Add the Reasoning Gym set of environments by zafstojano · Pull Request #326 · meta-pytorch/OpenEnv

zafstojano · 2026-01-25T16:52:23Z

Summary

Hey there, I am one of the core contributors of Reasoning Gym - a suite of 100+ environments with verifiable rewards. I would be really happy to contribute this set of procedural data generators to OpenEnv!

Since these are all single-step environments, I went with the following design philosophy:

When the user connects to an environment, calling env.reset(...) creates an environment with the passed arguments:

env.reset(
    dataset_name='figlet_font',
    dataset_config={"min_word_len": 4, "max_word_len": 6},
    seed=42,
    size=20
)

Since it is a single step environment, right after doing a step, the episode is done=True. This time, simply calling env.reset() with no arguments will yield a new generated sample from the previously instantiated environment.

Once the user decides to call env.reset(...) with new dataset configs, it will re-instantiate a new dataset and continue yielding data from there:

# Create composite dataset - note dataset_specs is list of dicts
# User constructs the specs according to reasoning_gym API
dataset_specs = [
    {"name": "leg_counting", "weight": 2, "config": {}},
    {"name": "figlet_font", "weight": 1, "config": {"min_word_len": 4, "max_word_len": 6}},
]
result = env.reset(
    dataset_name='composite',
    dataset_specs=dataset_specs,
    seed=42,
    size=30
)

Type of Change

Alignment Checklist

Before submitting, verify:

I have read .claude/docs/PRINCIPLES.md and this PR aligns with our principles
I have checked .claude/docs/INVARIANTS.md and no invariants are violated
I have run /pre-submit-pr (or bash .claude/hooks/lint.sh and tests) and addressed all issues

RFC Status

Not required (bug fix, docs, minor refactoring)
RFC exists: #___
RFC needed (will create before merge)

Test Plan

After building the Docker image, I have created a small script to test out the calls to the environment

Sample script

from reasoning_gym_env import ReasoningGymAction, ReasoningGymObservation, ReasoningGymEnv


def test_simple_dataset():
    """Test simple dataset with leg_counting questions."""
    print("\n" + "="*70)
    print("TEST 1: Simple Dataset (leg_counting)")
    print("="*70)
    
    env = ReasoningGymEnv(base_url="http://localhost:8000")

    print("\n[1/3] Creating dataset with 10 leg_counting questions...")
    result = env.reset(
        dataset_name='leg_counting',
        seed=42,
        size=10
    )
    print(f"  → First question received: {result.observation.question}")

    print("\n[2/3] Submitting answer '4'...")    
    result = env.step(ReasoningGymAction(answer="4"))
    print(f"  → Score: {result.observation.score}")
    print(f"  → Correct answer: {result.observation.correct_answer}")
    print(f"  → Episode done: {result.done}")

    print("\n[3/3] Getting next question from same dataset...")
    result = env.reset()
    print(f"  → Next question: {result.observation.question}")

    env.close()
    print("\n✓ Simple dataset test complete")


def test_simple_dataset_with_config():
    """Test simple dataset with configuration."""
    print("\n" + "="*70)
    print("TEST 2: Simple Dataset with Config (figlet_font)")
    print("="*70)
    
    env = ReasoningGymEnv(base_url="http://localhost:8000")

    print("\n[1/1] Creating figlet_font dataset with config...")
    print("  → Config: min_word_len=4, max_word_len=6")
    print("  → Dataset size: 20 questions")
    result = env.reset(
        dataset_name='figlet_font',
        dataset_config={"min_word_len": 4, "max_word_len": 6},
        seed=42,
        size=20
    )
    print(f"  → Question received:")
    print(f"     {result.observation.question}")

    env.close()
    print("\n✓ Simple dataset with config test complete")


def test_composite_dataset():
    """Test composite dataset with multiple question types."""
    print("\n" + "="*70)
    print("TEST 3: Composite Dataset (leg_counting + figlet_font)")
    print("="*70)
    
    env = ReasoningGymEnv(base_url="http://localhost:8000")

    # Create composite dataset - note dataset_specs is list of dicts
    # User constructs the specs according to reasoning_gym API
    dataset_specs = [
        {"name": "leg_counting", "weight": 2, "config": {}},
        {"name": "figlet_font", "weight": 1, "config": {"min_word_len": 4, "max_word_len": 6}},
    ]

    print("\n[1/2] Creating composite dataset...")
    print("  → Dataset specs:")
    print(f"     - leg_counting (weight: 2)")
    print(f"     - figlet_font (weight: 1, min_word_len=4, max_word_len=6)")
    print(f"  → Total size: 30 questions")
    result = env.reset(
        dataset_name='composite',
        dataset_specs=dataset_specs,
        seed=42,
        size=30
    )
    print(f"  → Question received: {result.observation.question}...")
    print(f"  → Dataset metadata: {result.observation.dataset_metadata}")

    print("\n[2/2] Submitting answer 'my answer'...")
    result = env.step(ReasoningGymAction(answer="my answer"))
    print(f"  → Score: {result.observation.score}")
    print(f"  → Correct answer: {result.observation.correct_answer}")
    print(f"  → Episode done: {result.done}")

    env.close()
    print("\n✓ Composite dataset test complete")


def test_dataset_persistence():
    """Test dataset persistence across multiple resets."""
    print("\n" + "="*70)
    print("TEST 4: Dataset Persistence & Iterator Looping")
    print("="*70)
    
    env = ReasoningGymEnv(base_url="http://localhost:8000")

    print("\n[Step 1] Creating dataset with 5 questions (seed=42)...")
    result = env.reset(
        dataset_name='leg_counting',
        seed=42,
        size=5
    )
    print(f"  → Question 1: {result.observation.question}")

    print("\n[Step 2] Calling reset() with no params - should get question 2...")
    result = env.reset()  # No params - get question 2 from same dataset
    print(f"  → Question 2: {result.observation.question}")
    
    print("\n[Step 3] Getting question 3...")
    result = env.reset()  # Question 3
    print(f"  → Question 3: {result.observation.question}")
    
    print("\n[Step 4] Getting question 4...")
    result = env.reset()  # Question 4
    print(f"  → Question 4: {result.observation.question}")
    
    print("\n[Step 5] Getting question 5 (last question)...")
    result = env.reset()  # Question 5
    print(f"  → Question 5: {result.observation.question}")
    
    print("\n[Step 6] Getting next question - should loop back to question 1...")
    result = env.reset()  # Question 1 again (iterator loops)
    print(f"  → Question (looped): {result.observation.question}")

    print("\n[Step 7] Creating new dataset with different seed (seed=99, size=10)...")
    print("  → This should rebuild the dataset entirely")
    result = env.reset(
        dataset_name='leg_counting',
        seed=99,
        size=10
    )
    print(f"  → New dataset question: {result.observation.question}")

    env.close()
    print("\n✓ Dataset persistence test complete")


if __name__ == "__main__":
    import traceback
    
    tests = [
        ("Simple Dataset", test_simple_dataset),
        ("Simple Dataset with Config", test_simple_dataset_with_config),
        ("Composite Dataset", test_composite_dataset),
        ("Dataset Persistence", test_dataset_persistence),
    ]
    
    results = []
    
    for test_name, test_func in tests:
        try:
            test_func()
            results.append((test_name, "PASSED", None))
        except Exception as e:
            results.append((test_name, "FAILED", e))
            print(f"\n❌ {test_name} FAILED with error:")
            print(f"   {str(e)}")
            traceback.print_exc()
    
