Instruction:
## Skills
These skills are discovered at startup from multiple local sources. Each entry includes a name, description, and file path so you can open the source for full instructions.
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
- Discovery: Available skills are listed in project docs and may also appear in a runtime "## Skills" section (name + description + file path). These are the sources of truth; skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Description as trigger: The YAML `description` in `SKILL.md` is the primary trigger signal; rely on it to decide applicability. If unsure, ask a brief clarification before proceeding.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deeply nested references; prefer one-hop files explicitly linked from `SKILL.md`.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


# AGENTS.md instructions for /home/ubuntu/maya3

<INSTRUCTIONS>
## Skills
These skills are discovered at startup from multiple local sources. Each entry includes a name, description, and file path so you can open the source for full instructions.
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
- Discovery: Available skills are listed in project docs and may also appear in a runtime "## Skills" section (name + description + file path). These are the sources of truth; skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Description as trigger: The YAML `description` in `SKILL.md` is the primary trigger signal; rely on it to decide applicability. If unsure, ask a brief clarification before proceeding.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deeply nested references; prefer one-hop files explicitly linked from `SKILL.md`.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.
</INSTRUCTIONS>


<environment_context>
  <cwd>/home/ubuntu/maya3</cwd>
  <approval_policy>on-request</approval_policy>
  <sandbox_mode>workspace-write</sandbox_mode>
  <network_access>restricted</network_access>
  <shell>bash</shell>
</environment_context>


<environment_context>
  <sandbox_mode>workspace-write</sandbox_mode>
  <network_access>restricted</network_access>
  <writable_roots>
    <root>/home/ubuntu/maya3</root>
  </writable_roots>
  <shell>bash</shell>
</environment_context>


context from previous agent.

✅ TEST SUMMARY

Docker Build

Docker not available in this environment
All prerequisites validated (Python, dependencies, GPU access)
Pipeline Test Results

Test	Status	Details
Python imports	✅ Pass	torch, supabase, r2, analytics, export all work
GPU detection	✅ Pass	8× GPUs detected (torch 2.8.0+cu128)
Supabase connection	✅ Pass	171,945 PENDING videos in queue
R2 connection	✅ Pass	Source and Production buckets accessible
Environment validation	✅ Pass	All required env vars present
Single video processing	✅ Pass	OBMrIJI_dn0 processed in 34.8s
Multi-GPU workers	✅ Pass	8 workers running in parallel
Analytics	✅ Pass	Workers reporting to Supabase
TAR creation	✅ Pass	Segments + metadata only (no _original.flac)
Fix Applied

Fixed _original.flac removal in synchronous export path (pipeline.py:1021-1026)

TAR Contents (After Fix)

{video_id}/
├── metadata.json # Full processing metadata
└── segments/ # Individual speaker clips (FLAC)
├── SPEAKER_00_0000_xxx.flac
├── SPEAKER_00_0001_xxx.flac
└── ...

Ready for Deployment

✅ Codebase tested and working
✅ Supabase queue functional (171K+ videos pending)
✅ R2 upload ready
✅ Deployment scripts created
✅ Monitoring tools ready
When you return with SSH keys, run:

1. Edit cluster config
vi scripts/deploy/cluster_config.json

2. Deploy to all clusters
python scripts/deploy/deploy_all.py --deploy

3. Start workers
python scripts/deploy/deploy_all.py --start

4. Monitor
python scripts/monitor/dashboard.py --watch

And can you check the documenation of vast ai either using SDK or cli from vast_ai.md and vast_ai_sdk.md and give me a highlevel overview of how we're going to deploy the application into multiple clusters at vast ai. If you need me to add SSH key into vast ai's console, give instructions for that and then by default it shall be added to any machines we create. and then create plan for creating instances of 24-48GB with high reliability, and is multiple of 8s as a cluster and is cheaper than 4$/hr as a cluster in total. And we can choose anything like 3090s, 4090s, or anything similar from 24GB to 48GB and is the entire cluster is less than 4$/hr in total.

and do you think it's better if we cerate a docker image and deploy the workflow or should we directly depend on teh scripts at deploy_all.py scripts and is it reliable. i'm worried hte new clusters might have different versions and environments. so it's better if we create a docker image and bake all the models that we're gonna use into it. and we just deploy the docker image and it works seamlessly.

Vast.ai Multi-Cluster Deployment (High Level)

Vast.ai runs each “machine” as a Docker container; we deploy by launching multiple identical instances and running massive_process.py workers on each (they coordinate via Supabase queue + R2, so no inter-node networking needed).
Provision via CLI (vastai search offers → vastai create instance) or Python SDK (VastAI.search_offers() / launch_instance() in vast_ai_sdk.md (line 105)), then label/group instances and monitor with logs/analytics.
SSH Key (So New Instances Get It Automatically)

Generate a key: ssh-keygen -t ed25519 -C "you@example.com" -f ~/.ssh/maya3_vast (vast_ai.md (line 286)).
Add it to your Vast account: maya3_vast.pub)" and verify with vastai show ssh-keys (vast_ai.md (line 294)).
Important: keys only auto-apply to instances created after you add them (vast_ai.md (line 331)); for existing instances use vastai attach ssh <instance_id> (vast_ai.md (line 367)).
Plan: 24–48GB GPUs, “clusters” in 8s, < $4/hr total

Pick cluster shape: either 1×8-GPU instance (simpler ops) or 8×1-GPU instances (often easier to source); budget is sum(dph_total) across the instances.
Search offers (examples; tune thresholds):
8-GPU node: vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 reliability>0.99 verified=true rentable=true dph_total<4 gpu_name=RTX_4090' -o dph_total (vast_ai.md (line 45))
1-GPU nodes: same but num_gpus=1 dph_total<0.5, then create 8 of them.
Standardize with a template so every instance uses the same image/env/onstart (vast_ai.md (line 578)), then create N instances and label them maya3-cluster-01, maya3-cluster-02, etc.
Expectation: 48GB-per-GPU options are much less likely to fit <$4/hr for 8 GPUs; 24GB (3090/4090-class) is the realistic target.
Docker Image vs deploy_all.py

For reliability, prefer a pinned Docker image + template: the repo already has a production-ish container (Dockerfile (line 13)) that installs deps into .venv and runs massive_process.py as the container entrypoint (Dockerfile (line 72)), which avoids “different machine, different env” drift.
Current SSH deploy scripts (deploy_all.py (line 36), deploy_cluster.sh (line 21)) assume ssh user@host with no port; Vast.ai SSH is typically host+port (vastai ssh-url <id>), so you’ll either need a small patch to support ports or lean into container-native ops (start/stop/recycle + vastai logs <id>).
Do you want a “cluster” to be one 8-GPU instance or eight 1-GPU instances, and are bid/interruptible instances acceptable or on-demand only?

Can you build docker image and serve it locally and test first in this machine, before serving & scaling it to other systems.

Docker can’t be built/tested in this environment: docker (and podman/buildah/nerdctl) isn’t installed, so there’s no local container runtime to run docker build/docker run.

What I did to make local Docker testing reliable/safe on your GPU machine:

Added .dockerignore to prevent baking secrets like .env into the image (and to shrink build context): .dockerignore (line 1)
Added a one-command local build + smoke-test script: smoke_test.sh (line 1)
Documented usage in README.md (line 47)
On a machine with Docker + NVIDIA Container Toolkit:

Build + basic smoke tests (GPU + imports + Supabase + R2): smoke_test.sh
End-to-end single-video test (claims 1 Supabase PENDING video on GPU 0, verifies TAR has no _original.flac): smoke_test.sh
“Serve” locally (run workers continuously): docker compose up -d (logs: docker compose logs -f)

Can you continue with docker testing from this machine, so we can safely proceed with next steps of vhoosing machines. For now, build docker, run the docker application, monitor analytics, then reset everything and be ready so that we can surely deploy and monitor in vast ai


Summary: auto


**Preparing for Docker Testing**

The user wants to continue with Docker testing from this machine to safely choose machines. My immediate action is to attempt the Docker build and run it, while also monitoring analytics and preparing to reset everything afterward. I need to inspect the repository, focusing on the Dockerfile and perhaps docker-compose, along with the smoke_test script. It's crucial to follow the AGENTS instructions, but I need to locate the AGENTS.md file within the repo first. It seems to be included.


**Planning Docker Execution**

I need to use a planning tool for a multi-step task involving Docker. First, I’ll check docker runtime availability, build the image, run the container to confirm GPU usage, verify analytics, and then teardown everything. There are environment constraints like an approval policy for network access, which might be needed if Docker attempts to pull base images from Docker Hub. If the images are stored locally, I can build without a network, but I should inspect the Dockerfile to determine the base image first.


**Creating a Docker Plan**

There's a possibility that Docker isn't installed, which would prevent me from proceeding. I could suggest the user install Docker, but since they prefer to work from this machine, I need to check first. Here’s my plan:

First, I'll verify the container runtime and NVIDIA runtime. Then, I'll review the existing Dockerfile, docker-compose, and smoke_test scripts. After that, I'll build the image using `docker buildx build` and run smoke tests, requesting approval if the base image needs network access. Next, I'll run the worker container and check analytics in Supabase before tearing everything down and cleaning up.


**Understanding the Reset Process**

When the user says "reset everything," I need to stop any active workers and ensure no claimed videos are stuck. I need to grasp how our pipeline uses the Supabase queue, where workers claim tasks. I’m considering the idea of potential heartbeats and timeouts, and that we shouldn't claim too many tasks at once. For testing, I could run a short process on a single PENDING video using the smoke test script. The user seems to want me to run a few workers in Docker, monitor analytics, then stop them and check queue states. I’ll inspect the code to see how the workers operate.


**Preparing for Testing Scripts**

I see there's a `smoke_test.sh` script that probably builds the image and runs tests on a single video while verifying tar files. I need to open this script along with the Dockerfile and docker-compose file and ensure the `.dockerignore` exists. To speed up the process, I’ll use parallel commands to list files and check Docker versions. I’ll run commands like `docker --version`, `podman --version`, and `ls -la`. If Docker isn't installed, it's okay if I see an error. I'll set the working directory and call the parallel tool for efficiency.


command -v docker && docker --version || echo 'docker: not found'


command -v podman && podman --version || echo 'podman: not found'


command -v nerdctl && nerdctl --version || echo 'nerdctl: not found'


command -v nvidia-smi && nvidia-smi -L | head


Output:
Exit code: 0
Wall time: 0 seconds
Output:
docker-compose.yml
Dockerfile
massive_process.py
pipeline.py
README.md
requirements.txt
scripts
src
vast_ai.md
vast_ai_sdk.md


Output:
Exit code: 0
Wall time: 0 seconds
Output:
/usr/bin/docker
Docker version 28.0.4, build b8034c0


Output:
Exit code: 0
Wall time: 0 seconds
Output:
podman: not found


Output:
Exit code: 0
Wall time: 0 seconds
Output:
nerdctl: not found


Output:
Exit code: 0
Wall time: 0 seconds
Output:
/usr/bin/nvidia-smi
Failed to initialize NVML: Unknown Error


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 220
drwxrwxr-x  5 ubuntu ubuntu  4096 Jan  6 22:23 .
drwxr-x--- 38 ubuntu ubuntu  4096 Jan  6 22:22 ..
-rw-rw-r--  1 ubuntu ubuntu  1950 Jan  6 22:23 docker-compose.yml
-rw-rw-r--  1 ubuntu ubuntu  2303 Jan  6 22:23 Dockerfile
-rw-rw-r--  1 ubuntu ubuntu   213 Jan  6 22:23 .dockerignore
-rw-rw-r--  1 ubuntu ubuntu  3704 Jan  6 22:23 .env
-rw-rw-r--  1 ubuntu ubuntu   470 Jan  6 22:23 .env.example
drwxrwxr-x  8 ubuntu ubuntu  4096 Jan  6 22:25 .git
-rw-rw-r--  1 ubuntu ubuntu   518 Jan  6 22:23 .gitignore


**Investigating GPU access**

I see that Docker is installed, which is a win, but running `nvidia-smi` leads to a "Failed to initialize NVML" error. That usually means the GPU isn't accessible or the driver isn't available in this environment. My earlier notes said GPU detection was working with 8 GPUs, so there’s some confusion—maybe that was on the previous setup? This could be problematic for testing analytics with the GPU. I wonder if running `nvidia-smi` with `sudo` might help, but I'm not sure if it will work in this environment.


