Upload folder using huggingface_hub

Dockerfile

@@ -1,3 +1,4 @@
+# syntax=docker/dockerfile:1
 ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest
 FROM ${BASE_IMAGE} AS builder
 
@@ -24,15 +25,13 @@ RUN if ! command -v uv >/dev/null 2>&1; then \
     fi
 
 # Install dependencies
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ -f uv.lock ]; then \
+RUN if [ -f uv.lock ]; then \
         uv sync --frozen --no-install-project --no-editable; \
     else \
         uv sync --no-install-project --no-editable; \
     fi
 
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ -f uv.lock ]; then \
+RUN if [ -f uv.lock ]; then \
         uv sync --frozen --no-editable; \
     else \
         uv sync --no-editable; \

README.md

@@ -25,6 +25,12 @@ ML practitioners spend enormous time on hyperparameter tuning. This environment
 
 The small dataset subsets (5k–10k samples) make overfitting a **real, tangible problem** the agent must address — exactly like real low-data regimes practitioners face daily.
 
+## Vision & Scalability
+
+The long-term vision for this environment is to **teach AI agents to monitor and optimize the training of large-scale models on distributed systems** — multi-GPU clusters, sharded data pipelines, and fault-tolerant training loops. In production ML, human engineers spend significant time babysitting training runs: watching for loss spikes, adjusting learning rates, restarting from checkpoints, and rebalancing resources across nodes. An agent that masters these skills could dramatically accelerate the development cycle of foundation models.
+
+To fit within current compute constraints (and the OpenEnv specification), the environment currently operates on small models trainable on standard CPUs. However, the core abstractions — observing training curves, adjusting hyperparameters mid-run, detecting convergence/divergence, and deciding when to stop — are **identical to those required at scale**. An agent that learns effective optimization strategies here can transfer those skills to larger, distributed settings as the environment scales up.
+
 ## Tasks
 
 ### Task 1: MNIST Digit Classifier (Easy)

inference.py

@@ -43,7 +43,8 @@ API_BASE_URL = os.environ.get(
 )
 MODEL_NAME = os.environ.get("MODEL_NAME", "gpt-4o-mini")
 OPENAI_API_KEY = (
-    os.environ.get("OPENAI_API_KEY")
+    os.environ.get("HF_TOKEN")
+    or os.environ.get("OPENAI_API_KEY")
     or os.environ.get("OPENROUTER_API_KEY")
     or os.environ.get("GEMINI_API_KEY")
 )
@@ -705,108 +706,143 @@ def run_task(client: OpenAI, scheduler: RequestScheduler, task_id: str) -> Dict[
     task_stats = TaskStats()
     task_started_at = time.time()
 
-    print(f"\n{'=' * 60}")
-    print(f"Task: {task_id}")
-    print(f"{'=' * 60}")
-
-    with MLTrainerEnv(base_url=ENV_URL).sync() as env:
-        reset_result = env.reset(task_id=task_id)
-        metadata = merge_task_metadata(task_id, extract_observation_metadata(reset_result.observation))
-        latest_data: Dict[str, Any] = {}
-        action_summaries: List[str] = []
-        total_reward = 0.0
-        final_score = None
-
-        print(f"  Task: {metadata.get('task_name', task_id)}")
-        print(f"  Difficulty: {metadata.get('difficulty', '?')}")
-        print(f"  Dataset: {metadata.get('dataset', '?')}")
-        print(f"  Max epochs: {metadata.get('max_epochs', '?')}")
-        print(f"  LLM decision budget: {max_decisions}")
-
-        for decision_index in range(max_decisions):
-            messages = build_messages(
-                metadata=metadata,
-                latest_data=latest_data,
-                action_summaries=action_summaries,
-                decision_index=decision_index,
-                max_decisions=max_decisions,
-            )
-            tool_call = request_action(
-                client=client,
-                scheduler=scheduler,
-                messages=messages,
-                stats=task_stats,
-                decision_index=decision_index,
-                max_decisions=max_decisions,
-            )
-            task_stats.decisions += 1
+    print(f"[START] task={task_id} env=ml_training_optimizer model={MODEL_NAME}", flush=True)
 
