From c7a9e2eb7654da4f91c4c32ec28cd3f7f1dc44e3 Mon Sep 17 00:00:00 2001 From: Robbe Sneyders Date: Tue, 12 Dec 2023 00:10:07 +0100 Subject: [PATCH] Update evaluate ragas component as reusable component --- .coverage.robbe-XPS-13-9305.45948.640622 | Bin 0 -> 53248 bytes .fondant/compose.yaml | 3 + .fondant/sagemaker-pipeline.yaml | 1 + .../Dockerfile | 13 +- components/evaluate_ragas/README.md | 55 +++++++ .../fondant_component.yaml | 16 +- components/evaluate_ragas/requirements.txt | 1 + .../src/main.py | 10 +- .../tests/component_test.py} | 10 +- .../tests/pytest.ini | 0 .../tests/requirements.txt | 0 .../retriever_eval_ragas/requirements.txt | 5 - .../1c3f24802e87ed0d7c46242e1f9dbc2f.txt | 1 + .../6d120fd3dd8ec8f882b7a3c91d112b9c.txt | 1 + .../f566da7d30e5bc64cec6144cc2a57c27.txt | 1 + .../download_images/manifest.json | 46 ++++++ .../download_images/part.0.parquet | Bin 0 -> 173286 bytes .../download_images/part.1.parquet | Bin 0 -> 367487 bytes .../download_images/part.2.parquet | 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+services: {} +version: '3.8' diff --git a/.fondant/sagemaker-pipeline.yaml b/.fondant/sagemaker-pipeline.yaml new file mode 100644 index 000000000..7daacd5db --- /dev/null +++ b/.fondant/sagemaker-pipeline.yaml @@ -0,0 +1 @@ +foo: bar \ No newline at end of file diff --git a/components/retriever_eval_ragas/Dockerfile b/components/evaluate_ragas/Dockerfile similarity index 63% rename from components/retriever_eval_ragas/Dockerfile rename to components/evaluate_ragas/Dockerfile index 35977190d..a5c3fa17a 100644 --- a/components/retriever_eval_ragas/Dockerfile +++ b/components/evaluate_ragas/Dockerfile @@ -9,15 +9,20 @@ RUN apt-get update && \ COPY requirements.txt / RUN pip3 install --no-cache-dir -r requirements.txt -# Set the working directory to the component folder -WORKDIR /component/src +# Install Fondant +# This is split from other requirements to leverage caching +ARG FONDANT_VERSION=main +RUN pip3 install fondant[component,aws,azure,gcp]@git+https://github.com/ml6team/fondant@${FONDANT_VERSION} -# Copy over src-files -COPY src/ . +# Set the working directory to the component folder +WORKDIR /component +COPY src/ src/ FROM base as test COPY tests/ tests/ RUN pip3 install --no-cache-dir -r tests/requirements.txt +ARG OPENAI_KEY +ENV OPENAI_KEY=${OPENAI_KEY} RUN python -m pytest tests FROM base diff --git a/components/evaluate_ragas/README.md b/components/evaluate_ragas/README.md new file mode 100644 index 000000000..c18a89002 --- /dev/null +++ b/components/evaluate_ragas/README.md @@ -0,0 +1,55 @@ +# retriever_eval_ragas + +### Description +Component that evaluates the retriever using RAGAS + +### Inputs / outputs + +**This component consumes:** + +- text: string +- retrieved_chunks: list + +**This component produces no data.** + +### Arguments + +The component takes the following arguments to alter its behavior: + +| argument | type | description | default | +| -------- | ---- | ----------- | ------- | +| module | str | Module from which the LLM is imported. Defaults to langchain.llms | langchain.llms | +| llm_name | str | Name of the selected llm | / | +| llm_kwargs | dict | Arguments of the selected llm | / | +| metrics | list | RAGAS metrics to compute | / | + +### Usage + +You can add this component to your pipeline using the following code: + +```python +from fondant.pipeline import Pipeline + + +pipeline = Pipeline(...) + +dataset = pipeline.read(...) + +dataset = dataset.apply( + "evaluate_ragas", + arguments={ + # Add arguments + # "module": "langchain.llms", + # "llm_name": , + # "llm_kwargs": {}, + # "metrics": [], + } +) +``` + +### Testing + +You can run the tests using docker with BuildKit. From this directory, run: +``` +docker build . --target test +``` diff --git a/components/retriever_eval_ragas/fondant_component.yaml b/components/evaluate_ragas/fondant_component.yaml similarity index 67% rename from components/retriever_eval_ragas/fondant_component.yaml