    # Print summary
    print("\n" + "="*70)
    print("TEST SUMMARY")
    print("="*70)
    for test_name, status, error in results:
        icon = "✅" if status == "PASSED" else "❌"
        print(f"{icon} {test_name}: {status}")
        if error:
            print(f"   Error: {str(error)}")
    
    passed = sum(1 for _, status, _ in results if status == "PASSED")
    total = len(results)
    print(f"\nResults: {passed}/{total} tests passed")
    
    if passed == total:
        print("🎉 All tests passed!")
    else:
        print(f"⚠️  {total - passed} test(s) failed")

Script Output

======================================================================
TEST 1: Simple Dataset (leg_counting)
======================================================================

[1/3] Creating dataset with 10 leg_counting questions...
  → First question received: Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 3 sea slugs, 12 deers, 2 giraffes, 11 elephants?


[2/3] Submitting answer '4'...
  → Score: 0.0
  → Correct answer: 100
  → Episode done: True

[3/3] Getting next question from same dataset...
  → Next question: Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 6 sheeps, 11 dogs, 12 praying mantiss?


✓ Simple dataset test complete

======================================================================
TEST 2: Simple Dataset with Config (figlet_font)
======================================================================

[1/1] Creating figlet_font dataset with config...
  → Config: min_word_len=4, max_word_len=6
  → Dataset size: 20 questions
  → Question received:
     What word does this say?

                                                     
                                                     
##         ####  ######   ######   #######  ######   
##          ##   ##   ##  ##   ##  ##       ##   ##  
##          ##   ######   ######   #####    ######   
##          ##   ##       ##       ##       ## ##    
#######    ####  ##       ##       #######  ##  ##   
                                                     


✓ Simple dataset with config test complete

======================================================================
TEST 3: Composite Dataset (leg_counting + figlet_font)
======================================================================

[1/2] Creating composite dataset...
  → Dataset specs:
     - leg_counting (weight: 2)
     - figlet_font (weight: 1, min_word_len=4, max_word_len=6)
  → Total size: 30 questions
  → Question received: Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 6 sheeps, 11 dogs, 12 praying mantiss?
...
  → Dataset metadata: None

[2/2] Submitting answer 'my answer'...
  → Score: 0.0
  → Correct answer: 140
  → Episode done: True

✓ Composite dataset test complete

======================================================================
TEST 4: Dataset Persistence & Iterator Looping
======================================================================

[Step 1] Creating dataset with 5 questions (seed=42)...
  → Question 1: Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 3 sea slugs, 12 deers, 2 giraffes, 11 elephants?


[Step 2] Calling reset() with no params - should get question 2...
  → Question 2: Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 6 sheeps, 11 dogs, 12 praying mantiss?


[Step 3] Getting question 3...
  → Question 3: Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 2 crabs, 10 lobsters, 1 human, 2 cows, 3 bees, 13 elephants, 9 dogs, 12 snakes, 5 shrimps?


[Step 4] Getting question 4...
  → Question 4: Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 2 grasshoppers, 8 spiders, 1 tiger, 2 chickens, 5 starfishs, 13 ants, 2 snakes?


[Step 5] Getting question 5 (last question)...
  → Question 5: Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 3 wasps, 10 jellyfishs, 9 elephants, 13 crabs?


[Step 6] Getting next question - should loop back to question 1...
  → Question (looped): Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 3 sea slugs, 12 deers, 2 giraffes, 11 elephants?


[Step 7] Creating new dataset with different seed (seed=99, size=10)...
  → This should rebuild the dataset entirely
  → New dataset question: Your task is to count how many legs there are in total when given a list of animals.

Now, how many legs are there in total if you have 12 bees, 7 horses, 9 cows, 11 elephants, 12 giraffes, 9 ducks, 2 woodlouses, 10 jellyfishs, 8 spiders?


✓ Dataset persistence test complete

======================================================================
TEST SUMMARY
======================================================================
✅ Simple Dataset: PASSED
✅ Simple Dataset with Config: PASSED
✅ Composite Dataset: PASSED
✅ Dataset Persistence: PASSED

Results: 4/4 tests passed
🎉 All tests passed!

Claude Code Review

Alignment Review Report                                                           
                                                                                  
Automated Checks                                                                  
                                                                                  
- Lint: ✅ PASS - 80 files already formatted                                      
- Debug code: ⚠️ FOUND - Multiple print statements detected (see details below)   
                                                                                  
Open RFCs Context                                                                 
                                                                                  
All RFCs are in "In Review" status:                                               
- RFC 000: Project Phases (In Review)                                             
- RFC 001: Abstractions (In Review)                                               
- RFC 002: Environment Spec (In Review)                                           
- RFC 003: MCP Support (In Review)                                                
                                                                                  
No direct conflicts identified between these changes and active RFCs.             
                                                                                  
Tier 1: Fixes Required                                                            
                                                                                  
Debug Code Analysis:                                                              
The check-debug.sh script flagged many print statements. However, upon analysis of
 the diff, all print statements in the new reasoning_gym_env code are in          
docstrings/examples, not in actual executable code:                               
                                                                                  
- Lines 76, 81-88, 92, 300, 305: Print statements in README examples ✅           
- Lines 483, 487-488, 492: Print statements in docstring examples in client.py ✅ 
                                                                                  
The debug script also found print statements in existing src/openenv files, but   
those are:                                                                        
1. Outside the scope of this PR (pre-existing)                                    
2. Primarily in docstrings and example code                                       
3. One test file (test_local_docker_provider.py) that is marked with a TODO to be 
removed/refactored                                                                
                                                                                  
Conclusion: ✅ No Tier 1 fixes required in this PR's changes.                     
                                                                                  