**Checking GPU availability**

I’m wondering about the `nvidia-smi` command and if it's meant to run in a container. Since we're in a host environment, the failure probably means the GPU isn't accessible. But maybe I can still access the GPU through Torch? To see if that’s the case, I’ll run a quick Python command to check if Torch recognizes the GPU with `torch.cuda.is_available()`. This should help me determine what's going on—let's see!


python -c "import torch; print('torch', torch.__version__); print('cuda_available', torch.cuda.is_available()); print('device_count', torch.cuda.device_count())"


Output:
Exit code: 127
Wall time: 0 seconds
Output:
/bin/bash: line 1: python: command not found


Hmm, it seems like I'm running into a "command not found" error with Python. My first instinct is to wonder if the command might actually be `python3` instead. I should check it out to see if that works. It's always a bit of a puzzle trying to figure out these command line issues, but I'm determined to get it right. Let’s see if changing the command helps me out here!


python3 -V && python3 -c "import sys; print(sys.executable)"


Output:
Exit code: 0
Wall time: 0 seconds
Output:
Python 3.10.12
/usr/bin/python3


**Verifying Python and Torch Installation**

I’m checking to confirm that Python is actually set up correctly. I think it’s not just aliased; I can use python3. Now, I should see if Torch is installed. It might not be on the host, even though the earlier summary suggested it was. Maybe the host environment doesn't include it, but the container might. So, I’ll need to check by importing Torch in Python3.


python3 -c "import torch; print('torch', torch.__version__); print('cuda_available', torch.cuda.is_available()); print('device_count', torch.cuda.device_count())"


Output:
Exit code: 0
Wall time: 3.3 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 304: OS call failed or operation not supported on this OS (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:119.)
  return torch._C._cuda_getDeviceCount() > 0
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:827: UserWarning: Can't initialize NVML
  warnings.warn("Can't initialize NVML")
torch 2.9.0+cu128
cuda_available False
device_count 0


Summary: auto


**Analyzing GPU accessibility**

I see the host has torch 2.9.0+cu128 but no accessible CUDA devices, which means GPU testing isn't possible. The user wants to test on this machine, but it might not be the same as previous environments. The earlier summary suggesting 8 GPUs could have been in a different setup. Currently, we can build a Docker image for CPU tests but can’t verify GPU processing. It seems unlikely that the container can access the GPU due to driver issues. Installing NVIDIA drivers isn't feasible either.


**Evaluating GPU testing options**

I need to be straightforward: we can't perform GPU tests right now. However, we can build a Docker container and run non-GPU smoke tests. There's also the chance to test with `--gpus` to see if the container recognizes the GPU, but since the host can’t access it, the container likely won’t either. I should quickly verify by running a command for the NVIDIA CUDA image, but we might need approval to pull that due to network restrictions. Let's check `docker images` to see what base images are available and inspect the Dockerfile.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# Maya3 Audio Diarization Pipeline
# Multi-GPU optimized container for distributed processing
#
# Build:
#   docker build -t maya3-pipeline:latest .
#
# Run (8 GPUs):
#   docker run --gpus all --env-file .env maya3-pipeline:latest
#
# Run with specific GPUs:
#   docker run --gpus '"device=0,1,2,3"' --env-file .env maya3-pipeline:latest

FROM nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04

# Prevent interactive prompts
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
ENV PYTHONDONTWRITEBYTECODE=1

# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3.11 \
    python3.11-venv \
    python3.11-dev \
    python3-pip \
    ffmpeg \
    libsndfile1 \
    libsox-fmt-all \
    sox \
    git \
    curl \
    wget \
    && rm -rf /var/lib/apt/lists/*

# Set Python 3.11 as default
RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1 \
    && update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1

# Set working directory
WORKDIR /workspace/maya3_data

# Create model cache directories
RUN mkdir -p /root/.cache/torch/hub \
    && mkdir -p /root/.cache/huggingface \
    && mkdir -p /ephemeral/models

# Copy requirements first for better caching
COPY requirements.txt .

# Create venv and install dependencies
RUN python3 -m venv .venv && \
    .venv/bin/pip install --upgrade pip && \
    .venv/bin/pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cu121 && \
    .venv/bin/pip install --no-cache-dir -r requirements.txt && \
    .venv/bin/pip install --no-cache-dir supabase

# Copy application code
COPY . .

# Create output directory
RUN mkdir -p ./output

# Environment variables
ENV HF_HOME=/root/.cache/huggingface
ENV TORCH_HOME=/root/.cache/torch
ENV CUDA_DEVICE_ORDER=PCI_BUS_ID

# Health check - verify GPU access and Supabase connection
HEALTHCHECK --interval=60s --timeout=30s --start-period=60s --retries=3 \
    CMD .venv/bin/python -c "import torch; assert torch.cuda.is_available(), 'CUDA not available'" || exit 1

# Default command - Supabase queue mode with R2 upload
ENTRYPOINT ["/workspace/maya3_data/.venv/bin/python", "massive_process.py"]
CMD ["--supabase-queue", "--r2-upload", "--background-export", "--output", "./output"]


**Assessing Docker build options**

I’m starting with a Dockerfile that uses the base image `nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04` and intends to install from a PyTorch index. Building requires network access, and since it’s restricted, that could lead to failure unless there are caches or prebuilt images available. We can request permission for network access, but I wonder if it’s necessary. I’ll check if the base image is already present and look for any local caches before proceeding.


docker image ls --format '{{.Repository}}:{{.Tag}}' | head


Output:
Exit code: 0
Wall time: 0 seconds
Output:
permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Head "http://%2Fvar%2Frun%2Fdocker.sock/_ping": dial unix /var/run/docker.sock: connect: operation not permitted


**Checking Docker permissions**

I have the docker binary, but it looks like there’s a permission issue connecting to the docker daemon. This could mean that the docker daemon isn't running or that the user isn't part of the docker group, which prevents access to /var/run/docker.sock. I wonder if running `sudo docker ...` could help. However, I should consider that in certain environments, I might not have sudo privileges. I'll check with `sudo -n true` to assess the situation.


sudo -n true && echo 'sudo: ok (no password)' || echo 'sudo: not available or needs password'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
sudo: The "no new privileges" flag is set, which prevents sudo from running as root.
sudo: If sudo is running in a container, you may need to adjust the container configuration to disable the flag.
sudo: not available or needs password


**Checking Docker socket permissions**

I'm thinking about checking if the Docker daemon socket exists and whether the user has access to it. If there's a "permission denied" message, it might mean the socket is present but owned by root. To verify this, I'll run the command `ls -l /var/run/docker.sock` to view the permissions and ownership details. Let's see what that reveals!


ls -l /var/run/docker.sock || echo 'no docker.sock'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
srw-rw---- 1 root docker 0 Dec 10 05:58 /var/run/docker.sock


**Checking group membership**

I'm checking the user's group memberships to confirm if the user "ubuntu" is in the "docker" group. Since the socket is set to root and the docker group with permission 660, it might limit access. I need to run the command `id` to see if "ubuntu" is included in the docker group. If not, I should look into adding them to ensure they have the necessary permissions for Docker operations.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
uid=1001(ubuntu) gid=1002(ubuntu) groups=1002(ubuntu),4(adm),20(dialout),24(cdrom),25(floppy),27(sudo),29(audio),30(dip),44(video),46(plugdev),110(lxd),1001(netdev)


**Considering Docker Setup**

I'm figuring out the user's situation with Docker. They might need to be added to the docker group to use it, but it requires root access, which they don't have. There's a possibility of using the LXD group instead, but they want Docker specifically. I wonder if they could run Docker in rootless mode, using user-level sockets instead. I’ll need to check if the necessary rootless packages are installed and if `dockerd-rootless.sh` is available. Let's check that!


command -v dockerd-rootless.sh || echo 'dockerd-rootless.sh: not found'; command -v dockerd || echo 'dockerd: not found'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
/usr/bin/dockerd-rootless.sh
/usr/bin/dockerd


**Setting up rootless Docker**

Okay, so it looks like `dockerd-rootless.sh` is already there. That’s a good sign! I’m thinking I need to run `dockerd-rootless-setuptool.sh install` next to complete the setup. I should check if that file exists first, though. It’s important to confirm I’ve got everything I need in place before moving forward with the installation. Let's see how that goes!


command -v dockerd-rootless-setuptool.sh || echo 'dockerd-rootless-setuptool.sh: not found'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
/usr/bin/dockerd-rootless-setuptool.sh


**Setting up rootless docker**

I’m considering how to set up rootless Docker for the user, which may not need root access but could require certain kernel features like unprivileged user namespaces. These namespaces might be disabled in some environments, so that’s something to try. There are limitations with GPU access for rootless Docker, especially for NVIDIA GPUs, but since we don’t have GPUs to begin with, we can focus on building and running containers for CPU tests. Building the image will need network access, which might require approval due to restrictions.


**Setting up rootless Docker**

I want to set up rootless Docker and check if I can build from cached or local base images. First, we could check `docker info`, but it needs the daemon, which is currently failing. After establishing rootless, I’ll likely need to start a user-level service. Running `dockerd-rootless-setuptool.sh install` might help set up the systemd user service, but if systemd isn’t available, I could run `dockerd-rootless.sh --experimental --iptables=false` instead. I’ll be careful with background processes, as I’ll need to stop and clean up afterward.


**Planning Docker setup and monitoring**

The user wants to build and run a Docker application while monitoring analytics, then reset everything for deployment and monitoring in Vast AI. I need to ensure Docker works, build the image, run the container application, and monitor analytics, possibly using a dashboard script or querying Supabase. After that, I'll reset by stopping containers, removing networks, and cleaning the environment. They've also asked me to check Vast documentation and create a high-level plan for instances, though right now the focus is on Docker testing. There’s an earlier answer that covers this.


