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/train.py

@@ -0,0 +1,45 @@
+"""
+Runpod train entrypoint
+"""
+
+import asyncio
+
+
+async def train(config_path: str, gpu_id: str = "0", preprocess: bool = True):
+    """
+    Run preprocessing (if enabled) and training with the given config file
+    :param config_path: Path to the YAML config file
+    :param gpu_id: GPU ID to use (default: "0")
+    :param preprocess: Whether to run preprocessing (default: True)
+
+    """
+    # First check if preprocessing is needed
+    if preprocess:
+        # Preprocess command
+        preprocess_cmd = (
+            f"CUDA_VISIBLE_DEVICES={gpu_id} axolotl preprocess {config_path}"
+        )
+        process = await asyncio.create_subprocess_shell(
+            preprocess_cmd,
+            stdout=asyncio.subprocess.PIPE,
+            stderr=asyncio.subprocess.STDOUT,
+        )
+
+        if process.stdout is not None:
+            async for line in process.stdout:
+                yield f"Preprocessing: {line.decode().strip()}"
+        await process.wait()
+        yield "Preprocessing completed."
+    else:
+        yield "Skipping preprocessing step."
+
+    # Training command
+    train_cmd = f"axolotl train {config_path}"
+    process = await asyncio.create_subprocess_shell(
+        train_cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.STDOUT
+    )
+
+    if process.stdout is not None:
+        async for line in process.stdout:
+            yield f"Training: {line.decode().strip()}"
+    await process.wait()