rename to components/evaluate_ragas/fondant_component.yaml index d25404fc6..8afb55de1 100644 --- a/components/retriever_eval_ragas/fondant_component.yaml +++ b/components/evaluate_ragas/fondant_component.yaml @@ -1,12 +1,11 @@ -#metadata: to be matched w/ docker image name: retriever_eval_ragas description: Component that evaluates the retriever using RAGAS -image: ghcr.io/ml6team/retriever_eval:dev +image: fndnt/retriever_eval:dev tags: - - Data writing + - Text processing consumes: - text: #TODO: same as previous component produces + text: type: string retrieved_chunks: type: array @@ -14,11 +13,10 @@ consumes: type: string produces: - #TODO: add/retrieve chosen metrics to compute - context_precision: - type: float32 - context_relevancy: - type: float32 + additionalProperties: true + # Overwrite with metrics to be computed by ragas + # (https://docs.ragas.io/en/latest/concepts/metrics/index.html) + args: module: diff --git a/components/evaluate_ragas/requirements.txt b/components/evaluate_ragas/requirements.txt new file mode 100644 index 000000000..64c1d6905 --- /dev/null +++ b/components/evaluate_ragas/requirements.txt @@ -0,0 +1 @@ +ragas==0.0.21 \ No newline at end of file diff --git a/components/retriever_eval_ragas/src/main.py b/components/evaluate_ragas/src/main.py similarity index 91% rename from components/retriever_eval_ragas/src/main.py rename to components/evaluate_ragas/src/main.py index 5c655e078..b2295ed90 100644 --- a/components/retriever_eval_ragas/src/main.py +++ b/components/evaluate_ragas/src/main.py @@ -1,3 +1,5 @@ +import typing as t + import pandas as pd from datasets import Dataset from fondant.component import PandasTransformComponent @@ -12,7 +14,7 @@ def __init__( module: str, llm_name: str, llm_kwargs: dict, - metrics: list, + produces: t.Dict[str, t.Any], **kwargs, ) -> None: """ @@ -20,7 +22,7 @@ def __init__( module: Module from which the LLM is imported. Defaults to langchain.llms llm_name: Name of the selected llm llm_kwargs: Arguments of the selected llm - metrics: RAGAS metrics to compute. + produces: RAGAS metrics to compute. kwargs: Unhandled keyword arguments passed in by Fondant. """ self.llm = self.extract_llm( @@ -29,7 +31,9 @@ def __init__( model_kwargs=llm_kwargs, ) self.gpt_wrapper = LangchainLLM(llm=self.llm) - self.metric_functions = self.extract_metric_functions(metrics=metrics) + self.metric_functions = self.extract_metric_functions( + metrics=list(produces.keys()), + ) self.set_llm(self.metric_functions) # import the metric functions selected diff --git a/components/retriever_eval_ragas/tests/retriever_eval_test.py b/components/evaluate_ragas/tests/component_test.py similarity index 96% rename from components/retriever_eval_ragas/tests/retriever_eval_test.py rename to components/evaluate_ragas/tests/component_test.py index 7f25cba7f..3a2f79be4 100644 --- a/components/retriever_eval_ragas/tests/retriever_eval_test.py +++ b/components/evaluate_ragas/tests/component_test.py @@ -1,4 +1,7 @@ +import os + import pandas as pd +import pyarrow as pa from main import RetrieverEval @@ -49,8 +52,11 @@ def test_transform(): component = RetrieverEval( module="langchain.llms", llm_name="OpenAI", - llm_kwargs={"openai_api_key": ""}, - metrics=["context_precision", "context_relevancy"], + llm_kwargs={"openai_api_key": os.environ["OPENAI_KEY"]}, + produces={ + "context_precision": pa.float32(), + "context_relevancy": pa.float32(), + }, ) output_dataframe = component.transform(input_dataframe) diff --git a/components/retriever_eval_ragas/tests/pytest.ini b/components/evaluate_ragas/tests/pytest.ini similarity index 100% rename from components/retriever_eval_ragas/tests/pytest.ini rename to components/evaluate_ragas/tests/pytest.ini diff --git a/components/retriever_eval_ragas/tests/requirements.txt b/components/evaluate_ragas/tests/requirements.txt similarity index 100% rename from components/retriever_eval_ragas/tests/requirements.txt