Tier 2: Alignment Discussion                                                      
                                                                                  
Principle Conflicts                                                               
                                                                                  
None identified                                                                   
                                                                                  
RFC Conflicts                                                                     
                                                                                  
None identified                                                                   
                                                                                  
Architecture Review                                                               
                                                                                  
The Reasoning Gym environment implementation follows OpenEnv patterns correctly:  
                                                                                  
✅ Gymnasium API compliance - Uses standard reset/step/state signatures           
✅ Type safety - Properly typed with generics: Environment[ReasoningGymAction,    
ReasoningGymObservation, State]                                                   
✅ Pydantic models - Action and Observation extend correct base classes           
✅ Client-server separation - No server imports in client code; shared models in  
models.py                                                                         
✅ Rewards in environment - Scoring handled server-side via                       
reasoning_gym.score_answer()                                                      
✅ WebSocket support - Uses EnvClient with WebSocket connections                  
✅ Container isolation - Dockerfile follows standard patterns                     
✅ Concurrent sessions - Declares SUPPORTS_CONCURRENT_SESSIONS = True             
                                                                                  
Summary                                                                           
                                                                                  
- ✅ 0 mechanical issues to fix                                                   
- ✅ 0 alignment points for human review                                          
- ✅ 0 RFC conflicts to discuss                                                   
                                                                                  
Overall Assessment: This PR is well-aligned with OpenEnv principles and           
invariants. The implementation follows established patterns from echo_env and     
other reference environments. All automated checks pass, and no architectural     
concerns were identified.

Integrate reasoning_gym library to provide single-step reasoning tasks. Each episode presents one question from a configurable dataset, the agent submits an answer, and receives a score (0.0 to 1.0). Features: - Single-step episodes: reset() provides question, step() validates answer - Dataset persistence: Dataset reused across resets until config changes - Flexible configuration: Supports simple and composite datasets - Concurrent sessions: Multiple clients can connect simultaneously Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

Replace EchoEnv template content with accurate documentation for Reasoning Gym environment. Update includes: - Single-step reasoning task workflow - Dataset configuration (simple and composite) - Dataset persistence behavior - Correct action/observation models (answer, score, question) - Reward structure (score-based, not length-based) - Use cases for LLM evaluation and agent training Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

Show how to access the dataset_metadata field in the Quick Start example, demonstrating the full observation interface. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

- Add comprehensive test suite with 26 tests covering environment behavior, models, client, and integration workflows - Fix imports in server files to support both Docker (direct import) and local testing (relative import) - Fix minor formatting issue in docstring Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

greptile-apps · 2026-01-25T16:55:49Z

Greptile Overview

Greptile Summary

Added Reasoning Gym environment integration to OpenEnv, providing 100+ single-step reasoning tasks with verifiable rewards.

Key Implementation Details:

Single-step episodes where reset() provides a question and step() validates the answer and returns done=True
Dataset persistence across resets - calling reset() without parameters reuses the existing dataset and provides the next question
Sequential iteration through questions with automatic wrap-around when the dataset is exhausted
Support for both simple datasets (e.g., leg_counting) with optional config parameters and composite datasets that blend multiple task types
Rewards computed inside environment using reasoning_gym.score_answer(), following the "rewards inside environment" principle

Architecture Alignment:

✅ Follows Gymnasium API patterns with proper reset() and step() signatures
✅ Type-safe implementation using generics: Environment[ReasoningGymAction, ReasoningGymObservation, State]
✅ Client-server separation maintained (client imports only from models.py, not from server/)
✅ WebSocket support via EnvClient base class
✅ Container isolation with multi-stage Dockerfile
✅ Comprehensive test coverage (18 tests covering reset, step, dataset persistence, error cases, and integration workflows)
✅ Declares SUPPORTS_CONCURRENT_SESSIONS = True for concurrent WebSocket sessions

Design Philosophy:
The environment implements a stateful dataset pattern where the dataset configuration persists until explicitly changed. This allows efficient iteration through questions without recreating the dataset on each reset. When reset() is called with new parameters, the dataset is rebuilt; otherwise, it advances to the next question in the sequence.

Confidence Score: 5/5

This PR is safe to merge with minimal risk - it's a well-implemented new environment that follows all OpenEnv patterns and principles
Score reflects comprehensive adherence to OpenEnv invariants and principles: proper client-server separation, rewards computed inside environment, Gymnasium API compliance, full type safety with generics, WebSocket support, container isolation, and comprehensive test coverage. The implementation follows established patterns from reference environments and includes 18 tests covering normal operations, edge cases, and integration workflows. No bugs, security issues, or alignment violations were found.
No files require special attention - all implementations are clean and follow established patterns

Important Files Changed

Filename	Overview
envs/reasoning_gym_env/models.py	Pydantic models for actions and observations - clean, properly typed, follows OpenEnv patterns
envs/reasoning_gym_env/client.py	Client implementation - correctly extends EnvClient with proper serialization methods, no server imports
envs/reasoning_gym_env/server/reasoning_gym_environment.py	Core environment logic - implements single-step reasoning tasks with dataset persistence, proper reward computation inside environment
envs/reasoning_gym_env/server/app.py	FastAPI app creation - follows standard create_app pattern with proper imports
tests/envs/test_reasoning_gym_environment.py	Comprehensive test suite - covers reset, step, dataset persistence, error cases, and integration workflows

Sequence Diagram

sequenceDiagram
    participant Client as ReasoningGymEnv<br/>(Client)
    participant WS as WebSocket<br/>Connection
    participant Server as FastAPI<br/>Server
    participant Env as ReasoningGymEnvironment
    participant RG as reasoning_gym<br/>Library

    Note over Client,RG: Initial Setup & First Episode
    Client->>Server: Connect (WebSocket)
    Server->>Env: Create environment instance
    
    Client->>WS: reset(dataset_name='leg_counting',<br/>seed=42, size=10)
    WS->>Server: Forward reset request
    Server->>Env: reset(...)
    Env->>RG: create_dataset('leg_counting',<br/>seed=42, size=10)
    RG-->>Env: Dataset instance
    Env->>Env: Create iterator from dataset
    Env->>Env: Get next question from iterator
    Env-->>Server: ReasoningGymObservation<br/>(question, done=False)
    Server-->>WS: Serialize observation
    WS-->>Client: StepResult with question
    
    Note over Client,RG: Agent Answers Question
    Client->>WS: step(ReasoningGymAction(answer="4"))
    WS->>Server: Forward step request
    Server->>Env: step(action)
    Env->>RG: score_answer(answer, entry)
    RG-->>Env: score (0.0-1.0)
    Env-->>Server: ReasoningGymObservation<br/>(score, correct_answer, done=True)
    Server-->>WS: Serialize observation
    WS-->>Client: StepResult with score
    
