feat(python): Python bindings for the Dynamo CLI tools

examples/cli/cli.py

@@ -0,0 +1,156 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Example cli using the Python bindings, similar to `dynamo-run`.
+# Usage: `python cli.py in=text out=mistralrs <your-model>`.
+# Must be in a virtualenv with the Dynamo bindings (or wheel) installed.
+
+import argparse
+import asyncio
+import sys
+from pathlib import Path
+
+import uvloop
+
+from dynamo.llm import EngineType, EntrypointArgs, make_engine, run_input
+from dynamo.runtime import DistributedRuntime
+
+
+def parse_args():
+    in_mode = "text"
+    out_mode = "echo"
+    batch_file = None  # Specific to in_mode="batch"
+
+    # List to hold arguments that argparse will process (flags and model path)
+    argparse_args = []
+
+    # --- Step 1: Manual Pre-parsing for 'in=' and 'out=' ---
+    # Iterate through sys.argv[1:] to extract in= and out=
+    # and collect remaining arguments for argparse.
+    for arg in sys.argv[1:]:
+        if arg.startswith("in="):
+            in_val = arg[len("in=") :]
+            if in_val.startswith("batch:"):
+                in_mode = "batch"
+                batch_file = in_val[len("batch:") :]
+            else:
+                in_mode = in_val
+        elif arg.startswith("out="):
+            out_mode = arg[len("out=") :]
+        else:
+            # This argument is not 'in=' or 'out=', so it's either a flag or the model path
+            argparse_args.append(arg)
+
+    # --- Step 2: Argparse for flags and the model path ---
+    parser = argparse.ArgumentParser(
+        description="Dynamo CLI: Connect inputs to an engine",
+        formatter_class=argparse.RawTextHelpFormatter,  # To preserve multi-line help formatting
+    )
+
+    # model_name: Option<String>
+    parser.add_argument("--model-name", type=str, help="Name of the model to load.")
+    # model_config: Option<PathBuf>
+    parser.add_argument(
+        "--model-config", type=Path, help="Path to the model configuration file."
+    )
+    # context_length: Option<u32>
+    parser.add_argument(
+        "--context-length", type=int, help="Maximum context length for the model (u32)."
+    )
+    # template_file: Option<PathBuf>
+    parser.add_argument(
+        "--template-file",
+        type=Path,
+        help="Path to the template file for text generation.",
+    )
+    # kv_cache_block_size: Option<u32>
+    parser.add_argument(
+        "--kv-cache-block-size", type=int, help="KV cache block size (u32)."
+    )
+    # http_port: Option<u16>
+    parser.add_argument("--http-port", type=int, help="HTTP port for the engine (u16).")
+
+    # TODO: Not yet used here
+    parser.add_argument(
+        "--tensor-parallel-size",
+        type=int,
+        help="Tensor parallel size for the model (e.g., 4).",
+    )
+
+    # Add the positional model argument.
+    # It's made optional (nargs='?') because its requirement depends on 'out_mode',
+    # which is handled in post-parsing validation.
+    parser.add_argument(
+        "model",
+        nargs="?",  # Make it optional for argparse, we'll validate manually
+        help="Path to the model (e.g., Qwen/Qwen3-0.6B).\n" "Required unless out=dyn.",
+    )
+
+    # Parse the arguments that were not 'in=' or 'out='
+    flags = parser.parse_args(argparse_args)
+
+    # --- Step 3: Post-parsing Validation and Final Assignment ---
+
+    # Validate 'batch' mode requires a file path
+    if in_mode == "batch" and not batch_file:
+        parser.error("Batch mode requires a file path: in=batch:FILE")
+
+    # Validate model path requirement based on 'out_mode'
+    if out_mode != "dyn" and flags.model is None:
+        parser.error("Model path is required unless out=dyn.")
+
+    # Consolidate all parsed arguments into a dictionary
+    parsed_args = {
+        "in_mode": in_mode,
+        "out_mode": out_mode,
+        "batch_file": batch_file,  # Will be None if in_mode is not "batch"
+        "model_path": flags.model,
+        "flags": flags,
+    }
+
+    return parsed_args
+
+
+async def run():
+    loop = asyncio.get_running_loop()
+    runtime = DistributedRuntime(loop, False)
+
+    args = parse_args()
+
+    engine_type_map = {
+        "echo": EngineType.Echo,
+        "mistralrs": EngineType.MistralRs,
+        "llamacpp": EngineType.LlamaCpp,
+        "dyn": EngineType.Dynamic,
+    }
+    out_mode = args["out_mode"]
+    engine_type = engine_type_map.get(out_mode)
+    if engine_type is None:
+        print(f"Unsupported output type: {out_mode}")
+        sys.exit(1)
+
+    # TODO: The "vllm", "sglang" and "trtllm" cases should call Python directly
+
+    entrypoint_kwargs = {"model_path": args["model_path"]}
+
+    flags = args["flags"]
+    if flags.model_name is not None:
+        entrypoint_kwargs["model_name"] = flags.model_name
+    if flags.model_config is not None:
+        entrypoint_kwargs["model_config"] = flags.model_config
+    if flags.context_length is not None:
+        entrypoint_kwargs["context_length"] = flags.context_length
+    if flags.template_file is not None:
+        entrypoint_kwargs["template_file"] = flags.template_file
+    if flags.kv_cache_block_size is not None:
+        entrypoint_kwargs["kv_cache_block_size"] = flags.kv_cache_block_size
+    if flags.http_port is not None:
+        entrypoint_kwargs["http_port"] = flags.http_port
+
+    e = EntrypointArgs(engine_type, **entrypoint_kwargs)
+    engine = await make_engine(runtime, e)
+    await run_input(runtime, args["in_mode"], engine)
+
+
+if __name__ == "__main__":
+    uvloop.run(run())