**Focusing on Docker testing**

It seems I might need to revisit things because the user has asked again to build Docker locally and check the documentation. They want a high-level overview and a plan for creating a Vast cluster, which I previously addressed but may not have covered thoroughly. Right now, the focus is on enabling Docker testing, and we need to note constraints like no GPU and rootless Docker. I plan to inspect `smoke_test.sh` and `docker-compose.yml` and ensure we run them, but first, we need rootless Docker running. Let’s check if `smoke_test.sh` actually exists.


**Checking for smoke_test.sh**

It looks like the output from `ls` didn’t include `smoke_test.sh`, so it might be missing. The `ls -la` command didn’t show it either, which is a bit puzzling. I’m considering running a more thorough `ls -la` to see if it might be further down the list, or I could search specifically for `smoke_test`. I want to make sure I locate it, as it's important for our Docker testing setup!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
4:-rw-rw-r--  1 ubuntu ubuntu  1950 Jan  6 22:23 docker-compose.yml


Summary: auto


**Investigating missing files**

There's no `smoke_test.sh`, which makes me think the previous claim might not match the actual workspace. I do see files like `.dockerignore`, `.env`, and `.env.example`. It's possible that `smoke_test.sh` was never created or maybe they forgot to commit it. I need to check the README for instructions on what should be there or if there's any mention of the missing script. Let's see what I can find!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# Maya3 Audio Diarization Pipeline

High-throughput, GPU-accelerated audio diarization and segmentation pipeline. Processes podcast audio from R2 storage, performs speaker diarization, music detection, and outputs clean speaker-segmented data.

## Performance

- **361x real-time** throughput (8x RTX 4090)
- **90+ hours** of audio processed in ~15 minutes
- Distributed processing via Supabase queue

## Architecture

```
R2 (Source Audio) → Supabase Queue → GPU Workers → R2 (Output .tar)
                         ↓
                    Status Updates
```

## Pipeline Stages

| Stage | Description |
|-------|-------------|
| 1. Download | Fetch audio from R2 bucket |
| 2. VAD | Voice activity detection (Silero) |
| 3. Chunking | Split into processable chunks |
| 4. Diarization | Speaker identification (PyAnnote) |
| 5. Embeddings | Speaker embedding extraction |
| 6. Clustering | Speaker clustering & refinement |
| 7. Music Detection | INA + PANNs hybrid detection |
| 8. Finalize | Quality filtering & validation |
| 9. Output | Generate segmented JSON |
| 10. Export | Upload .tar to R2 |

## Quick Start

### Prerequisites

- NVIDIA GPU with CUDA 12.1+
- Docker with NVIDIA Container Toolkit
- Hugging Face token (for PyAnnote models)
- Supabase project (for queue)
- Cloudflare R2 bucket (for storage)

### Setup

1. Clone and configure:
```bash
cp .env.example .env
# Edit .env with your credentials
```

2. Build container:
```bash
docker-compose build
```

3. Run workers:
```bash
docker-compose up -d
```

### Local Docker Smoke Test

On a machine with Docker + NVIDIA Container Toolkit:

```bash
chmod +x scripts/docker/smoke_test.sh
./scripts/docker/smoke_test.sh
```

Optional end-to-end test (process 1 Supabase-queued video on GPU 0 and validate TAR contents):

```bash
E2E=1 GPU_DEVICE=0 ./scripts/docker/smoke_test.sh
```

### Environment Variables

| Variable | Description |
|----------|-------------|
| `HF_TOKEN` | Hugging Face API token |
| `URL` | Supabase project URL |
| `SUPABASE_ADMIN` | Supabase service role key |
| `ACCOUNT_ID` | Cloudflare account ID |
| `API_TOKEN` | Cloudflare R2 API token |

## Local Development

```bash
# Create virtual environment
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run with Supabase queue
python massive_process.py --supabase-queue --output ./output
```

## Output Format

Each processed video produces a `.tar` containing:
- `{video_id}_segments.json` - Speaker-segmented data
- Metadata and quality statistics

## Scaling

Workers can be deployed across multiple machines. Each worker:
1. Claims videos from Supabase queue
2. Processes independently
3. Reports completion/failure back to Supabase

## Files

```
├── massive_process.py    # Main entry point
├── pipeline.py           # Core pipeline logic
├── src/
│   ├── config.py         # Configuration
│   ├── download.py       # R2 download
│   ├── vad.py            # Voice activity detection
│   ├── diarization.py    # Speaker diarization
│   ├── embeddings.py     # Speaker embeddings
│   ├── clustering.py     # Speaker clustering
│   ├── music_detection.py # Music detection
│   ├── export.py         # Output generation
│   ├── r2_client.py      # R2 storage client
│   ├── supabase_client.py # Supabase queue client
│   └── logger.py         # Clean logging
├── Dockerfile            # Container build
├── docker-compose.yml    # Container orchestration
└── requirements.txt      # Python dependencies
```

## License

MIT


**Checking directory structure**

I see that the README mentions the `scripts/docker/smoke_test.sh` file, but my earlier `ls` command didn’t show `scripts/docker`. So, I’m wondering if this folder is actually in there. I think it might be best to look more closely at the `scripts` directory — it could be hiding in there! I’ll run another command to dig around that folder and see what I can find.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
scripts/monitor/retry_failed.py
scripts/monitor/dashboard.py
scripts/monitor/lease_recovery.py
scripts/prepare_benchmark.py
scripts/docker/smoke_test.sh
scripts/fix_rtf.py
scripts/check_videos.py
scripts/run_migration.sh
scripts/test_analytics.py
scripts/process_specific_videos.py
scripts/monitor_analytics.py
scripts/supabase_migrations.sql
scripts/find_valid_videos.py
scripts/run_analytics_migration.py
scripts/deploy/start_workers.sh
scripts/deploy/deploy_all.py
scripts/deploy/deploy_cluster.sh
scripts/sync_csv_to_supabase.py


**Checking docker setup**

I see that the smoke_test script is located at scripts/docker/smoke_test.sh, which is great for when Docker is up and running. Next, I need to verify that rootless Docker can execute correctly. So, I'll follow the rootless setup instructions and create a plan. Importantly, I want to make sure we don't leak any secrets, as the `.env` file contains credentials. I'll check the `.dockerignore` file to ensure it excludes `.env`.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
.git
.vscode
.claude

# Python
.venv
__pycache__/
*.pyc
*.pyo
*.pyd

# Local outputs / large artifacts
output/
data/
*.tar
*.webm
*.flac

# Secrets (never bake credentials into images)
.env
.env.*
!/.env.example


**Using Docker Compose**

Alright, we’ve excluded the .env file, so that’s good! Now, I need to focus on using Docker Compose with the docker-compose.yml file. I'll look into inspecting that file next to see what configurations we've got going on. It’s important to ensure everything is set up correctly before proceeding, so I'll take my time here to double-check everything and make sure I’m not missing any details. Let’s go ahead and inspect it!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# Maya3 Distributed Pipeline - Docker Compose
#
# Usage:
#   docker-compose up -d                    # Start workers
#   docker-compose logs -f                  # View logs
#   docker-compose down                     # Stop workers
#
# Note: Each container uses ALL available GPUs by default.

version: '3.8'

services:
  # Main GPU worker - processes videos from Supabase queue
  worker:
    build:
      context: .
      dockerfile: Dockerfile
    image: maya3-pipeline:latest
    container_name: maya-worker
    restart: unless-stopped
    env_file:
      - .env
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - CUDA_DEVICE_ORDER=PCI_BUS_ID
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    volumes:
      # Persist output (optional if using R2)
      - ./output:/workspace/maya3_data/output
      # Model cache (speeds up restarts)
      - model-cache:/root/.cache
    shm_size: '16gb'
    logging:
      driver: "json-file"
      options:
        max-size: "100m"
        max-file: "3"
    command: >
      --supabase-queue
      --r2-upload
      --background-export
      --export-workers 4
      --output ./output

  # Lease recovery service - reclaims expired leases from crashed workers
  # CRITICAL: Run exactly ONE of these per cluster
  lease-recovery:
    build:
      context: .
      dockerfile: Dockerfile
    image: maya3-pipeline:latest
    container_name: maya-lease-recovery
    restart: unless-stopped
    env_file:
      - .env
    entrypoint: ["/workspace/maya3_data/.venv/bin/python"]
    command: ["scripts/monitor/lease_recovery.py", "--daemon", "--interval", "300"]
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 512M
    logging:
      driver: "json-file"
      options:
        max-size: "10m"
        max-file: "1"

volumes:
  model-cache:
    driver: local


**Considering Docker configurations**

I'm thinking about Docker configurations and how the user wants to test without a GPU. The .dockerignore excludes the .env file, which is good, but the image build with rootless Docker needs network access for apt-get and pip install. I might need to seek approval for network commands. The pipeline requires a GPU, and the health check confirms if torch.cuda.is_available, which would fail without one. I wonder if the user assumed this machine has a GPU. Can we access GPU through Docker if the host doesn’t have one?


**Investigating GPU issues**

I'm considering the possibility that the host GPU driver is present, but `nvidia-smi` could be failing because of NVML or MIG issues. I think we should investigate further. There's a chance we're in a container where `nvidia-smi` fails due to missing files like `/dev/nvidia0`. I should check with `ls /dev/nvidia*` to see what devices are available. This could help clarify the situation. Let’s dig into this!


ls -l /dev/nvidia* 2>/dev/null || echo 'no /dev/nvidia* devices'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
crw-rw-rw- 1 root root 195,   0 Dec 10 05:58 /dev/nvidia0
crw-rw-rw- 1 root root 195, 255 Dec 10 05:58 /dev/nvidiactl
crw-rw-rw- 1 root root 195, 254 Dec 10 05:58 /dev/nvidia-modeset
crw-rw-rw- 1 root root 236, 255 Dec 10 05:58 /dev/nvidia-nvswitchctl
crw-rw-rw- 1 root root 235,   0 Dec 10 05:58 /dev/nvidia-uvm
crw-rw-rw- 1 root root 235,   1 Dec 10 05:58 /dev/nvidia-uvm-tools

/dev/nvidia-caps:
total 0
cr-------- 1 root root 238, 1 Dec 10 05:58 nvidia-cap1
cr--r--r-- 1 root root 238, 2 Dec 10 05:58 nvidia-cap2


**Investigating GPU issues**

I see there are /dev/nvidia devices, so there must be at least one GPU available. However, nvidia-smi isn’t initializing NVML, which might be due to a driver mismatch or missing libraries within the container. The presence of /dev/nvidia0 indicates something's there, but the driver version might not be installed correctly or is inaccessible. I think I should check `cat /proc/driver/nvidia/version` next to get more information.


cat /proc/driver/nvidia/version 2>/dev/null || echo 'no /proc/driver/nvidia/version'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
NVRM version: NVIDIA UNIX x86_64 Kernel Module  560.35.05  Wed Oct 30 01:43:57 UTC 2024
GCC version:  gcc version 12.3.0 (Ubuntu 12.3.0-1ubuntu1~22.04)


**Diagnosing nvidia-smi issues**

I’m trying to figure out why the `nvidia-smi` command is failing. Could it be because the NVML library doesn't match the driver? I’ll check the output of `ldd $(which nvidia-smi)`, even though it’s in `/usr/bin`. If `nvidia-smi -q` fails but `nvidia-smi -h` works, that could indicate initialization issues with NVML. Maybe the GPU isn’t available or there's a mismatch with the driver, especially if it's in a container environment.


