seed, self._state.episode_id, view) + return observation + + def _finish(self, observation, before, action): + observation.done = self.replay.done + observation.reward = self.replay.previous["reward"] if self.replay.turn > before else 0.0 + self._recorder.append(action, observation, self._state.step_count) + return observation + + def step(self, action, timeout_s=None, **kwargs): + before = getattr(self.replay, "turn", 0) + return self._finish(super().step(action, timeout_s=timeout_s, **kwargs), before, action) + + async def step_async(self, action, timeout_s=None, **kwargs): + before = getattr(self.replay, "turn", 0) + result = await super().step_async(action, timeout_s=timeout_s, **kwargs) + return self._finish(result, before, action) + + def _step_impl(self, action, timeout_s=None, **kwargs): + raise ValueError("Use MCP tools: observe_demand_history or reserve_capacity") + + @property + def state(self): + return self._state.model_copy(deep=True) diff --git a/envs/llm_capacity_env/server/data/LICENSE-BurstGPT.txt b/envs/llm_capacity_env/server/data/LICENSE-BurstGPT.txt new file mode 100644 index 0000000000..4ea99c213c --- /dev/null +++ b/envs/llm_capacity_env/server/data/LICENSE-BurstGPT.txt @@ -0,0 +1,395 @@ +Attribution 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. 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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/envs/llm_capacity_env/server/data/PROVENANCE.json b/envs/llm_capacity_env/server/data/PROVENANCE.json new file mode 100644 index 0000000000..5aa4ff4c35 --- /dev/null +++ b/envs/llm_capacity_env/server/data/PROVENANCE.json @@ -0,0 +1,32 @@ +{ + "dataset": "BurstGPT v2.0", + "upstream": "https://github.com/HPMLL/BurstGPT", + "release": "https://github.com/HPMLL/BurstGPT/releases/tag/v2.0", + "license": "CC-BY-4.0", + "citation": "Yuxin Wang et al. BurstGPT: A Real-World Workload Dataset to Optimize LLM Serving Systems. KDD 2025. doi:10.1145/3711896.3737413", + "adaptation": "Conversation log entries; sum Total tokens by original relative day, hour and model; zero-fill missing hours on logged days; select ChatGPT and GPT-4. No raw prompts or completions. No target replacement, noise injection or score-based episode filtering. The original S11 stream selection preceded this OpenEnv adaptation.", + "log_day_segments": [ + [ + 0, + 120 + ], + [ + 225, + 334 + ] + ], + "days": 231, + "rows": 11088, + "sha256": "de9db57e9dd8aa1c96f33789e45eaf5fe0b09726160e9554645d2cb8ca58c130", + "source_case": "famou-bench-v3.1/cases/S11_system_llm_capacity", + "source_aggregates": [ + { + "file": "data/history/load.csv", + "sha256": "f685ffd1e2981fd4fa8641af14035d37c56bcb2473795ca34356fc3632a3bf5f" + }, + { + "file": "world/stream/truth.csv", + "sha256": "fc9da238b830f15d75755aa2abfef3dad507b9af47487f8db6c286518827c895" + } + ] +} diff --git a/envs/llm_capacity_env/server/data/hourly_demand.csv b/envs/llm_capacity_env/server/data/hourly_demand.csv new file mode 100644 index 0000000000..c3ddf02896 --- /dev/null +++ b/envs/llm_capacity_env/server/data/hourly_demand.csv @@ -0,0 +1,11089 @@ +log_day,hour,model,total_tokens +0,0,ChatGPT,28704 +0,0,GPT-4,11873 +0,1,ChatGPT,8879 +0,1,GPT-4,1438 +0,2,ChatGPT,16767 +0,2,GPT-4,3879 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No OpenEnv imports or network access.""" + +import csv +import math +import random +from pathlib import Path + +MODELS = ("ChatGPT", "GPT-4") +UNIT_COST = {"ChatGPT": 0.002, "GPT-4": 0.0375} +SHORTFALL_MULTIPLIER = 4.0 +RESERVATION_CAP = 1_000_000.0 # Numerical action bound, not hardware capacity. +REWARD_SCALE = 1000.0 +HISTORY_DAYS = 14 +EPISODE_DAYS = 14 +DATA = Path(__file__).resolve().parent / "data/hourly_demand.csv" + + +def