{ "session_id": "20260519_164833_60a691", "model": "gpt-5.4-mini", "base_url": "https://api.nwafu-ai.cn/v1", "platform": "qqbot", "session_start": "2026-05-19T16:48:33.340405", "last_updated": "2026-05-19T16:52:18.278614", "system_prompt": "You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. You communicate clearly, admit uncertainty when appropriate, and prioritize being genuinely useful over being verbose unless otherwise directed below. Be targeted and efficient in your exploration and investigations.\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n\n\n\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n\n\n\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n\n\n\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n\n\n\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n\n\n\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n\n\nHost: Linux (6.8.0-101-generic)\nUser home directory: /home/ubuntu\nCurrent working directory: /home/ubuntu/.hermes/hermes-agent\n\nYou are on QQ, a popular Chinese messaging platform. QQ supports markdown formatting and emoji. You can send media files natively: include MEDIA:/absolute/path/to/file in your response. Images are sent as native photos, and other files arrive as downloadable documents.\n\nConversation started: Tuesday, May 19, 2026 04:48 PM\nModel: gpt-5.4-mini\nProvider: nwafu_backup", "tools": [ { "type": "function", "function": { "name": "mcp_excalidraw_align_elements", "description": "Align elements to a specific position", "parameters": { "type": "object", "properties": { "elementIds": { "type": "array", "items": { "type": "string" } }, "alignment": { "type": "string", "enum": [ "left", "center", "right", "top", "middle", "bottom" ] } }, "required": [ "elementIds", "alignment" ] } } }, { "type": "function", "function": { "name": "mcp_excalidraw_create_element", "description": "Create a new Excalidraw element", "parameters": { "type": "object", "properties": { "type": { "type": "string", "enum": [ "rectangle", "ellipse", "diamond", "arrow", "text", "label", "freedraw", "line", "arrowLabel" ] }, "x": { "type": "number" }, "y": { "type": "number" }, "width": { "type": "number" }, "height": { "type": "number" }, "backgroundColor": { "type": "string" }, "strokeColor": { "type": "string" }, "strokeWidth": { "type": "number" }, "roughness": { "type": "number" }, "opacity": { "type": "number" }, "text": { "type": "string" }, "fontSize": { "type": "number" }, "fontFamily": { "type": "string" } }, "required": [ "type", "x", "y" ] } } }, { "type": "function", "function": { "name": "mcp_excalidraw_delete_element", "description": "Delete an Excalidraw element", "parameters": { "type": "object", "properties": { "id": { "type": "string" } }, "required": [ "id" ] } } }, { "type": "function", "function": { "name": "mcp_excalidraw_distribute_elements", "description": "Distribute elements evenly", "parameters": { "type": "object", "properties": { "elementIds": { "type": "array", "items": { "type": "string" } }, "direction": { "type": "string", "enum": [ "horizontal", "vertical" ] } }, "required": [ "elementIds", "direction" ] } } }, { "type": "function", "function": { "name": "mcp_excalidraw_get_resource", "description": "Get an Excalidraw resource", "parameters": { "type": "object", "properties": { "resource": { "type": "string", "enum": [ "scene", "library", "theme", "elements" ] } }, "required": [ "resource" ] } } }, { "type": "function", "function": { "name": "mcp_excalidraw_group_elements", "description": "Group multiple elements together", "parameters": { "type": "object", "properties": { "elementIds": { "type": "array", "items": { "type": "string" } } }, "required": [ "elementIds" ] } } }, { "type": "function", "function": { "name": "mcp_excalidraw_lock_elements", "description": "Lock elements to prevent modification", "parameters": { "type": "object", "properties": { "elementIds": { "type": "array", "items": { "type": "string" } } }, "required": [ "elementIds" ] } } }, { "type": "function", "function": { "name": "mcp_excalidraw_query_elements", "description": "Query Excalidraw elements with optional filters", "parameters": { "type": "object", "properties": { "type": { "type": "string", "enum": [ "rectangle", "ellipse", "diamond", "arrow", "text", "label", "freedraw", "line", "arrowLabel" ] }, "filter": { "type": "object", "additionalProperties": true, "properties": {} } } } } }, { "type": "function", "function": { "name": "mcp_excalidraw_ungroup_elements", "description": "Ungroup a group of elements", "parameters": { "type": "object", "properties": { "groupId": { "type": "string" } }, "required": [ "groupId" ] } } }, { "type": "function", "function": { "name": "mcp_excalidraw_unlock_elements", "description": "Unlock elements to allow modification", "parameters": { "type": "object", "properties": { "elementIds": { "type": "array", "items": { "type": "string" } } }, "required": [ "elementIds" ] } } }, { "type": "function", "function": { "name": "mcp_excalidraw_update_element", "description": "Update an existing Excalidraw element", "parameters": { "type": "object", "properties": { "id": { "type": "string" }, "type": { "type": "string", "enum": [ "rectangle", "ellipse", "diamond", "arrow", "text", "label", "freedraw", "line", "arrowLabel" ] }, "x": { "type": "number" }, "y": { "type": "number" }, "width": { "type": "number" }, "height": { "type": "number" }, "backgroundColor": { "type": "string" }, "strokeColor": { "type": "string" }, "strokeWidth": { "type": "number" }, "roughness": { "type": "number" }, "opacity": { "type": "number" }, "text": { "type": "string" }, "fontSize": { "type": "number" }, "fontFamily": { "type": "string" } }, "required": [ "id" ] } } }, { "type": "function", "function": { "name": "mcp_tavily_get_prompt", "description": "Get a prompt by name from MCP server 'tavily'", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "Name of the prompt to retrieve" }, "arguments": { "type": "object", "description": "Optional arguments to pass to the prompt", "properties": {}, "additionalProperties": true } }, "required": [ "name" ] } } }, { "type": "function", "function": { "name": "mcp_tavily_list_prompts", "description": "List available prompts from MCP server 'tavily'", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "mcp_tavily_list_resources", "description": "List available resources from MCP server 'tavily'", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "mcp_tavily_read_resource", "description": "Read a resource by URI from MCP server 'tavily'", "parameters": { "type": "object", "properties": { "uri": { "type": "string", "description": "URI of the resource to read" } }, "required": [ "uri" ] } } }, { "type": "function", "function": { "name": "mcp_tavily_tavily_crawl", "description": "Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.", "parameters": { "additionalProperties": false, "properties": { "url": { "description": "The root URL to begin the crawl", "type": "string" }, "max_depth": { "default": 1, "description": "Max depth of the crawl. Defines how far from the base URL the crawler can explore.", "minimum": 1, "type": "integer" }, "max_breadth": { "default": 20, "description": "Max number of links to follow per level of the tree (i.e., per page)", "minimum": 1, "type": "integer" }, "limit": { "default": 50, "description": "Total number of links the crawler will process before stopping", "minimum": 1, "type": "integer" }, "instructions": { "default": "", "description": "Natural language instructions for the crawler. Instructions specify which types of pages the crawler should return.", "type": "string" }, "select_paths": { "default": [], "description": "Regex patterns to select only URLs with specific path patterns (e.g., /docs/.