    Note over Client,RG: Next Question (Reuse Dataset)
    Client->>WS: reset() [no params]
    WS->>Server: Forward reset request
    Server->>Env: reset()
    Env->>Env: Reuse existing dataset
    Env->>Env: Get next question from iterator
    Note over Env: If iterator exhausted,<br/>wrap around to start
    Env-->>Server: ReasoningGymObservation<br/>(question, done=False)
    Server-->>WS: Serialize observation
    WS-->>Client: StepResult with question
    
    Note over Client,RG: New Dataset Configuration
    Client->>WS: reset(dataset_name='composite',<br/>dataset_specs=[...], seed=99, size=30)
    WS->>Server: Forward reset request
    Server->>Env: reset(...)
    Env->>RG: create_dataset('composite',<br/>datasets=specs, seed=99, size=30)
    RG-->>Env: New dataset instance
    Env->>Env: Create new iterator
    Env->>Env: Get first question
    Env-->>Server: ReasoningGymObservation<br/>(question, done=False)
    Server-->>WS: Serialize observation
    WS-->>Client: StepResult with question

zafstojano · 2026-01-28T08:50:21Z

tagging @burtenshaw @Darktex for visibility :)

burtenshaw · 2026-02-05T13:42:38Z

This looks good. Have you also deployed it to the HF hub, and updated the environments page

…oning-gym-env

…v into feat/reasoning-gym-env

zafstojano · 2026-02-08T09:02:53Z

I have added the env card.

Regarding deploying to HF spaces, I have an issue. First, building the image with openenv build seems to work:

openenv build logs

❯ uv run openenv build ./envs/reasoning_gym_env
Building Docker image for: reasoning_gym_env
============================================================
Build mode detected: in-repo
Preparing in-repo build...
Copied OpenEnv package to: 
/var/folders/_6/3tz7ldb102s8qbj66zjytvgw0000gn/T/tmpqkc_by50/reasoning_gym_env/open
env
Updated pyproject.toml to use local core
Removed outdated uv.lock
Build directory prepared: 
/var/folders/_6/3tz7ldb102s8qbj66zjytvgw0000gn/T/tmpqkc_by50/reasoning_gym_env
Building Docker image: openenv-reasoning_gym
Build context: 
/var/folders/_6/3tz7ldb102s8qbj66zjytvgw0000gn/T/tmpqkc_by50/reasoning_gym_env
Dockerfile: 
/var/folders/_6/3tz7ldb102s8qbj66zjytvgw0000gn/T/tmpqkc_by50/reasoning_gym_env/serv
er/Dockerfile
Running: docker build -t openenv-reasoning_gym -f 
/var/folders/_6/3tz7ldb102s8qbj66zjytvgw0000gn/T/tmpqkc_by50/reasoning_gym_env/serv
er/Dockerfile --build-arg BUILD_MODE=in-repo --build-arg ENV_NAME=reasoning_gym 
/var/folders/_6/3tz7ldb102s8qbj66zjytvgw0000gn/T/tmpqkc_by50/reasoning_gym_env
#0 building with "desktop-linux" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 2.70kB 0.0s done
#1 DONE 0.0s

#2 [internal] load metadata for ghcr.io/meta-pytorch/openenv-base:latest
#2 DONE 1.5s

#3 [internal] load .dockerignore
#3 transferring context: 171B done
#3 DONE 0.0s

#4 [builder 1/8] FROM ghcr.io/meta-pytorch/openenv-base:latest@sha256:144c93341e2830b03a6c9520ab89a5002f19f55d8eaf4a2698669a183fcb2f29
#4 resolve ghcr.io/meta-pytorch/openenv-base:latest@sha256:144c93341e2830b03a6c9520ab89a5002f19f55d8eaf4a2698669a183fcb2f29 done
#4 ...

#5 [internal] load build context
#5 transferring context: 978.50kB 0.1s done
#5 DONE 0.1s