-            tool_name = tool_call["tool_name"]
-            arguments = tool_call["arguments"]
-            print(f"  Decision {decision_index + 1}/{max_decisions}: {tool_name}({json.dumps(arguments, default=str)[:100]})")
+    rewards: List[float] = []
+    total_reward = 0.0
+    final_score = 0.0
+    steps_taken = 0
+    success = False
 
+    try:
+        env_instance = MLTrainerEnv(base_url=ENV_URL).sync()
+        with env_instance as env:
             try:
-                step_result = env.step(CallToolAction(tool_name=tool_name, arguments=arguments))
-            except Exception as exc:
-                raise InferenceError(f"Environment step failed for {tool_name}: {exc}") from exc
-
-            observation = step_result.observation
-            reward = step_result.reward or 0.0
-            done = step_result.done
-            total_reward += reward
-
-            result_data = normalize_tool_result(extract_tool_result_from_observation(observation))
-            if result_data:
-                latest_data = apply_tool_context(tool_name, arguments, latest_data, result_data)
-            else:
-                latest_data = apply_tool_context(
-                    tool_name,
-                    arguments,
-                    latest_data,
-                    extract_observation_metadata(observation),
-                )
-            action_summaries.append(action_summary(tool_name, arguments, latest_data, reward))
-
-            metadata_grade = extract_observation_metadata(observation).get("grade", {})
-            result_grade = latest_data.get("grade", {}) if isinstance(latest_data, dict) else {}
-            if result_grade:
-                final_score = result_grade.get("score")
-                print(f"    Score: {final_score}")
-            elif metadata_grade:
-                final_score = metadata_grade.get("score")
-                print(f"    Score: {final_score}")
-
-            print(f"    Reward: {reward} | Done: {done}")
-            print(f"    {compact_state_summary(metadata, latest_data)}")
-
-            if done:
-                break
-        else:
-            # LLM exhausted its decision budget without submitting.
-            # Auto-submit so the task still gets a score.
-            print(f"  [Decision budget exhausted — auto-submitting model...]")
-            try:
-                step_result = env.step(CallToolAction(tool_name="submit_model", arguments={}))
-                observation = step_result.observation
-                reward = step_result.reward or 0.0
-                total_reward += reward
+                reset_result = env.reset(task_id=task_id)
+            except Exception as e:
+                # Need to swallow to trigger finally block properly
+                print(f"[DEBUG] env.reset() failed: {e}", flush=True)
+                raise e
+
+            metadata = merge_task_metadata(task_id, extract_observation_metadata(reset_result.observation))
+            latest_data: Dict[str, Any] = {}
+            action_summaries: List[str] = []
 