**Investigating GPU access issues**

I suspect we're running in a container with GPU device files but limited capabilities due to cgroup restrictions. Maybe `nvidia-smi` can't access `/dev/nvidiactl`. Even though `/dev/nvidia*` is world-writable, there could be other issues. Torch warnings indicate it can't initialize NVML, and `cudaGetDeviceCount` returns an OS error, possibly due to sandbox limitations. We're in an unprivileged container, so the GPU may be inaccessible. I might need to set the `NVIDIA_VISIBLE_DEVICES` environment variable or check for mismatches in driver and CUDA versions. I’ll also verify the presence of the NVML library.


ldconfig -p | rg -n "libnvidia-ml" || true


Output:
Exit code: 0
Wall time: 0 seconds
Output:
277:	libnvidia-ml.so.1 (libc6,x86-64) => /lib/x86_64-linux-gnu/libnvidia-ml.so.1


**Checking library version**

I'm considering checking the library version possibly by using `strings`, but that might yield a lot of data. Another option is to run `nvidia-smi` with `LD_LIBRARY_PATH`, although it might not work due to the driver module possibly not being loaded correctly. The `/proc/driver/nvidia/version` tells me it is loaded, but I wonder if it's because there's no GPU detected. I think I'll check with `lspci | rg -i nvidia` next.


command -v lspci && lspci | rg -i 'nvidia|3d|vga' || echo 'lspci not available'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
/usr/bin/lspci
00:01.0 VGA compatible controller: Device 1234:1111 (rev 02)
01:00.0 3D controller: NVIDIA Corporation GA100 [A100 SXM4 80GB] (rev a1)


**Troubleshooting GPU access**

I see an A100 80GB, which indicates there’s GPU hardware, but I’m concerned that `nvidia-smi` fails. This could be due to issues like MIG or the NVML not communicating properly. I wonder if this environment is a container impacting GPU access, possibly because of `no_new_privileges`. The `lspci` sees the GPU, so that's promising. I’ll consider running `nvidia-smi` with `sudo` next, despite not being root. There might be a known issue with WSL too. I'll try `strace nvidia-smi` to dig into the error.


**Checking GPU commands**

Running `nvidia-smi -L` results in an error, which is pretty frustrating. I wonder if setting `NVIDIA_VISIBLE_DEVICES=all` would help, but that doesn't seem to work. I could try `nvidia-smi --query-gpu=name --format=csv,noheader`, but that fails too. My next step is to check the error code and run `nvidia-smi` again to see the output and figure out what might be causing these issues. This process is a bit of a head-scratcher!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
Failed to initialize NVML: Unknown Error


Summary: auto


ls -l /lib/x86_64-linux-gnu/libnvidia-ml.so.1 && readlink -f /lib/x86_64-linux-gnu/libnvidia-ml.so.1


Output:
Exit code: 0
Wall time: 0 seconds
Output:
lrwxrwxrwx 1 root root 25 Nov  1  2024 /lib/x86_64-linux-gnu/libnvidia-ml.so.1 -> libnvidia-ml.so.560.35.05
/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.560.35.05


dmesg | tail -n 30 2>/dev/null || echo 'dmesg not permitted'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
dmesg: read kernel buffer failed: Operation not permitted


python3 - <<'PY'
import os
paths = ['/dev/nvidia0','/dev/nvidiactl','/dev/nvidia-uvm','/dev/nvidia-caps/nvidia-cap1','/dev/nvidia-caps/nvidia-cap2']
for p in paths:
    try:
        fd = os.open(p, os.O_RDONLY)
        os.close(fd)
        print(p, 'OK')
    except Exception as e:
        print(p, 'ERR', type(e).__name__, e)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
/dev/nvidia0 OK
/dev/nvidiactl OK
/dev/nvidia-uvm OK
/dev/nvidia-caps/nvidia-cap1 ERR PermissionError [Errno 13] Permission denied: '/dev/nvidia-caps/nvidia-cap1'
/dev/nvidia-caps/nvidia-cap2 OK


Output:
Exit code: 0
Wall time: 0 seconds
Output:


Output:
Exit code: 0
Wall time: 0 seconds
Output:
8:nvidia_uvm           4956160  8
79:nvidia_drm            122880  0
80:nvidia_modeset       1392640  1 nvidia_drm
83:nvidia              65900544  62 nvidia_uvm,nvidia_modeset


**Troubleshooting NVML error**

It seems like the NVML unknown error could be due to missing permission to access `/dev/nvidiactl`. I’m considering trying to run `nvidia-smi` with `CUDA_DISABLE_UNIFIED_MEMORY=1`, but that didn’t work. Maybe I should try running `nvidia-persistenced`, though it seems it's not necessary for persistence mode. There’s a chance that the A100 is in MIG mode, which might prevent querying. I want to check if MIG is enabled, but it looks like I can’t run `nvidia-smi` to verify that.


ls /proc/driver/nvidia/gpus 2>/dev/null || echo 'no /proc/driver/nvidia/gpus'; find /proc/driver/nvidia/gpus -maxdepth 2 -type f -name information -print -exec cat {} \; 2>/dev/null || true


Output:
Exit code: 0
Wall time: 0 seconds
Output:
0000:01:00.0
/proc/driver/nvidia/gpus/0000:01:00.0/information
Model: 		 NVIDIA A100-SXM4-80GB
IRQ:   		 16
GPU UUID: 	 GPU-ada3fc23-5709-2bd4-dd15-24f3b0c3cd2d
Video BIOS: 	 92.00.9e.00.16
Bus Type: 	 PCIe
DMA Size: 	 47 bits
DMA Mask: 	 0x7fffffffffff
Bus Location: 	 0000:01:00.0
Device Minor: 	 0
GPU Firmware: 	 560.35.05
GPU Excluded:	 No


**Troubleshooting GPU issues**

It seems the GPU is recognized by the driver, but NVML isn't functioning, possibly due to environment restrictions. I wonder if I can use CUDA runtime APIs directly instead. Torch typically relies on NVML, but the failure of `cudaGetDeviceCount` hints at a deeper issue, possibly with accessibility due to compute mode. I’ll need to verify if the CUDA runtime library is installed and if there are any missing components regarding `libcuda.so`. Let's check.


ldconfig -p | rg -n "libcuda\.so" || true


Output:
Exit code: 0
Wall time: 0 seconds
Output:
606:	libcuda.so.1 (libc6,x86-64) => /lib/x86_64-linux-gnu/libcuda.so.1
607:	libcuda.so (libc6,x86-64) => /lib/x86_64-linux-gnu/libcuda.so


ls -l /lib/x86_64-linux-gnu/libcuda.so.1 && readlink -f /lib/x86_64-linux-gnu/libcuda.so.1


Output:
Exit code: 0
Wall time: 0 seconds
Output:
lrwxrwxrwx 1 root root 20 Nov  1  2024 /lib/x86_64-linux-gnu/libcuda.so.1 -> libcuda.so.560.35.05
/usr/lib/x86_64-linux-gnu/libcuda.so.560.35.05


**Investigating CUDA error 304**

I’m trying to understand why `cudaGetDeviceCount` returns error 304. It seems like this error refers to `cudaErrorOperatingSystem`, which could happen if a system call fails, like opening `/dev/nvidiactl`. Though I can open that path, maybe subsequent calls fail due to a missing capability. I'll attempt to use the `cuda` driver API directly and run a small program with `ctypes` to invoke `cuInit`.  It looks like I should execute: `python3 -c "import ctypes; lib=ctypes.CDLL('libcuda.so'); print(lib.cuInit(0))"`.


python3 - <<'PY'
import ctypes
lib = ctypes.CDLL('libcuda.so.1')
lib.cuInit.argtypes = [ctypes.c_uint]
lib.cuInit.restype = ctypes.c_int
res = lib.cuInit(0)
print('cuInit', res)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
cuInit 304


**Assessing Docker options**

The user wants me to respond based on what's possible on this machine. Right now, I can’t use the docker daemon due to permission issues, but rootless docker might be an option to run containers. However, there are some limitations with rootless docker, like not being able to use `--gpus` due to cgroup restrictions. First, I should attempt to set up rootless docker and run simple container tests. Then we can request network access to pull base images and run builds.


dockerd-rootless-setuptool.sh check 2>&1 | head -n 120


Output:
Exit code: 0
Wall time: 0 seconds
Output:
[ERROR] Missing system requirements. Run the following commands to
[ERROR] install the requirements and run this tool again.

########## BEGIN ##########
sudo sh -eux <<EOF
# Install newuidmap & newgidmap binaries
apt-get install -y uidmap
EOF
########## END ##########


Hmm, it seems we might not be able to run Docker in this environment at all. I wonder if running Docker rootless with `--userns=...` could work, but that requires `uidmap`, and it looks like we can't install it. Oh wait, unless `uidmap` is already installed somewhere. I should check if `newuidmap` exists to see if that's an option! It's a little tricky, but I'm determined to figure it out!


command -v newuidmap || echo 'newuidmap not found'; command -v newgidmap || echo 'newgidmap not found'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
newuidmap not found
newgidmap not found


**Exploring uid mapping options**

I'm considering whether we can vendor a static binary of uidmap, but it seems like we can't. I’m thinking about using `unshare` to map one ID, potentially with `unshare -Ur` to create a user namespace. It's essential to check if `unshare` works, especially for rootless Docker. We should look into mapping multiple IDs from user to root and see if we have subuid/subgid configured by checking `/etc/subuid` and `/etc/subgid`.


ls -l /etc/subuid /etc/subgid 2>/dev/null && (rg -n '^ubuntu:' /etc/subuid /etc/subgid || true) || echo 'no subuid/subgid files'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
-rw-r--r-- 1 root root 20 Apr 10  2025 /etc/subgid
-rw-r--r-- 1 root root 20 Apr 10  2025 /etc/subuid
/etc/subgid:1:ubuntu:165536:65536
/etc/subuid:1:ubuntu:165536:65536


Summary: auto


Output:
Exit code: 0
Wall time: 0 seconds
Output:
NAME        DESCRIPTION                               DOCKER ENDPOINT               ERROR
default *   Current DOCKER_HOST based configuration   unix:///var/run/docker.sock


command -v lxc && lxc --version || echo 'lxc not found'


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
/snap/bin/lxc
cmd_run.go:1400: WARNING: cannot create user data directory: cannot create snap home dir: mkdir /home/ubuntu/snap: permission denied
cannot set privileged capabilities: Operation not permitted
lxc not found


**Evaluating Docker Limitations**

I'm running into permission issues with LXC installed from Snap, which prevents me from creating necessary folders and setting capabilities. So, LXD isn't usable. I can't run Docker either because of the lack of permissions for the daemon and needed binaries. Although I considered using Docker commands through `docker.sock`, that's not an option. The permissions can't be changed since the files are root-owned. I need to explain these limitations and suggest running local tests in Python instead, along with instructions for a suitable machine.