#4 [builder 1/8] FROM ghcr.io/meta-pytorch/openenv-base:latest@sha256:144c93341e2830b03a6c9520ab89a5002f19f55d8eaf4a2698669a183fcb2f29
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 0B / 14.31MB 0.2s
#4 sha256:05c5a241432232a0a200b8395f09c5da514ed2d1d0fff93fb57bbd229f308114 0B / 1.27MB 0.2s
#4 sha256:cb62e24d8d3208d00cb0aaa3b4a68245a68d22d99267b240d82a91fb9d5adfbc 2.01kB / 2.01kB done
#4 sha256:8c9d886ce28d995f7682117fbe938f82d7e4b4e06d9f7855f307c731c4a6aaae 8.54kB / 8.54kB done
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 0B / 30.14MB 0.2s
#4 sha256:144c93341e2830b03a6c9520ab89a5002f19f55d8eaf4a2698669a183fcb2f29 1.61kB / 1.61kB done
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 2.10MB / 30.14MB 0.5s
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 6.29MB / 30.14MB 0.8s
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 8.39MB / 30.14MB 0.9s
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 11.28MB / 30.14MB 1.1s
#4 sha256:05c5a241432232a0a200b8395f09c5da514ed2d1d0fff93fb57bbd229f308114 1.05MB / 1.27MB 1.3s
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 13.63MB / 30.14MB 1.3s
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 1.05MB / 14.31MB 1.5s
#4 sha256:05c5a241432232a0a200b8395f09c5da514ed2d1d0fff93fb57bbd229f308114 1.27MB / 1.27MB 1.4s done
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 16.78MB / 30.14MB 1.5s
#4 sha256:93304c4769c26f8efc1479b815e3d84a136217013671ad87e5fa98da27cea089 0B / 251B 1.5s
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 20.97MB / 30.14MB 1.7s
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 25.17MB / 30.14MB 2.0s
#4 sha256:93304c4769c26f8efc1479b815e3d84a136217013671ad87e5fa98da27cea089 251B / 251B 1.9s done
#4 sha256:e61c5e8184c8211960c42f637dad7ba317a20f4cd6fb0bfff99ed99515200338 0B / 4.99MB 2.0s
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 27.26MB / 30.14MB 2.1s
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 2.10MB / 14.31MB 2.3s
#4 sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 30.14MB / 30.14MB 2.3s done
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 3.11MB / 14.31MB 2.4s
#4 sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001 0B / 15.36MB 2.4s
#4 extracting sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 3.90MB / 14.31MB 2.5s
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 4.68MB / 14.31MB 2.6s
#4 sha256:e61c5e8184c8211960c42f637dad7ba317a20f4cd6fb0bfff99ed99515200338 1.05MB / 4.99MB 2.6s
#4 sha256:e61c5e8184c8211960c42f637dad7ba317a20f4cd6fb0bfff99ed99515200338 2.10MB / 4.99MB 2.7s
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 6.29MB / 14.31MB 2.8s
#4 sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001 2.10MB / 15.36MB 2.9s
#4 sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001 5.24MB / 15.36MB 3.1s
#4 sha256:e61c5e8184c8211960c42f637dad7ba317a20f4cd6fb0bfff99ed99515200338 3.15MB / 4.99MB 3.2s
#4 sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001 6.34MB / 15.36MB 3.2s
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 7.34MB / 14.31MB 3.3s
#4 sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001 8.39MB / 15.36MB 3.3s
#4 sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001 9.44MB / 15.36MB 3.4s
#4 sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001 12.58MB / 15.36MB 3.6s
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 8.39MB / 14.31MB 3.8s
#4 sha256:e61c5e8184c8211960c42f637dad7ba317a20f4cd6fb0bfff99ed99515200338 4.19MB / 4.99MB 3.8s
#4 sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001 15.36MB / 15.36MB 3.8s done
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 9.44MB / 14.31MB 3.9s
#4 sha256:e61c5e8184c8211960c42f637dad7ba317a20f4cd6fb0bfff99ed99515200338 4.99MB / 4.99MB 3.9s done
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 0B / 25.20MB 3.9s
#4 sha256:1de9592b4b71557f9b26c0d765f61800360794d67ff00329acab5c48ad3a8310 0B / 362B 3.9s
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 10.78MB / 14.31MB 4.1s
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 12.58MB / 14.31MB 4.2s
#4 sha256:1de9592b4b71557f9b26c0d765f61800360794d67ff00329acab5c48ad3a8310 362B / 362B 4.2s done
#4 sha256:783b7c917b690db743671ad20e3cdc7c0b4ba7011c777f783efb248a97dbdb88 0B / 93B 4.2s
#4 sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 14.31MB / 14.31MB 4.3s done
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 2.00MB / 25.20MB 4.4s
#4 sha256:783b7c917b690db743671ad20e3cdc7c0b4ba7011c777f783efb248a97dbdb88 93B / 93B 4.5s done
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 4.19MB / 25.20MB 4.6s
#4 extracting sha256:3ea009573b472d108af9af31ec35a06fe3649084f6611cf11f7d594b85cf7a7c 2.4s done
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 7.34MB / 25.20MB 4.9s
#4 extracting sha256:05c5a241432232a0a200b8395f09c5da514ed2d1d0fff93fb57bbd229f308114
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 9.44MB / 25.20MB 5.0s
#4 extracting sha256:05c5a241432232a0a200b8395f09c5da514ed2d1d0fff93fb57bbd229f308114 0.1s done
#4 extracting sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 12.58MB / 25.20MB 5.2s
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 15.50MB / 25.20MB 5.4s
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 17.03MB / 25.20MB 5.5s
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 18.87MB / 25.20MB 5.6s
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 20.97MB / 25.20MB 5.7s
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 23.07MB / 25.20MB 5.8s
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 24.92MB / 25.20MB 5.9s
#4 sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 25.20MB / 25.20MB 5.9s done
#4 extracting sha256:e9e50e44f49d8e67d2fb6488786b2879eeb473e5ea7f238fc2655b7154abde80 1.5s done
#4 extracting sha256:93304c4769c26f8efc1479b815e3d84a136217013671ad87e5fa98da27cea089 done
#4 extracting sha256:e61c5e8184c8211960c42f637dad7ba317a20f4cd6fb0bfff99ed99515200338 0.1s
#4 extracting sha256:e61c5e8184c8211960c42f637dad7ba317a20f4cd6fb0bfff99ed99515200338 0.2s done
#4 extracting sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001
#4 extracting sha256:c95ac89af49c8b53e0e7a92f9ed7eac5a4f2281adbdbd76da84a5fa174b94001 0.3s done
#4 extracting sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1
#4 extracting sha256:7b29706aad968eb0c2dafd2e0f6e02026339ceb1d792a586518c87db72d01fc1 2.3s done
#4 extracting sha256:1de9592b4b71557f9b26c0d765f61800360794d67ff00329acab5c48ad3a8310 done
#4 extracting sha256:783b7c917b690db743671ad20e3cdc7c0b4ba7011c777f783efb248a97dbdb88 done
#4 DONE 9.5s

