# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-02 10:54:55
- **Session ID**: `789c18d3-04c3-4ee8-8091-63cb7f39a022`

## Conversation

### 👤 You

test_output_gen_batch_padded = self.async_rollout_manager.generate_sequences(test_gen_batch_padded)
  File "/root/verl/verl/experimental/agent_loop/agent_loop.py", line 846, in generate_sequences
    outputs = ray.get(
        [
    ...<2 lines>...
        ]
    )
                                  ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ray.exceptions.ActorDiedError: The actor died because of an error raised in its creation task, ray::agent_loop_worker_0:AgentLoopWorker.__init__() (pid=716404, ip=172.17.0.2, actor_id=165327c4e7f51af17312d1f801000000, repr=<verl.experimental.agent_loop.agent_loop.AgentLoopWorker object at 0x71ce0b9e2660>)
  File "/root/miniconda3/lib/python3.13/concurrent/futures/_base.py", line 449, in result
    return self.__get_result()
           ~~~~~~~~~~~~~~~~~^^
  File "/root/miniconda3/lib/python3.13/concurrent/futures/_base.py", line 401, in __get_result
    raise self._exception
  File "/root/verl/verl/experimental/agent_loop/agent_loop.py", line 414, in __init__
    RolloutTraceConfig.init(
    ~~~~~~~~~~~~~~~~~~~~~~~^
        self.config.trainer.project_name,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    ...<2 lines>...
        trace_config.get("token2text", False),
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/root/verl/verl/utils/rollout_trace.py", line 69, in init
    import weave
ModuleNotFoundError: No module named 'weave'

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
(RewardManagerWorker pid=717670) using customized reward function 'compute_score' from '/root/verl/recipe/retool/retool.py' [repeated 7x across cluster]
(AgentLoopWorker pid=716408) Exception raised in creation task: The actor died because of an error raised in its creation task, ray::agent_loop_worker_7:AgentLoopWorker.__init__() (pid=716408, ip=172.17.0.2, actor_id=30109684f52b73c677aa4fa401000000, repr=<verl.experimental.agent_loop.agent_loop.AgentLoopWorker object at 0x7e453d5e6660>) [repeated 7x across cluster]
(AgentLoopWorker pid=716408)   File "/root/miniconda3/lib/python3.13/concurrent/futures/_base.py", line 456, in result [repeated 7x across cluster]
(AgentLoopWorker pid=716408)     return self.__get_result() [repeated 7x across cluster]
(AgentLoopWorker pid=716408)            ~~~~~~~~~~~~~~~~~^^ [repeated 7x across cluster]
(AgentLoopWorker pid=716408)   File "/root/miniconda3/lib/python3.13/concurrent/futures/_base.py", line 401, in __get_result [repeated 7x across cluster]
(AgentLoopWorker pid=716408)     raise self._exception [repeated 7x across cluster]
(AgentLoopWorker pid=716408)   File "/root/verl/verl/experimental/agent_loop/agent_loop.py", line 414, in __init__ [repeated 7x across cluster]
(AgentLoopWorker pid=716408)     RolloutTraceConfig.init( [repeated 7x across cluster]
(AgentLoopWorker pid=716408)     ~~~~~~~~~~~~~~~~~~~~~~~^ [repeated 7x across cluster]
(AgentLoopWorker pid=716408)         self.config.trainer.project_name, [repeated 7x across cluster]
(AgentLoopWorker pid=716408)         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 7x across cluster]
(AgentLoopWorker pid=716408)     ...<2 lines>... [repeated 7x across cluster]
(AgentLoopWorker pid=716408)         trace_config.get("token2text", False), [repeated 7x across cluster]
(AgentLoopWorker pid=716408)         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ [repeated 7x across cluster]
(AgentLoopWorker pid=716408)     ) [repeated 7x across cluster]
(AgentLoopWorker pid=716408)     ^ [repeated 7x across cluster]
(AgentLoopWorker pid=716408)   File "/root/verl/verl/utils/rollout_trace.py", line 69, in init [repeated 7x across cluster]
(AgentLoopWorker pid=716408)     import weave [repeated 7x across cluster]
(AgentLoopWorker pid=716408) ModuleNotFoundError: No module named 'weave' [repeated 7x across cluster]

