Delete dataset_card.yaml

dataset_card.yaml

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-dataset_name: hlo-feature-dataset
-pretty_name: HLO Feature Dataset for Deep Learning Resource Estimation
-dataset_type: graph-and-tabular
-license: MIT
-task_categories:
-- time-series-forecasting
-- regression
-- graph-machine-learning
-language: en
-tags:
-- HPC
-- resource-prediction
-- XLA
-- compiler-features
-- deep-learning
-- graph-learning
-- scheduling
-size_categories:
-- 1K<n<10K
-source_datasets:
-- custom
-dataset_summary: The HLO Feature Dataset contains High-Level Optimizer (HLO) graph
-  features and metadata extracted from deep learning training workloads. It is designed
-  for tasks such as runtime prediction, resource estimation, and graph-based machine
-  learning in HPC environments.
-structured_data:
-  features:
-  - name: name
-    type: string
-    description: ''
-  - name: samples
-    type: float
-    description: ''
-  - name: input_dim_w
-    type: float
-    description: ''
-  - name: input_dim_h
-    type: float
-    description: ''
-  - name: input_dim_c
-    type: float
-    description: ''
-  - name: output_dim
-    type: float
-    description: ''
-  - name: optimizer
-    type: string
-    description: ''
-  - name: epochs
-    type: float
-    description: ''
-  - name: batch
-    type: float
-    description: ''
-  - name: learn_rate
-    type: float
-    description: ''
-  - name: tf_version
-    type: string
-    description: ''
-  - name: cuda_version
-    type: string
-    description: ''
-  - name: batch_time
-    type: float
-    description: ''
-  - name: epoch_time
-    type: float
-    description: ''
-  - name: fit_time
-    type: float
-    description: ''
-  - name: npz_path
-    type: string
-    description: ''
-  - name: gpu_make
-    type: string
-    description: ''
-  - name: gpu_name
-    type: string
-    description: ''
-  - name: gpu_arch
-    type: string
-    description: ''
-  - name: gpu_cc
-    type: string
-    description: ''
-  - name: gpu_core_count
-    type: string
-    description: ''
-  - name: gpu_sm_count
-    type: string
-    description: ''
-  - name: gpu_memory_size
-    type: string
-    description: ''
-  - name: gpu_memory_type
-    type: string
-    description: ''
-  - name: gpu_memory_bw
-    type: string
-    description: ''
-  - name: gpu_tensor_core_count
-    type: string
-    description: ''
-  - name: max_memory_util
-    type: float
-    description: ''
-  - name: avg_memory_util
-    type: float
-    description: ''
-  - name: max_gpu_util
-    type: string
-    description: ''
-  - name: avg_gpu_util
-    type: string
-    description: ''
-  - name: max_gpu_temp
-    type: string
-    description: ''
-  - name: avg_gpu_temp
-    type: string
-    description: ''
-graph_data:
-  node_features: node_feat
-  edge_index: edge_index
-  additional_keys:
-  - node_opcode
-  - node_config_ids
-  - node_splits
-usage_example: '```python
-
-  from datasets import load_dataset
-
-  import numpy as np
-
-
-  dataset = load_dataset(''your-username/hlo-feature-dataset'')
-
-  sample = dataset[''train''][0]
-
-
-  graph_data = np.load(sample[''npz_path''])
-
-  node_features = graph_data[''node_feat'']
-
-  edges = graph_data[''edge_index'']
-
-  ```'
-citation: "@misc{hlofeatures2025,\n  title={HLO Feature Dataset for AI Resource Estimation},\n\
-  \  author={Your Name},\n  year={2025},\n  url={https://huggingface.co/datasets/your-username/hlo-feature-dataset}\n\
-  }"