**Assessing Docker Group Membership**

I’m considering whether it’s possible to get added to the Docker group in this environment, but we don’t have sudo privileges to make that change. I’m wondering if the user can run commands outside the sandbox, but as an AI here, I can’t do that. There might be some escalation abilities, but the `sandbox_permissions require_escalated` parameter only allows command execution without sandbox restrictions, which doesn’t grant root privileges. So, we still can’t modify the Docker group or install packages.


**Exploring Escalation Possibilities**

I’m considering if the harness, when escalated, could run commands on the host with full privileges. The "require_escalated" parameter suggests running without sandbox restrictions, but it only allows commands to execute as the same user without certain limitations. This doesn’t seem to help with our OS-level permission issues. While the harness could potentially run commands with higher access, it looks like it wouldn't grant root access. I’m also thinking about running Docker inside Docker, but there’s no permission to communicate with the daemon.


**Investigating Rootless Docker Options**

I’m thinking about whether we can run `dockerd` rootless without needing `newuidmap`. If we could install the `newuidmap` binary ourselves and use setuid, that might help, but it seems like we can’t setuid here. However, I wonder if we can utilize a rootless Docker setup without `newuidmap` by using the `--userns=host` and `--rootlesskit` options. I should check the rootless documentation and also look at the `dockerd-rootless.sh` help for more insights.


dockerd-rootless.sh --help 2>&1 | head -n 80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
+ [ -w /run/user/1001 ]
+ [ -d /home/ubuntu ]
+ rootlesskit=
+ command -v docker-rootlesskit
+ command -v rootlesskit
+ rootlesskit=rootlesskit
+ break
+ [ -z rootlesskit ]
+ : /run/user/1001/dockerd-rootless
+ :
+ :
+ : builtin
+ : auto
+ : auto
+ :
+ net=
+ mtu=
+ [ -z  ]
+ command -v slirp4netns
+ slirp4netns --help
+ grep -qw -- --netns-type
+ net=slirp4netns
+ [ -z  ]
+ mtu=65520
+ [ -z slirp4netns ]
+ [ -z 65520 ]
+ host_loopback=--disable-host-loopback
+ [  = false ]
+ dockerd=dockerd
+ [ -z  ]
+ _DOCKERD_ROOTLESS_CHILD=1
+ export _DOCKERD_ROOTLESS_CHILD
+ id -u
+ [ 1001 = 0 ]
+ command -v selinuxenabled
+ exec rootlesskit --state-dir=/run/user/1001/dockerd-rootless --net=slirp4netns --mtu=65520 --slirp4netns-sandbox=auto --slirp4netns-seccomp=auto --disable-host-loopback --port-driver=builtin --copy-up=/etc --copy-up=/run --propagation=rslave /usr/bin/dockerd-rootless.sh --help
[rootlesskit:parent] error: mkdir /run/user/1001/dockerd-rootless: permission denied


**Checking directory permissions**

I'm looking into an issue where the script is trying to create a directory under /run/user/1001 but is getting a permission denied error. That’s a bit concerning since it suggests a lack of write access to that directory. Typically, it should be owned by the user for their systemd session, but there might be some restrictions or it could be missing. I'll check the directory permissions next to see what's going on.


ls -ld /run/user/1001 || echo 'no /run/user/1001'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
drwx------ 5 ubuntu ubuntu 2320 Jan  6 22:23 /run/user/1001


**Investigating permission issues**

I see that there's a rootlesskit error saying permission is denied for creating the directory /run/user/1001/dockerd-rootless. That does seem strange. I’m wondering if it’s related to the “no new privileges” setting? But you’d think that mkdir would still work. To check, I should test creating a different directory with the command `mkdir /run/user/1001/testdir`. Let's see if that works!


mkdir -p /run/user/1001/testdir && echo ok && rmdir /run/user/1001/testdir


Output:
Exit code: 1
Wall time: 0 seconds
Output:
mkdir: cannot create directory ‘/run/user/1001/testdir’: Permission denied


**Exploring directory creation issues**

I'm trying to figure out why I can't create directories under /run/user/1001, even though it appears I own them. `ls` shows ubuntu:ubuntu, but mkdir denies access. This might be due to the filesystem being mounted read-only or under specific restrictions. It seems /run/user/1001 could be on a "tmpfs" with limitations that don't actually prevent mkdir. However, since I'm in a workspace-write sandbox, I can only write within /home/ubuntu/maya3, which explains the permission denial.


**Considering permission escalation**

I’m thinking about whether I can request escalated permissions to write outside my current working directory. It appears that with sandbox_mode set to workspace-write, I can only write in specific writable locations unless I get approval. I can set `sandbox_permissions=require_escalated` to bypass restrictions, allowing writing to /run/user/1001. This might also help with connecting to /var/run/docker.sock, though I face permission issues tied to OS group membership. A rootless setup might help, but it involves some additional complexities like `uidmap` installation.


**Exploring rootless Docker options**

I'm considering that we might still lack `newuidmap`, but could run rootless Docker in "single UID" mode using `--userns=host`. However, rootless Docker relies on user namespaces and needs `unshare` to function correctly. Even if it might work without newuidmap and newgidmap, there could be limitations.

I’m noticing that the setup tool fails due to the absence of `newuidmap` and `newgidmap`, but I think `dockerd-rootless.sh` might still run. If I use `unshare` without newuidmap, it maps only the current user, possibly leading to file permission issues. There's also a "rootless without subuid" mode that intrigues me.


**Checking rootless Docker options**

I want to quickly review the rootless Docker documentation, though I'm currently unable to access the network. I could attempt to run `dockerd-rootless.sh` after setting up the `DOCKERD_ROOTLESS_ROOTLESSKIT_STATE_DIR`, which typically uses /run/user/1001/dockerd-rootless. However, I can't create this directory yet due to sandbox restrictions. With permission escalation, I could.

I'm hoping that running `dockerd-rootless.sh --help` with escalated permissions will work. Though my prior attempt failed because of directory creation issues, perhaps now I'll be able to see the help output. I just need to request approval first to run rootless dockerd in /run.


**Planning rootless Docker execution**

I think we should start by explaining to the user that we need approval for running commands outside the workspace, like creating a socket in /run and accessing the network for pulling images. Since they want to proceed, we'll request approval with escalated permissions. I need to create a plan using the update_plan tool, outlining the steps: checking Docker daemon access, setting up rootless Docker if necessary, and building the image.

I also need to explore if we can use rootless Docker without `newuidmap` or consider running `dockerd-rootless.sh` directly while storing state in the workspace to avoid writing to /run. I'll adjust the `XDG_RUNTIME_DIR` to a writable path within my workspace. Let's attempt this while monitoring the output.


rm -rf .xdg_run .docker-rootless-state || true
XDG_RUNTIME_DIR=/home/ubuntu/maya3/.xdg_run DOCKERD_ROOTLESS_ROOTLESSKIT_STATE_DIR=/home/ubuntu/maya3/.docker-rootless-state dockerd-rootless.sh --help 2>&1 | head -n 40


Output:
Exit code: 0
Wall time: 0 seconds
Output:
+ [ -w /home/ubuntu/maya3/.xdg_run ]
+ echo XDG_RUNTIME_DIR needs to be set and writable
XDG_RUNTIME_DIR needs to be set and writable
+ exit 1


Summary: auto


**Creating runtime directory**

I’m seeing that the XDG_RUNTIME_DIR needs to be set and writable, but it looks like the directory might not exist. It’s checking the write permissions, so I realize the first step is to create this directory. I'll go ahead and create a directory named `.xdg_run`, and then I should be able to run whatever process is needing that. Let's make sure everything's in order before proceeding!


mkdir -p .xdg_run .docker-rootless-state
XDG_RUNTIME_DIR=/home/ubuntu/maya3/.xdg_run DOCKERD_ROOTLESS_ROOTLESSKIT_STATE_DIR=/home/ubuntu/maya3/.docker-rootless-state dockerd-rootless.sh --help 2>&1 | head -n 60


Output:
Exit code: 0
Wall time: 0 seconds
Output:
+ [ -w /home/ubuntu/maya3/.xdg_run ]
+ [ -d /home/ubuntu ]
+ rootlesskit=
+ command -v docker-rootlesskit
+ command -v rootlesskit
+ rootlesskit=rootlesskit
+ break
+ [ -z rootlesskit ]
+ : /home/ubuntu/maya3/.docker-rootless-state
+ :
+ :
+ : builtin
+ : auto
+ : auto
+ :
+ net=
+ mtu=
+ [ -z  ]
+ command -v slirp4netns
+ slirp4netns --help
+ grep -qw -- --netns-type
+ net=slirp4netns
+ [ -z  ]
+ mtu=65520
+ [ -z slirp4netns ]
+ [ -z 65520 ]
+ host_loopback=--disable-host-loopback
+ [  = false ]
+ dockerd=dockerd
+ [ -z  ]
+ _DOCKERD_ROOTLESS_CHILD=1
+ export _DOCKERD_ROOTLESS_CHILD
+ id -u
+ [ 1001 = 0 ]
+ command -v selinuxenabled
+ exec rootlesskit --state-dir=/home/ubuntu/maya3/.docker-rootless-state --net=slirp4netns --mtu=65520 --slirp4netns-sandbox=auto --slirp4netns-seccomp=auto --disable-host-loopback --port-driver=builtin --copy-up=/etc --copy-up=/run --propagation=rslave /usr/bin/dockerd-rootless.sh --help
[rootlesskit:parent] error: failed to setup UID/GID map: newuidmap 803964 [0 1001 1 1 165536 65536] failed: : exec: "newuidmap": executable file not found in $PATH
[rootlesskit:child ] error: EOF


**Troubleshooting rootlesskit issues**

I’m looking at the problem where rootlesskit fails because `newuidmap` isn’t found. It seems necessary for starting rootless Docker, but we’re stuck unless we can install the uidmap package, which needs sudo or root access.

Maybe there's a way to run rootlesskit without `newuidmap` by using `--disable-uidmap`, though I’m not sure. I’ll check the rootlesskit help to see if there are options like `--disable-host-loopback` we can use. Let’s figure this out!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
NAME:
   rootlesskit - Linux-native fakeroot using user namespaces

USAGE:
   rootlesskit [global options] [arguments...]

VERSION:
   2.3.4

DESCRIPTION:
   RootlessKit is a Linux-native implementation of "fake root" using user_namespaces(7).

   Web site: https://github.com/rootless-containers/rootlesskit

   Examples:
     # spawn a shell with a new user namespace and a mount namespace
     rootlesskit bash

     # make /etc writable
     rootlesskit --copy-up=/etc bash

     # set mount propagation to rslave
     rootlesskit --propagation=rslave bash

     # create a network namespace with slirp4netns, and expose 80/tcp on the namespace as 8080/tcp on the host
     rootlesskit --copy-up=/etc --net=slirp4netns --disable-host-loopback --port-driver=builtin -p 127.0.0.1:8080:80/tcp bash

   Note: RootlessKit requires /etc/subuid and /etc/subgid to be configured by the real root user.
   See https://rootlesscontaine.rs/getting-started/common/ .

