post training CLI (#51)

## What does this PR do?
Add post training related CLI to client SDK

## user experience
Since kick off supervised finetune job need to setup several configs and
hyper-parameters, to make user experience friendly, we provide an
example script under
examples/post_training/supervised_fine_tune_client.py to kick off the
post training job

## test 
**kick off training**
python supervised_fine_tune_client.py "devgpu018.nha2.facebook.com" 5000
"1236" "meta-llama/Llama-3.2-3B-Instruct"
<img width="880" alt="Screenshot 2024-12-18 at 11 39 35 AM"
src="https://github.com/user-attachments/assets/f5f011c1-005d-4887-8eeb-41c3aa1604d6"
/>




**get job list**
llama-stack-client --endpoint http://devgpu018.nha2.facebook.com:5000
post_training list

<img width="880" alt="Screenshot 2024-12-18 at 11 40 27 AM"
src="https://github.com/user-attachments/assets/433b7b82-b3fa-473b-8a8f-3a0d046f2959"
/>




**get job status**
llama-stack-client --endpoint http://devgpu018.nha2.facebook.com:5000
post_training status --job-uuid "1235"


<img width="894" alt="Screenshot 2024-12-18 at 11 41 34 AM"
src="https://github.com/user-attachments/assets/2275cd56-9367-4903-85cb-6000be35f0d9"
/>

**get job artifacts**
llama-stack-client --endpoint http://devgpu018.nha2.facebook.com:5000
post_training artifacts --job-uuid "1235"
<img width="888" alt="Screenshot 2024-12-18 at 11 42 22 AM"
src="https://github.com/user-attachments/assets/dad2d521-e9b1-409a-93a9-f040c4d6cd24"
/>

examples/post_training/supervised_fine_tune_client.py

@@ -0,0 +1,111 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
+
+import asyncio
+from typing import Optional
+
+import fire
+from llama_stack_client import LlamaStackClient
+
+from llama_stack_client.types.post_training_supervised_fine_tune_params import (
+    AlgorithmConfigLoraFinetuningConfig,
+    TrainingConfig,
+    TrainingConfigDataConfig,
+    TrainingConfigEfficiencyConfig,
+    TrainingConfigOptimizerConfig,
+)
+
+
+async def run_main(
+    host: str,
+    port: int,
+    job_uuid: str,
+    model: str,
+    use_https: bool = False,
+    checkpoint_dir: Optional[str] = None,
+    cert_path: Optional[str] = None,
+):
+
+    # Construct the base URL with the appropriate protocol
+    protocol = "https" if use_https else "http"
+    base_url = f"{protocol}://{host}:{port}"
+
+    # Configure client with SSL certificate if provided
+    client_kwargs = {"base_url": base_url}
+    if use_https and cert_path:
+        client_kwargs["verify"] = cert_path
+
+    client = LlamaStackClient(**client_kwargs)
+
+    algorithm_config = AlgorithmConfigLoraFinetuningConfig(
+        type="LoRA",
+        lora_attn_modules=["q_proj", "v_proj", "output_proj"],
+        apply_lora_to_mlp=True,
+        apply_lora_to_output=False,
+        rank=8,
+        alpha=16,
+    )
+
+    data_config = TrainingConfigDataConfig(
+        dataset_id="alpaca",
+        validation_dataset_id="alpaca",
+        batch_size=1,
+        shuffle=False,
+    )
+
+    optimizer_config = TrainingConfigOptimizerConfig(
+        optimizer_type="adamw",
+        lr=3e-4,
+        weight_decay=0.1,
+        num_warmup_steps=100,
+    )
+
+    effiency_config = TrainingConfigEfficiencyConfig(
+        enable_activation_checkpointing=True,
+    )
+
+    training_config = TrainingConfig(
+        n_epochs=1,
+        data_config=data_config,
+        efficiency_config=effiency_config,
+        optimizer_config=optimizer_config,
+        max_steps_per_epoch=30,
+        gradient_accumulation_steps=1,
+    )
+
+    training_job = client.post_training.supervised_fine_tune(
+        job_uuid=job_uuid,
+        model=model,
+        algorithm_config=algorithm_config,
+        training_config=training_config,
+        checkpoint_dir=checkpoint_dir,
+        # logger_config and hyperparam_search_config haven't been used yet
+        logger_config={},
+        hyperparam_search_config={},
+    )
+
+    print(f"finished the training job: {training_job.job_uuid}")
+
+
+def main(
+    host: str,
+    port: int,
+    job_uuid: str,
+    model: str,
+    use_https: bool = False,
+    checkpoint_dir: Optional[str] = "null",
+    cert_path: Optional[str] = None,
+):
+    job_uuid = str(job_uuid)
+    asyncio.run(
+        run_main(host, port, job_uuid, model, use_https, checkpoint_dir, cert_path)
+    )
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