*, /api/v1.*)", "items": { "type": "string" }, "type": "array" }, "select_domains": { "default": [], "description": "Regex patterns to restrict crawling to specific domains or subdomains (e.g., ^docs\\.example\\.com$)", "items": { "type": "string" }, "type": "array" }, "allow_external": { "default": true, "description": "Whether to return external links in the final response", "type": "boolean" }, "extract_depth": { "default": "basic", "description": "Advanced extraction retrieves more data, including tables and embedded content, with higher success but may increase latency", "enum": [ "basic", "advanced" ], "type": "string" }, "format": { "default": "markdown", "description": "The format of the extracted web page content. markdown returns content in markdown format. text returns plain text and may increase latency.", "enum": [ "markdown", "text" ], "type": "string" }, "include_favicon": { "default": false, "description": "Whether to include the favicon URL for each result", "type": "boolean" } }, "required": [ "url" ], "type": "object" } } }, { "type": "function", "function": { "name": "mcp_tavily_tavily_extract", "description": "Extract content from URLs. Returns raw page content in markdown or text format.", "parameters": { "additionalProperties": false, "properties": { "urls": { "description": "List of URLs to extract content from", "items": { "type": "string" }, "type": "array" }, "extract_depth": { "default": "basic", "description": "Use 'advanced' for LinkedIn, protected sites, or tables/embedded content", "enum": [ "basic", "advanced" ], "type": "string" }, "include_images": { "default": false, "description": "Include images from pages", "type": "boolean" }, "format": { "default": "markdown", "description": "Output format", "enum": [ "markdown", "text" ], "type": "string" }, "include_favicon": { "default": false, "description": "Include favicon URLs", "type": "boolean" }, "query": { "default": "", "description": "Query to rerank content chunks by relevance", "type": "string" } }, "required": [ "urls" ], "type": "object" } } }, { "type": "function", "function": { "name": "mcp_tavily_tavily_map", "description": "Map a website's structure. Returns a list of URLs found starting from the base URL.", "parameters": { "additionalProperties": false, "properties": { "url": { "description": "The root URL to begin the mapping", "type": "string" }, "max_depth": { "default": 1, "description": "Max depth of the mapping. Defines how far from the base URL the crawler can explore", "minimum": 1, "type": "integer" }, "max_breadth": { "default": 20, "description": "Max number of links to follow per level of the tree (i.e., per page)", "minimum": 1, "type": "integer" }, "limit": { "default": 50, "description": "Total number of links the crawler will process before stopping", "minimum": 1, "type": "integer" }, "instructions": { "default": "", "description": "Natural language instructions for the crawler", "type": "string" }, "select_paths": { "default": [], "description": "Regex patterns to select only URLs with specific path patterns (e.g., /docs/.*, /api/v1.*)", "items": { "type": "string" }, "type": "array" }, "select_domains": { "default": [], "description": "Regex patterns to restrict crawling to specific domains or subdomains (e.g., ^docs\\.example\\.com$)", "items": { "type": "string" }, "type": "array" }, "allow_external": { "default": true, "description": "Whether to return external links in the final response", "type": "boolean" } }, "required": [ "url" ], "type": "object" } } }, { "type": "function", "function": { "name": "mcp_tavily_tavily_research", "description": "Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources, including web pages, documents, and other resources, to answer a question or complete a task. Returns a detailed response based on the research findings. Rate limit: 20 requests per minute.", "parameters": { "additionalProperties": false, "properties": { "input": { "description": "A comprehensive description of the research task", "type": "string" }, "model": { "default": "auto", "description": "Defines the degree of depth of the research. 'mini' is good for narrow tasks with few subtopics. 'pro' is good for broad tasks with many subtopics", "enum": [ "mini", "pro", "auto" ], "type": "string" } }, "required": [ "input" ], "type": "object" } } }, { "type": "function", "function": { "name": "mcp_tavily_tavily_search", "description": "Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.", "parameters": { "additionalProperties": false, "properties": { "query": { "description": "Search query", "type": "string" }, "max_results": { "default": 5, "description": "The maximum number of search results to return", "type": "integer" }, "search_depth": { "default": "basic", "description": "The depth of the search. 'basic' for generic results, 'advanced' for more thorough search, 'fast' for optimized low latency with high relevance, 'ultra-fast' for prioritizing latency above all else", "enum": [ "basic", "advanced", "fast", "ultra-fast" ], "type": "string" }, "topic": { "const": "general", "default": "general", "description": "The category of the search. This will determine which of our agents will be used for the search", "type": "string" }, "time_range": { "enum": [ "day", "week", "month", "year" ], "type": "string", "nullable": true, "description": "The time range back from the current date to include in the search results", "default": null }, "include_images": { "default": false, "description": "Include a list of query-related images in the response", "type": "boolean" }, "include_image_descriptions": { "default": false, "description": "Include a list of query-related images and their descriptions in the response", "type": "boolean" }, "include_raw_content": { "default": false, "description": "Include the cleaned and parsed HTML content of each search result", "type": "boolean" }, "include_domains": { "default": [], "description": "A list of domains to specifically include in the search results, if the user asks to search on specific sites set this to the domain of the site", "items": { "type": "string" }, "type": "array" }, "exclude_domains": { "default": [], "description": "List of domains to specifically exclude, if the user asks to exclude a domain set this to the domain of the site", "items": { "type": "string" }, "type": "array" }, "country": { "default": "", "description": "Boost search results from a specific country. Must be a full country name (e.g., 'United States', 'Japan', 'Germany'). ISO country codes (e.g., 'us', 'jp') are not supported. Available only if topic is general. See https://docs.tavily.com/documentation/api-reference/search for the full list of supported countries.", "type": "string" }, "include_favicon": { "default": false, "description": "Whether to include the favicon URL for each result", "type": "boolean" }, "start_date": { "default": "", "description": "Will return all results after the specified start date. Required to be written in the format