OPTIONS:
  Misc:
    --debug                                                  debug mode (default: false)
    --print-semver value                                     print a version component as a decimal integer [major, minor, patch]
    --help, -h                                               show help
    --version, -v                                            print the version

  Mount:
    --copy-up value [ --copy-up value ]                      mount a filesystem and copy-up the contents. e.g. "--copy-up=/etc" (typically required for non-host network)
    --copy-up-mode value                                     copy-up mode [tmpfs+symlink] (default: "tmpfs+symlink")
    --propagation value                                      mount propagation [rprivate, rslave] (default: "rprivate")

  Network:
    --net value                                              network driver [host, none, pasta(experimental), slirp4netns, vpnkit, lxc-user-nic(experimental)] (default: "host")
    --mtu value                                              MTU for non-host network (default: 65520 for pasta and slirp4netns, 1500 for others) (default: 0)
    --cidr value                                             CIDR for pasta and slirp4netns networks (default: 10.0.2.0/24)
    --ifname value                                           Network interface name (default: tap0 for pasta, slirp4netns, and vpnkit; eth0 for lxc-user-nic)
    --disable-host-loopback                                  prohibit connecting to 127.0.0.1:* on the host namespace (default: false)
    --ipv6                                                   enable IPv6 routing. Unrelated to port forwarding. Only supported for pasta and slirp4netns. (experimental) (default: false)
    --detach-netns                                           detach network namespaces  (default: false)

  Network [lxc-user-nic]:
    --lxc-user-nic-binary value                              path of lxc-user-nic binary for --net=lxc-user-nic
    --lxc-user-nic-bridge value                              lxc-user-nic bridge name (default: "lxcbr0")

  Network [pasta]:
    --pasta-binary value                                     path of pasta binary for --net=pasta (default: "pasta")

  Network [slirp4netns]:
    --slirp4netns-binary value                               path of slirp4netns binary for --net=slirp4netns (default: "slirp4netns")
    --slirp4netns-sandbox value                              enable slirp4netns sandbox (experimental) [auto, true, false] (the default is planned to be "auto" in future) (default: "false")
    --slirp4netns-seccomp value                              enable slirp4netns seccomp (experimental) [auto, true, false] (the default is planned to be "auto" in future) (default: "false")

  Network [vpnkit]:
    --vpnkit-binary value                                    path of VPNKit binary for --net=vpnkit (default: "vpnkit")

  Port:
    --port-driver value                                      port driver for non-host network. [none, implicit (for pasta), builtin, slirp4netns] (default: "none")
    --publish value, -p value [ --publish value, -p value ]  publish ports. e.g. "127.0.0.1:8080:80/tcp"

  Process:
    --pidns                                                  create a PID namespace (default: false)
    --cgroupns                                               create a cgroup namespace (default: false)
    --utsns                                                  create a UTS namespace (default: false)
    --ipcns                                                  create an IPC namespace (default: false)
    --reaper value                                           enable process reaper. Requires --pidns. [auto,true,false] (default: "auto")
    --evacuate-cgroup2 value                                 evacuate processes into the specified subgroup. Requires --pidns and --cgroupns

  State:
    --state-dir value                                        state directory

  SubID:
    --subid-source value                                     the source of the subids. "dynamic" executes /usr/bin/getsubids. "static" reads /etc/{subuid,subgid}. [auto,dynamic,static] (default: "auto")


ls -l /usr/sbin/newuidmap /usr/bin/newuidmap 2>/dev/null || echo 'newuidmap binary not present'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
newuidmap binary not present


rootlesskit --state-dir=/home/ubuntu/maya3/.rk-state --net=host bash -lc 'id' 2>&1 | head -n 40


Output:
Exit code: 0
Wall time: 0 seconds
Output:
[rootlesskit:parent] error: failed to setup UID/GID map: newuidmap 804217 [0 1001 1 1 165536 65536] failed: : exec: "newuidmap": executable file not found in $PATH


**Testing user namespaces**

I’m thinking about rootlesskit, which requires newuidmap, and I can’t seem to use it. I wonder if `unshare -Ur` could let me create a user namespace to run rootless dockerd without needing rootlesskit. It might work if unprivileged user namespaces are allowed. So, I’ll test this out by running `unshare -Ur bash -lc 'id'` and see what happens. Let's find out if this approach works!


unshare -Ur bash -lc 'id' 2>&1 | head -n 40 || true


Output:
Exit code: 0
Wall time: 0 seconds
Output:
unshare: cannot open /proc/self/uid_map: Permission denied


python3 - <<'PY'
import socket
s=socket.socket(socket.AF_UNIX)
try:
    s.connect('/var/run/docker.sock')
    print('connected')
except Exception as e:
    print(type(e).__name__, e)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
PermissionError [Errno 1] Operation not permitted


**Analyzing permission errors**

I'm encountering a PermissionError, specifically Errno 1, which indicates "Operation not permitted." This is EPERM, not EACCES. For socket files, an error due to insufficient permissions typically shows as Errno 13. It seems the environment is denying connections to the Docker daemon, likely for security reasons, even if group memberships are correct. I might explore running Docker in escalated mode to bypass these restrictions since the sandbox could be blocking Unix socket connections, including mkdir operations.


**Testing Docker in escalated mode**

I want to try running `docker version` or `docker image ls` using the require_escalated option to see if it works. While `docker --version` was successful, that doesn't connect to the daemon, which seems blocked by the sandbox. If escalated mode has fewer restrictions, I might get through. I plan to request user approval to run `docker ps`, as it involves connecting to `/var/run/docker.sock`. I should create a plan to inform the user about this request and consider potential network access implications.


**Considering network restrictions**

It looks like network access might be restricted, and while running in escalated mode could potentially allow network usage, I'd need to specify that in the command. Since any network action triggers approval, running a `docker build` that pulls from the internet might need separate approvals for each escalated command. First, I want to verify if we can access the Docker daemon by requesting approval to run `docker info` or `docker ps`. I should also plan to read the vast_ai docs, but I'll focus on updating the plan right now.


Output: Plan updated


Need to verify we can talk to the Docker daemon (required to build/run containers for the smoke test).


Output:
Exit code: 1
Wall time: 0 seconds
Output:
permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Get "http://%2Fvar%2Frun%2Fdocker.sock/v1.48/containers/json": dial unix /var/run/docker.sock: connect: permission denied


sudo -n true && echo 'sudo ok' || echo 'sudo not available'

Need to check if we can run docker via sudo to access the Docker daemon socket.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
sudo ok


**Evaluating docker command privileges**

It's interesting that in escalated mode, sudo works, meaning I can run Docker commands as root without a password. That's great! I can use `sudo docker` or `sudo docker compose` to build or run containers. In non-escalated mode, no new privileges were granted, but now I can proceed with escalated permissions since these commands write outside of the workspace. I wonder if we need user approval for each command... It seems like previous commands might have already received user approval for this session.


**Clarifying escalated command approvals**

Alright, we previously requested an escalated command, which ran successfully and returned output, suggesting automatic approval was granted. Maybe the harness didn’t require explicit user intervention for this? Still, I can’t assume this will be the case for future commands. I'll continue using require_escalated for Docker commands and network operations. Now, about GPU access: I notice CUDA calls are failing due to OS limitations. Perhaps escalated mode could bypass sandbox restrictions, allowing proper GPU access. I'll have to check on that!


**Testing CUDA initialization**

In sandbox mode, certain syscalls like `ioctl` on /dev/nvidia can be blocked, which might lead to error 304. Escalated permissions could potentially resolve this issue. I think it’s a good idea to test the CUDA initialization with the following command: `python3 -c "import ctypes; lib=ctypes.CDLL('libcuda.so.1'); print(lib.cuInit(0))"`. This way, I can see if escalated mode allows proper function here. Let's see how that goes!


python3 - <<'PY'
import ctypes
lib = ctypes.CDLL('libcuda.so.1')
lib.cuInit.argtypes = [ctypes.c_uint]
lib.cuInit.restype = ctypes.c_int
print('cuInit', lib.cuInit(0))
PY

Need to check if CUDA can initialize in the escalated environment (required for GPU Docker testing).


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
cuInit 0


Summary: auto


**Assessing GPU operations**

I’m discovering that the escalated mode now allows CUDA initialization, which implies that the sandbox was previously blocking GPU operations. This is a key insight since we can now perform GPU tests with escalated commands. I’ll need to run Docker and GPU operations while using the required permissions. With that settled, I can mark step 1 complete and move on to step 2.


Explanation: Docker daemon and CUDA/GPU access require escalated execution; with escalation, sudo works and CUDA initializes correctly.


Output: Plan updated


**Enabling container runtime**

I'm currently working on enabling a usable container runtime. Since we have system Docker accessible via sudo, I can utilize `sudo docker` commands. First, I need to verify that everything is set up correctly, so I'll run `sudo docker info` to get the system information. After that, I'll check the Docker version quickly by executing `sudo docker version`. It’s important to make sure everything is functioning properly before proceeding!


Verify Docker daemon access via sudo before building and running the container.


Output: exec command rejected by user


<environment_context>
  <approval_policy>never</approval_policy>
  <sandbox_mode>danger-full-access</sandbox_mode>
  <network_access>enabled</network_access>
  <shell>bash</shell>
</environment_context>


continue, now you have full access


**Evaluating Docker and GPU setup**

I need to focus on building the Docker image locally, starting from the Dockerfile. Running smoke tests is crucial, so I’ll inspect and run the script to check GPU connectivity. Since building will be heavy, I want to ensure the Docker environment has access to the GPU. I’ll check if the NVIDIA Container Toolkit is installed and run relevant commands to validate that the setup works correctly. Previously, `nvidia-smi` failed due to restrictions, so I’ll retest it now.


**Preparing to run Docker commands**

I want to check if the GPU is available, so I’ll run the necessary commands. I should also verify access with `docker ps`, possibly using sudo. Since we’re in "never" mode, I can proceed without asking for approval, but I need to be careful. I’ll check the Docker version and start the service if needed before building the image. The build might take time due to dependencies, so I’ll use `--progress=plain` to manage output size. Finally, I'll list previous images with `sudo docker image ls`.


**Considering Docker image options**

I wonder if an image already exists from a previous build, even though the earlier agent indicated Docker wasn't available. Now, it seems we can proceed with building and running. There’s also a `docker-compose.yml` file that uses `deploy` for swarm, but I think Docker Compose can ignore that. I plan to use `sudo docker compose`, checking for any necessary plugins.

I’ll need to monitor analytics, likely through Supabase, and might run `scripts/test_analytics.py` while ensuring that container processes videos effectively. We can also inspect Supabase logs or use `scripts/monitor_analytics.py`.


**Managing container leases**

After tests, I need to stop the containers and ensure we release any claimed leases with Supabase. There’s a chance that stopping the container might leave a lease in progress, so I should check for lease expiry. I might manually reset if necessary, or there could be a script for lease recovery or resetting specific videos.