src/llama_stack_client/lib/cli/llama_stack_client.py

@@ -19,6 +19,7 @@
 from .inference import inference
 from .memory_banks import memory_banks
 from .models import models
+from .post_training import post_training
 from .providers import providers
 from .scoring_functions import scoring_functions
 from .shields import shields
@@ -75,6 +76,7 @@ def cli(ctx, endpoint: str, config: str | None):
 cli.add_command(scoring_functions, "scoring_functions")
 cli.add_command(eval, "eval")
 cli.add_command(inference, "inference")
+cli.add_command(post_training, "post_training")
 
 
 def main():

src/llama_stack_client/lib/cli/post_training/__init__.py

@@ -0,0 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .post_training import post_training
+
+__all__ = ["post_training"]

src/llama_stack_client/lib/cli/post_training/post_training.py

@@ -0,0 +1,117 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Optional
+
+import click
+
+from llama_stack_client.types.post_training_supervised_fine_tune_params import (
+    AlgorithmConfig,
+    TrainingConfig,
+)
+from rich.console import Console
+
+from ..common.utils import handle_client_errors
+
+
+@click.group()
+def post_training():
+    """Query details about available post_training endpoints on distribution."""
+    pass
+
+
+@click.command("supervised_fine_tune")
+@click.option("--job-uuid", required=True, help="Job UUID")
+@click.option("--model", required=True, help="Model ID")
+@click.option("--algorithm-config", required=True, help="Algorithm Config")
+@click.option("--training-config", required=True, help="Training Config")
+@click.option(
+    "--checkpoint-dir", required=False, help="Checkpoint Config", default=None
+)
+@click.pass_context
+@handle_client_errors("post_training supervised_fine_tune")
+def supervised_fine_tune(
+    ctx,
+    job_uuid: str,
+    model: str,
+    algorithm_config: AlgorithmConfig,
+    training_config: TrainingConfig,
+    checkpoint_dir: Optional[str],
+):
+    """Kick off a supervised fine tune job"""
+    client = ctx.obj["client"]
+    console = Console()
+
+    post_training_job = client.post_training.supervised_fine_tune(
+        job_uuid=job_uuid,
+        model=model,
+        algorithm_config=algorithm_config,
+        training_config=training_config,
+        checkpoint_dir=checkpoint_dir,
+        # logger_config and hyperparam_search_config haven't been used yet
+        logger_config={},
+        hyperparam_search_config={},
+    )
+    console.print(post_training_job.job_uuid)
+
+
+@click.command("list")
+@click.pass_context
+@handle_client_errors("post_training get_training_jobs")
+def get_training_jobs(ctx):
+    """Show the list of available post training jobs"""
+    client = ctx.obj["client"]
+    console = Console()
+
+    post_training_jobs = client.post_training.job.list()
+    console.print(
+        [post_training_job.job_uuid for post_training_job in post_training_jobs]
+    )
+
+
+@click.command("status")
+@click.option("--job-uuid", required=True, help="Job UUID")
+@click.pass_context
+@handle_client_errors("post_training get_training_job_status")
+def get_training_job_status(ctx, job_uuid: str):
+    """Show the status of a specific post training job"""
+    client = ctx.obj["client"]
+    console = Console()
+
+    job_status_reponse = client.post_training.job.status(job_uuid=job_uuid)
+    console.print(job_status_reponse)
+
+
+@click.command("artifacts")
+@click.option("--job-uuid", required=True, help="Job UUID")
+@click.pass_context
+@handle_client_errors("post_training get_training_job_artifacts")
+def get_training_job_artifacts(ctx, job_uuid: str):
+    """Get the training artifacts of a specific post training job"""
+    client = ctx.obj["client"]
+    console = Console()
+
+    job_artifacts = client.post_training.job.artifacts(job_uuid=job_uuid)
+    console.print(job_artifacts)
+
+
+@click.command("cancel")
+@click.option("--job-uuid", required=True, help="Job UUID")
+@click.pass_context
+@handle_client_errors("post_training cancel_training_job")
+def cancel_training_job(ctx, job_uuid: str):
+    """Cancel the training job"""
+    client = ctx.obj["client"]
+
+    client.post_training.job.cancel(job_uuid=job_uuid)
+
+
+# Register subcommands
+post_training.add_command(supervised_fine_tune)
+post_training.add_command(get_training_jobs)
+post_training.add_command(get_training_job_status)
+post_training.add_command(get_training_job_artifacts)
+post_training.add_command(cancel_training_job)