YYYY-MM-DD.", "type": "string" }, "end_date": { "default": "", "description": "Will return all results before the specified end date. Required to be written in the format YYYY-MM-DD", "type": "string" }, "exact_match": { "type": "boolean", "nullable": true, "description": "Only return results containing the exact phrase(s) in quotes in your query", "default": null } }, "required": [ "query" ], "type": "object" } } }, { "type": "function", "function": { "name": "patch", "description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.", "parameters": { "type": "object", "properties": { "mode": { "type": "string", "enum": [ "replace", "patch" ], "description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.", "default": "replace" }, "path": { "type": "string", "description": "REQUIRED when mode='replace'. File path to edit." }, "old_string": { "type": "string", "description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness." }, "new_string": { "type": "string", "description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text." }, "replace_all": { "type": "boolean", "description": "Replace all occurrences instead of requiring a unique match (default: false)", "default": false }, "patch": { "type": "string", "description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch" } }, "required": [ "mode" ] } } }, { "type": "function", "function": { "name": "process", "description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "list", "poll", "log", "wait", "kill", "write", "submit", "close" ], "description": "Action to perform on background processes" }, "session_id": { "type": "string", "description": "Process session ID (from terminal background output). Required for all actions except 'list'." }, "data": { "type": "string", "description": "Text to send to process stdin (for 'write' and 'submit' actions)" }, "timeout": { "type": "integer", "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.", "minimum": 1 }, "offset": { "type": "integer", "description": "Line offset for 'log' action (default: last 200 lines)" }, "limit": { "type": "integer", "description": "Max lines to return for 'log' action", "minimum": 1 } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "read_file", "description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to read (absolute, relative, or ~/path)" }, "offset": { "type": "integer", "description": "Line number to start reading from (1-indexed, default: 1)", "default": 1, "minimum": 1 }, "limit": { "type": "integer", "description": "Maximum number of lines to read (default: 500, max: 2000)", "default": 500, "maximum": 2000 } }, "required": [ "path" ] } } }, { "type": "function", "function": { "name": "search_files", "description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.", "parameters": { "type": "object", "properties": { "pattern": { "type": "string", "description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search" }, "target": { "type": "string", "enum": [ "content", "files" ], "description": "'content' searches inside file contents, 'files' searches for files by name", "default": "content" }, "path": { "type": "string", "description": "Directory or file to search in (default: current working directory)", "default": "." }, "file_glob": { "type": "string", "description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)" }, "limit": { "type": "integer", "description": "Maximum number of results to return (default: 50)", "default": 50 }, "offset": { "type": "integer", "description": "Skip first N results for pagination (default: 0)", "default": 0 }, "output_mode": { "type": "string", "enum": [ "content", "files_only", "count" ], "description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file", "default": "content" }, "context": { "type": "integer", "description": "Number of context lines before and after each match (grep mode only)", "default": 0 } }, "required": [ "pattern" ] } } }, { "type": "function", "function": { "name": "terminal", "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n (1) Long-lived processes that never exit (servers, watchers).\n (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n", "parameters": { "type": "object", "properties": { "command": { "type": "string", "description": "The command to execute on the VM" }, "background": { "type": "boolean", "description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.", "default": false }, "timeout": { "type": "integer", "description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.", "minimum": 1 }, "workdir": { "type": "string", "description": "Working directory for this command (absolute path). Defaults to the session working directory." }, "pty": { "type": "boolean", "description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.", "default": false }, "notify_on_complete": { "type": "boolean", "description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.", "default": false }, "watch_patterns": { "type": "array", "items": { "type": "string" }, "description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both." } }, "required": [ "command" ] } } }, { "type": "function", "function": { "name": "write_file", "description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)" }, "content": { "type": "string", "description": "Complete content to write to the file" } }, "required": [ "path", "content" ] } } } ], "message_count": 53, "messages": [ { "role": "user", "content": "Inspect the latest FedNova training log and relevant source code to identify why the new algorithm appears ineffective. Do not modify files. Summarize evidence and give a minimal diagnosis with next-step suggestions." }, { "role": "assistant", "content": "", "reasoning": "**Exploring file access**\n\nI need to inspect files in a remote path, but I currently have a local workspace at /home/ubuntu. I'll have to consider using tools for this. The repository might be accessible, maybe even mounted, so I need to check the log and code in the specified work directory. The user mentioned a remote training server path, yet I only have the local environment. Maybe I can discover files via SSH, starting with a search for logs in /home/ubuntu. I’ll also check if the repository is cloned locally.\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Exploring file access**\n\nI need to inspect files in a remote path, but I currently have a local workspace at /home/ubuntu. I'll have to consider using tools for this. The repository might be accessible, maybe even mounted, so I need to check the log and code in the specified work directory. The user mentioned a remote training server path, yet I only have the local environment. Maybe I can discover files via SSH, starting with a search for logs in /home/ubuntu. I’ll also check if the repository is cloned locally.