Fix Flyte Types Upload Issues in Default Input

Dockerfile.dev

@@ -44,6 +44,7 @@ RUN SETUPTOOLS_SCM_PRETEND_VERSION_FOR_FLYTEKIT=$PSEUDO_VERSION \
             pandas \
             pillow \
             plotly \
+            pyarrow \
             pygments \
             scikit-learn \
             ydata-profiling \

flytekit/clis/sdk_in_container/run.py

@@ -458,9 +458,13 @@ def to_click_option(
     description_extra = ""
     if literal_var.type.simple == SimpleType.STRUCT:
         if default_val and not isinstance(default_val, ArtifactQuery):
-            if type(default_val) == dict or type(default_val) == list:
-                default_val = json.dumps(default_val)
-            else:
+            """
+            1. Convert default_val to a JSON string for click.Option, which uses json.loads to parse it.
+            2. Set a new context with remote access to allow Flyte types (e.g., files) to be uploaded.
+            3. Use FlyteContextManager for Flyte Types with custom serialization.
+                If no custom logic exists, fall back to json.dumps.
+            """
+            with FlyteContextManager.with_context(flyte_ctx.new_builder()):
                 encoder = JSONEncoder(python_type)
                 default_val = encoder.encode(default_val)
         if literal_var.type.metadata:

flytekit/types/directory/types.py

@@ -537,7 +537,8 @@ async def async_to_literal(
                 remote_directory = ctx.file_access.get_random_remote_directory()
             if not pathlib.Path(source_path).is_dir():
                 raise FlyteAssertion("Expected a directory. {} is not a directory".format(source_path))
-            await ctx.file_access.async_put_data(
+            # remote_directory will convert the path from `flyte://` to `s3://` or `gs://`
+            remote_directory = await ctx.file_access.async_put_data(
                 source_path, remote_directory, is_multipart=True, batch_size=batch_size
             )
             return Literal(scalar=Scalar(blob=Blob(metadata=meta, uri=remote_directory)))

flytekit/types/file/file.py

@@ -551,6 +551,7 @@ async def async_to_literal(
                 )
             else:
                 remote_path = await ctx.file_access.async_put_raw_data(source_path, **headers)
+            # If the source path is a local file, the remote path will be a remote storage path.
             return Literal(scalar=Scalar(blob=Blob(metadata=meta, uri=unquote(str(remote_path)))))
         # If not uploading, then we can only take the original source path as the uri.
         else:

tests/flytekit/integration/remote/test_remote.py

@@ -9,14 +9,14 @@
 import tempfile
 import time
 import typing
-
+import re
 import joblib
 from urllib.parse import urlparse
 import uuid
 import pytest
 from mock import mock, patch
 
-from flytekit import LaunchPlan, kwtypes
+from flytekit import LaunchPlan, kwtypes, WorkflowExecutionPhase
 from flytekit.configuration import Config, ImageConfig, SerializationSettings
 from flytekit.core.launch_plan import reference_launch_plan
 from flytekit.core.task import reference_task
@@ -62,7 +62,8 @@ def register():
     assert out.returncode == 0
 
 
-def run(file_name, wf_name, *args):
+def run(file_name, wf_name, *args) -> str:
+    # Copy the environment and set the environment variable
     out = subprocess.run(
         [
             "pyflyte",
@@ -82,9 +83,20 @@ def run(file_name, wf_name, *args):
             MODULE_PATH / file_name,
             wf_name,
             *args,
-        ]
+        ],
+        capture_output=True,  # Capture the output streams
+        text=True,  # Return outputs as strings (not bytes)
     )
-    assert out.returncode == 0
+    assert out.returncode == 0, (f"Command failed with return code {out.returncode}.\n"
+                                 f"Standard Output: {out.stdout}\n"
+                                 f"Standard Error: {out.stderr}\n")
+
+    match = re.search(r'executions/([a-zA-Z0-9]+)', out.stdout)
+    if match:
+        execution_id = match.group(1)
+        return execution_id
+
+    return "Unknown"
 
 
 def test_remote_run():
@@ -93,7 +105,28 @@ def test_remote_run():
 