rename to components/evaluate_ragas/tests/requirements.txt diff --git a/components/retriever_eval_ragas/requirements.txt b/components/retriever_eval_ragas/requirements.txt deleted file mode 100644 index d519950fe..000000000 --- a/components/retriever_eval_ragas/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -ragas==0.0.20 -transformers==4.35.2 -torch==2.1.1 -torchaudio==2.1.1 -torchvision==0.16.1 \ No newline at end of file diff --git a/data/creative_commons_pipline/cache/1c3f24802e87ed0d7c46242e1f9dbc2f.txt b/data/creative_commons_pipline/cache/1c3f24802e87ed0d7c46242e1f9dbc2f.txt new file mode 100644 index 000000000..1a285361f --- /dev/null +++ b/data/creative_commons_pipline/cache/1c3f24802e87ed0d7c46242e1f9dbc2f.txt @@ -0,0 +1 @@ +/data/creative_commons_pipline/creative_commons_pipline-20231206224045/filter_languages/manifest.json \ No newline at end of file diff --git a/data/creative_commons_pipline/cache/6d120fd3dd8ec8f882b7a3c91d112b9c.txt b/data/creative_commons_pipline/cache/6d120fd3dd8ec8f882b7a3c91d112b9c.txt new file mode 100644 index 000000000..2fa036091 --- /dev/null +++ b/data/creative_commons_pipline/cache/6d120fd3dd8ec8f882b7a3c91d112b9c.txt @@ -0,0 +1 @@ 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It will introduce the concepts and clarify +the trade-offs made. + +## High level overview + +Fondant has been designed with composability of the pipelines and reusability of the components +as a goal. It achieves this in two ways: +- Datasets as the interface between components +- Components designed for reusability + +![Main pipeline](./art/design/pipeline_main.png) + +### Datasets as the interface between components + +Datasets as the interface between components means that each component produces a dataset which +is consumed by the next component. These datasets follow a prescribed format, which makes it +easier for components to work with datasets produced by different components. + +The datasets are written to storage and described by a manifest. Only this manifest is passed +between the components. The manifest describes what the dataset looks like and where it can be +found. + +![Pipeline with manifests](./art/design/pipeline_manifest.png) + +### Components designed for reusability + +Each component is described by a specification, which describes the format of the dataset it +produces, and which format of dataset it can consume. This allows static analysis of the +compatibility of two components and increases the composibility of the pipeline. + +Finally, each component is implemented as a docker container, which makes them easy to +distribute and orchestrate. + +![Pipeline with docker components](./art/design/pipeline_component.png) + +## Datasets + +!!! warning "This is the to-be state" + + We are simplifying our old dataset design to the one described here. You can follow the + progress in https://github.com/ml6team/fondant/issues/564 + +As mentioned above, each fondant component produces a dataset and passes a manifest describing +the dataset to the next component. The datasets are designed to make this as efficient as possible: +- Only the diff between each dataset is stored +- The data can be efficiently queried both row- and column-based + +This approach has a couple of benefits: +- Components only load the data they use, and only write the data they change +- Data can be processed partition-wise, which allows processing of larger-than-memory datasets +- Filtering is cheap since it only needs to write the updated index +- Fondant keeps a snapshot of the data between each pipeline step, which enables: + - Built-in lineage and tracking data entries through the pipeline + - Rerunning a pipeline from any step (caching) + - Inspecting the data at each stage throughout your pipeline + +### Datasets throughout a pipeline + +It's easiest to explain this using an example. Let's envision a pipeline with the following steps: +1. `load_image_urls`, which