load_trace(path=DATA): + trace = {} + with Path(path).open() as f: + for row in csv.DictReader(f): + day, hour = int(row["log_day"]), int(row["hour"]) + model, demand = row["model"], int(row["total_tokens"]) + if model not in MODELS or not 0 <= hour < 24 or demand < 0: + raise ValueError("Invalid trace row") + bucket = trace.setdefault(day, {m: [None] * 24 for m in MODELS}) + if bucket[model][hour] is not None: + raise ValueError("Duplicate trace row") + bucket[model][hour] = demand + if not trace or any(v is None for d in trace.values() for xs in d.values() for v in xs): + raise ValueError("Incomplete hourly trace") + # Under this recorded bound, 48 hourly costs / 1000 are strictly below 2. + if max(v for d in trace.values() for xs in d.values() for v in xs) > 500_000: + raise ValueError("Trace exceeds declared reward-bound assumptions") + return trace + + +def eligible_starts(trace, split): + if split not in ("train", "test"): + raise ValueError("split must be train or test") + target_ranges = ((14, 89), (239, 304)) if split == "train" else ((90, 120), (305, 334)) + return [ + start + for a, b in target_ranges + for start in range(a, b - EPISODE_DAYS + 2) + if all(day in trace for day in range(start - HISTORY_DAYS, start + EPISODE_DAYS)) + ] + + +def validate_reservations(reservations): + if not isinstance(reservations, dict) or set(reservations) != set(MODELS): + raise ValueError("Provide exactly ChatGPT and GPT-4") + result = {} + for model in MODELS: + xs = reservations[model] + if not isinstance(xs, list) or len(xs) != 24: + raise ValueError("Each deployment requires exactly 24 hourly reservations") + if any( + isinstance(x, bool) + or not isinstance(x, (int, float)) + or not math.isfinite(x) + or not 0 <= x <= RESERVATION_CAP + for x in xs + ): + raise ValueError("Reservations must be finite numbers between 0 and 1000000") + result[model] = [float(x) for x in xs] + return result + + +def modeled_cost(reservations, demand): + return sum( + UNIT_COST[m] * (r + SHORTFALL_MULTIPLIER * max(d - r, 0)) / 1000 + for m in MODELS + for r, d in zip(reservations[m], demand[m], strict=True) + ) + + +class Replay: + def __init__(self, split="train"): + self.trace = load_trace() + self.split = split + self.starts = eligible_starts(self.trace, split) + self.active = False + self.turn = 0 + + def reset(self, seed=None): + self.start = random.Random(seed).choice(self.starts) + self.turn = 0 + self.total_cost = 0.0 + self.previous = None + self.active = True + return self.observe() + + @property + def done(self): + return self.active and self.turn == EPISODE_DAYS + + def observe(self): + if not self.active: + raise RuntimeError("Environment has not been initialized by orchestration") + day = self.start + self.turn + # Copy nested structures; consumers must not mutate replay data. + return { + "episode_day": self.turn, + "episode_days": EPISODE_DAYS, + "next_log_day": None if self.done else day, + "done": self.done, + "split": self.split, + "history": [ + {"log_day": d, "tokens": {m: list(self.trace[d][m]) for m in MODELS}} + for d in range(day - HISTORY_DAYS, day) + ], + "unit_cost_per_1000_tokens": dict(UNIT_COST), + "shortfall_multiplier": SHORTFALL_MULTIPLIER, + "reservation_cap": RESERVATION_CAP, + "reward_scale": REWARD_SCALE, + "cumulative_modeled_cost": self.total_cost, + "previous": None + if self.previous is None + else { + **self.previous, + "realized_tokens": { + m: list(xs) for m, xs in self.previous["realized_tokens"].items() + }, + }, + } + + def reserve(self, reservations): + if not self.active or self.done: + raise RuntimeError("No active decision day") + plan = validate_reservations(reservations) # Validate before any