-                result_data = normalize_tool_result(extract_tool_result_from_observation(observation))
-                if result_data:
-                    latest_data = apply_tool_context("submit_model", {}, latest_data, result_data)
-
-                metadata_grade = extract_observation_metadata(observation).get("grade", {})
-                result_grade = latest_data.get("grade", {}) if isinstance(latest_data, dict) else {}
-                if result_grade:
-                    final_score = result_grade.get("score")
-                    print(f"    Auto-submit Score: {final_score}")
-                elif metadata_grade:
-                    final_score = metadata_grade.get("score")
-                    print(f"    Auto-submit Score: {final_score}")
-            except Exception as exc:
-                print(f"  [Auto-submit failed: {exc}]")
-
-    task_stats.elapsed_seconds = round(time.time() - task_started_at, 1)
+            for decision_index in range(max_decisions):
+                try:
+                    messages = build_messages(
+                        metadata=metadata,
+                        latest_data=latest_data,
+                        action_summaries=action_summaries,
+                        decision_index=decision_index,
+                        max_decisions=max_decisions,
+                    )
+                    tool_call = request_action(
+                        client=client,
+                        scheduler=scheduler,
+                        messages=messages,
+                        stats=task_stats,
+                        decision_index=decision_index,
+                        max_decisions=max_decisions,
+                    )
+                except Exception as e:
+                    print(f"[DEBUG] model request failed: {e}", flush=True)
+                    break
+                    
+                task_stats.decisions += 1
+                tool_name = tool_call["tool_name"]
+                arguments = tool_call["arguments"]
+                
+                args_str = json.dumps(arguments, separators=(',', ':'))
+                action_str = f"{tool_name}({args_str})"
+                
+                error = None
+                reward = 0.0
+                done = False
+
+                try:
+                    step_result = env.step(CallToolAction(tool_name=tool_name, arguments=arguments))
+                    observation = step_result.observation
+                    reward = step_result.reward or 0.0
+                    done = step_result.done
+                    
+                    result_data = normalize_tool_result(extract_tool_result_from_observation(observation))
+                    if result_data:
+                        latest_data = apply_tool_context(tool_name, arguments, latest_data, result_data)
+                    else:
+                        latest_data = apply_tool_context(
+                            tool_name,
+                            arguments,
+                            latest_data,
+                            extract_observation_metadata(observation),
+                        )
+                    
+                    # Update score
+                    metadata_grade = extract_observation_metadata(observation).get("grade", {})
+                    result_grade = latest_data.get("grade", {}) if isinstance(latest_data, dict) else {}
+                    if result_grade:
+                        final_score = result_grade.get("score") or 0.0
+                    elif metadata_grade:
+                        final_score = metadata_grade.get("score") or 0.0
+                        
+                except Exception as exc:
+                    error = str(exc)
+                    done = True
+
+                rewards.append(reward)
+                total_reward += reward
+                steps_taken = decision_index + 1
+                
+                error_val = error if error else "null"
+                error_val = error_val.replace('\n', ' ')
+                done_val = str(done).lower()
+                
+                print(f"[STEP] step={steps_taken} action={action_str} reward={reward:.2f} done={done_val} error={error_val}", flush=True)
+
+                action_summaries.append(action_summary(tool_name, arguments, latest_data, reward))
+
+                if done:
+                    break
+            else:
+                # auto-submit
+                try:
+                    action_str = "submit_model({})"
+                    step_result = env.step(CallToolAction(tool_name="submit_model", arguments={}))
+                    observation = step_result.observation
+                    reward = step_result.reward or 0.0
+                    total_reward += reward
+                    done = step_result.done
+                    
+                    result_data = normalize_tool_result(extract_tool_result_from_observation(observation))
+                    if result_data:
+                        latest_data = apply_tool_context("submit_model", {}, latest_data, result_data)
+
+                    metadata_grade = extract_observation_metadata(observation).get("grade", {})
+                    result_grade = latest_data.get("grade", {}) if isinstance(latest_data, dict) else {}
+                    if result_grade:
+                        final_score = result_grade.get("score") or 0.0
+                    elif metadata_grade:
+                        final_score = metadata_grade.get("score") or 0.0
+                        
+                    steps_taken += 1
+                    rewards.append(reward)
+                    print(f"[STEP] step={steps_taken} action={action_str} reward={reward:.2f} done={str(done).lower()} error=null", flush=True)
+
+                except Exception as exc:
+                    pass
+
+    except Exception as e:
+        print(f"[DEBUG] task container failed: {e}", flush=True)
+
+    finally:
+        task_stats.elapsed_seconds = round(time.time() - task_started_at, 1)
+        if final_score is None:
+            final_score = 0.0
+        final_score = min(max(float(final_score), 0.0), 1.0)
+        success = final_score >= 0.1
+        
+        rewards_str = ",".join(f"{float(r):.2f}" for r in rewards) if rewards else "0.00"
+        print(f"[END] success={str(success).lower()} steps={steps_taken} score={final_score:.3f} rewards={rewards_str}", flush=True)
 