downloads a dataset containing image urls +2. `download_images`, which downloads the images from the url and extracts their width and height +3. `filter_resolution`, which filters the images based on their resolution +4. `process_images`, which applies some aesthetic processing to the images + +and see how the datasets evolve throughout the dataset. + +#### 1. `load_image_urls` + +The first component writes its data to a component specific path. the dataset produced by the +first component thus contains an `index` and an `image_url` field: + +| /load_image_urls | +|------------------| +| - index | +| - image_urls | + +#### 2. `download_images` + +The second component also writes its output to a component specific path. It +contains the `index`, `image`, `width`, and `height` field: + +| /load_image_urls | /download_images | _dataset_ | +|------------------|------------------|-----------| +| - index | `- index` | <-- | +| `- image_urls` | | <-- | +| | `- image` | <-- | +| | `- width` | <-- | +| | `- height` | <-- | + +The dataset not only contains the data written by the latest component though. It provides a +view on the written data looking back through the pipeline. The dataset after the second +component will thus contain the highlighted fields from the different components. + +#### 3. `filter_resolution` + +The third component only filters the data and only writes the filtered index. + +| /load_image_urls | /download_images | /filter_resolution | _dataset_ | +|------------------|------------------|--------------------|-----------| +| - index | - index | `- index` | <-- | +| `- image_urls` | | | <-- | +| | `- image` | | <-- | +| | `- width` | | <-- | +| | `- height` | | <-- | + +The dataset will use the last index and join the data from previous components using this index. +The dataset will therefore only contain the filtered data of all fields. + +#### 4. `image_processing` + +The 4th component processes the image data. It writes the new processed image data with the +index. Since the component starts from the output of the `filter_resolution` component, and +doesn't make any changes to the index, the index will stay the same. The index always needs to +be written however since it's needed to merge data from the different components. + +| /load_image_urls | /download_images | /filter_resolution | /image_processing | _dataset_ | +|------------------|------------------|--------------------|-------------------|-----------| +| - index | - index | - index | `- index` | <-- | +| `- image_urls` | | | | <-- | +| | - image | | `- image` | <-- | +| | `- width` | | | <-- | +| | `- height` | | | <-- | + +The dataset again provides a view on the written data looking back through the pipeline. The +dataset after the fourth component will thus contain the following fields: +- `index` from `/image_processing` +- `image_urls` from `/load_image_urls` +- `image` from `/image_processing` +- `width` from `/download_images` +- `height` from `/download_images` + +#### Conclusion + +This shows how each component only writes the data they change, and how only the diff between each +dataset is stored. Note that we can still reproduce each intermediate dataset of the pipeline, +which allows some of the key features mentioned above: +- Built-in lineage and tracking data entries through the pipeline +- Rerunning a pipeline from any step (caching) +- Inspecting the data at each stage throughout your pipeline + +### Parquet + +To understand how the other features are achieved, we have to look at the format used to store +the data, which is `parquet`. + +## Manifest + +## Component specification + +## Component + +## FAQ \ No newline at end of file diff --git a/examples/read_chunk_write/.fondant/compose.yaml b/examples/read_chunk_write/.fondant/compose.yaml new file mode 100644 index 000000000..4a786eea3 --- /dev/null +++ b/examples/read_chunk_write/.fondant/compose.yaml @@ -0,0 +1,114 @@ +name: example-pipeline +services: + chunk_text: + build: + args: [] + context: /home/robbe/workspace/fondant/src/fondant/components/chunk_text + command: + - --metadata + - '{"base_path": "/data", "pipeline_name": "example-pipeline", "run_id": "example-pipeline-20231205131003", + "component_id": "chunk_text", "cache_key": "8d8b9109a925a8c50bafdba72d791d20"}' + - --output_manifest_path + - /data/example-pipeline/example-pipeline-20231205131003/chunk_text/manifest.json + - --chunk_size + - '10' + - --chunk_overlap + - '2' + - --cache + - 'True' + - --cluster_type + - default + - --component_spec + - '{"name": "Chunk text", "description": "Component that chunks text into smaller + segments \n\nThis component takes a body of text and chunks into small chunks. + The id of the returned dataset\nconsists of the id of the original document + followed by the chunk index.\n", "image": "fndnt/chunk_text:dev", "tags": ["Text + processing"], "consumes": {"text": {"type": "string"}}, "produces": {"text": + {"type": "string"}, "original_document_id": {"type": "string"}}, "args": {"chunk_size": + {"description": "Maximum size of chunks to return", "type": "int"}, "chunk_overlap": + {"description": "Overlap in characters between chunks", "type": "int"}}}' + - --input_manifest_path + - /data/example-pipeline/example-pipeline-20231205131003/load_from_parquet/manifest.json + depends_on: + load_from_parquet: + condition: service_completed_successfully + labels: + pipeline_description: null + ports: + - 8787:8787 + volumes: + - source: /home/robbe/workspace/fondant/examples/read_chunk_write/data + target: /data + type: bind + load_from_parquet: + command: + - --metadata + - '{"base_path": "/data", "pipeline_name": "example-pipeline", "run_id": "example-pipeline-20231205131003", + "component_id": "load_from_parquet", "cache_key": "52dbe6ceffb5d6e7a3751d6518fc5761"}' + - --output_manifest_path + - /data/example-pipeline/example-pipeline-20231205131003/load_from_parquet/manifest.json + - --dataset_uri + - /data/input/sample.parquet + - --n_rows_to_load + - '5' + - --cache + - 'True' + - --cluster_type + - default + - --component_spec + - '{"name": "Load from parquet", "description": "Component that loads a dataset + from a parquet uri", "image": "fndnt/load_from_parquet:dev", "produces": {"text": + {"type": "string"}}, "args": {"dataset_uri": {"description": "The remote path + to the parquet file/folder containing the dataset", "type": "str"}, "column_name_mapping": + {"description": "Mapping of the consumed dataset", "type": "dict", "default": + {}}, "n_rows_to_load": {"description": "Optional argument that defines the number + of rows to load. Useful for testing pipeline runs on a small scale", "type": + "int"}, "index_column": {"description": "Column to set index to in the load + component, if not specified a default globally unique index will be set", "type": + "str", "default": "None"}}}' + depends_on: {} + image: fndnt/load_from_parquet:dev + labels: + pipeline_description: null + ports: + - 8787:8787 + volumes: + - source: /home/robbe/workspace/fondant/examples/read_chunk_write/data + target: /data + type: bind + write_to_parquet: + build: + args: [] + context: components/write_to_parquet + command: + - --metadata + - '{"base_path": "/data", "pipeline_name": "example-pipeline", "run_id": "example-pipeline-20231205131003", + "component_id": "write_to_parquet", "cache_key": "707e4cf9a9056104034133b41df35b82"}' + - --output_manifest_path + - /data/example-pipeline/example-pipeline-20231205131003/write_to_parquet/manifest.json + - --output_path + - /data/output + - --cache + - 'True' + - --cluster_type + - default + - --component_spec + - '{"name": "Write to parquet", "description": "Component that writes to a parquet + uri", "image": ":dev", "consumes": {"text": {"type": "string"}, "original_document_id": + {"type": "string"}}, "args": {"output_path": {"description": "The path to write + the dataset to", "type": "str"}, "file_name": {"description": "The