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artifacts = [
{ source = "/var/lib/are", service = "grader" },
]
[task]
name = "bespokelabs/cpu-llm-decode-throughput"
version = "1.1.0"
description = ""
keywords = [
"autoresearch",
"machine-learning",
"benchmark",
]
authors = [
{ name = "Bespoke Labs" },
]
[metadata]
category = "Model Training and ML Infrastructure"
subcategory = "Systems and Efficiency"
task_objective = [
"Optimize",
"Implement",
]
artifact_type = [
"Codebase",
"Single script or program",
]
expert_time_estimate_hours = 6.0
are_version = "1.1"
budget_seconds = 86400
[verifier]
timeout_sec = 600.0
environment_mode = "separate"
[[verifier.collect]]
service = "grader"
command = "/opt/are/venv/bin/python /opt/are/drain.py"
timeout_sec = 4800.0
[verifier.environment]
network_mode = "no-network"
build_timeout_sec = 600.0
cpus = 1
memory_mb = 1024
gpus = 0
[agent]
network_mode = "allowlist"
allowed_hosts = [
"api.openai.com",
"api.anthropic.com",
"grader",
]
timeout_sec = 86400.0
user = "agent"
[environment]
network_mode = "public"
build_timeout_sec = 3600.0
os = "linux"
cpus = 8
memory_mb = 14336
storage_mb = 153600
gpus = 0
mcp_servers = [
{ name = "are", transport = "streamable-http", url = "http://grader:8000/mcp" },
]
[environment.env]
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