--
python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=$adv_estimator \
    algorithm.use_kl_in_reward=$use_kl_in_reward \
    algorithm.kl_ctrl.kl_coef=$kl_coef \
    data.train_files="$train_files" \
    data.val_files="$test_files" \
    data.return_raw_chat=True \
    data.train_batch_size=$train_batch_size \
    data.max_prompt_length=$max_prompt_length \
    data.max_response_length=$max_response_length \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    data.custom_cls.path=recipe/retool/retool.py \
    data.custom_cls.name=CustomRLHFDataset \
    custom_reward_function.path=recipe/retool/retool.py \
    custom_reward_function.name=compute_score \
    actor_rollout_ref.model.path=$model_path \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.use_kl_loss=$use_kl_loss \
    actor_rollout_ref.actor.kl_loss_coef=$kl_loss_coef \
    actor_rollout_ref.actor.clip_ratio_low=$clip_ratio_low \
    actor_rollout_ref.actor.clip_ratio_high=$clip_ratio_high \
    actor_rollout_ref.actor.clip_ratio_c=10.0 \
    actor_rollout_ref.actor.optim.lr=$actor_lr \
    actor_rollout_ref.actor.use_dynamic_bsz=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=$ppo_mini_batch_size \
    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=$actor_max_token_len_per_gpu \
    actor_rollout_ref.actor.ulysses_sequence_parallel_size=$train_sp \
    actor_rollout_ref.actor.fsdp_config.param_offload=$offload \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=$offload \
    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=$log_prob_max_token_len_per_gpu \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.mode=async \
    actor_rollout_ref.rollout.tensor_model_parallel_size=$infer_tp \
    actor_rollout_ref.rollout.multi_turn.enable=True \
    actor_rollout_ref.rollout.multi_turn.max_user_turns=$max_turns \
    actor_rollout_ref.rollout.multi_turn.max_assistant_turns=$max_turns \
    actor_rollout_ref.rollout.multi_turn.tool_config_path=$tool_config_path \
    actor_rollout_ref.rollout.multi_turn.format=hermes \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.9 \
    actor_rollout_ref.rollout.n=$n_resp_per_prompt \
    actor_rollout_ref.rollout.val_kwargs.top_p=0.6 \
    actor_rollout_ref.rollout.val_kwargs.temperature=1.0 \
    actor_rollout_ref.rollout.val_kwargs.n=$n_resp_per_prompt_val \
    actor_rollout_ref.rollout.trace.backend=weave \
    actor_rollout_ref.rollout.trace.token2text=True \
    trainer.logger=['console','wandb'] \
    trainer.project_name=$project_name \
    trainer.experiment_name=$experiment_name \

---

### 🤖 Cursor Assistant

I can see the error clearly - the training script is failing because the `weave` module is not installed, but it's configured to use weave as the trace backend.

Let me check the relevant files to understand the setup and provide you with the best solution:

---

### 🤖 Cursor Assistant

The error is clear: the training script is configured to use `weave` as the trace backend, but the `weave` module is not installed in your environment.

Based on the code, VERL supports three tracing backends:
1. **weave** (W&B Weave) - requires `pip install weave`
2. **mlflow** - requires `pip install mlflow`  
3. **None** - no tracing (disabled)

## Solution Options

### Option 1: Disable Tracing (Quickest)
Remove or comment out these two lines from your training command:

```bash
actor_rollout_ref.rollout.trace.backend=weave \
actor_rollout_ref.rollout.trace.token2text=True \
```

### Option 2: Install Weave
If you want to keep using Weave for tracing:

---

### 👤 You

File "/root/miniconda3/lib/python3.13/site-packages/urllib3/connection.py", line 205, in _new_conn
    raise NameResolutionError(self.host, self, e) from e
urllib3.exceptions.NameResolutionError: <urllib3.connection.HTTPSConnection object at 0x765a141842f0>: Failed to resolve 'api.wandb.ai' ([Errno -3] Temporary failure in name resolution)

The above exception was the direct cause of the following exception:

ray::agent_loop_worker_4:AgentLoopWorker.__init__() (pid=818488, ip=172.17.0.2, actor_id=34e0e9f97f3f31729fabe1f901000000, repr=<verl.experimental.agent_loop.agent_loop.AgentLoopWorker object at 0x765a59a8f8c0>)
  File "/root/miniconda3/lib/python3.13/site-packages/requests/a
42f0>: Failed to resolve 'api.wandb.ai' ([Errno -3] Temporary failure in name resolution)"))

During handling of the above exception, another exception occurred:

ray::agent_loop_worker_4:AgentLoopWorker.__init__() (pid=818488, ip=172.17.0.2, actor_id=34e0e9f97f3f31729fabe1f901000000, repr=<verl.experimental.agent_loop.agent_loop.AgentLoopWorker object at 0x765a59a8f8c0>)
  File "/root/miniconda3/lib/python3.13/site-packages/gql/transport/requests.py", line 292, in execute
    response = self.session.request(self.method, self.url, **post_args)
  File "/root/miniconda3/lib/python3.13/site-packages/requests/sessions.py", line 589, in request
    resp = self.send(prep, **send_kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/requests/sessions.py", line 703, in send
    r = adapter.send(request, **kwargs)
  File "/root/miniconda3/lib/python3.13/site-packages/requests/adapters.py", line 700, in send
    raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='api.wandb.ai', port=443): Max retries exceeded with url: /graphql (Caused by NameResolutionError("<urllib3.connection.HTTPSConnection object at 0x765a141842f0>: Failed to resolve 'api.wandb.ai' ([Errno -3] Temporary failure in name resolution)"))

The above exception was the direct cause of the following exception:



--
Why this error? I can access the api.wandb.ai on the host

---

### 🤖 Cursor Assistant

Now you're getting a networking error - the Docker container where the training is running can't reach `api.wandb.ai`. This is a Docker networking issue, even though your host machine can access it.