     return {
         "task_id": task_id,

pyproject.toml

@@ -35,4 +35,13 @@ server = "ml_trainer_env.server.app:main"
 [tool.setuptools]
 include-package-data = true
 packages = ["ml_trainer_env", "ml_trainer_env.server"]
-package-dir = { "ml_trainer_env" = ".", "ml_trainer_env.server" = "server" }
+package-dir = { "ml_trainer_env" = ".", "ml_trainer_env.server" = "server" }
+
+[tool.uv.sources]
+torch = { index = "pytorch-cpu" }
+torchvision = { index = "pytorch-cpu" }
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true

uv.lock

@@ -1,17 +1,21 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
     "python_full_version >= '3.14' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
     "python_full_version == '3.13.*' and sys_platform == 'win32'",
     "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
-    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
     "python_full_version >= '3.11' and python_full_version < '3.13' and sys_platform == 'win32'",
     "python_full_version >= '3.11' and python_full_version < '3.13' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.11' and python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version < '3.11'",
+    "python_full_version >= '3.11' and python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and sys_platform != 'darwin'",
+    "python_full_version >= '3.14' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin'",
+    "python_full_version >= '3.11' and python_full_version < '3.13' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
 ]
 
 [[package]]
@@ -456,14 +460,14 @@ wheels = [
 
 [[package]]
 name = "click"
-version = "8.3.1"
+version = "8.3.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/75/31212c6bf2503fdf920d87fee5d7a86a2e3bcf444984126f13d8e4016804/click-8.3.2.tar.gz", hash = "sha256:14162b8b3b3550a7d479eafa77dfd3c38d9dc8951f6f69c78913a8f9a7540fd5", size = 302856, upload-time = "2026-04-03T19:14:45.118Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/20/71885d8b97d4f3dde17b1fdb92dbd4908b00541c5a3379787137285f602e/click-8.3.2-py3-none-any.whl", hash = "sha256:1924d2c27c5653561cd2cae4548d1406039cb79b858b747cfea24924bbc1616d", size = 108379, upload-time = "2026-04-03T19:14:43.505Z" },
 ]
 
 [[package]]
@@ -653,79 +657,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1a/89/843b53614b47f97fe1abc13f9a86efa5ec9e275292c457af1d4a60dc80e0/cryptography-46.0.6-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:6728c49e3b2c180ef26f8e9f0a883a2c585638db64cf265b49c9ba10652d430e", size = 3409955, upload-time = "2026-03-25T23:34:48.465Z" },
 ]
 