To be safe, processing one video at a time to completion seems smart. However, since they want to "reset everything," I’ll interpret that as stopping containers, clearing the output directory, and maybe removing Docker volumes to start fresh. I also need to ensure the Supabase queue isn’t stuck. If I claim videos and then stop, they'll be pending, so running `scripts/monitor/lease_recovery.py` might help release those leases if needed, even if it’s not ideal.


**Planning a minimal safe test**

I want to conduct a minimal safe test, which means building the image and running the container in smoke-test mode. I could choose to either not claim any videos or use a `--dry-run` option if that's available. I should check the `massive_process.py` CLI options for a potential `--test` flag to verify the environment. The `scripts/docker/smoke_test.sh` also looks promising for doing environment checks within the container without claiming any tasks, so I’ll inspect that script closely.


sed -n '1,200p' scripts/docker/smoke_test.sh


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env bash
set -euo pipefail

IMAGE_TAG="${IMAGE_TAG:-maya3-pipeline:local}"
OUTPUT_DIR="${OUTPUT_DIR:-./output}"
GPU_DEVICE="${GPU_DEVICE:-0}"
E2E="${E2E:-0}"
R2_UPLOAD="${R2_UPLOAD:-0}"

if ! command -v docker >/dev/null 2>&1; then
  echo "Error: docker is not installed or not on PATH."
  exit 1
fi

if [ ! -f ".env" ]; then
  echo "Error: .env not found. Create one from .env.example before running this."
  exit 1
fi

echo "==> Building image: ${IMAGE_TAG}"
docker build -t "${IMAGE_TAG}" .

echo "==> Basic entrypoint check (help output)"
docker run --rm --gpus all --env-file .env "${IMAGE_TAG}" --help >/dev/null

echo "==> GPU + imports smoke test"
docker run --rm --gpus all --env-file .env \
  --entrypoint /workspace/maya3_data/.venv/bin/python \
  "${IMAGE_TAG}" -c "import torch; print('torch', torch.__version__); print('cuda_available', torch.cuda.is_available()); print('gpu_count', torch.cuda.device_count())"

echo "==> Supabase connectivity smoke test"
docker run --rm --gpus all --env-file .env \
  --entrypoint /workspace/maya3_data/.venv/bin/python \
  "${IMAGE_TAG}" -c "from src.supabase_client import SupabaseClient; print(SupabaseClient().get_stats())"

echo "==> R2 connectivity smoke test"
docker run --rm --gpus all --env-file .env \
  --entrypoint /workspace/maya3_data/.venv/bin/python \
  "${IMAGE_TAG}" -c "from src.r2_client import R2Client; print('source_bucket', R2Client(bucket_type='source').bucket); print('production_bucket', R2Client(bucket_type='production').bucket)"

if [ "${E2E}" = "1" ]; then
  mkdir -p "${OUTPUT_DIR}"
  ts="$(date +%Y%m%d_%H%M%S)"
  upload_prefix="smoke_test/${ts}"

  extra_args=(
    --supabase-queue
    --gpus "${GPU_DEVICE}"
    --workers-per-gpu 1
    --max-videos 1
    --background-export
    --export-workers 2
    --output ./output
  )

  if [ "${R2_UPLOAD}" = "1" ]; then
    extra_args+=(
      --r2-upload
      --r2-upload-prefix "${upload_prefix}"
      --r2-upload-skip-if-exists
    )
  fi

  echo "==> E2E test: process 1 Supabase-queued video on GPU ${GPU_DEVICE}"
  docker run --rm \
    --gpus "device=${GPU_DEVICE}" \
    --shm-size 16g \
    --env-file .env \
    -v "${OUTPUT_DIR}:/workspace/maya3_data/output" \
    "${IMAGE_TAG}" "${extra_args[@]}"

  latest_tar="$(ls -t "${OUTPUT_DIR}"/*.tar 2>/dev/null | head -n 1 || true)"
  if [ -z "${latest_tar}" ]; then
    echo "Warning: no .tar found in ${OUTPUT_DIR} (maybe R2 upload deleted it, or export disabled)."
    exit 0
  fi

  echo "==> Verifying TAR does not contain _original.flac: ${latest_tar}"
  if tar -tf "${latest_tar}" | grep -q '_original\.flac$'; then
    echo "FAIL: _original.flac found inside ${latest_tar}"
    exit 2
  fi
  echo "OK: _original.flac not present in TAR"
fi

echo "==> Done"


**Checking file and Docker access**

I think the file is set to 664, which should be readable. There’s a chance the output directory could become root-owned, but we can address that later. I could update the script to use `sudo docker` if we’re not running as root. Still, since the request was to test, maybe I should just run it with sudo for now. I'll check `docker ps` to see if the harness can interact with Docker without using sudo.


docker ps --format '{{.ID}} {{.Image}}' | head


Output:
Exit code: 0
Wall time: 0 seconds
Output:
permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Get "http://%2Fvar%2Frun%2Fdocker.sock/v1.48/containers/json": dial unix /var/run/docker.sock: connect: permission denied


sudo docker ps --format '{{.ID}} {{.Image}}' | head


Output:
Exit code: 0
Wall time: 0 seconds
Output:
0a96e08dc49c bharathkumar192/qwen-image-edit:v2.0


nvidia-smi -L | head -n 20 && nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader | head -n 20


Output:
Exit code: 0
Wall time: 0 seconds
Output:
GPU 0: NVIDIA A100-SXM4-80GB (UUID: GPU-ada3fc23-5709-2bd4-dd15-24f3b0c3cd2d)
NVIDIA A100-SXM4-80GB, 81920 MiB, 560.35.05


Summary: auto


**Accessing GPU functionality**

I'm excited that hosting `nvidia-smi` is working now! It seems the earlier restrictions were causing issues with NVML, but now the GPU is accessible, which is fantastic. Additionally, I think the previous Python Torch error related to CUDA might be resolved because of this improvement. Overall, things are looking up with GPU availability, and I'm eager to see how it all turns out!


python3 -c "import torch; print('torch', torch.__version__); print('cuda_available', torch.cuda.is_available()); print('device_count', torch.cuda.device_count()); print('device0', torch.cuda.get_device_name(0) if torch.cuda.is_available() else None)"


Output:
Exit code: 0
Wall time: 1.5 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
torch 2.9.0+cu128
cuda_available True
device_count 1
device0 NVIDIA A100-SXM4-80GB


**Running tests with CUDA**

Alright, I can see that torch is recognizing CUDA, which is great! We have an A100 80GB GPU available for testing, so I think we're all set for the smoke test, although running E2E could take some time because it needs to process a video. Meanwhile, we'll run `massive_process.py` with `--max-videos 1` to keep things simple. First though, I need to ensure that `docker compose` works by checking its version – let’s do that!


sudo docker compose version || (command -v docker-compose && sudo docker-compose version) || true


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
Docker Compose version v2.34.0


**Considering Docker image options**

I'm thinking about how we might want to customize the Dockerfile to pre-download models, as it could help avoid repeated downloads when starting up. The user suggested baking all models, but we previously decided not to bake certain elements like `.env`. Still, we could run scripts to download model weights for caching during build. While that could increase image size, it might be manageable with a persistent volume. Now, we need to focus on running the Docker build and testing.


**Planning Docker testing steps**

To move forward with deploying to Vast, I need to focus on integrating containers while keeping the user's emphasis on Docker testing in mind. They previously requested a high-level plan, so I'll make sure to stay concise. The user wants to build and run the Docker application, monitor analytics, and reset everything, but I need to be careful not to over-scope. I’ll limit processing to 1 video with the script's E2E parameters, ensuring we test the R2 upload as well since they’ve validated the connection before.


**Considering R2 upload and testing**

I need to think through potential side effects of uploading to R2. To avoid collisions, I can prefix uploads with `smoke_test/<timestamp>` and use `--r2-upload-skip-if-exists` to keep things safe. The user might want to test the full pipeline, which includes R2 uploads, so I could run the E2E test with `R2_UPLOAD=1`. Given they have many pending videos, processing just one should be fine, but I’ll need to monitor the queue. Additionally, I should run `scripts/test_analytics.py` to confirm analytics reporting.


sed -n '1,200p' scripts/test_analytics.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""Test analytics reporting system."""

import os
import sys
import time

# Add project root to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

# Load .env
try:
    from dotenv import load_dotenv
    load_dotenv()
except ImportError:
    pass

# Check credentials
url = os.environ.get('URL') or os.environ.get('SUPABASE_URL')
key = os.environ.get('SUPABASE_ADMIN')

if not url or not key:
    print("ERROR: Missing Supabase credentials")
    print(f"  URL: {'SET' if url else 'NOT SET'}")
    print(f"  SUPABASE_ADMIN: {'SET' if key else 'NOT SET'}")
    sys.exit(1)

print(f"Supabase URL: {url[:50]}...")
print()

# Test 1: Check queue status
print("=== Queue Status ===")
from src.supabase_client import SupabaseClient
client = SupabaseClient()
stats = client.get_stats()
print(f"PENDING: {stats.get('PENDING', 0)}")
print(f"COMPLETED: {stats.get('COMPLETED', 0)}")
print(f"FAILED: {stats.get('FAILED', 0)}")
print()

# Test 2: Test analytics reporter
print("=== Testing Analytics Reporter ===")
from src.analytics import create_reporter

reporter = create_reporter(
    gpu_id=0,
    supabase_url=url,
    supabase_key=key,
    machine_id="test-machine",
    enabled=True
)

print(f"Worker ID: {reporter.worker_id}")

# Simulate session start
print("  Sending session_start...")
reporter.session_start()

# Simulate pick
print("  Sending pick(test_video_001)...")
reporter.pick("test_video_001")

# Wait for flush
print("  Waiting 6s for background flush...")
time.sleep(6)

# Simulate done
print("  Sending done(test_video_001)...")
reporter.done(
    video_id="test_video_001",
    total_time=45.5,
    audio_min=10.2,
    speakers=3,
    usable_pct=78.5
)

# Wait for flush
print("  Waiting 8s for background flush...")
time.sleep(8)

# Debug: Check queue size
print(f"  Queue size: {reporter._queue.qsize()}")
print(f"  Flush thread alive: {reporter._flush_thread.is_alive()}")

# Check local stats
print()
print("=== Local Stats ===")
local_stats = reporter.get_local_stats()
for k, v in local_stats.items():
    print(f"  {k}: {v}")

# Shutdown (flush remaining)
print()
print("Shutting down reporter (flushing events)...")
reporter.shutdown()

# Give it time to flush
time.sleep(2)

# Query Supabase to verify
print()
print("=== Verifying in Supabase ===")
from supabase import create_client
sb = create_client(url, key)

# Check heartbeats
result = sb.table('worker_heartbeats').select('*').eq('worker_id', reporter.worker_id).execute()
if result.data:
    print(f"worker_heartbeats: Found {len(result.data)} row(s)")
    for row in result.data:
        print(f"  status: {row.get('status')}")
        print(f"  session_videos_done: {row.get('session_videos_done')}")
        print(f"  session_audio_minutes: {row.get('session_audio_minutes')}")
else:
    print("worker_heartbeats: No data found")

# Check events
result = sb.table('processing_events').select('*').eq('worker_id', reporter.worker_id).execute()
if result.data:
    print(f"processing_events: Found {len(result.data)} row(s)")
    for row in result.data:
        print(f"  video_id: {row.get('video_id')}, type: {row.get('event_type')}")
else:
    print("processing_events: No data found")

print()
print("=== Test Complete ===")

# Cleanup test data
print("Cleaning up test data...")
try:
    sb.table('worker_heartbeats').delete().eq('worker_id', reporter.worker_id).execute()
    sb.table('processing_events').delete().eq('worker_id', reporter.worker_id).execute()
    sb.table('worker_metrics').delete().eq('worker_id', reporter.worker_id).execute()
    print("  Cleanup complete")
except Exception as e:
    print(f"  Cleanup failed: {e}")


**Testing analytics reporting**

I'm looking at a script that tests analytics reporting by writing to Supabase and then performing a cleanup afterward. That seems solid! I’m considering running it inside a container or on the host. For monitoring analytics, I might use the command `scripts/monitor/dashboard.py --watch` to keep an eye on everything. It sounds like a good plan, so I’ll check that out further!


sed -n '1,200p' scripts/monitor/dashboard.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Real-time Monitoring Dashboard for Maya3 Distributed Pipeline.

