Dagster

@asset(
    name="my_artifact",
    metadata={
        "wandb_artifact_arguments": {
            "type": "dataset",
        }
    },
    io_manager_key="wandb_artifacts_manager",
)
def create_dataset():
    return [1, 2, 3] # ceci sera stocké dans un Artifact

@asset(
   name="my_advanced_artifact",
   metadata={
       "wandb_artifact_configuration": {
           "type": "dataset",
           "description": "My *Markdown* description",
           "aliases": ["my_first_alias", "my_second_alias"],
           "add_dirs": [
               {
                   "name": "My directory",
                   "local_path": "path/to/directory",
               }
           ],
           "add_files": [
               {
                   "name": "validation_dataset",
                   "local_path": "path/to/data.json",
               },
               {
                   "is_tmp": True,
                   "local_path": "path/to/temp",
               },
           ],
           "add_references": [
               {
                   "uri": "https://picsum.photos/200/300",
                   "name": "External HTTP reference to an image",
               },
               {
                   "uri": "s3://my-bucket/datasets/mnist",
                   "name": "External S3 reference",
               },
           ],
       }
   },
   io_manager_key="wandb_artifacts_manager",
)
def create_advanced_artifact():
   return [1, 2, 3]

@asset(
    partitions_def=DailyPartitionsDefinition(start_date="2023-01-01", end_date="2023-02-01"),
    name="my_daily_partitioned_asset",
    compute_kind="wandb",
    metadata={
        "wandb_artifact_configuration": {
            "type": "dataset",
        }
    },
)
def create_my_daily_partitioned_asset(context):
    partition_key = context.asset_partition_key_for_output()
    context.log.info(f"Creating partitioned asset for {partition_key}")
    return random.randint(0, 100)

@asset(
   ins={
       "table": AssetIn(
           key="my_artifact_with_table",
           metadata={
               "wandb_artifact_configuration": {
                   "get": "my_table",
               }
           },
           input_manager_key="wandb_artifacts_manager",
       )
   }
)
def get_table(context, table):
   context.log.info(table.get_column("a"))

@asset(
   ins={
       "path": AssetIn(
           key="my_artifact_with_file",
           metadata={
               "wandb_artifact_configuration": {
                   "get_path": "name_of_file",
               }
           },
           input_manager_key="wandb_artifacts_manager",
       )
   }
)
def get_path(context, path):
   context.log.info(path)

@asset(
   ins={
       "artifact": AssetIn(
           key="my_artifact",
           input_manager_key="wandb_artifacts_manager",
       )
   },
)
def get_artifact(context, artifact):
   context.log.info(artifact.name)

@asset(
    name="my_joblib_serialized_model",
    compute_kind="Python",
    metadata={
        "wandb_artifact_configuration": {
            "type": "model",
            "serialization_module": {
                "name": "joblib"
            },
        }
    },
    io_manager_key="wandb_artifacts_manager",
)
def create_model_serialized_with_joblib():
    # Ce n'est pas un vrai modèle ML, mais cela ne serait pas possible avec le module pickle
    return lambda x, y: x + y

@asset(
    name="inference_result_from_joblib_serialized_model",
    compute_kind="Python",
    ins={
        "my_joblib_serialized_model": AssetIn(
            input_manager_key="wandb_artifacts_manager",
        )
    },
    metadata={
        "wandb_artifact_configuration": {
            "type": "results",
        }
    },
    io_manager_key="wandb_artifacts_manager",
)
def use_model_serialized_with_joblib(
    context: OpExecutionContext, my_joblib_serialized_model
):
    inference_result = my_joblib_serialized_model(1, 2)
    context.log.info(inference_result)  # Affiche : 3
    return inference_result

import numpy
import onnxruntime as rt
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

from dagster import AssetIn, AssetOut, asset, multi_asset

@multi_asset(
    compute_kind="Python",
    outs={
        "my_onnx_model": AssetOut(
            metadata={
                "wandb_artifact_configuration": {
                    "type": "model",
                }
            },
            io_manager_key="wandb_artifacts_manager",
        ),
        "my_test_set": AssetOut(
            metadata={
                "wandb_artifact_configuration": {
                    "type": "test_set",
                }
            },
            io_manager_key="wandb_artifacts_manager",
        ),
    },
    group_name="onnx_example",
)
def create_onnx_model():
    # Inspiré de https://onnx.ai/sklearn-onnx/

