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major refactor 2

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Jai Suphavadeeprasit 2026-01-22 13:21:50 -05:00
parent 2e6c3b3e12
commit 1b1d8a5909
2 changed files with 45 additions and 27 deletions
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example_trainer/README.md
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@ -108,10 +108,12 @@ The simplest mode. Trainer manages vLLM internally.
run-api --port 8000
# Terminal 2: Start the environment server (generates rollouts)
python environments/gsm8k_server.py serve \
--slurm.num_gpus 0 \
python -u environments/gsm8k_server.py serve \
--env.tokenizer_name $MODEL \
--openai.base_url http://localhost:9001/v1
--env.use_wandb=False \
--openai.model_name $MODEL \
--openai.base_url http://localhost:9001/v1 \
--openai.server_type vllm
# Terminal 3: Run training (trainer will launch its own vLLM)
CUDA_VISIBLE_DEVICES=0 python -m example_trainer.grpo \
@ -141,10 +143,12 @@ CUDA_VISIBLE_DEVICES=0 python example_trainer/vllm_api_server.py \
--gpu-memory-utilization 0.45
# Terminal 3: Start the environment server
python environments/gsm8k_server.py serve \
--slurm.num_gpus 0 \
python -u environments/gsm8k_server.py serve \
--env.tokenizer_name $MODEL \
--openai.base_url http://localhost:9001/v1
--env.use_wandb=False \
--openai.model_name $MODEL \
--openai.base_url http://localhost:9001/v1 \
--openai.server_type vllm
# Terminal 4: Run training (attaches to vLLM's tensors)
CUDA_VISIBLE_DEVICES=0 python -m example_trainer.grpo \
@ -177,10 +181,12 @@ CUDA_VISIBLE_DEVICES=0 python example_trainer/vllm_api_server.py \
--enforce-eager
# Terminal 3: Start the environment server
python environments/gsm8k_server.py serve \
--slurm.num_gpus 0 \
python -u environments/gsm8k_server.py serve \
--env.tokenizer_name $MODEL \
--openai.base_url http://localhost:9001/v1
--env.use_wandb=False \
--openai.model_name $MODEL \
--openai.base_url http://localhost:9001/v1 \
--openai.server_type vllm
# Terminal 4: Run LoRA training
CUDA_VISIBLE_DEVICES=1 python -m example_trainer.grpo \
@ -240,10 +246,12 @@ mkdir -p $LOGDIR
run-api --port 8001
# Terminal 2: Environment server
python environments/gsm8k_server.py serve \
--slurm.num_gpus 0 \
python -u environments/gsm8k_server.py serve \
--env.tokenizer_name $MODEL \
--env.use_wandb=False \
--openai.model_name $MODEL \
--openai.base_url http://localhost:9001/v1 \
--openai.server_type vllm \
--server.port 8001
# Terminal 3: Trainer (manages its own vLLM)
@ -269,10 +277,12 @@ CUDA_VISIBLE_DEVICES=2 python example_trainer/vllm_api_server.py \
--model $MODEL --port 9002 --gpu-memory-utilization 0.45
# Terminal 6: Environment server
python environments/gsm8k_server.py serve \
--slurm.num_gpus 0 \
python -u environments/gsm8k_server.py serve \
--env.tokenizer_name $MODEL \
--env.use_wandb=False \
--openai.model_name $MODEL \
--openai.base_url http://localhost:9002/v1 \
--openai.server_type vllm \
--server.port 8002
# Terminal 7: Trainer (attaches to vLLM)
@ -299,10 +309,12 @@ CUDA_VISIBLE_DEVICES=4 python example_trainer/vllm_api_server.py \
--enable-lora --max-lora-rank 32 --enforce-eager
# Terminal 10: Environment server
python environments/gsm8k_server.py serve \
--slurm.num_gpus 0 \
python -u environments/gsm8k_server.py serve \
--env.tokenizer_name $MODEL \
--env.use_wandb=False \
--openai.model_name $MODEL \
--openai.base_url http://localhost:9003/v1 \
--openai.server_type vllm \
--server.port 8003
# Terminal 11: Trainer