name to assign + to the written file(s)", "type": "str", "default": "data"}}}' + - --input_manifest_path + - /data/example-pipeline/example-pipeline-20231205131003/chunk_text/manifest.json + depends_on: + chunk_text: + condition: service_completed_successfully + labels: + pipeline_description: null + ports: + - 8787:8787 + volumes: + - source: /home/robbe/workspace/fondant/examples/read_chunk_write/data + target: /data + type: bind +version: '3.8' diff --git a/interface.py b/interface.py new file mode 100644 index 000000000..a98f4c268 --- /dev/null +++ b/interface.py @@ -0,0 +1,38 @@ +import typing as t + + +class Dataset: + + def __init__(self, pipeline: 'Pipeline') -> None: + self.pipeline = pipeline + + def apply(self, component_op): + return self.pipeline.apply(component_op, self) + + def write(self, component_op): + self.pipeline.apply(component_op, self) + + +class Pipeline: + + def __init__( + self, + base_path: str, + name: str, + description: t.Optional[str] = None, + ): + self.base_path = base_path + self.name = name + self.description = description + + self._graph = {} + + def apply(self, component_op, dataset: t.Optional[Dataset] = None) -> Dataset: + result = Dataset(self) + self._graph[result.id] = (component_op, []) + + def read(self, component_op): + return self.apply(component_op) + + def run(self): + pass diff --git a/interface2.py b/interface2.py new file mode 100644 index 000000000..e35bd892a --- /dev/null +++ b/interface2.py @@ -0,0 +1,36 @@ +from fondant.pipeline import Pipeline +import pyarrow as pa + + +pipeline = Pipeline("my-pipe", base_path="/base_path") + +dataset = pipeline.read( + "read_component", + arguments={}, + produces={ + "image": pa.binary() + } +) + +dataset = dataset.apply( + "generic_transform", + arguments={}, + consumes={ + "image": pa.binary() + }, + produces={ + "caption": pa.string() + } +) + +dataset = dataset.rename( + caption="text", +) + +dataset.write( + "write_component", + consumes={ + "text": pa.string(), + "image": pa.binary(), + } +) \ No newline at end of file diff --git a/new_spec b/new_spec new file mode 100644 index 000000000..4df1564e1 --- /dev/null +++ b/new_spec @@ -0,0 +1 @@ +{"components": {"comp-example-component": {"executorLabel": "exec-example-component", "inputDefinitions": {"parameters": {"input_manifest_path": {"parameterType": "STRING", "description": "Path to the input manifest", "isOptional": true}, "component_spec": {"parameterType": "STRUCT", "description": "The component specification as a dictionary"}, "input_partition_rows": {"parameterType": "NUMBER_INTEGER", "description": "The number of rows to load per partition. Set to override the automatic partitioning", "isOptional": true}, "cache": {"parameterType": "BOOLEAN", "description": "Set to False to disable caching, True by default.", "defaultValue": true}, "cluster_type": {"parameterType": "STRING", "description": "The cluster type to use for the execution", "defaultValue": "default"}, "client_kwargs": {"parameterType": "STRUCT", "description": "Keyword arguments to pass to the Dask client", "defaultValue": {}}, "metadata": {"parameterType": "STRING", "description": "Metadata arguments containing the run id and base path"}, "output_manifest_path": {"parameterType": "STRING", "description": "Path to the output manifest"}, "consumes": {"parameterType": "STRUCT", "description": "A mapping to update the fields consumed by the operation as defined in the component spec. The keys are the names of the fields to be received by the component, while the values are the type of the field, or the name of the field to map from the input dataset.", "defaultValue": {}}, "produces": {"parameterType": "STRUCT", "description": "A mapping to update the fields produced by the operation as defined in the component spec. The keys are the names of the fields to be produced by the component, while the values are the type of the field, or the name that