mutation. + day = self.start + self.turn + demand = self.trace[day] + cost = modeled_cost(plan, demand) + self.total_cost += cost + self.turn += 1 + self.previous = { + "log_day": day, + "realized_tokens": demand, + "modeled_cost": cost, + "reward": -cost / REWARD_SCALE, + } + return self.observe() diff --git a/envs/llm_capacity_env/server/trajectory.py b/envs/llm_capacity_env/server/trajectory.py new file mode 100644 index 0000000000..65a571f79c --- /dev/null +++ b/envs/llm_capacity_env/server/trajectory.py @@ -0,0 +1,54 @@ +"""Server-owned structured trajectory files, never exposed as agent tools.""" + +import json +import threading +from pathlib import Path +from uuid import uuid4 + + +class TrajectoryRecorder: + def __init__(self, directory=None): + self.directory = Path(directory) if directory else None + self.path = None + self.lock = threading.Lock() + + def start(self, seed, episode_id, observation): + if self.directory is None: + return + self.directory.mkdir(parents=True, exist_ok=True) + # User-supplied episode IDs are data, never filesystem path components. + self.path = self.directory / (uuid4().hex + ".jsonl") + with self.path.open("x", encoding="utf-8") as stream: + stream.write( + json.dumps( + { + "type": "reset", + "seed": seed, + "episode_id": episode_id, + "observation": observation, + }, + allow_nan=False, + ) + + "\n" + ) + + def append(self, action, observation, step_count): + if self.path is None: + return + payload = { + "type": "transition", + "action": action.model_dump(mode="json"), + "observation": observation.model_dump(mode="json"), + "reward": observation.reward, + "terminal": observation.done, + "step_count": step_count, + } + # Serialization may fail on an invalid nonfinite action. 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-0.12228742, + -0.11169109899999999, + -0.18154478599999999, + -0.166916298, + -0.19073091650000001, + -0.158290421, + -0.124141896, + -0.11279151 + ], + "invalid_action_no_advance": true, + "seed_reset_reproducible": true, + "read_tool_zero_reward": true, + "terminal_extra_action_no_reward": true, + "declared_tools": [ + "observe_demand_history", + "reserve_capacity" + ], + "web_ui_status": 200 +} diff --git a/envs/llm_capacity_env/validation-evidence/README.md b/envs/llm_capacity_env/validation-evidence/README.md new file mode 100644 index 0000000000..08d8a35861 --- /dev/null +++ b/envs/llm_capacity_env/validation-evidence/README.md @@ -0,0 +1,23 @@ +# Validation evidence + +CI_MATRIX.json records the Python 3.11/3.12 monorepo checks, source hashes, +retries and dependency setup on 2026-11-02. SUMMARY.json and SOURCE_MANIFEST.json +record the separate OpenEnv 0.11.0 / Python 3.13 standalone container-validation +snapshot; paths in that manifest refer to that standalone archive. + +- 21 environment/MCP/trajectory tests passed; Ruff lint passed. +- Official static manifest validation: one registered static check passed. +- Official URL/API validation: 6/6 checks passed. +- Actual pinned-base Docker build and 14-decision network episode passed. +- Concurrent isolated sessions, invalid/terminal reward handling, seeded reset + and the web UI passed. +- The actual API server emitted per-session JSONL files with reset, action, + observation, reward, terminal flag and step count; a complete 14-decision + trajectory was inspected. See SERVER_TRAJECTORIES.json. +- All three upstream raw releases reproduced the exact aggregate SHA-256. +- Wheel build, installed client/data and offline official push staging passed. + +No upload or upstream acceptance is implied. These checks are not full semantic +or hub certification, and do not establish frontier-agent difficulty. Costs are +modeled