Add runpod sls handler (#2530) [skip ci]

* Add runpod sls handler

* remove LICENSE and fix README

* chore: lint

* use axolotl cloud image as base and various fixes

* fix: trim allowed cuda versions

* restore dockerfile

* chore: update title

* use axolotl cloud image

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: NanoCode012 <nano@axolotl.ai>

.runpod/src/handler.py

@@ -0,0 +1,64 @@
+"""
+Runpod serverless entrypoint handler
+"""
+
+import os
+
+import runpod
+import yaml
+from huggingface_hub._login import login
+from train import train
+from utils import get_output_dir
+
+BASE_VOLUME = os.environ.get("BASE_VOLUME", "/runpod-volume")
+if not os.path.exists(BASE_VOLUME):
+    os.makedirs(BASE_VOLUME)
+
+logger = runpod.RunPodLogger()
+
+
+async def handler(job):
+    runpod_job_id = job["id"]
+    inputs = job["input"]
+    run_id = inputs.get("run_id", "default_run_id")
+    args = inputs.get("args", {})
+
+    # Set output directory
+    output_dir = os.path.join(BASE_VOLUME, get_output_dir(run_id))
+    args["output_dir"] = output_dir
+
+    # First save args to a temporary config file
+    config_path = "/workspace/test_config.yaml"
+
+    # Add run_name and job_id to args before saving
+    args["run_name"] = run_id
+    args["runpod_job_id"] = runpod_job_id
+
+    yaml_data = yaml.dump(args, default_flow_style=False)
+    with open(config_path, "w", encoding="utf-8") as file:
+        file.write(yaml_data)
+
+    # Handle credentials
+    credentials = inputs.get("credentials", {})
+
+    if "wandb_api_key" in credentials:
+        os.environ["WANDB_API_KEY"] = credentials["wandb_api_key"]
+    if "hf_token" in credentials:
+        os.environ["HF_TOKEN"] = credentials["hf_token"]
+
+    if os.environ.get("HF_TOKEN"):
+        login(token=os.environ["HF_TOKEN"])
+    else:
+        logger.info("No HF_TOKEN provided. Skipping login.")
+
+    logger.info("Starting Training.")
+    async for result in train(config_path):  # Pass the config path instead of args
+        logger.info(result)
+    logger.info("Training Complete.")
+
+    # Cleanup
+    del os.environ["WANDB_API_KEY"]
+    del os.environ["HF_TOKEN"]
+
+
+runpod.serverless.start({"handler": handler, "return_aggregate_stream": True})