#6 [builder 2/8] WORKDIR /app
#6 DONE 0.2s

#7 [builder 3/8] RUN apt-get update &&     apt-get install -y --no-install-recommends git &&     rm -rf /var/lib/apt/lists/*
#7 0.270 Get:1 http://deb.debian.org/debian trixie InRelease [140 kB]
#7 0.299 Get:2 http://deb.debian.org/debian trixie-updates InRelease [47.3 kB]
#7 0.309 Get:3 http://deb.debian.org/debian-security trixie-security InRelease [43.4 kB]
#7 0.321 Get:4 http://deb.debian.org/debian trixie/main arm64 Packages [9607 kB]
#7 0.865 Get:5 http://deb.debian.org/debian trixie-updates/main arm64 Packages [5404 B]
#7 0.874 Get:6 http://deb.debian.org/debian-security trixie-security/main arm64 Packages [101 kB]
#7 1.547 Fetched 9944 kB in 1s (7559 kB/s)
#7 1.547 Reading package lists...
#7 1.915 Reading package lists...
#7 2.297 Building dependency tree...
#7 2.377 Reading state information...
#7 2.497 The following additional packages will be installed:
#7 2.497   git-man libcurl3t64-gnutls liberror-perl libexpat1 libgdbm-compat4t64
#7 2.498   libngtcp2-16 libngtcp2-crypto-gnutls8 libperl5.40 perl perl-modules-5.40
#7 2.498 Suggested packages:
#7 2.498   gettext-base git-doc git-email git-gui gitk gitweb git-cvs git-mediawiki
#7 2.498   git-svn sensible-utils perl-doc libterm-readline-gnu-perl
#7 2.498   | libterm-readline-perl-perl make libtap-harness-archive-perl
#7 2.498 Recommended packages:
#7 2.498   patch less ssh-client
#7 2.550 The following NEW packages will be installed:
#7 2.551   git git-man libcurl3t64-gnutls liberror-perl libexpat1 libgdbm-compat4t64
#7 2.551   libngtcp2-16 libngtcp2-crypto-gnutls8 libperl5.40 perl perl-modules-5.40
#7 2.590 0 upgraded, 11 newly installed, 0 to remove and 0 not upgraded.
#7 2.590 Need to get 19.0 MB of archives.
#7 2.590 After this operation, 108 MB of additional disk space will be used.
#7 2.590 Get:1 http://deb.debian.org/debian trixie/main arm64 libexpat1 arm64 2.7.1-2 [93.3 kB]
#7 2.610 Get:2 http://deb.debian.org/debian trixie/main arm64 perl-modules-5.40 all 5.40.1-6 [3019 kB]
#7 2.779 Get:3 http://deb.debian.org/debian trixie/main arm64 libgdbm-compat4t64 arm64 1.24-2 [50.3 kB]
#7 2.787 Get:4 http://deb.debian.org/debian trixie/main arm64 libperl5.40 arm64 5.40.1-6 [4142 kB]
#7 3.017 Get:5 http://deb.debian.org/debian trixie/main arm64 perl arm64 5.40.1-6 [267 kB]
#7 3.031 Get:6 http://deb.debian.org/debian trixie/main arm64 libngtcp2-16 arm64 1.11.0-1 [121 kB]
#7 3.036 Get:7 http://deb.debian.org/debian trixie/main arm64 libngtcp2-crypto-gnutls8 arm64 1.11.0-1 [28.2 kB]
#7 3.046 Get:8 http://deb.debian.org/debian trixie/main arm64 libcurl3t64-gnutls arm64 8.14.1-2+deb13u2 [354 kB]
#7 3.067 Get:9 http://deb.debian.org/debian trixie/main arm64 liberror-perl all 0.17030-1 [26.9 kB]
#7 3.078 Get:10 http://deb.debian.org/debian trixie/main arm64 git-man all 1:2.47.3-0+deb13u1 [2205 kB]
#7 3.200 Get:11 http://deb.debian.org/debian trixie/main arm64 git arm64 1:2.47.3-0+deb13u1 [8666 kB]
#7 3.811 debconf: unable to initialize frontend: Dialog
#7 3.811 debconf: (TERM is not set, so the dialog frontend is not usable.)
#7 3.811 debconf: falling back to frontend: Readline
#7 3.812 debconf: unable to initialize frontend: Readline
#7 3.812 debconf: (Can't locate Term/ReadLine.pm in @INC (you may need to install the Term::ReadLine module) (@INC entries checked: /etc/perl /usr/local/lib/aarch64-linux-gnu/perl/5.40.1 /usr/local/share/perl/5.40.1 /usr/lib/aarch64-linux-gnu/perl5/5.40 /usr/share/perl5 /usr/lib/aarch64-linux-gnu/perl-base /usr/lib/aarch64-linux-gnu/perl/5.40 /usr/share/perl/5.40 /usr/local/lib/site_perl) at /usr/share/perl5/Debconf/FrontEnd/Readline.pm line 8, <STDIN> line 11.)
#7 3.812 debconf: falling back to frontend: Teletype
#7 3.814 debconf: unable to initialize frontend: Teletype
#7 3.814 debconf: (This frontend requires a controlling tty.)
#7 3.814 debconf: falling back to frontend: Noninteractive
#7 4.265 Fetched 19.0 MB in 1s (17.0 MB/s)
#7 4.277 Selecting previously unselected package libexpat1:arm64.
#7 4.277 (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 5865 files and directories currently installed.)
#7 4.284 Preparing to unpack .../00-libexpat1_2.7.1-2_arm64.deb ...
#7 4.288 Unpacking libexpat1:arm64 (2.7.1-2) ...
#7 4.312 Selecting previously unselected package perl-modules-5.40.
#7 4.313 Preparing to unpack .../01-perl-modules-5.40_5.40.1-6_all.deb ...
#7 4.314 Unpacking perl-modules-5.40 (5.40.1-6) ...
#7 4.603 Selecting previously unselected package libgdbm-compat4t64:arm64.
#7 4.604 Preparing to unpack .../02-libgdbm-compat4t64_1.24-2_arm64.deb ...
#7 4.605 Unpacking libgdbm-compat4t64:arm64 (1.24-2) ...
#7 4.619 Selecting previously unselected package libperl5.40:arm64.
#7 4.620 Preparing to unpack .../03-libperl5.40_5.40.1-6_arm64.deb ...
#7 4.621 Unpacking libperl5.40:arm64 (5.40.1-6) ...
#7 4.939 Selecting previously unselected package perl.
#7 4.940 Preparing to unpack .../04-perl_5.40.1-6_arm64.deb ...
#7 4.941 Unpacking perl (5.40.1-6) ...
#7 4.971 Selecting previously unselected package libngtcp2-16:arm64.
#7 4.971 Preparing to unpack .../05-libngtcp2-16_1.11.0-1_arm64.deb ...
#7 4.973 Unpacking libngtcp2-16:arm64 (1.11.0-1) ...
#7 4.994 Selecting previously unselected package libngtcp2-crypto-gnutls8:arm64.
#7 4.994 Preparing to unpack .../06-libngtcp2-crypto-gnutls8_1.11.0-1_arm64.deb ...
#7 4.995 Unpacking libngtcp2-crypto-gnutls8:arm64 (1.11.0-1) ...
#7 5.006 Selecting previously unselected package libcurl3t64-gnutls:arm64.
#7 5.007 Preparing to unpack .../07-libcurl3t64-gnutls_8.14.1-2+deb13u2_arm64.deb ...
#7 5.009 Unpacking libcurl3t64-gnutls:arm64 (8.14.1-2+deb13u2) ...
#7 5.039 Selecting previously unselected package liberror-perl.
#7 5.040 Preparing to unpack .../08-liberror-perl_0.17030-1_all.deb ...
#7 5.041 Unpacking liberror-perl (0.17030-1) ...
#7 5.051 Selecting previously unselected package git-man.
#7 5.052 Preparing to unpack .../09-git-man_1%3a2.47.3-0+deb13u1_all.deb ...
#7 5.053 Unpacking git-man (1:2.47.3-0+deb13u1) ...
#7 5.121 Selecting previously unselected package git.
#7 5.122 Preparing to unpack .../10-git_1%3a2.47.3-0+deb13u1_arm64.deb ...
#7 5.125 Unpacking git (1:2.47.3-0+deb13u1) ...
#7 5.503 Setting up libexpat1:arm64 (2.7.1-2) ...
#7 5.506 Setting up libgdbm-compat4t64:arm64 (1.24-2) ...
#7 5.508 Setting up perl-modules-5.40 (5.40.1-6) ...
#7 5.511 Setting up git-man (1:2.47.3-0+deb13u1) ...
#7 5.514 Setting up libngtcp2-16:arm64 (1.11.0-1) ...
#7 5.517 Setting up libngtcp2-crypto-gnutls8:arm64 (1.11.0-1) ...
#7 5.520 Setting up libcurl3t64-gnutls:arm64 (8.14.1-2+deb13u2) ...
#7 5.523 Setting up libperl5.40:arm64 (5.40.1-6) ...
#7 5.526 Setting up perl (5.40.1-6) ...
#7 5.530 Setting up liberror-perl (0.17030-1) ...
#7 5.533 Setting up git (1:2.47.3-0+deb13u1) ...
#7 5.544 Processing triggers for libc-bin (2.41-12+deb13u1) ...
#7 DONE 5.6s

