Database for AI powered by a storage format optimized for deep-learning applications.
75
Evaluation — 75%
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{
"context": "This evaluation assesses the engineer's proficiency in using Deep Lake's data import and export capabilities. The focus is on correct usage of Deep Lake's from_csv(), from_parquet(), and to_csv() functions to handle data ingestion and export operations.",
"type": "weighted_checklist",
"checklist": [
{
"name": "CSV Import Usage",
"description": "Uses deeplake.from_csv() to import CSV data. The function should be called with the CSV file path and output dataset path parameters.",
"max_score": 30
},
{
"name": "Parquet Import Usage",
"description": "Uses deeplake.from_parquet() to import Parquet data. The function should be called with the Parquet file path and output dataset path parameters.",
"max_score": 30
},
{
"name": "CSV Export Usage",
"description": "Uses dataset.to_csv() method to export dataset to CSV format. Should open the dataset first using deeplake.open() or deeplake.open_read_only(), then call to_csv() with the output path.",
"max_score": 25
},
{
"name": "Dataset Opening",
"description": "Uses deeplake.open() or deeplake.open_read_only() to open existing datasets before performing operations on them.",
"max_score": 15
}
]
}Install with Tessl CLI
npx tessl i tessl/pypi-deeplakedocs
evals
scenario-1
scenario-2
scenario-3
scenario-4
scenario-5
scenario-6
scenario-7
scenario-8
scenario-9
scenario-10