should be used to write the field to the output dataset.", "defaultValue": {}}, "storage_args": {"parameterType": "STRING", "description": "Storage arguments"}}}}}, "deploymentSpec": {"executors": {"exec-example-component": {"container": {"command": ["fondant", "execute", "main"], "image": "example_component:latest"}}}}, "pipelineInfo": {"name": "example-component"}, "root": {"dag": {"tasks": {"example-component": {"cachingOptions": {"enableCache": true}, "componentRef": {"name": "comp-example-component"}, "inputs": {"parameters": {"input_manifest_path": {"componentInputParameter": "input_manifest_path"}, "component_spec": {"componentInputParameter": "component_spec"}, "input_partition_rows": {"componentInputParameter": "input_partition_rows"}, "cache": {"componentInputParameter": "cache"}, "cluster_type": {"componentInputParameter": "cluster_type"}, "client_kwargs": {"componentInputParameter": "client_kwargs"}, "metadata": {"componentInputParameter": "metadata"}, "output_manifest_path": {"componentInputParameter": "output_manifest_path"}, "consumes": {"componentInputParameter": "consumes"}, "produces": {"componentInputParameter": "produces"}, "storage_args": {"componentInputParameter": "storage_args"}}}, "taskInfo": {"name": "example-component"}}}}, "inputDefinitions": {"parameters": {"input_manifest_path": {"parameterType": "STRING", "description": "Path to the input manifest", "isOptional": true}, "component_spec": {"parameterType": "STRUCT", "description": "The component specification as a dictionary"}, "input_partition_rows": {"parameterType": "NUMBER_INTEGER", "description": "The number of rows to load per partition. Set to override the automatic partitioning", "isOptional": true}, "cache": {"parameterType": "BOOLEAN", "description": "Set to False to disable caching, True by default.", "defaultValue": true}, "cluster_type": {"parameterType": "STRING", "description": "The cluster type to use for the execution", "defaultValue": "default"}, "client_kwargs": {"parameterType": "STRUCT", "description": "Keyword arguments to pass to the Dask client", "defaultValue": {}}, "metadata": {"parameterType": "STRING", "description": "Metadata arguments containing the run id and base path"}, "output_manifest_path": {"parameterType": "STRING", "description": "Path to the output manifest"}, "consumes": {"parameterType": "STRUCT", "description": "A mapping to update the fields consumed by the operation as defined in the component spec. The keys are the names of the fields to be received by the component, while the values are the type of the field, or the name of the field to map from the input dataset.", "defaultValue": {}}, "produces": {"parameterType": "STRUCT", "description": "A mapping to update the fields produced by the operation as defined in the component spec. The keys are the names of the fields to be produced by the component, while the values are the type of the field, or the name that should be used to write the field to the output dataset.", "defaultValue": {}}, "storage_args": {"parameterType": "STRING", "description": "Storage arguments"}}}}, "schemaVersion": "2.1.0", "sdkVersion": "kfp-2.0.1"} \ No newline at end of file diff --git a/pipeline.py b/pipeline.py new file mode 100644 index 000000000..e8a3ceff5 --- /dev/null +++ b/pipeline.py @@ -0,0 +1,37 @@ +from fondant.pipeline import Pipeline + +pipeline = Pipeline( + name="creative_commons_pipline", + base_path="./data" +) + +import pyarrow as pa + +dataset = pipeline.read( + "load_from_hf_hub", + arguments={ + "dataset_name": "fondant-ai/fondant-cc-25m", + "n_rows_to_load": 100, + }, + produces={ + "alt_text": pa.string(), + "image_url": pa.string(), + "license_location": pa.string(), + "license_type": pa.string(), + "webpage_url": pa.string(), + }, +) + +images = dataset.apply( + "download_images", +) + +english_images = images.apply( + "filter_language", + arguments={ + "language": "en" + }, + consumes={ + "text": "alt_text" + } +)