-[[package]]
-name = "cuda-bindings"
-version = "13.2.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "cuda-pathfinder", marker = "(python_full_version < '3.11' and sys_platform == 'emscripten') or (python_full_version < '3.11' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
-]
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/1a/fe/7351d7e586a8b4c9f89731bfe4cf0148223e8f9903ff09571f78b3fb0682/cuda_bindings-13.2.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:08b395f79cb89ce0cd8effff07c4a1e20101b873c256a1aeb286e8fd7bd0f556", size = 5744254, upload-time = "2026-03-11T00:12:29.798Z" },
-    { url = "https://files.pythonhosted.org/packages/aa/ef/184aa775e970fc089942cd9ec6302e6e44679d4c14549c6a7ea45bf7f798/cuda_bindings-13.2.0-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6f3682ec3c4769326aafc67c2ba669d97d688d0b7e63e659d36d2f8b72f32d6", size = 6329075, upload-time = "2026-03-11T00:12:32.319Z" },
-    { url = "https://files.pythonhosted.org/packages/e0/a9/3a8241c6e19483ac1f1dcf5c10238205dcb8a6e9d0d4d4709240dff28ff4/cuda_bindings-13.2.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:721104c603f059780d287969be3d194a18d0cc3b713ed9049065a1107706759d", size = 5730273, upload-time = "2026-03-11T00:12:37.18Z" },
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+# validate-submission.sh — OpenEnv Submission Validator
+#
+# Checks that your HF Space is live, Docker image builds, and openenv validate passes.
+#
+# Prerequisites:
+#   - Docker:       https://docs.docker.com/get-docker/
+#   - openenv-core: pip install openenv-core
+#   - curl (usually pre-installed)
+#
+# Run:
+#   curl -fsSL https://raw.githubusercontent.com/<owner>/<repo>/main/scripts/validate-submission.sh | bash -s -- <ping_url> [repo_dir]
+#
+#   Or download and run locally:
+#     chmod +x validate-submission.sh
+#     ./validate-submission.sh <ping_url> [repo_dir]
+#
+# Arguments:
+#   ping_url   Your HuggingFace Space URL (e.g. https://your-space.hf.space)
+#   repo_dir   Path to your repo (default: current directory)
+#
+# Examples:
+#   ./validate-submission.sh https://my-team.hf.space
+#   ./validate-submission.sh https://my-team.hf.space ./my-repo
+#
+
+set -uo pipefail
+
+DOCKER_BUILD_TIMEOUT=600
+if [ -t 1 ]; then
+  RED='\033[0;31m'
+  GREEN='\033[0;32m'
+  YELLOW='\033[1;33m'
+  BOLD='\033[1m'
+  NC='\033[0m'
+else
+  RED='' GREEN='' YELLOW='' BOLD='' NC=''
+fi
+
+run_with_timeout() {
+  local secs="$1"; shift
+  if command -v timeout &>/dev/null; then
+    timeout "$secs" "$@"
+  elif command -v gtimeout &>/dev/null; then
+    gtimeout "$secs" "$@"
+  else
+    "$@" &
+    local pid=$!
+    ( sleep "$secs" && kill "$pid" 2>/dev/null ) &
+    local watcher=$!
+    wait "$pid" 2>/dev/null
+    local rc=$?
+    kill "$watcher" 2>/dev/null
+    wait "$watcher" 2>/dev/null
+    return $rc
+  fi
+}
+
+portable_mktemp() {
+  local prefix="${1:-validate}"
+  mktemp "${TMPDIR:-/tmp}/${prefix}-XXXXXX" 2>/dev/null || mktemp
+}
+
+CLEANUP_FILES=()
+cleanup() { rm -f "${CLEANUP_FILES[@]+"${CLEANUP_FILES[@]}"}"; }
+trap cleanup EXIT
+
+PING_URL="${1:-}"
+REPO_DIR="${2:-.}"
+
+if [ -z "$PING_URL" ]; then
+  printf "Usage: %s <ping_url> [repo_dir]\n" "$0"
+  printf "\n"
+  printf "  ping_url   Your HuggingFace Space URL (e.g. https://your-space.hf.space)\n"
+  printf "  repo_dir   Path to your repo (default: current directory)\n"
+  exit 1
+fi
+