## Best Solutions (in order of simplicity):

### **Option 1: Disable Tracing (Recommended for now)**

Simply remove these two lines from your training command:

```bash
actor_rollout_ref.rollout.trace.backend=weave \
actor_rollout_ref.rollout.trace.token2text=True \
```

Tracing is optional and mainly useful for debugging multi-turn conversations. You can train successfully without it.

### **Option 2: Use MLflow with Local Storage (Alternative)**

If you want tracing but without external dependencies, use MLflow instead:

---

### 🤖 Cursor Assistant

Replace the weave lines with mlflow:

```bash
# Remove these lines:
actor_rollout_ref.rollout.trace.backend=weave \
actor_rollout_ref.rollout.trace.token2text=True \

# Replace with:
actor_rollout_ref.rollout.trace.backend=mlflow \
actor_rollout_ref.rollout.trace.token2text=True \
```

MLflow will store traces locally in SQLite (`/tmp/mlruns.db` by default), no internet connection needed.

### **Option 3: Fix Docker Networking (If you need Weave)**

If you really need Weave tracing, you'll need to configure Docker networking. This typically involves:

1. Using `--network host` mode when running the container
2. Or configuring DNS properly in Docker
3. Ensuring the container has internet access

However, **I recommend Option 1 (disable tracing)** for now to get your training running. Tracing is mainly useful for debugging multi-turn agent conversations, not essential for training itself. You can always enable it later when needed.

**Which option would you like to proceed with?** Based on your goal (getting the RL training running), I'd suggest removing the trace configuration lines entirely.

---

### 👤 You

I'm not inside a docker.

---

### 🤖 Cursor Assistant

Ah, I understand! Even though you're not inside Docker yourself, the error shows that the **Ray workers** (`ip=172.17.0.2`) are running inside Docker containers. The IP `172.17.0.2` is a typical Docker bridge network address, which means VERL is spawning Ray worker processes in containers for distributed training.

So the issue is: your host can access `api.wandb.ai`, but the **Ray worker containers** cannot.

Let me check the training script to see how to configure this:

---

### 🤖 Cursor Assistant

Good catch! So the training command runs on your host, but **Ray spawns worker processes in Docker containers** for distributed training. Those containers can't reach `api.wandb.ai`.

## **Recommended Solutions (easiest first):**

### **Solution 1: Disable Tracing (Simplest)**

Edit the training script to remove the trace configuration:

---

### 🤖 Cursor Assistant

I notice your script **doesn't** include the trace configuration lines - they're not in lines 84-107. So you must have added them when running the command.

**Simply run the script without adding those trace lines:**

```bash
bash recipe/retool/run_qwen2-32b_dapo.sh
```

Don't add:
- `actor_rollout_ref.rollout.trace.backend=weave`
- `actor_rollout_ref.rollout.trace.token2text=True`

The script already has `trainer.logger=['console','wandb']` on line 97, which gives you basic wandb logging without needing the trace feature.

---

### **Solution 2: Use MLflow (If you need detailed tracing)**

If you do want detailed multi-turn conversation tracing, use MLflow which stores locally:

---

### 🤖 Cursor Assistant

Actually, let me revert that change since you should try **Solution 1 first** (no tracing):

---

### 🤖 Cursor Assistant

## Summary

Your error occurred because you manually added these lines to your training command:
- `actor_rollout_ref.rollout.trace.backend=weave`
- `actor_rollout_ref.rollout.trace.token2text=True`

The Ray worker containers can't reach `api.wandb.ai`, causing the failure.

### **Just run the script as-is:**

```bash
bash recipe/retool/run_qwen2-32b_dapo.sh
```

The script already includes `trainer.logger=['console','wandb']` for basic wandb logging. You don't need the trace feature - it's only useful for debugging specific multi-turn conversation flows, not for training.

### **If you later want tracing:**

You can add MLflow tracing (local storage, no network needed) by adding these two lines after line 96 in the script:

```bash
actor_rollout_ref.rollout.trace.backend=mlflow \
actor_rollout_ref.rollout.trace.token2text=True \
```

But for now, just run it without any trace configuration and your training should start successfully! 🚀

---


*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*