> ## Documentation Index > Fetch the complete documentation index at: https://miles.radixark.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # SWE-Agent training with Harbor on Docker sandboxes > Trains coding and terminal agents with Harbor-managed local Docker sandboxes and verifier rewards. This example trains GLM-4.7-Flash on agentic coding and terminal tasks. Miles runs synchronous GRPO and serves the policy through its session server; a separate [Harbor](https://github.com/harbor-framework/harbor) agent server creates the task sandboxes, runs the agents, and returns verifier rewards. The same pipeline supports Terminal-Bench, SWE-bench, and custom Harbor tasks. Training records must contain a `prompt` and `metadata.instance_id` identifying the Harbor task. ## Files | File | Purpose | | ---------------------------------- | ----------------------------------------------------------------------- | | `run.py` | Validated synchronous GLM-4.7-Flash launcher. | | `run-glm47-flash-agentic-async.py` | Disaggregated fully asynchronous launcher. | | `run_glm52_lora_tb2_daytona.py` | Multi-node GLM-5.2 744B-A40B LoRA launcher (bf16 trainer, fp8 rollout). | | `swe_agent_function.py` | Sends each rollout to the Harbor agent server. | | `generate.py` | Builds rewards, metrics, and training samples. | | `download_and_process_data.py` | Converts supported datasets to Miles JSONL. | ## 1. Start the Harbor agent server Use the `harbor-miles-v0.20.0` branch of the `harbor-framework/harbor` repository, which carries the Miles integration: ```bash theme={null} git clone https://github.com/harbor-framework/harbor.git cd harbor git checkout harbor-miles-v0.20.0 uv sync HARBOR_TASKS_DIR=/path/to/harbor_tasks uv run python miles_agent_server.py \ --host 0.0.0.0 \ --port 30000 \ --dashboard-port 0 \ --max-concurrent 32 \ --agent-timeout 5400 \ --trials-dir /path/to/trials ``` `HARBOR_TASKS_DIR` must contain one Harbor task directory for every `metadata.instance_id` in the training data. The agent-server machine must have Docker and enough capacity for the requested number of concurrent sandboxes; set `--max-concurrent` to at least one sandbox per trajectory in a rollout step (`--rollout-batch-size` times `--n-samples-per-prompt`). Keep `--agent-timeout` generous — agentic trials routinely run past an hour, and a short timeout kills them mid-episode. Verify `http://:30000/health` before launching Miles. The two per-trial timeouts must be ordered. `--agent-timeout` is the authoritative one: when it fires, the agent server ends the trial and frees its sandbox. The rollout client applies a second ceiling, `AGENT_TRIAL_TIMEOUT` (default 7200 seconds), which has to stay above `--agent-timeout`. If the client gives up first, the trial is recorded as aborted while the agent server keeps running it, so the sandbox and its `--max-concurrent` slot stay busy for the remaining difference, and the aborted sample takes its whole GRPO group down with it. Raise it through the launcher's generic env-var hook: ```bash theme={null} python examples/swe-agent-harbor-docker/run.py ... --extra-env-vars 'AGENT_TRIAL_TIMEOUT=10800' ``` If the trainer reaches the agent server through a proxy or an in-cluster service rather than directly, point `--agent-server-url` at that stable name rather than an ephemeral pod address. The rollout client enables TCP keepalive probes so long-running trials do not lose an idle connection while Harbor is working. ## 2. Prepare Terminal-Bench data Convert a local JSONL whose rows include a task instruction and instance name: ```bash theme={null} python examples/swe-agent-harbor-docker/download_and_process_data.py \ --input /path/to/terminal-bench.jsonl \ --output /path/to/tb2_train.jsonl \ --agent-name mini-swe-agent \ --prompt-key instruction ``` The resulting `metadata.instance_id` values must match task directories known to the Harbor agent server. ## 3. Launch synchronous GLM-4.7-Flash training The shape below is what a multi-day Terminal-Bench 2 run used on one node of 8 H200 GPUs: 32 trajectories per GRPO step (4 prompts times 8 samples), each one a full mini-swe-agent episode in its own Harbor sandbox. ```bash theme={null} python examples/swe-agent-harbor-docker/run.py \ --num-nodes 1 \ --num-gpus-per-node 8 \ --skip-prepare \ --megatron-path /root/Megatron-LM \ --hf-checkpoint /path/to/GLM-4.7-Flash \ --ref-load /path/to/GLM-4.7-Flash_torch_dist \ --save-dir /path/to/checkpoints \ --prompt-data /path/to/tb2_train.jsonl \ --max-seq-len 65536 \ --rollout-batch-size 4 \ --n-samples-per-prompt 8 \ --global-batch-size 32 \ --num-rollout 200 \ --save-interval 20 \ --agent-server-url http://:30000 \ --router-external-host \ --miles-host-ip 0.0.0.0 \ --save-traces-dir /path/to/traces ``` For a smoke test, set `--num-rollout 1`. Expect roughly 10 minutes per step at this shape; because synchronous rollout waits for the slowest trajectory in the batch, a step that draws an unusually slow task can take several times that. `--router-external-host` is the address Harbor sandboxes use to call the Miles session server and SGLang router. It must resolve and route from the agent-server machine. `--miles-host-ip 0.0.0.0` is useful when those services must accept connections forwarded from another host. Ensure ports 30000 and 31000 are reachable end to end; Tailscale is one option when the machines are on different networks. ## 4. Verify progress Check both layers: 1. Miles logs emit rollout metrics and write `rollout_data/*.pt` under the trace directory. 2. Megatron logs emit `train/step` and the Ray job exits successfully. Confirm a suspected stall on disk before believing a dashboard. W\&B uploads can fail partway through a long run — dropping some metric rows while others keep arriving — which looks exactly like a frozen reward curve. The per-step `train_data/` and `rollout_data/.pt` dumps under `--save-traces-dir` are written by the trainer itself and are the authoritative progress signal. The synchronous launcher uses GLM-4.7 tool-call and reasoning parsers, TITO, the Miles session server, and the Megatron backend.