\n\n", "tool_calls": [ { "id": "call_yl14ZHEntzERS8mhjYM69eLf", "call_id": "call_yl14ZHEntzERS8mhjYM69eLf", "response_item_id": "fc_yl14ZHEntzERS8mhjYM69eLf", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*FedNova_cifar10.log\",\"limit\":20,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/ubuntu\",\"pattern\":\"*FedNova_cifar10.log\",\"target\":\"files\"}" } }, { "id": "call_tZB2Pa7Kad78ke7IzckylyzN", "call_id": "call_tZB2Pa7Kad78ke7IzckylyzN", "response_item_id": "fc_tZB2Pa7Kad78ke7IzckylyzN", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*FedNova*\",\"limit\":50,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/ubuntu\",\"pattern\":\"*FedNova*\",\"target\":\"files\"}" } }, { "id": "call_3pXBe34hpbAOrFJWQOSZP4XB", "call_id": "call_3pXBe34hpbAOrFJWQOSZP4XB", "response_item_id": "fc_3pXBe34hpbAOrFJWQOSZP4XB", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*\",\"limit\":20,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/ubuntu\",\"pattern\":\"PFLlib-master\",\"target\":\"files\"}" } } ] }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_yl14ZHEntzERS8mhjYM69eLf" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_tZB2Pa7Kad78ke7IzckylyzN" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_3pXBe34hpbAOrFJWQOSZP4XB" }, { "role": "assistant", "content": "", "reasoning": "**Exploring repository paths**\n\nI need to find the repository path, but it might not be under /home/ubuntu. The context mentions a remote server path, which is /home/user-lbhzj/pw/PFLlib-master/system, while my local workspace is at /home/ubuntu. I wonder if I can inspect the current directories through the terminal to see if anything is mounted. Since searching under /home/ubuntu yielded no files, maybe I should try using terminal commands like pwd or ls, and searching under /home instead.\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Exploring repository paths**\n\nI need to find the repository path, but it might not be under /home/ubuntu. The context mentions a remote server path, which is /home/user-lbhzj/pw/PFLlib-master/system, while my local workspace is at /home/ubuntu. I wonder if I can inspect the current directories through the terminal to see if anything is mounted. Since searching under /home/ubuntu yielded no files, maybe I should try using terminal commands like pwd or ls, and searching under /home instead.\n\n", "tool_calls": [ { "id": "call_cJtaFgjgF73n7Ycu3vsy1IbW", "call_id": "call_cJtaFgjgF73n7Ycu3vsy1IbW", "response_item_id": "fc_cJtaFgjgF73n7Ycu3vsy1IbW", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*FedNova_cifar10.log\",\"limit\":20,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home\",\"pattern\":\"*FedNova_cifar10.log\",\"target\":\"files\"}" } }, { "id": "call_Mi47nUBE4Zrpk0evYl4kMckI", "call_id": "call_Mi47nUBE4Zrpk0evYl4kMckI", "response_item_id": "fc_Mi47nUBE4Zrpk0evYl4kMckI", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*PFLlib*\",\"limit\":50,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home\",\"pattern\":\"*PFLlib*\",\"target\":\"files\"}" } }, { "id": "call_Hl0F19bWVqSm88H0UWZ4O7tC", "call_id": "call_Hl0F19bWVqSm88H0UWZ4O7tC", "response_item_id": "fc_Hl0F19bWVqSm88H0UWZ4O7tC", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*system*\",\"limit\":50,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home\",\"pattern\":\"*system*\",\"target\":\"files\"}" } } ] }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_cJtaFgjgF73n7Ycu3vsy1IbW" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_Mi47nUBE4Zrpk0evYl4kMckI" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 17, \"files\": [\"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-xxhdpi/ic_perm_group_system_tools.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-xxhdpi/ic_perm_group_system_clock.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-xxhdpi/perm_group_system_clock.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-xxhdpi/perm_group_system_tools.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-mdpi/perm_group_system_clock.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-mdpi/perm_group_system_tools.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/color/system_bar_background_semi_transparent.xml\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-hdpi/perm_group_system_clock.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-hdpi/perm_group_system_tools.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-xhdpi/perm_group_system_clock.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-xhdpi/perm_group_system_tools.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/layout/app_language_picker_system_default.xml\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/layout/system_user_home.xml\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/layout/app_language_picker_system_current.xml\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-xxxhdpi/perm_group_system_clock.png\", \"/home/ubuntu/android-sdk/platforms/android-34/data/res/drawable-xxxhdpi/perm_group_system_tools.png\", \"/home/ubuntu/android-sdk/platforms/android-34/core-for-system-modules.jar\"]}", "tool_call_id": "call_Hl0F19bWVqSm88H0UWZ4O7tC" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_JYqk0r3fa2AIrQZJzHfQEDHs", "call_id": "call_JYqk0r3fa2AIrQZJzHfQEDHs", "response_item_id": "fc_JYqk0r3fa2AIrQZJzHfQEDHs", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"pwd && whoami && hostname\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":30,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } }, { "id": "call_PxLa04fbNfYi7YSivSp3RFLq", "call_id": "call_PxLa04fbNfYi7YSivSp3RFLq", "response_item_id": "fc_PxLa04fbNfYi7YSivSp3RFLq", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"ssh -o StrictHostKeyChecking=no -o ConnectTimeout=10 lbhzj@100.78.7.50 'pwd && whoami && hostname'\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":30,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"/home/ubuntu\\nubuntu\\npw\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_JYqk0r3fa2AIrQZJzHfQEDHs" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"Permission denied, please try again.\\r\\nPermission denied, please try again.\\r\\nlbhzj@100.78.7.50: Permission denied (publickey,password).\", \"exit_code\": 255, \"error\": null}", "tool_call_id": "call_PxLa04fbNfYi7YSivSp3RFLq" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_8tikU9ZgkKPoWvmFhFgf1xnM", "call_id": "call_8tikU9ZgkKPoWvmFhFgf1xnM", "response_item_id": "fc_8tikU9ZgkKPoWvmFhFgf1xnM", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*PFLlib-master*\",\"limit\":200,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/ubuntu\",\"pattern\":\"*PFLlib-master*\",\"target\":\"files\"}" } }, { "id": "call_Hmnk23IyAz9ZkA5WZnA1AmV7", "call_id": "call_Hmnk23IyAz9ZkA5WZnA1AmV7", "response_item_id": "fc_Hmnk23IyAz9ZkA5WZnA1AmV7", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*FedNova_cifar10.log\",\"limit\":200,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/ubuntu\",\"pattern\":\"*FedNova_cifar10.log\",\"target\":\"files\"}" } }, { "id": "call_d5HO5Mh7EShCqrTLjjKaeYd6", "call_id": "call_d5HO5Mh7EShCqrTLjjKaeYd6", "response_item_id": "fc_d5HO5Mh7EShCqrTLjjKaeYd6", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*system\",\"limit\":200,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/ubuntu\",\"pattern\":\"*system\",\"target\":\"files\"}" } } ] }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_8tikU9ZgkKPoWvmFhFgf1xnM" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_Hmnk23IyAz9ZkA5WZnA1AmV7" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_d5HO5Mh7EShCqrTLjjKaeYd6" }, { "role": "assistant", "content": "", "reasoning": "**Searching for repo access**\n\nI need to find the repository on my local machine. The context mentions a remote server path, but the local workspace might have mounted files. I should check /home/ubuntu, but maybe it’s hidden? The search_files tool didn’t come up with results. I can consider using the terminal find command, but I prefer search_files. Perhaps the repo is locally accessible through some mounted directory, so I might need to broaden my search to /mnt/data. Let's check by pattern PFLlib in search_files from root.