#8 [builder 4/8] COPY . /app/env
#8 DONE 0.0s

#9 [builder 5/8] WORKDIR /app/env
#9 DONE 0.0s

#10 [builder 6/8] RUN if ! command -v uv >/dev/null 2>&1; then         curl -LsSf https://astral.sh/uv/install.sh | sh &&         mv /root/.local/bin/uv /usr/local/bin/uv &&         mv /root/.local/bin/uvx /usr/local/bin/uvx;     fi
#10 DONE 0.1s

#11 [builder 7/8] RUN --mount=type=cache,target=/root/.cache/uv     if [ -f uv.lock ]; then         uv sync --frozen --no-install-project --no-editable;     else         uv sync --no-install-project --no-editable;     fi
#11 0.220 Using CPython 3.11.14 interpreter at: /usr/local/bin/python3
#11 0.220 Creating virtual environment at: .venv
#11 1.024 Resolved 133 packages in 800ms
#11 2.828 Prepared 19 packages in 1.77s
#11 2.829 warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
#11 2.829          If the cache and target directories are on different filesystems, hardlinking may not be supported.
#11 2.829          If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
#11 3.686 Installed 120 packages in 858ms
#11 3.686  + annotated-doc==0.0.4
#11 3.686  + annotated-types==0.7.0
#11 3.686  + anyio==4.12.1
#11 3.686  + arckit==0.1.0
#11 3.686  + attrs==25.4.0
#11 3.686  + authlib==1.6.7
#11 3.686  + backports-tarfile==1.2.0
#11 3.686  + beartype==0.22.9
#11 3.686  + bfi==1.0.4
#11 3.686  + cachetools==7.0.0
#11 3.686  + cellpylib==2.4.0
#11 3.686  + certifi==2026.1.4
#11 3.686  + cffi==2.0.0
#11 3.686  + charset-normalizer==3.4.4
#11 3.686  + click==8.3.1
#11 3.686  + cloudpickle==3.1.2
#11 3.686  + contourpy==1.3.3
#11 3.686  + croniter==6.0.0
#11 3.686  + cryptography==46.0.4
#11 3.686  + cycler==0.12.1
#11 3.686  + cyclopts==4.5.1
#11 3.686  + diskcache==5.6.3
#11 3.686  + distro==1.9.0
#11 3.686  + dnspython==2.8.0
#11 3.686  + docstring-parser==0.17.0
#11 3.686  + docutils==0.22.4
#11 3.686  + drawsvg==2.4.1
#11 3.686  + email-validator==2.3.0
#11 3.686  + exceptiongroup==1.3.1
#11 3.686  + fakeredis==2.33.0
#11 3.686  + fastapi==0.128.4
#11 3.686  + fastmcp==2.14.5
#11 3.687  + filelock==3.20.3
#11 3.687  + fonttools==4.61.1
#11 3.687  + fsspec==2026.2.0
#11 3.687  + h11==0.16.0
#11 3.687  + hf-xet==1.2.0
#11 3.687  + httpcore==1.0.9
#11 3.687  + httpx==0.28.1
#11 3.687  + httpx-sse==0.4.3
#11 3.687  + huggingface-hub==1.4.1
#11 3.687  + idna==3.11
#11 3.687  + importlib-metadata==8.7.1
#11 3.687  + jaraco-classes==3.4.0
#11 3.687  + jaraco-context==6.1.0
#11 3.687  + jaraco-functools==4.4.0
#11 3.687  + jeepney==0.9.0
#11 3.687  + jiter==0.13.0
#11 3.687  + jsonref==1.1.0
#11 3.687  + jsonschema==4.26.0
#11 3.687  + jsonschema-path==0.3.4
#11 3.687  + jsonschema-specifications==2025.9.1
#11 3.687  + keyring==25.7.0
#11 3.687  + kiwisolver==1.4.9
#11 3.687  + lupa==2.6
#11 3.687  + magiccube==0.3.0
#11 3.687  + markdown-it-py==4.0.0
#11 3.687  + matplotlib==3.10.8
#11 3.687  + mcp==1.26.0
#11 3.687  + mdurl==0.1.2
#11 3.687  + more-itertools==10.8.0
#11 3.687  + mpmath==1.3.0
#11 3.687  + numpy==2.4.2
#11 3.687  + openai==2.17.0
#11 3.687  + openapi-pydantic==0.5.1
#11 3.687  + openenv-core==0.2.0 (from file:///app/env/openenv)
#11 3.687  + opentelemetry-api==1.39.1
#11 3.687  + packaging==26.0
#11 3.687  + pathable==0.4.4
#11 3.687  + pathvalidate==3.3.1
#11 3.687  + pillow==12.1.0
#11 3.687  + platformdirs==4.5.1
#11 3.687  + prometheus-client==0.24.1
#11 3.687  + py-key-value-aio==0.3.0
#11 3.687  + py-key-value-shared==0.3.0
#11 3.687  + pycosat==0.6.6
#11 3.687  + pycparser==3.0
#11 3.687  + pydantic==2.12.5
#11 3.687  + pydantic-core==2.41.5
#11 3.687  + pydantic-settings==2.12.0
#11 3.687  + pydocket==0.17.5
#11 3.687  + pyfiglet==1.0.2
#11 3.687  + pygments==2.19.2
#11 3.687  + pyjwt==2.11.0
#11 3.687  + pyparsing==3.3.2
#11 3.687  + pyperclip==1.11.0
#11 3.687  + python-dateutil==2.9.0.post0
#11 3.687  + python-dotenv==1.2.1
#11 3.687  + python-json-logger==4.0.0
#11 3.687  + python-multipart==0.0.22
#11 3.687  + pytz==2025.2
#11 3.687  + pyyaml==6.0.3
#11 3.687  + reasoning-gym==0.1.24
#11 3.687  + redis==7.1.0
#11 3.687  + referencing==0.36.2
#11 3.687  + requests==2.32.5
#11 3.687  + rich==14.3.2
#11 3.687  + rich-rst==1.3.2
#11 3.687  + rpds-py==0.30.0
#11 3.687  + secretstorage==3.5.0
#11 3.687  + shellingham==1.5.4
#11 3.687  + six==1.17.0
#11 3.687  + sniffio==1.3.1
#11 3.688  + sortedcontainers==2.4.0
#11 3.688  + sse-starlette==3.2.0
#11 3.688  + starlette==0.52.1
#11 3.688  + sympy==1.14.0
#11 3.688  + tabulate==0.9.0
#11 3.688  + tomli==2.4.0
#11 3.688  + tomli-w==1.2.0
#11 3.688  + tqdm==4.67.3
#11 3.688  + typer==0.21.1
#11 3.688  + typer-slim==0.21.1
#11 3.688  + typing-extensions==4.15.0
#11 3.688  + typing-inspection==0.4.2
#11 3.688  + urllib3==2.6.3
#11 3.688  + uvicorn==0.40.0
#11 3.688  + websockets==16.0
#11 3.688  + zipp==3.23.0
#11 3.688  + zss==1.2.0
#11 DONE 4.0s

