add memray example

Signed-off-by: Jan Fiedler <[email protected]>

examples/memray_plugin/Dockerfile

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+FROM python:3.11-slim-bookworm
+LABEL org.opencontainers.image.source=https://github.com/flyteorg/flytesnacks
+
+WORKDIR /root
+ENV VENV /opt/venv
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONPATH /root
+
+WORKDIR /root
+
+ENV VENV /opt/venv
+# Virtual environment
+RUN python3 -m venv ${VENV}
+ENV PATH="${VENV}/bin:$PATH"
+
+# Install Python dependencies
+COPY requirements.in /root
+RUN pip install -r /root/requirements.in
+
+# Copy the actual code
+COPY . /root
+
+# This tag is supplied by the build script and will be used to determine the version
+# when registering tasks, workflows, and launch plans
+ARG tag
+ENV FLYTE_INTERNAL_IMAGE $tag

examples/memray_plugin/README.md

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+(memray_plugin)=
+
+# Memray Profiling
+
+```{eval-rst}
+.. tags:: Integration, Profiling, Observability
+```
+
+Memray tracks and reports memory allocations, both in python code and in compiled extension modules.
+This Memray Profiling plugin enables memory tracking on the Flyte task level and renders a memgraph profiling graph on Flyte Deck.
+
+First, install the Memray plugin:
+
+```bash
+pip install flytekitplugins-memray
+```
+
+```{auto-examples-toc}
+memray_example
+```

examples/memray_plugin/memray_plugin/memray_example.py

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+# %% [markdown]
+# (memray_example)=
+#
+# # Memray Profiling Example
+# Memray tracks and reports memory allocations, both in python code and in compiled extension modules.
+# This Memray Profiling plugin enables memory tracking on the Flyte task level and renders a memgraph profiling graph on Flyte Deck.
+# %%
+from flytekit import workflow, task, ImageSpec
+from flytekitplugins.memray import memray_profiling
+import time
+
+# %% [markdown]
+# First, we use `ImageSpec` to construct a container that contains the dependencies for the
+# tasks, we want to profile:
+# %%
+image = ImageSpec(
+    name="memray_demo",
+    packages=["flytekitplugins_memray"],
+    registry="<your_cr_registry>",
+)
+
+
+# %% [markdown]
+# Next, we define a dummy function that generates data in memory without releasing:
+# %%
+def generate_data(n: int):
+    leak_list = []
+    for _ in range(n):  # Arbitrary large number for demonstration
+        large_data = " " * 10**6  # 1 MB string
+        leak_list.append(large_data)  # Keeps appending without releasing
+        time.sleep(0.1)  # Slow down the loop to observe memory changes
+
+
+# %% [markdown]
+# Example of profiling the memory usage of `generate_data()` via the memray `table` html reporter
+# %%
+@task(container_image=image, enable_deck=True)
+@memray_profiling(memray_html_reporter="table")
+def memory_usage(n: int) -> str:
+    generate_data(n=n)
+
+    return "Well"
+
+
+# %% [markdown]
+# Example of profiling the memory leackage of `generate_data()` via the memray `flamegraph` html reporter
+# %%
+
+
+@task(container_image=image, enable_deck=True)
+@memray_profiling(trace_python_allocators=True, memray_reporter_args=["--leaks"])
+def memory_leakage(n: int) -> str:
+    generate_data(n=n)
+
+    return "Well"
+
+
+# %% [markdown]
+# Put everything together in a workflow.
+# %%
+
+
+@workflow
+def wf(n: int = 500):
+    memory_usage(n=n)
+    memory_leakage(n=n)

examples/memray_plugin/requirements.in

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+flytekitplugins-memray