\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Searching for repo access**\n\nI need to find the repository on my local machine. The context mentions a remote server path, but the local workspace might have mounted files. I should check /home/ubuntu, but maybe it’s hidden? The search_files tool didn’t come up with results. I can consider using the terminal find command, but I prefer search_files. Perhaps the repo is locally accessible through some mounted directory, so I might need to broaden my search to /mnt/data. Let's check by pattern PFLlib in search_files from root.\n\n", "tool_calls": [ { "id": "call_HdzqncEjZvAXA5OowiJmjVzM", "call_id": "call_HdzqncEjZvAXA5OowiJmjVzM", "response_item_id": "fc_HdzqncEjZvAXA5OowiJmjVzM", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*FedNova_cifar10.log\",\"limit\":50,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/\",\"pattern\":\"*FedNova_cifar10.log\",\"target\":\"files\"}" } }, { "id": "call_RkbUD4IKVDiDd0gL1erf2hMd", "call_id": "call_RkbUD4IKVDiDd0gL1erf2hMd", "response_item_id": "fc_RkbUD4IKVDiDd0gL1erf2hMd", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*PFLlib-master*\",\"limit\":50,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/\",\"pattern\":\"*PFLlib-master*\",\"target\":\"files\"}" } }, { "id": "call_H769Nkf7KNo2LGD8Uxbg3l7U", "call_id": "call_H769Nkf7KNo2LGD8Uxbg3l7U", "response_item_id": "fc_H769Nkf7KNo2LGD8Uxbg3l7U", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*FedNova*\",\"limit\":100,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/\",\"pattern\":\"*FedNova*\",\"target\":\"files\"}" } } ] }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_HdzqncEjZvAXA5OowiJmjVzM" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_RkbUD4IKVDiDd0gL1erf2hMd" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_H769Nkf7KNo2LGD8Uxbg3l7U" }, { "role": "assistant", "content": "", "reasoning": "**Inspecting local repository**\n\nI'm considering how to inspect the local repository, possibly accessible through the current working directory. I think searching for files in the /home/ubuntu directory could help me understand the repository structure, even if it’s not named clearly. I'll use the files for Python scripts related to FedNova as a guide for my search. I want to make sure I gather useful information to assist with this task!\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Inspecting local repository**\n\nI'm considering how to inspect the local repository, possibly accessible through the current working directory. I think searching for files in the /home/ubuntu directory could help me understand the repository structure, even if it’s not named clearly. I'll use the files for Python scripts related to FedNova as a guide for my search. I want to make sure I gather useful information to assist with this task!\n\n", "tool_calls": [ { "id": "call_JeFcVNff55Vu2KCh4IFG0dHB", "call_id": "call_JeFcVNff55Vu2KCh4IFG0dHB", "response_item_id": "fc_JeFcVNff55Vu2KCh4IFG0dHB", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*.py\",\"limit\":200,\"offset\":0,\"output_mode\":\"content\",\"path\":\"/home/ubuntu\",\"pattern\":\"FedNova\",\"target\":\"content\"}" } }, { "id": "call_txkUicLMnkMVXBxWYhMYSDY0", "call_id": "call_txkUicLMnkMVXBxWYhMYSDY0", "response_item_id": "fc_txkUicLMnkMVXBxWYhMYSDY0", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*.py\",\"limit\":200,\"offset\":0,\"output_mode\":\"content\",\"path\":\"/home/ubuntu\",\"pattern\":\"clientnova|servernova|fednova\",\"target\":\"content\"}" } }, { "id": "call_4muPeVVYXVFZwQsShcyNXGQu", "call_id": "call_4muPeVVYXVFZwQsShcyNXGQu", "response_item_id": "fc_4muPeVVYXVFZwQsShcyNXGQu", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*.log\",\"limit\":200,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/ubuntu\",\"pattern\":\"*.log\",\"target\":\"files\"}" } } ] }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_JeFcVNff55Vu2KCh4IFG0dHB" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_txkUicLMnkMVXBxWYhMYSDY0" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 19, \"files\": [\"/home/ubuntu/midscene_run/log/android-device.log\", \"/home/ubuntu/midscene_run/log/cli-runner.log\", \"/home/ubuntu/midscene_run/log/commonContextParser.log\", \"/home/ubuntu/midscene_run/log/ai-call.log\", \"/home/ubuntu/midscene_run/log/device-task-executor.log\", \"/home/ubuntu/midscene_run/log/ai-profile-detail.log\", \"/home/ubuntu/midscene_run/log/planning.log\", \"/home/ubuntu/midscene_run/log/ai-profile-stats.log\", \"/home/ubuntu/midscene_run/log/task-runner.log\", \"/home/ubuntu/midscene_run/log/agent-task-builder.log\", \"/home/ubuntu/midscene_run/log/img.log\", \"/home/ubuntu/midscene_run/log/ai-config.log\", \"/home/ubuntu/midscene_run/log/agent.log\", \"/home/ubuntu/midscene_run/log/mcp-base-tools.log\", \"/home/ubuntu/midscene_run/log/mcp-android-tools.log\", \"/home/ubuntu/midscene_run/log/android-utils.log\", \"/home/ubuntu/midscene_run/log/android-agent.log\", \"/home/ubuntu/hermes_install.log\", \"/home/ubuntu/adb-keeper.log\"]}", "tool_call_id": "call_4muPeVVYXVFZwQsShcyNXGQu" }, { "role": "assistant", "content": "", "reasoning": "**Investigating remote access**\n\nI need to inspect a potentially inaccessible remote training server path. I want to investigate the latest log and relevant source code, which might be accessible via mounted SSH, but I’m unsure if that will work. There's a possibility of hidden repositories in the /home/ubuntu directory. I should think about whether we have the necessary credentials to SSH or use a different username. I need to check if there's a file sync or if I can access it through other means, like terminal commands.\n\n**Checking file existence**\n\nI want to check if certain files exist by using the search_files function on the /home/user-lbhzj directory. It's possible that the files could be on the host, but I'm feeling a bit unsure. I should double-check this path and see what can be found there. I'm just trying to make sure I don't miss anything important. It’s all about ensuring I have the right information to proceed effectively!\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Investigating remote access**\n\nI need to inspect a potentially inaccessible remote training server path. I want to investigate the latest log and relevant source code, which might be accessible via mounted SSH, but I’m unsure if that will work. There's a possibility of hidden repositories in the /home/ubuntu directory. I should think about whether we have the necessary credentials to SSH or use a different username. I need to check if there's a file sync or if I can access it through other means, like terminal commands.\n\n**Checking file existence**\n\nI want to check if certain files exist by using the search_files function on the /home/user-lbhzj directory. It's possible that the files could be on the host, but I'm feeling a bit unsure. I should double-check this path and see what can be found there. I'm just trying to make sure I don't miss anything important. It’s all about ensuring I have the right information to proceed effectively!