accounting units; the perfect-information row reads future demand and +is only an offline lower bound. Public partitions are not sealed holdouts. diff --git a/envs/llm_capacity_env/validation-evidence/RECONSTRUCTION.json b/envs/llm_capacity_env/validation-evidence/RECONSTRUCTION.json new file mode 100644 index 0000000000..773e9a0ed5 --- /dev/null +++ b/envs/llm_capacity_env/validation-evidence/RECONSTRUCTION.json @@ -0,0 +1,5 @@ +{ + "passed": true, + "sha256": "de9db57e9dd8aa1c96f33789e45eaf5fe0b09726160e9554645d2cb8ca58c130", + "source": "all three upstream BurstGPT v2.0 CSV.gz release assets" +} diff --git a/envs/llm_capacity_env/validation-evidence/RUNTIME.json b/envs/llm_capacity_env/validation-evidence/RUNTIME.json new file mode 100644 index 0000000000..2919a806ab --- /dev/null +++ b/envs/llm_capacity_env/validation-evidence/RUNTIME.json @@ -0,0 +1,113 @@ +{ + "target": "http://127.0.0.1:44969", + "validation_type": "running_environment", + "standard_version": "1.0.0", + "standard_profile": "openenv-http/1.x", + "mode": "simulation", + "passed": true, + "summary": { + "passed_count": 6, + "total_count": 6, + "failed_criteria": [], + "required_passed_count": 6, + "required_total_count": 6 + }, + "criteria": [ + { + "id": "openapi_version_available", + "description": "GET /openapi.json returns OpenAPI info.version", + "passed": true, + "required": true, + "expected": { + "status_code": 200, + "info.version": "string" + }, + "actual": { + "status_code": 200, + "info.version": "1.0.0" + } + }, + { + "id": "health_endpoint", + "description": "GET /health returns healthy status", + "passed": true, + "required": true, + "expected": { + "status_code": 200, + "status": "healthy" + }, + "actual": { + "status_code": 200, + "status": "healthy" + } + }, + { + "id": "metadata_endpoint", + "description": "GET /metadata returns name and description", + "passed": true, + "required": true, + "expected": { + "status_code": 200, + "fields": [ + "name", + "description" + ] + }, + "actual": { + "status_code": 200, + "name": "CapacityEnvironment", + "description": "CapacityEnvironment environment" + } + }, + { + "id": "schema_endpoint", + "description": "GET /schema returns action, observation, and state schemas", + "passed": true, + "required": true, + "expected": { + "status_code": 200, + "fields": [ + "action", + "observation", + "state" + ] + }, + "actual": { + "status_code": 200, + "has_action": true, + "has_observation": true, + "has_state": true + } + }, + { + "id": "mcp_endpoint", + "description": "POST /mcp is reachable and returns JSON-RPC payload", + "passed": true, + "required": true, + "expected": { + "status_code": 200, + "jsonrpc": "2.0" + }, + "actual": { + "status_code": 200, + "jsonrpc": "2.0" + } + }, + { + "id": "mode_endpoint_consistency", + "description": "OpenAPI endpoint set matches OpenEnv mode contract", + "passed": true, + "required": true, + "expected": { + "/reset": true, + "/step": true, + "/state": true + }, + "actual": { + "/reset": true, + "/step": true, + "/state": true + } + } + ] +} diff --git a/envs/llm_capacity_env/validation-evidence/SERVER_TRAJECTORIES.json b/envs/llm_capacity_env/validation-evidence/SERVER_TRAJECTORIES.json new file mode 100644 index 0000000000..18d076410b --- /dev/null +++ b/envs/llm_capacity_env/validation-evidence/SERVER_TRAJECTORIES.json @@ -0,0 +1,8 @@ +{ + "passed": true, + "session_files": 3, + "complete_episodes": [ + "40d24c841c304ef3b8bbc4dc25c0632c.jsonl" + ], + "records": "reset, action, observation, reward, terminal and step_count" +} diff --git a/envs/llm_capacity_env/validation-evidence/SOURCE_MANIFEST.json b/envs/llm_capacity_env/validation-evidence/SOURCE_MANIFEST.json new file mode 