     # run twice to make sure it will register a new version of the workflow.
     run("default_lp.py", "my_wf")
-    run("default_lp.py", "my_wf")
+
+
+def test_generic_idl_flytetypes():
+    os.environ["FLYTE_USE_OLD_DC_FORMAT"] = "true"
+    # default inputs for flyte types in dataclass
+    execution_id = run("generic_idl_flytetypes.py", "wf")
+    remote = FlyteRemote(Config.auto(config_file=CONFIG), PROJECT, DOMAIN)
+    execution = remote.fetch_execution(name=execution_id)
+    execution = remote.wait(execution=execution, timeout=datetime.timedelta(minutes=5))
+    print("Execution Error:", execution.error)
+    assert execution.closure.phase == WorkflowExecutionPhase.SUCCEEDED, f"Execution failed with phase: {execution.closure.phase}"
+    os.environ["FLYTE_USE_OLD_DC_FORMAT"] = "false"
+
+
+def test_msgpack_idl_flytetypes():
+    # default inputs for flyte types in dataclass
+    execution_id = run("msgpack_idl_flytetypes.py", "wf")
+    remote = FlyteRemote(Config.auto(config_file=CONFIG), PROJECT, DOMAIN)
+    execution = remote.fetch_execution(name=execution_id)
+    execution = remote.wait(execution=execution, timeout=datetime.timedelta(minutes=5))
+    print("Execution Error:", execution.error)
+    assert execution.closure.phase == WorkflowExecutionPhase.SUCCEEDED, f"Execution failed with phase: {execution.closure.phase}"
 
 
 def test_fetch_execute_launch_plan(register):
@@ -736,7 +769,6 @@ def test_execute_workflow_remote_fn_with_maptask():
     )
     assert out.outputs["o0"] == [4, 5, 6]
 
-
 def test_register_wf_fast(register):
     from workflows.basic.subworkflows import parent_wf
 

tests/flytekit/integration/remote/workflows/basic/generic_idl_flytetypes.py

@@ -0,0 +1,48 @@
+import typing
+import os
+from dataclasses import dataclass, fields, field
+from typing import Dict, List
+from flytekit.types.file import FlyteFile
+from flytekit.types.structured import StructuredDataset
+from flytekit.types.directory import FlyteDirectory
+from flytekit import task, workflow, ImageSpec
+import datetime
+from enum import Enum
+import pandas as pd
+
+@dataclass
+class DC:
+    ff: FlyteFile
+    sd: StructuredDataset
+    fd: FlyteDirectory
+
+
+@task
+def t1(dc: DC = DC(ff=FlyteFile(os.path.realpath(__file__)),
+                   sd=StructuredDataset(
+                       uri="tests/flytekit/integration/remote/workflows/basic/data/df.parquet",
+                       file_format="parquet"),
+                    fd=FlyteDirectory("tests/flytekit/integration/remote/workflows/basic/data/")
+                   )):
+
+    with open(dc.ff, "r") as f:
+        print("File Content: ", f.read())
+
+    print("sd:", dc.sd.open(pd.DataFrame).all())
+
+    df_path = os.path.join(dc.fd.path, "df.parquet")
+    print("fd: ", os.path.isdir(df_path))
+
+    return dc
+
+@workflow
+def wf(dc: DC = DC(ff=FlyteFile(os.path.realpath(__file__)),
+                   sd=StructuredDataset(
+                       uri="tests/flytekit/integration/remote/workflows/basic/data/df.parquet",
+                       file_format="parquet"),
+                    fd=FlyteDirectory("tests/flytekit/integration/remote/workflows/basic/data/")
+                   )):
+    t1(dc=dc)
+
+if __name__ == "__main__":
+    wf()

tests/flytekit/integration/remote/workflows/basic/msgpack_idl_flytetypes.py

@@ -0,0 +1,48 @@
+import typing
+import os
+from dataclasses import dataclass, fields, field
+from typing import Dict, List
+from flytekit.types.file import FlyteFile
+from flytekit.types.structured import StructuredDataset
+from flytekit.types.directory import FlyteDirectory
+from flytekit import task, workflow, ImageSpec
+import datetime
+from enum import Enum
+import pandas as pd
+
+@dataclass
+class DC:
+    ff: FlyteFile
+    sd: StructuredDataset
+    fd: FlyteDirectory
+
+
+@task
+def t1(dc: DC = DC(ff=FlyteFile(os.path.realpath(__file__)),
+                   sd=StructuredDataset(
+                       uri="tests/flytekit/integration/remote/workflows/basic/data/df.parquet",
+                       file_format="parquet"),
+                    fd=FlyteDirectory("tests/flytekit/integration/remote/workflows/basic/data/")
+                   )):
+
+    with open(dc.ff, "r") as f:
+        print("File Content: ", f.read())
+
+    print("sd:", dc.sd.open(pd.DataFrame).all())
+
+    df_path = os.path.join(dc.fd.path, "df.parquet")
+    print("fd: ", os.path.isdir(df_path))
+
+    return dc
+
+@workflow
+def wf(dc: DC = DC(ff=FlyteFile(os.path.realpath(__file__)),
+                   sd=StructuredDataset(
+                       uri="tests/flytekit/integration/remote/workflows/basic/data/df.parquet",
+                       file_format="parquet"),
+                    fd=FlyteDirectory("tests/flytekit/integration/remote/workflows/basic/data/")
+                   )):
+    t1(dc=dc)
+
+if __name__ == "__main__":
+    wf()