launch/dynamo-run/Cargo.toml

@@ -34,6 +34,7 @@ anyhow = { workspace = true }
 async-openai = { workspace = true }
 async-stream = { workspace = true }
 async-trait = { workspace = true }
+either = { workspace = true }
 futures = { workspace = true }
 libc = { workspace = true }
 serde = { workspace = true }

launch/dynamo-run/src/lib.rs

@@ -11,6 +11,7 @@ use dynamo_llm::local_model::{LocalModel, LocalModelBuilder};
 use dynamo_runtime::CancellationToken;
 
 mod flags;
+use either::Either;
 pub use flags::Flags;
 mod opt;
 pub use dynamo_llm::request_template::RequestTemplate;
@@ -41,14 +42,19 @@ pub async fn run(
         .kv_cache_block_size(flags.kv_cache_block_size)
         // Only set if user provides. Usually loaded from tokenizer_config.json
         .context_length(flags.context_length)
-        .http_port(flags.http_port)
+        .http_port(Some(flags.http_port))
         .router_config(flags.router_config())
         .request_template(flags.request_template.clone());
 
     // If `in=dyn` we want the trtllm/sglang/vllm subprocess to listen on that endpoint.
     // If not, then the endpoint isn't exposed so we let LocalModel invent one.
+    let mut rt = Either::Left(runtime.clone());
     if let Input::Endpoint(path) = &in_opt {
-        builder.endpoint_id(path.parse().with_context(|| path.clone())?);
+        builder.endpoint_id(Some(path.parse().with_context(|| path.clone())?));
+
+        let distributed_runtime =
+            dynamo_runtime::DistributedRuntime::from_settings(runtime.clone()).await?;
+        rt = Either::Right(distributed_runtime);
     };
 
     let local_model = builder.build().await?;
@@ -70,8 +76,7 @@ pub async fn run(
     //
     // Run in from an input
     //
-
-    dynamo_llm::entrypoint::input::run_input(in_opt, runtime, engine_config).await?;
+    dynamo_llm::entrypoint::input::run_input(rt, in_opt, engine_config).await?;
 
     // Allow engines to ask main thread to wait on an extra future.
     // We use this to stop the vllm and sglang sub-process