100644 index 0000000000..c567a816d6 --- /dev/null +++ b/envs/llm_capacity_env/validation-evidence/SOURCE_MANIFEST.json @@ -0,0 +1,45 @@ +{ + ".dockerignore": "a1820da7b4728b93d073c9d9a52980df400a22fd4d819c090fe7b692befd361f", + ".gitignore": "bd3bf4820c23454e718b67dad09186139c5e07931016b8988a0f6191e37932d1", + "Dockerfile": 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"74e15658678ec60534e435716cd241535e0ec6e3ecd0b34e857341ee0a690985" +} diff --git a/envs/llm_capacity_env/validation-evidence/STAGING.json b/envs/llm_capacity_env/validation-evidence/STAGING.json new file mode 100644 index 0000000000..bdd10812ba --- /dev/null +++ b/envs/llm_capacity_env/validation-evidence/STAGING.json @@ -0,0 +1,4 @@ +{ + "passed": true, + "scope": "Official push validation and staging only; no authentication or upload" +} diff --git a/envs/llm_capacity_env/validation-evidence/STATIC.json b/envs/llm_capacity_env/validation-evidence/STATIC.json new file mode 100644 index 0000000000..fd7b184e9c --- /dev/null +++ b/envs/llm_capacity_env/validation-evidence/STATIC.json @@ -0,0 +1,88 @@ +{ + "report_schema_version": "1", + "target": ".", + "source_digest": "b2da2b4b8f52251a7e7270d537805f872f4d69d7318a647a02b8d2934eb56c17", + "signature": "openenv.yaml", + "manifest": { + "manifest_schema_version": "1", + "name": "llm_capacity_env", + "version": "0.1.0", + "signature": "openenv.yaml", + "reward": { + "range": [ + -2.0, + 0.0 + ], + "oracle_tolerance": 0.0, + "floor_margin": 0.1, + "variance_tolerance": null + }, + "judge": null, + "resources": { + "cpu": 1.0, + "memory_mb": 2048, + "disk_mb": 2048, + "episode_timeout_s": 120.0, + "gpus": 0, + "gpu_types": [] + }, + "task_distribution": null, + "network": { + "mode": "public", + "allowed_hosts": [] + }, + "capabilities": { + "oracle": null, + "verifier": { + "kind": "reward_channel", + "entry": null + }, + "set_state": false, + "llm_judged": false, + "rubric_tree": false, + "task_api": false, + "canaries": null, + "declared_tools": [ + "observe_demand_history", + "reserve_capacity" + ], + "declared_task_count": {} + }, + "types": { + "tags": [ + "resource-allocation", + "llm-serving", + "trace-replay" + ] + } + }, + "policy_version": "v1", + "lane": "local", + "levels_run": [ + 1 + ], + "results": [ + { + "check_id": "static.manifest", + "status": "pass", + "measured": { + "oracle_tolerance": 0.0, + "floor_margin": 0.1, + "variance_tolerance": null, + "episode_timeout_s": 120.0, + "bounds": { + "max_oracle_tolerance": 0.1, + "min_floor_margin": 0.1, + "max_variance_tolerance": 0.2, + "max_episode_timeout_s": 3600.0 + } + }, + "evidence": [ + "manifest is schema-valid and within policy bounds" + ], + "remediation": null, + "duration_s": 0.000020341016352176666 + } + ], + "verdict": "pass" +} diff --git a/envs/llm_capacity_env/validation-evidence/SUMMARY.json b/envs/llm_capacity_env/validation-evidence/SUMMARY.json new file mode 100644 index 0000000000..1512dbe474 --- /dev/null +++ b/envs/llm_capacity_env/validation-evidence/SUMMARY.json @@ -0,0 +1,12 @@ +{ + "passed": true, + "openenv": "0.11.0", + "python": "3.13.15 (main, Sep 1 2026, 00:09:11) [GCC 12.2.0]", + "cluster": "center", + "image_id": "sha256:a0432d085337286bfd65a1e17944906e1f2016e2f3923ed00d24cc462c0f6071", + "unit_tests": "..................... [100%]\n21 passed in 9.14s\n", + "static_report": "/mnt/cfs_bj_fm/workspace/lihuaiming/external-benchmark-contributions/openenv/cases/llm-capacity-reservation/validation/attempt-20261102T025613Z/STATIC.json", + "runtime_report": "/mnt/cfs_bj_fm/workspace/lihuaiming/external-benchmark-contributions/openenv/cases/llm-capacity-reservation/validation/attempt-20261102T025613Z/RUNTIME.json", + "protocol_report": "/mnt/cfs_bj_fm/workspace/lihuaiming/external-benchmark-contributions/openenv/cases/llm-capacity-reservation/validation/attempt-20261102T025613Z/PROTOCOL.json", + "scope": "Raw-data