+if ! REPO_DIR="$(cd "$REPO_DIR" 2>/dev/null && pwd)"; then
+  printf "Error: directory '%s' not found\n" "${2:-.}"
+  exit 1
+fi
+PING_URL="${PING_URL%/}"
+export PING_URL
+PASS=0
+
+log()  { printf "[%s] %b\n" "$(date -u +%H:%M:%S)" "$*"; }
+pass() { log "${GREEN}PASSED${NC} -- $1"; PASS=$((PASS + 1)); }
+fail() { log "${RED}FAILED${NC} -- $1"; }
+hint() { printf "  ${YELLOW}Hint:${NC} %b\n" "$1"; }
+stop_at() {
+  printf "\n"
+  printf "${RED}${BOLD}Validation stopped at %s.${NC} Fix the above before continuing.\n" "$1"
+  exit 1
+}
+
+printf "\n"
+printf "${BOLD}========================================${NC}\n"
+printf "${BOLD}  OpenEnv Submission Validator${NC}\n"
+printf "${BOLD}========================================${NC}\n"
+log "Repo:     $REPO_DIR"
+log "Ping URL: $PING_URL"
+printf "\n"
+
+log "${BOLD}Step 1/3: Pinging HF Space${NC} ($PING_URL/reset) ..."
+
+CURL_OUTPUT=$(portable_mktemp "validate-curl")
+CLEANUP_FILES+=("$CURL_OUTPUT")
+HTTP_CODE=$(curl -s -o "$CURL_OUTPUT" -w "%{http_code}" -X POST \
+  -H "Content-Type: application/json" -d '{}' \
+  "$PING_URL/reset" --max-time 30 2>"$CURL_OUTPUT" || printf "000")
+
+if [ "$HTTP_CODE" = "200" ]; then
+  pass "HF Space is live and responds to /reset"
+elif [ "$HTTP_CODE" = "000" ]; then
+  fail "HF Space not reachable (connection failed or timed out)"
+  hint "Check your network connection and that the Space is running."
+  hint "Try: curl -s -o /dev/null -w '%%{http_code}' -X POST $PING_URL/reset"
+  stop_at "Step 1"
+else
+  fail "HF Space /reset returned HTTP $HTTP_CODE (expected 200)"
+  hint "Make sure your Space is running and the URL is correct."
+  hint "Try opening $PING_URL in your browser first."
+  stop_at "Step 1"
+fi
+
+log "${BOLD}Step 2/3: Running docker build${NC} ..."
+
+if ! command -v docker &>/dev/null; then
+  fail "docker command not found"
+  hint "Install Docker: https://docs.docker.com/get-docker/"
+  stop_at "Step 2"
+fi
+
+if [ -f "$REPO_DIR/Dockerfile" ]; then
+  DOCKER_CONTEXT="$REPO_DIR"
+elif [ -f "$REPO_DIR/server/Dockerfile" ]; then
+  DOCKER_CONTEXT="$REPO_DIR/server"
+else
+  fail "No Dockerfile found in repo root or server/ directory"
+  stop_at "Step 2"
+fi
+
+log "  Found Dockerfile in $DOCKER_CONTEXT"
+
+BUILD_OK=false
+BUILD_OUTPUT=$(run_with_timeout "$DOCKER_BUILD_TIMEOUT" docker build "$DOCKER_CONTEXT" 2>&1) && BUILD_OK=true
+
+if [ "$BUILD_OK" = true ]; then
+  pass "Docker build succeeded"
+else
+  fail "Docker build failed (timeout=${DOCKER_BUILD_TIMEOUT}s)"
+  printf "%s\n" "$BUILD_OUTPUT" | tail -20
+  stop_at "Step 2"
+fi
+
+log "${BOLD}Step 3/3: Running openenv validate${NC} ..."
+
+if ! command -v openenv &>/dev/null; then
+  fail "openenv command not found"
+  hint "Install it: pip install openenv-core"
+  stop_at "Step 3"
+fi
+
+VALIDATE_OK=false
+VALIDATE_OUTPUT=$(cd "$REPO_DIR" && openenv validate 2>&1) && VALIDATE_OK=true
+
+if [ "$VALIDATE_OK" = true ]; then
+  pass "openenv validate passed"
+  [ -n "$VALIDATE_OUTPUT" ] && log "  $VALIDATE_OUTPUT"
+else
+  fail "openenv validate failed"
+  printf "%s\n" "$VALIDATE_OUTPUT"
+  stop_at "Step 3"
+fi
+
+printf "\n"
+printf "${BOLD}========================================${NC}\n"
+printf "${GREEN}${BOLD}  All 3/3 checks passed!${NC}\n"
+printf "${GREEN}${BOLD}  Your submission is ready to submit.${NC}\n"
+printf "${BOLD}========================================${NC}\n"
+printf "\n"
+
+exit 0