#12 [builder 8/8] RUN --mount=type=cache,target=/root/.cache/uv     if [ -f uv.lock ]; then         uv sync --frozen --no-editable;     else         uv sync --no-editable;     fi
#12 0.551 Prepared 1 package in 413ms
#12 0.555 warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
#12 0.555          If the cache and target directories are on different filesystems, hardlinking may not be supported.
#12 0.555          If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
#12 0.556 Installed 1 package in 5ms
#12 0.556  + openenv-reasoning-gym==0.1.0 (from file:///app/env)
#12 DONE 0.6s

#13 [stage-1 3/4] COPY --from=builder /app/env/.venv /app/.venv
#13 DONE 1.2s

#14 [stage-1 4/4] COPY --from=builder /app/env /app/env
#14 DONE 1.4s

#15 exporting to image
#15 exporting layers
#15 exporting layers 0.6s done
#15 writing image sha256:a732f196a9c7a9d84f3bbde5433239bfe383e3034279b8ce816bdd46ba98c94c done
#15 naming to docker.io/library/openenv-reasoning_gym
#15 naming to docker.io/library/openenv-reasoning_gym done
#15 DONE 0.6s

View build details: docker-desktop://dashboard/build/desktop-linux/desktop-linux/ismfgmlof1ine15pr0kuu055y

✓ Docker build successful

Done!

Even though I successfully push the env from CLI:

❯ uv run openenv push ./envs/reasoning_gym_env
Validating OpenEnv environment in 
/Users/zafir/projects/OpenEnv/envs/reasoning_gym_env...
⚠ Recommended directory missing: outputs/
✓ Found OpenEnv environment: reasoning_gym
✓ Authenticated as: zafstojano
Preparing files for Hugging Face deployment (with web interface)...
Moved Dockerfile to repository root for deployment
✓ Updated Dockerfile: enabled web interface
Creating/verifying space: zafstojano/reasoning_gym
✓ Space zafstojano/reasoning_gym is ready
Uploading files to zafstojano/reasoning_gym...
No files have been modified since last commit. Skipping to prevent empty commit.
✓ Upload completed successfully
Space URL: https://huggingface.co/spaces/zafstojano/reasoning_gym

✓ Deployment complete!
Visit your space at: https://huggingface.co/spaces/zafstojano/reasoning_gym

I get a build error on the space:

Build error
Job failed with exit code: 1. Reason: cache miss: [builder 4/8] COPY . /app/env
cache miss: [builder 5/8] WORKDIR /app/env
cache miss: [stage-1 4/4] COPY --from=builder /app/env /app/env
cache miss: [builder 6/8] RUN if ! command -v uv >/dev/null 2>&1; then         curl -LsSf https://astral.sh/uv/install.sh | sh &&         mv /root/.local/bin/uv /usr/local/bin/uv &&         mv /root/.local/bin/uvx /usr/local/bin/uvx;     fi
cache miss: [builder 7/8] RUN --mount=type=cache,target=/root/.cache/uv     if [ -f uv.lock ]; then         uv sync --frozen --no-install-project --no-editable;     else         uv sync --no-install-project --no-editable;     fi
cache miss: [builder 3/8] RUN apt-get update &&     apt-get install -y --no-install-recommends git &&     rm -rf /var/lib/apt/lists/*
cache miss: [builder 2/8] WORKDIR /app
cache miss: [stage-1 3/4] COPY --from=builder /app/env/.venv /app/.venv
cache miss: [builder 8/8] RUN --mount=type=cache,target=/root/.cache/uv     if [ -f uv.lock ]; then         uv sync --frozen --no-editable;     else         uv sync --no-editable;     fi
{"total":15,"completed":12,"user_total":10,"user_cached":0,"user_completed":7,"user_cacheable":9,"from":1,"miss":9,"client_duration_ms":9890}
Build logs:

Failed to retrieve error logs: SSE is not enabled

Any help is appreciated

zafstojano and others added 6 commits January 25, 2026 16:51

Add dataset_metadata example to README

73e456c

Show how to access the dataset_metadata field in the Quick Start example, demonstrating the full observation interface. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

update example to pass dataset config

5f67eda

add to repo walkthrough

e4cd829

meta-cla bot added the CLA Signed This label is managed by the Meta Open Source bot. label Jan 25, 2026

zafstojano changed the title ~~Feat/reasoning gym env~~ Add the Reasoning Gym set of environments Jan 25, 2026

update empty dict passed to leg counting env

a5dad69

Merge branch 'main' into feat/reasoning-gym-env

639d2ca

burtenshaw added the New Environment label Feb 5, 2026

burtenshaw and others added 6 commits February 6, 2026 11:43

Merge branch 'main' into feat/reasoning-gym-env

cd22bb7

Merge branch 'main' into feat/reasoning-gym-env

a36a85f

Merge branch 'main' of github.com:meta-pytorch/OpenEnv into feat/reas…

910ca03

…oning-gym-env

Merge branch 'feat/reasoning-gym-env' of github.com:zafstojano/OpenEn…

54af22c

…v into feat/reasoning-gym-env

add uv.lock

2a15f4c

add env card

5bd7d38

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Add the Reasoning Gym set of environments#326

Add the Reasoning Gym set of environments#326
zafstojano wants to merge 14 commits intometa-pytorch:mainfrom
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