hash reconstruction, wheel data inclusion; official CLI static (skip build) and URL/API checks; separate actual Docker build and MCP/WebSocket episodes. No upstream acceptance or full semantic/hub certification." +} diff --git a/tests/envs/test_llm_capacity_mcp.py b/tests/envs/test_llm_capacity_mcp.py new file mode 100644 index 0000000000..62be2f6c16 --- /dev/null +++ b/tests/envs/test_llm_capacity_mcp.py @@ -0,0 +1,38 @@ +from llm_capacity_env.server.capacity_environment import CapacityEnvironment +from llm_capacity_env.server.engine import MODELS +from openenv.core.env_server.mcp_types import CallToolAction, ListToolsAction + + +def test_tools_controls_reward_and_error_accounting(): + env = CapacityEnvironment() + try: + env.reset(seed=11) + result = env.step(ListToolsAction()) + assert {t.name for t in result.tools} == { + "observe_demand_history", + "reserve_capacity", + } + assert result.reward == 0 + assert env.state.step_count == 0 + bad = env.step( + CallToolAction(tool_name="reserve_capacity", arguments={"reservations": {}}) + ) + assert bad.reward == 0 and env.state.step_count == 0 + # A successful decision emits its cost once; read-only calls cannot collect it again. + good = env.step( + CallToolAction( + tool_name="reserve_capacity", + arguments={"reservations": {m: [0] * 24 for m in MODELS}}, + ) + ) + assert -2 <= good.reward < 0 + assert env.state.step_count == 1 + view = env.step( + CallToolAction(tool_name="observe_demand_history", arguments={}) + ) + assert view.reward == 0 and not view.done + snapshot = env.state + snapshot.step_count = 99 + assert env.state.step_count == 1 + finally: + env.close() diff --git a/tests/envs/test_llm_capacity_replay.py b/tests/envs/test_llm_capacity_replay.py new file mode 100644 index 0000000000..d871215ba5 --- /dev/null +++ b/tests/envs/test_llm_capacity_replay.py @@ -0,0 +1,120 @@ +import copy +import hashlib +import json +import math +from pathlib import Path + +import pytest +from llm_capacity_env.server.engine import ( + eligible_starts, + EPISODE_DAYS, + HISTORY_DAYS, + load_trace, + modeled_cost, + MODELS, + Replay, + validate_reservations, +) + + +def plan(value=0): + return {m: [value] * 24 for m in MODELS} + + +def test_trace_integrity_and_license(): + data = Path(__file__).resolve().parents[2] / "envs/llm_capacity_env/server/data" + provenance = json.loads((data / "PROVENANCE.json").read_text()) + assert ( + hashlib.sha256((data / "hourly_demand.csv").read_bytes()).hexdigest() + == provenance["sha256"] + ) + assert ( + "Attribution 4.0 International" in (data / "LICENSE-BurstGPT.txt").read_text() + ) + assert len(load_trace()) == 231 + + +def test_chronology_and_split_disjointness(): + trace = load_trace() + target_sets = {} + for split in ("train", "test"): + starts = eligible_starts(trace, split) + assert starts + target_sets[split] = {d for s in starts for d in range(s, s + EPISODE_DAYS)} + for s in starts: + assert set(range(s - HISTORY_DAYS, s + EPISODE_DAYS)) <= trace.keys() + assert target_sets["train"].isdisjoint(target_sets["test"]) + + +def test_known_cost_and_perfect_information_lower_bound(): + demand = plan(1000) + assert modeled_cost(plan(0), demand) == pytest.approx(3.792) + assert modeled_cost(plan(1000), demand) == pytest.approx(0.948) + assert modeled_cost(plan(2000), demand) == pytest.approx(1.896) + + +@pytest.mark.parametrize( + "bad", + [ + None, + {}, + {"ChatGPT": [0] * 24}, + {**plan(), "extra": [0] * 24}, + {**plan(), "GPT-4": [0] * 