\n\n", "tool_calls": [ { "id": "call_kotX4cdOcXgVIFyf2N4SKgmd", "call_id": "call_kotX4cdOcXgVIFyf2N4SKgmd", "response_item_id": "fc_kotX4cdOcXgVIFyf2N4SKgmd", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*\",\"limit\":50,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/user-lbhzj\",\"pattern\":\"*\",\"target\":\"files\"}" } }, { "id": "call_qC3IzYtQTbhLmqg5giqjGrvb", "call_id": "call_qC3IzYtQTbhLmqg5giqjGrvb", "response_item_id": "fc_qC3IzYtQTbhLmqg5giqjGrvb", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*PFLlib-master*\",\"limit\":50,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home\",\"pattern\":\"*PFLlib-master*\",\"target\":\"files\"}" } }, { "id": "call_GkWdphE50ajtPnf9hFa457d5", "call_id": "call_GkWdphE50ajtPnf9hFa457d5", "response_item_id": "fc_GkWdphE50ajtPnf9hFa457d5", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*pw*\",\"limit\":50,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home\",\"pattern\":\"*pw*\",\"target\":\"files\"}" } } ] }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0, \"error\": \"Path not found: /home/user-lbhzj\"}", "tool_call_id": "call_kotX4cdOcXgVIFyf2N4SKgmd" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_qC3IzYtQTbhLmqg5giqjGrvb" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_GkWdphE50ajtPnf9hFa457d5" }, { "role": "assistant", "content": "", "reasoning": "**Investigating access issues**\n\nI need to investigate a path that seems inaccessible locally. I might need to use the terminal with SSH, but maybe the keys aren’t set up. I could still inspect the remote, but SSH to the server might not be permitted. I wonder if the repository is in the current working directory. Let’s check the contents of the current working directory using the search files function to see if that provides any clues!\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Investigating access issues**\n\nI need to investigate a path that seems inaccessible locally. I might need to use the terminal with SSH, but maybe the keys aren’t set up. I could still inspect the remote, but SSH to the server might not be permitted. I wonder if the repository is in the current working directory. Let’s check the contents of the current working directory using the search files function to see if that provides any clues!\n\n", "tool_calls": [ { "id": "call_zNkAp0BRpDDgVASAhfB9SksL", "call_id": "call_zNkAp0BRpDDgVASAhfB9SksL", "response_item_id": "fc_zNkAp0BRpDDgVASAhfB9SksL", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*\",\"limit\":200,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/ubuntu/.hermes/hermes-agent\",\"pattern\":\"*\",\"target\":\"files\"}" } }, { "id": "call_zvvMc0izBjaqOjYB1YsP6gDZ", "call_id": "call_zvvMc0izBjaqOjYB1YsP6gDZ", "response_item_id": "fc_zvvMc0izBjaqOjYB1YsP6gDZ", "type": "function", "function": { "name": "search_files", "arguments": "{\"context\":0,\"file_glob\":\"*\",\"limit\":200,\"offset\":0,\"output_mode\":\"files_only\",\"path\":\"/home/ubuntu\",\"pattern\":\"*\",\"target\":\"files\"}" } } ] }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 200, 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true}\n\n[Hint: Results truncated. Use offset=200 to see more, or narrow with a more specific pattern or file_glob.]\n\n[Subdirectory context discovered: .hermes/hermes-agent/AGENTS.md]\n# Hermes Agent - Development Guide\n\nInstructions for AI coding assistants and developers working on the hermes-agent codebase.\n\n## Development Environment\n\n```bash\n# Prefer .venv; fall back to venv if that's what your checkout has.\nsource .venv/bin/activate # or: source venv/bin/activate\n```\n\n`scripts/run_tests.sh` probes `.venv` first, then `venv`, then\n`$HOME/.hermes/hermes-agent/venv` (for worktrees that share a venv with the\nmain checkout).\n\n## Project Structure\n\nFile counts shift constantly — don't treat the tree below as exhaustive.\nThe canonical source is the filesystem. The notes call out the load-bearing\nentry points you'll actually edit.\n\n```\nhermes-agent/\n├── run_agent.py # AIAgent class — core conversation loop (~12k LOC)\n├── model_tools.py # Tool orchestration, discover_builtin_tools(), handle_function_call()\n├── toolsets.py # Toolset definitions, _HERMES_CORE_TOOLS list\n├── cli.py # HermesCLI class — interactive CLI orchestrator (~11k LOC)\n├── hermes_state.py # SessionDB — SQLite session store (FTS5 search)\n├── hermes_constants.py # get_hermes_home(), display_hermes_home() — profile-aware paths\n├── hermes_logging.py # setup_logging() — agent.log / errors.log / gateway.log (profile-aware)\n├── batch_runner.py # Parallel batch processing\n├── agent/ # Agent internals (provider adapters, memory, caching, compression, etc.)\n├── hermes_cli/ # CLI subcommands, setup wizard, plugins loader, skin engine\n├── tools/ # Tool implementations — auto-discovered via tools/registry.py\n│ └── environments/ # Terminal backends (local, docker, ssh, modal, daytona, singularity)\n├── gateway/ # Messaging gateway — run.py + session.py + platforms/\n│ ├── platforms/ # Adapter per platform (telegram, discord, slack, whatsapp,\n│ │ # homeassistant, signal, matrix, mattermost, email, sms,\n│ │ # dingtalk, wecom, weixin, feishu, qqbot, bluebubbles,\n│ │ # yuanbao, webhook, api_server, ...). See ADDING_A_PLATFORM.md.\n│ └── builtin_hooks/ # Extension point for always-registered gateway hooks (none shipped)\n├── plugins/ # Plugin system (see \"Plugins\" section below)\n│ ├── memory/ # Memory-provider plugins (honcho, mem0, supermemory, ...)\n│ ├── context_engine/ # Context-engine plugins\n│ ├── model-providers/ # Inference backend plugins (openrouter, anthropic, gmi, ...)\n│ ├── kanban/ # Multi-agent board dispatcher + worker plugin\n│ ├── hermes-achievements/ # Gamified achievement tracking\n│ ├── observability/ # Metrics / traces / logs plugin\n│ ├── image_gen/ # Image-generation providers\n│ └── / # disk-cleanup, example-dashboard, google_meet, platforms,\n│ # spotify, strike-freedom-cockpit, ...\n├── optional-skills/ # Heavier/niche skills shipped but NOT active by default\n├── skills/ # Built-in skills bundled with the repo\n├── ui-tui/ # Ink (React) terminal UI — `hermes --tui`\n│ └── src/ # entry.tsx, app.tsx, gatewayClient.ts + app/components/hooks/lib\n├── tui_gateway/ # Python JSON-RPC backend for the TUI\n├── acp_adapter/ # ACP server (VS Code / Zed / JetBrains integration)\n├── cron/ # Scheduler — jobs.py, scheduler.py\n├── environments/ # RL training environments (Atropos)\n├── scripts/ # run_tests.sh, release.py, auxiliary scripts\n├── website/ # Docusaurus docs site\n└── tests/ # Pytest suite (~17k tests across ~900 files as of May 2026)\n```\n\n**User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys only).\n**Logs:** `~/.hermes/logs/` — `agent.log` (INFO+), `errors.log` (WARNING+),\n`gateway.log` when running the gateway. Profile-aware via `get_hermes_home()`.\nBrowse with `hermes logs [--follow] [--level ...] [--session ...]`.\n\n## File Dependency Chain\n\n```\ntools/registry.py (no deps — imported by all tool files)\n ↑\ntools/*.py (each calls registry.register() at import time)\n ↑\nmodel_tools.py (imports tools/registry + triggers tool discovery)\n ↑\nrun_agent.py, cli.py, batch_runner.py, environments/\n```\n\n---\n\n## AIAgent Class (run_agent.py)\n\nThe real `AIAgent.