    # Entraîner un modèle.
    iris = load_iris()
    X, y = iris.data, iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y)
    clr = RandomForestClassifier()
    clr.fit(X_train, y_train)

    # Convertir au format ONNX
    initial_type = [("float_input", FloatTensorType([None, 4]))]
    onx = convert_sklearn(clr, initial_types=initial_type)

    # Écrire les Artifacts (modèle + test_set)
    return onx.SerializeToString(), {"X_test": X_test, "y_test": y_test}

@asset(
    name="experiment_results",
    compute_kind="Python",
    ins={
        "my_onnx_model": AssetIn(
            input_manager_key="wandb_artifacts_manager",
        ),
        "my_test_set": AssetIn(
            input_manager_key="wandb_artifacts_manager",
        ),
    },
    group_name="onnx_example",
)
def use_onnx_model(context, my_onnx_model, my_test_set):
    # Inspiré de https://onnx.ai/sklearn-onnx/

    # Calculer la prédiction avec ONNX Runtime
    sess = rt.InferenceSession(my_onnx_model)
    input_name = sess.get_inputs()[0].name
    label_name = sess.get_outputs()[0].name
    pred_onx = sess.run(
        [label_name], {input_name: my_test_set["X_test"].astype(numpy.float32)}
    )[0]
    context.log.info(pred_onx)
    return pred_onx

"*": {
    "get": "default_table_name",
},

"yellow": {
    "get": "custom_table_name",
},

"orange": {
    "version": "v0",
},

"blue": {
    "alias": "special_alias",
},

# ajoutez ceci à votre config.yaml
# vous pouvez également définir la configuration dans le Launchpad de Dagit ou dans JobDefinition.execute_in_process
# Référence : https://docs.dagster.io/concepts/configuration/config-schema#specifying-runtime-configuration
resources:
 wandb_config:
   config:
     entity: my_entity # remplacez par votre entité W&B
     project: my_project # remplacez par votre projet W&B
ops:
 run_launch_agent:
   config:
     max_jobs: -1
     queues: 
       - my_dagster_queue

from dagster_wandb.launch.ops import run_launch_agent
from dagster_wandb.resources import wandb_resource

from dagster import job, make_values_resource

@job(
   resource_defs={
       "wandb_config": make_values_resource(
           entity=str,
           project=str,
       ),
       "wandb_resource": wandb_resource.configured(
           {"api_key": {"env": "WANDB_API_KEY"}}
       ),
   },
)
def run_launch_agent_example():
   run_launch_agent()

# ajoutez ceci à votre config.yaml
# vous pouvez également définir la configuration dans le Launchpad de Dagit ou via JobDefinition.execute_in_process
# Référence : https://docs.dagster.io/concepts/configuration/config-schema#specifying-runtime-configuration
resources:
 wandb_config:
   config:
     entity: my_entity # remplacez par votre entité W&B
     project: my_project # remplacez par votre projet W&B
ops:
 my_launched_job:
   config:
     entry_point:
       - python
       - train.py
     queue: my_dagster_queue
     uri: https://github.com/wandb/example-dagster-integration-with-launch


from dagster_wandb.launch.ops import run_launch_job
from dagster_wandb.resources import wandb_resource

from dagster import job, make_values_resource


@job(resource_defs={
       "wandb_config": make_values_resource(
           entity=str,
           project=str,
       ),
       "wandb_resource": wandb_resource.configured(
           {"api_key": {"env": "WANDB_API_KEY"}}
       ),
   },
)
def run_launch_job_example():
   run_launch_job.alias("my_launched_job")() # on renomme le job avec un alias

wandb.init(
    id=context.run_id,
    resume="allow",
    ...
)

wandb.init(
    id="my_resumable_run_id",
    resume="allow",
    ...
)

@job(
   resource_defs={
       "io_manager": wandb_artifacts_io_manager.configured(
           {"wandb_run_id": "my_resumable_run_id"}
       ),
   }
)