23}, + plan(-1), + plan(1000001), + plan(float("nan")), + plan(float("inf")), + plan(True), + plan("1"), + ], +) +def test_invalid_actions_do_not_advance_or_change_cost(bad): + env = Replay() + before = copy.deepcopy(env.reset(7)) + with pytest.raises(ValueError): + env.reserve(bad) + assert env.observe() == before + + +def test_determinism_terminal_and_non_mutating_observations(): + a, b = Replay(), Replay() + assert a.reset(10) == b.reset(10) + view = a.observe() + view["history"][0]["tokens"]["GPT-4"][0] = -99 + assert a.observe() == b.observe() + for _ in range(EPISODE_DAYS): + x, y = a.reserve(plan(10000)), b.reserve(plan(10000)) + assert x == y + assert -2 <= x["previous"]["reward"] <= 0 + assert a.done + with pytest.raises(RuntimeError): + a.reserve(plan()) + assert a.observe() == b.observe() + + +def test_current_and_future_demand_cannot_affect_predecision_observation(): + a, b = Replay(), Replay() + assert a.reset(19) == b.reset(19) + # Causal counterfactual: change all current/future truth but no revealed history. + b.trace = copy.deepcopy(b.trace) + for d in range(b.start, b.start + EPISODE_DAYS): + b.trace[d] = plan(98765) + assert a.observe() == b.observe() + assert a.reserve(plan())["previous"] != b.reserve(plan())["previous"] + + +def test_all_legal_extremes_bound_reward_and_lower_bound(): + for demand in load_trace().values(): + lower = modeled_cost(demand, demand) + for reservations in (plan(0), plan(1000000)): + cost = modeled_cost(validate_reservations(reservations), demand) + assert math.isfinite(cost) and lower <= cost <= 2000 + + +def test_environment_instances_are_independent(): + a, b = Replay(), Replay() + a.reset(0) + before = b.reset(1) + a.reserve(plan()) + assert b.observe() == before diff --git a/tests/envs/test_llm_capacity_trajectory.py b/tests/envs/test_llm_capacity_trajectory.py new file mode 100644 index 0000000000..c1738c7f3f --- /dev/null +++ b/tests/envs/test_llm_capacity_trajectory.py @@ -0,0 +1,59 @@ +import asyncio +import json + +from llm_capacity_env.server.capacity_environment import CapacityEnvironment +from openenv.core.env_server.mcp_types import CallToolAction + + +def action(): + return CallToolAction( + tool_name="reserve_capacity", + arguments={"reservations": {"ChatGPT": [0] * 24, "GPT-4": [0] * 24}}, + ) + + +def test_server_emits_complete_trace_and_separate_reset_files(tmp_path): + env = CapacityEnvironment(trace_dir=tmp_path) + try: + env.reset(seed=3, episode_id="../../outside") + for _ in range(14): + env.step(action()) + files = list(tmp_path.glob("*.jsonl")) + assert len(files) == 1 + rows = [json.loads(line) for line in files[0].read_text().splitlines()] + assert len(rows) == 15 and rows[0]["type"] == "reset" + assert rows[0]["episode_id"] == "../../outside" + assert all(row["action"]["tool_name"] == "reserve_capacity" for row in rows[1:]) + assert rows[-1]["terminal"] and rows[-1]["step_count"] == 14 + assert ( + abs(sum(row["reward"] for row in rows[1:]) + env.replay.total_cost / 1000) + < 1e-10 + ) + assert all("observation" in row for row in rows) + env.reset(seed=3) + assert len(list(tmp_path.glob("*.jsonl"))) == 2 + finally: + env.close() + + +def test_async_calls_are_recorded_without_reward_repetition(tmp_path): + env = CapacityEnvironment(trace_dir=tmp_path) + + async def run(): + env.reset(seed=5) + await env.step_async(action()) + await env.step_async( + CallToolAction(tool_name="observe_demand_history", arguments={}) + ) + + try: + asyncio.run(run()) + rows = [ + json.loads(line) + for line in next(tmp_path.glob("*.jsonl")).read_text().splitlines() + ] + assert len(rows) == 3 + assert rows[1]["reward"] < 0 and rows[2]["reward"] == 0 + assert rows[1]["step_count"] == rows[2]["step_count"] == 1 + finally: + env.close()