__init__` takes ~60 parameters (credentials, routing, callbacks,\nsession context, budget, credential pool, etc.). The signature below is the\nminimum subset you'll usually touch — read `run_agent.py` for the full list.\n\n```python\nclass AIAgent:\n def __init__(self,\n base_url: str = None,\n api_key: str = None,\n provider: str = None,\n api_mode: str = None, # \"chat_completions\" | \"codex_responses\" | ...\n model: str = \"\", # empty → resolved from config/provider later\n max_iterations: int = 90, # tool-calling iterations (shared with subagents)\n enabled_toolsets: list = None,\n disabled_toolsets: list = None,\n quiet_mode: bool = False,\n save_trajectories: bool = False,\n platform: str = None, # \"cli\", \"telegram\", etc.\n session_id: str = None,\n skip_context_files: bool = False,\n skip_memory: bool = False,\n credential_pool=None,\n # ... plus callbacks, thread/user/chat IDs, iteration_budget, fallback_model,\n # checkpoints config, prefill_messages, service_tier, reasoning_config, etc.\n ): ...\n\n def chat(self, message: str) -> str:\n \"\"\"Simple interface — returns final response string.\"\"\"\n\n def run_conversation(self, user_message: str, system_message: str = None,\n conversation_history: list = None, task_id: str = None) -> dict:\n \"\"\"Full interface — returns dict with final_response + messages.\"\"\"\n```\n\n### Agent Loop\n\nThe core loop is inside `run_conversation()` — entirely synchronous, with\ninterrupt checks, budget tracking, and a one-turn grace call:\n\n```python\nwhile (api_call_count < self.max_iterations and self.iteration_budget.remaining > 0) \\\n or self._budget_grace_call:\n if self._interrupt_requested: break\n response = client.chat.completions.create(model=model, messages=messages, tools=tool_schemas)\n if response.tool_calls:\n for tool_call in response.tool_calls:\n result = handle_function_call(tool_call.name, tool_call.args, task_id)\n messages.append(tool_result_message(result))\n api_call_count += 1\n else:\n return response.content\n```\n\nMessages follow OpenAI format: `{\"role\": \"system/user/assistant/tool\", ...}`.\nReasoning content is stored in `assistant_msg[\"reasoning\"]`.\n\n---\n\n## CLI Architecture (cli.py)\n\n- **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete\n- **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results\n- `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML\n- **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text\n- `process_command()` is a method on `HermesCLI` — dispatches on canonical command name resolved via `resolve_command()` from the central registry\n- Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching\n\n### Slash Command Registry (`hermes_cli/commands.py`)\n\nAll slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandDef` objects. Every downstream consumer derives from this registry automatically:\n\n- **CLI** — `process_command()` resolves aliases via `resolve_command()`, dispatches on canonical name\n- **Gateway** — `GATEWAY_KNOWN_COMMANDS` frozenset for hook emission, `resolve_command()` f\n\n[...truncated AGENTS.md: 50,073 chars total]", "tool_call_id": "call_zNkAp0BRpDDgVASAhfB9SksL" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 200, \"files\": [\"/home/ubuntu/.hermes/logs/agent.log\", \"/home/ubuntu/.hermes/state.db-shm\", \"/home/ubuntu/.hermes/state.db-wal\", \"/home/ubuntu/.hermes/sessions/session_20260519_164833_60a691.json\", \"/home/ubuntu/.hermes/logs/errors.log\", \"/home/ubuntu/.local/share/tirith/log.jsonl\", \"/home/ubuntu/.hermes/cron/.tick.lock\", \"/home/ubuntu/.hermes/skills/.usage.json\", \"/home/ubuntu/.hermes/sessions/sessions.json\", \"/home/ubuntu/.hermes/logs/gateway.log\", \"/home/ubuntu/.hermes/gateway_state.json\", 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Use offset=200 to see more, or narrow with a more specific pattern or file_glob.]", "tool_call_id": "call_zvvMc0izBjaqOjYB1YsP6gDZ" }, { "role": "assistant", "content": "", "reasoning": "**Investigating logs and code**\n\nI need to inspect the logs and source code related to the task. I’ve found a folder at /home/ubuntu/.hermes skills mlops/pflib-experiments, but it seems like the repository might not be available locally. It’s possible that the skill documentation references a remote server and commands. To progress, I need to read the SKILL documentation and check for logs in .hermes. Specifically, I need the latest FedNova training log on the remote server 100.78.7.50. Let's dive into those details!\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Investigating logs and code**\n\nI need to inspect the logs and source code related to the task. I’ve found a folder at /home/ubuntu/.hermes skills mlops/pflib-experiments, but it seems like the repository might not be available locally. It’s possible that the skill documentation references a remote server and commands. To progress, I need to read the SKILL documentation and check for logs in .hermes. Specifically, I need the latest FedNova training log on the remote server 100.78.7.50. Let's dive into those details!\n\n", "tool_calls": [ { "id": "call_PWdKkdBcmD2XNsZA83AUaMjk", "call_id": "call_PWdKkdBcmD2XNsZA83AUaMjk", "response_item_id": "fc_PWdKkdBcmD2XNsZA83AUaMjk", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":240,\"offset\":1,\"path\":\"/home/ubuntu/.hermes/skills/mlops/pflib-experiments/SKILL.md\"}" } }, { "id": "call_BOCXfhctVTXeaBYm9IVtMX9e", "call_id": "call_BOCXfhctVTXeaBYm9IVtMX9e", "response_item_id": "fc_BOCXfhctVTXeaBYm9IVtMX9e", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":240,\"offset\":1,\"path\":\"/home/ubuntu/.hermes/skills/mlops/pflib-experiments/references/pflib-commands.md\"}" } } ] }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|---\\n 2|name: pflib-experiments\\n 3|description: 在西北农林科技大学训练服务器 (100.78.7.50) 上运行 PFLlib 联邦学习实验。覆盖 SSH 连接、参数陷阱、GPU 管理、实验日志追踪。\\n 4|trigger: PFLlib / 联邦学习 / federated learning / 跑实验 / 训练服务器 / 100.78.7.50 / FedAvg / FedProx / SCAFFOLD\\n 5|category: mlops\\n 6|---\\n 7|\\n 8|# PFLlib 实验管理\\n 9|\\n 10|## 训练服务器\\n 11|\\n 12|| 项目 | 值 |\\n 13||------|-----|\\n 14|| IP | 100.78.7.50 |\\n 15|| 用户 | user-lbhzj |\\n 16|| 密码 | lbhzj |\\n 17|| 工作目录 | `/home/user-lbhzj/pw/PFLlib-master/system` |\\n 18|| conda 环境 | `pw` |\\n 19|\\n 20|## 连接测试\\n 21|\\n 22|```bash\\n 23|sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"nvidia-smi\\\"\\n 24|```\\n 25|\\n 26|## GPU 监控\\n 27|\\n 28|```bash\\n 29|# 查看所有 GPU 利用率\\n 30|sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"nvidia-smi --query-gpu=index,name,memory.used,memory.total,utilization.gpu --format=csv\\\"\\n 31|\\n 32|# 空闲 GPU（利用率 0%）\\n 33|sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"nvidia-smi --query-gpu=index,utilization.gpu --format=csv,noheader\\\" | grep '0 %'\\n 34|```\\n 35|\\n 36|## 关键参数陷阱 ⚠️⚠️⚠️\\n 37|\\n 38|**【铁律】禁止在未获明确同意前启动或停止任何实验！**\\n 39|- 用户明确说：\\\"不能随便开始和停正在跑的实验，必须经过我的同意\\\"\\n 40|- 每次跑实验前必须：先给用户看完整命令 → 用户确认 → 再执行\\n 41|- 违反此铁律会被严厉批评\\n 42|\\n 43|**PFLlib 参数 `-ncl` 和 `-nc` 极易混淆（必须严格遵守）：**\\n 44|\\n 45|| 参数 | 含义 | CIFAR-10 | Crop |\\n 46||------|------|----------|------|\\n 47|| `-ncl, --num_classes` | **数据集类别数** | 10 | 3 |\\n 48|| `-nc, --num_clients` | **客户端总数** | 20 | 10 |\\n 49|| `-gr, --global_rounds` | 全局通信轮次 | 70 |\\n 50|| `-ls, --local_epochs` | 本地训练轮次 | 3 |\\n 51|| `-lbs, --batch_size` | 批次大小 | 64 |\\n 52|| `-lr` | 本地学习率 | 0.05 |\\n 53|| `-ldg` | 学习率衰减系数 | 0.2 |\\n 54|| `-ldm` | 衰减节点 | 40 60 |\\n 55|| `-mu` | 近端约束参数 | 0.01 或 0.1 |\\n 56|| `-ma` | mixup alpha | 0.2 |\\n 57|| `-did` | GPU 设备 ID | 0-7 |\\n 58|| `-m` | 模型 | ResNet18 |\\n 59|| `-data` | 数据集 | Cifar10 / crop |\\n 60|| `-algo` | 算法 | FedAvg / FedProx / FedProxV2 / FedDyn / FedNova |\\n 61|\\n 62|**【重要】基线实验必须用 `-at none -pp none -ma 0`（无注意力、无预训练、无Mixup）。