Displays:
- Cluster-wide progress
- Per-worker status
- Error rates and common failures
- Queue depth and throughput

Usage:
    python dashboard.py                    # One-time status
    python dashboard.py --watch            # Live updates (every 30s)
    python dashboard.py --watch --interval 10  # Custom interval
"""

import os
import sys
import time
import argparse
from datetime import datetime, timedelta
from typing import Dict, List, Any

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))

from dotenv import load_dotenv
load_dotenv()

from supabase import create_client

# ANSI colors
class C:
    RESET = '\033[0m'
    BOLD = '\033[1m'
    RED = '\033[91m'
    GREEN = '\033[92m'
    YELLOW = '\033[93m'
    BLUE = '\033[94m'
    CYAN = '\033[96m'
    DIM = '\033[2m'

def clear_screen():
    """Clear terminal screen."""
    print('\033[2J\033[H', end='')

def format_duration(seconds: float) -> str:
    """Format seconds as human-readable duration."""
    if seconds < 60:
        return f"{seconds:.0f}s"
    elif seconds < 3600:
        return f"{seconds/60:.1f}m"
    else:
        return f"{seconds/3600:.1f}h"

def format_number(n: int) -> str:
    """Format number with commas."""
    return f"{n:,}"

class Dashboard:
    def __init__(self):
        self.sb = create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
        self.start_time = datetime.utcnow()

    def get_queue_stats(self) -> Dict[str, int]:
        """Get video counts by status."""
        stats = {}
        for status in ['PENDING', 'CLAIMED', 'COMPLETED', 'FAILED', 'SKIPPED']:
            result = self.sb.table('videos').select('id', count='exact').eq('status', status).execute()
            stats[status] = result.count or 0
        return stats

    def get_worker_status(self) -> List[Dict]:
        """Get all worker heartbeats."""
        result = self.sb.table('worker_heartbeats').select('*').order('worker_id').execute()
        return result.data or []

    def get_recent_errors(self, limit: int = 10) -> List[Dict]:
        """Get recent error logs."""
        result = self.sb.table('error_logs').select(
            'video_id, stage, error_type, error_message, created_at'
        ).order('created_at', desc=True).limit(limit).execute()
        return result.data or []

    def get_error_summary(self) -> Dict[str, int]:
        """Get error counts by type."""
        result = self.sb.table('error_logs').select('error_type').execute()
        counts = {}
        for row in (result.data or []):
            err_type = row.get('error_type', 'Unknown')
            counts[err_type] = counts.get(err_type, 0) + 1
        return counts

    def get_hourly_throughput(self) -> List[Dict]:
        """Get videos completed per hour (last 24h)."""
        # Get recent processing events
        since = (datetime.utcnow() - timedelta(hours=24)).isoformat()
        result = self.sb.table('processing_events').select(
            'created_at, audio_minutes'
        ).eq('event_type', 'done').gte('created_at', since).execute()

        # Bucket by hour
        hourly = {}
        for row in (result.data or []):
            ts = row.get('created_at', '')[:13]  # YYYY-MM-DDTHH
            if ts not in hourly:
                hourly[ts] = {'count': 0, 'audio_min': 0}
            hourly[ts]['count'] += 1
            hourly[ts]['audio_min'] += row.get('audio_minutes', 0) or 0

        return [{'hour': k, **v} for k, v in sorted(hourly.items())]

    def get_stale_workers(self, threshold_sec: int = 300) -> List[Dict]:
        """Find workers that haven't reported in threshold seconds."""
        threshold = (datetime.utcnow() - timedelta(seconds=threshold_sec)).isoformat()
        result = self.sb.table('worker_heartbeats').select('*').neq(
            'status', 'offline'
        ).lt('last_heartbeat', threshold).execute()
        return result.data or []

    def print_header(self):
        """Print dashboard header."""
        now = datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S UTC')
        print(f"\n{C.BOLD}{C.CYAN}╔══════════════════════════════════════════════════════════════════════╗{C.RESET}")
        print(f"{C.BOLD}{C.CYAN}║{C.RESET}            {C.BOLD}MAYA3 DISTRIBUTED PIPELINE DASHBOARD{C.RESET}                      {C.CYAN}║{C.RESET}")
        print(f"{C.BOLD}{C.CYAN}║{C.RESET}                       {now}                       {C.CYAN}║{C.RESET}")
        print(f"{C.BOLD}{C.CYAN}╚══════════════════════════════════════════════════════════════════════╝{C.RESET}")

    def print_queue_stats(self, stats: Dict[str, int]):
        """Print queue statistics."""
        total = sum(stats.values())
        completed = stats.get('COMPLETED', 0)
        failed = stats.get('FAILED', 0)
        pending = stats.get('PENDING', 0)
        claimed = stats.get('CLAIMED', 0)

        progress = (completed / total * 100) if total > 0 else 0
        error_rate = (failed / (completed + failed) * 100) if (completed + failed) > 0 else 0

        print(f"\n{C.BOLD}📊 QUEUE STATUS{C.RESET}")
        print(f"{'─'*50}")

        # Progress bar
        bar_width = 40
        filled = int(bar_width * progress / 100)
        bar = '█' * filled + '░' * (bar_width - filled)
        print(f"  Progress: [{C.GREEN}{bar}{C.RESET}] {progress:.1f}%")

        # Stats grid
        print(f"  {C.GREEN}✓ Completed:{C.RESET} {format_number(completed):>8}  │  {C.YELLOW}⏳ Pending:{C.RESET} {format_number(pending):>8}")
        print(f"  {C.RED}✗ Failed:{C.RESET}    {format_number(failed):>8}  │  {C.BLUE}🔄 Claimed:{C.RESET} {format_number(claimed):>8}")
        print(f"  {C.DIM}Total:{C.RESET}       {format_number(total):>8}  │  {C.DIM}Error Rate:{C.RESET} {error_rate:.2f}%")

    def print_worker_status(self, workers: List[Dict]):
        """Print worker status table."""
        print(f"\n{C.BOLD}👷 WORKER STATUS ({len(workers)} workers){C.RESET}")
        print(f"{'─'*70}")

        if not workers:
            print(f"  {C.DIM}No workers registered{C.RESET}")
            return

        # Group by machine
        machines = {}
        for w in workers:
            machine = w.get('machine_id', 'unknown')
            if machine not in machines:
                machines[machine] = []
            machines[machine].append(w)

        for machine, machine_workers in sorted(machines.items()):
            print(f"\n  {C.BOLD}{machine}{C.RESET}")

            for w in sorted(machine_workers, key=lambda x: x.get('gpu_id', 0)):
                status = w.get('status', 'unknown')
                gpu = w.get('gpu_id', '?')
                video = w.get('current_video_id', '')
                stage = w.get('current_stage_name', '')
                done = w.get('session_videos_done', 0)
                failed = w.get('session_videos_failed', 0)
                audio_min = w.get('session_audio_minutes', 0)

                # Status indicator
                if status == 'processing':
                    status_icon = f"{C.GREEN}●{C.RESET}"
                    status_text = f"{C.GREEN}processing{C.RESET}"
                elif status == 'idle':
                    status_icon = f"{C.YELLOW}○{C.RESET}"
                    status_text = f"{C.YELLOW}idle{C.RESET}"
                else:
                    status_icon = f"{C.RED}◌{C.RESET}"
                    status_text = f"{C.RED}{status}{C.RESET}"

                # Last heartbeat age
                last_hb = w.get('last_heartbeat', '')
                if last_hb:
                    try:


**Considering monitoring options**

I don't think scrolling is necessary, but it seems like having a dashboard showing emojis can be useful. For analytics, I realize we can run the dashboard to monitor worker heartbeats. They want analytics while the container runs. We'll have the dashboard in watch mode and container running detached. I need to keep in mind that we can only run one process at a time in this environment. I'll start the container in the background for a short period and monitor the dashboard for 1-2 iterations.


**Clarifying dashboard and container setup**

I realize that using `dashboard.py --watch` will run indefinitely, but we can limit the time with the `timeout` command. For instance, I can use `timeout 65s python3 scripts/monitor/dashboard.py --watch --interval 15` to get some updates. We'll need the `.env` file for supabase credentials, which is available, but I notice that it's excluded from the image by `.dockerignore`. Docker-compose can still access it. Also, I need to confirm if the compose plugin will allocate GPUs correctly, since there might be some confusion with how to implement that.


**Testing GPU reliability**

To enhance reliability, I think about modifying the `docker-compose.yml` to use `deploy`, although that might not be enough. Using `docker run --gpus all` is a solid option. There’s no `gpus` field necessary if I’m using the NVIDIA runtime. I remember that in Docker Compose v2, I can specify `deploy.resources.reservations.devices`, which should support device reservations. To test this, I'll run the smoke test script, which ensures GPUs are accessible. I'll run `sudo bash scripts/docker/smoke_test.sh` to build the image and conduct GPU tests successfully.


**Planning Docker application execution**

I’m considering the GPU imports test and checking Supabase and R2 connectivity, which is promising. For running the Docker application and monitoring analytics, I have a couple of options. One idea is to run `sudo docker run ...` using `massive_process.py` in queue mode, limiting it to 1 video for about 2-3 minutes. While that runs, I could monitor analytics with the dashboard afterward or in parallel. Alternatively, I could use `docker compose up -d` for the worker, potentially starting it with `--max-videos 1`.


**Setting up E2E testing**

I’m considering running the E2E test using `sudo bash scripts/docker/smoke_test.sh`, which processes one video and verifies the output. While it runs, I could monitor the dashboard to check updates and worker heartbeats, although the worker might exit quickly. The user wants a setup for building the Docker image and running it like a continuous worker mode for analytics. I think I should start with `--max-videos 1` to ensure the container exits properly after completion, and I could run the dashboard using a timeout while managing the container in detached mode.