消融实验再加回来。**\\n 63|\\n 64|## 命令执行铁律 ⚠️⚠️⚠️\\n 65|\\n 66|**【最重要】每次跑实验前必须分三步：**\\n 67|1. **先给用户看完整命令**（粘贴在消息里，不执行）\\n 68|2. **等用户说\\\"可以跑\\\"或\\\"确认\\\"**\\n 69|3. **再执行 `terminal(background=True)` 启动训练**\\n 70|\\n 71|**禁止在未获明确同意前启动或停止任何实验！用户明确说过：\\\"不能随便开始和停正在跑的实验，必须经过我的同意\\\"。**\\n 72|\\n 73|**【铁律】报告任何训练结果前，必须先 SSH 验证，再报告！禁止假设/猜测结果。**\\n 74|\\n 75|教训：我说了\\\"FedNova初始化成功\\\"但没有实际 SSH 验证就瞎说，小旺骂我蠢货。\\n 76|\\n 77|**Python 代码注释陷阱 ⚠️：**\\n 78|- 中文注释必须加 `#` 前缀，否则被当成代码执行\\n 79|- 案例：`clientnova.py` 第87行 `返回累计梯度和归一化因子` 没有 `#` 导致 `NameError`\\n 80|- 每次添加新算法代码后，检查所有注释是否有 `#` 前缀\\n 81|\\n 82|**新算法代码检查清单（添加后必做）：**\\n 83|1. `grep -n '^[^#].*[\\\\u4e00-\\\\u9fff]' clientnova.py` — 查找无#前缀的中文\\n 84|2. `grep -n '返回\\\\|得到\\\\|计算' clientnova.py` — 查找可能的注释\\n 85|3. `python -c \\\"import flcore.clients.clientnova\\\"` — 验证语法正确\\n 86|4. 以上全过才能提交代码运行\\n 87|\\n 88|### 跑实验（后台）- 必须先问用户！\\n 89|\\n 90|```bash\\n 91|cd /home/user-lbhzj/pw/PFLlib-master/system && \\\\\\n 92|source /home/user-lbhzj/anaconda3/etc/profile.d/conda.sh && conda activate pw && \\\\\\n 93|CUDA_VISIBLE_DEVICES={GPU} nohup bash -c 'exec -a pw python main_v2.py \\\\\\n 94| -data {数据集} -ncl {类别数} -nc {客户端数} \\\\\\n 95| -m ResNet18 -algo {算法} \\\\\\n 96| -gr 70 -lbs 64 -lr 0.05 \\\\\\n 97| -ldg {衰减系数} -ldm {衰减节点} -ls {本地轮次} \\\\\\n 98| -mu 0.1 -ma 0.2 -at none -pp none \\\\\\n 99| -did {GPU}' > /home/user-lbhzj/pw/PFLlib-master/system/{日志名}.log 2>&1 &\\n 100|```\\n 101|\\n 102|**【关键】`exec -a pw python` — 进程名必须是 `pw`，方便 `nvidia-smi` 监控看到哪个GPU在跑训练。**\\n 103|\\n 104|**注意：基线实验必须用 `-at none`（无注意力），消融实验才加 `-at se`**\\n 105|\\n 106|**标准参数参考：**\\n 107|- CIFAR-10: `-ncl 10 -nc 20`\\n 108|- Crop: `-ncl 3 -nc 10`\\n 109|- 学习率衰减系数 CIFAR-10: `-ldg 0.2`，Crop: `-ldg 0.5`\\n 110|- 衰减节点 CIFAR-10: `-ldm 40 60`，Crop: `-ldm 20 40 60 80`\\n 111|\\n 112|### 检查训练进度\\n 113|\\n 114|```bash\\n 115|sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"tail -20 /home/user-lbhzj/pw/PFLlib-master/system/{日志名}.log\\\"\\n 116|```\\n 117|\\n 118|### 停掉训练\\n 119|\\n 120|```bash\\n 121|sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"pkill -9 -f 'main_v2.py.*{算法}'\\\"\\n 122|```\\n 123|\\n 124|## CIFAR-10 基线参数（来自 log 文件，2026-05-19 确认）\\n 125|\\n 126|| 算法 | 准确率 | local_epochs | mu | mixup | num_clients | global_rounds |\\n 127||------|--------|-------------|-----|-------|-------------|---------------|\\n 128|| FedAvg | 69.84% | 3 | 0.1 | 0.2 | 20 | 70 |\\n 129|| FedProx（固定μ） | 71.74% | 3 | 0.01 | 0.2 | 20 | 70 |\\n 130|| FedProxV2（动态μ） | 74.59% | ? | ? | ? | 20 | 70 |\\n 131|| SCAFFOLD | 待跑 | 3 | 0 | 0 | 20 | 70 |\\n 132|\\n 133|对应 log 文件：\\n 134|- FedAvg: `train_cifar10_resnet18_fedavg_base.log`\\n 135|- FedProx: `train_cifar10_resnet18_fedprox_mu0.01_nopre_nose.log`\\n 136|- FedProxV2 74.59%: **log 已删除**（Apr 28 被重新划分数据集）\\n 137|\\n 138|对应 h5 文件：\\n 139|- `/home/user-lbhzj/pw/PFLlib-master/results/Cifar10_ResNet18_FedAvg_test__acc0.6984.h5`\\n 140|- `/home/user-lbhzj/pw/PFLlib-master/results/Cifar10_ResNet18_FedProx_test__acc0.7174.h5`\\n 141|- `/home/user-lbhzj/pw/PFLlib-master/results/Cifar10_ResNet18_FedProxV2_test__acc0.7459.h5`\\n 142|\\n 143|**注意：CIFAR-10 数据集在 Apr 28 被重新划分，旧 log 和新数据不能直接对比。**\\n 144|\\n 145|## Crop 数据集基线参数\\n 146|\\n 147|| 参数 | 值 |\\n 148||------|-----|\\n 149|| 数据集 | crop |\\n 150|| num_classes | 3 |\\n 151|| num_clients | 10 |\\n 152|| global_rounds | 70 |\\n 153|| local_epochs | 3 |\\n 154|| local_learning_rate | 0.05 |\\n 155|| lr_decay_gamma | 0.5 |\\n 156|| lr_decay_milestones | [20, 40, 60, 80] |\\n 157|\\n 158|## 内置 SOTA 算法对比（无需改代码）\\n 159|\\n 160|**实测可用的基线/对比算法（2026-05-19）：**\\n 161|\\n 162|| 算法 | 能直接跑？ | 特点 |\\n 163||------|-----------|------|\\n 164|| **FedAvg** | ✅ 正常 | 纯联邦平均，最简单基线 |\\n 165|| **FedProx** | ✅ 正常 | 固定近端约束（mu=0.01） |\\n 166|| FedDyn | ✅ 正常 | 动态正则化 |\\n 167|| FedNova | ✅ 正常 | 归一化平均（需 clientnova.py + servernova.py） |\\n 168|| MOON | ✅ 正常 | 对比学习 |\\n 169|| **Ditto** | ✅ 正常 | 双重本地+全局训练 |\\n 170|| **FedProto** | ✅ 正常 | 原型学习 |\\n 171|| **PerAvg** | ✅ 正常 | 个性化平均 |\\n 172|| **pFedMe** | ✅ 正常 | 个性化方法 |\\n 173|| **FedNova** | ✅ 已验证 | FedProx变种，2026-05-19测试通过 |\\n 174|| **SCAFFOLD** | ❌ 有bug | 控制变量，报 `global_c is None` |\\n 175|\\n 176|### FedNova 测试结果（2026-05-19）\\n 177|- 第0轮准确率：**12.03%**（随机初始化，正常）\\n 178|- 代码状态：`clientnova.py:87` 注释bug已修复\\n 179|- freeze_backbone：仅在 `-pp` 指定有效预训练权重时触发，`-pp none` 时不冻结\\n 180|\\n 181|**推荐对比顺序：**\\n 182|1. **FedAvg** — 最基础基线\\n 183|2. **FedProx** — 固定近端约束（你论文里的基线）\\n 184|3. **FedDyn** — 动态正则化（和你的方法最接近）\\n 185|4. **FedNova** — 归一化平均（处理异构本地步数）\\n 186|5. **MOON** — 对比学习方法\\n 187|6. **Ditto** — 个性化方法\\n 188|\\n 189|### SCAFFOLD Bug 说明\\n 190|\\n 191|SCAFFOLD 报错：`AttributeError: 'NoneType' object has no attribute 'data'`\\n 192|\\n 193|**原因：** `clientSCAFFOLD.__init__` 中 `self.global_c = None`，在第一个 round 调用 `optimizer.step(self.global_c, self.client_c)` 时 `global_c` 还是 None。\\n 194|\\n 195|**用户明确表示不想改代码解决。** 直接用 FedAvg/Ditto/FedDyn 作为基线即可，不需要 SCAFFOLD。\\n 196|\\n 197|### 基线命令模板（FedAvg 为例）\\n 198|\\n 199|```bash\\n 200|# CIFAR-10 FedAvg 基线（必须先给用户看命令，等确认后再跑）\\n 201|cd /home/user-lbhzj/pw/PFLlib-master/system && \\\\\\n 202|source ~/anaconda3/etc/profile.d/conda.sh && conda activate pw && \\\\\\n 203|nohup python main_v2.py \\\\\\n 204| -data Cifar10 -ncl 10 -nc 20 \\\\\\n 205| -m ResNet18 -algo FedAvg \\\\\\n 206| -gr 70 -lbs 64 -lr 0.05 -ldg 0.2 -ldm 40 60 -ls 3 \\\\\\n 207| -mu 0.1 -ma 0 -at none -pp none \\\\\\n 208| -did 4 \\\\\\n 209| > FedAvg_cifar10.log 2>&1 &\\n 210|```\\n 211|\\n 212|**FedAvg 基线参数（CIFAR-10）：**\\n 213|- `-ncl 10`：CIFAR-10 有 10 个类\\n 214|- `-nc 20`：20 个客户端\\n 215|- `-ls 3`：local_epochs=3\\n 216|- `-mu 0.1`：近端约束参数（FedAvg 虽不用但有默认值）\\n 217|- `-ma 0`：无 Mixup（基线）\\n 218|- `-at none`：无注意力机制（基线）\\n 219|- `-pp none`：无预训练参数（基线）\\n 220|- `-did 4`：GPU 4\\n 221|- **必须用 `exec -a pw python`** 来命名进程，否则 `nvidia-smi` 里看不到 pw 进程\\n 222|\\n 223|## 从 h5 提取结果\\n 224|\\n 225|```bash\\n 226|sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 'source ~/anaconda3/etc/profile.d/conda.sh && conda activate pw && python3 << EOF\\n 227|import h5py\\n 228|import numpy as np\\n 229|f = h5py.File(\\\"/home/user-lbhzj/pw/PFLlib-master/results/{h5文件}\\\", \\\"r\\\")\\n 230|for k in f.keys():\\n 231| arr = f[k][:]\\n 232| if len(arr) > 0:\\n 233| print(k + \\\": len=\\\" + str(len(arr)) + \\\", last3=\\\" + str(arr[-3:].tolist()))\\n 234|EOF'\\n 235|```\\n 236|\\n 237|## 代码修改铁律 ⚠️⚠️⚠️\\n 238|\\n 239|**禁止随意修改 PFLlib 源代码！必须先汇报，等用户确认。**\\n 240|\\n 241|\", \"total_lines\": 268, \"file_size\": 11473, \"truncated\": true, \"hint\": \"Use offset=241 to continue reading (showing 1-240 of 268 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_PWdKkdBcmD2XNsZA83AUaMjk" }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|# PFLlib 正确命令速查\\n 2|\\n 3|## 基线实验参数\\n 4|\\n 5|**基线实验必须用：`-at none -pp none -ma 0`（无注意力、无预训练、无Mixup）**\\n 6|\\n 7|消融实验才加回来：`-at se -ma 0.2`\\n 8|\\n 9|## CIFAR-10 正确参数格式\\n 10|\\n 11|```bash\\n 12|cd /home/user-lbhzj/pw/PFLlib-master/system && \\\\\\n 13|source /home/user-lbhzj/anaconda3/etc/profile.d/conda.sh && conda activate pw && \\\\\\n 14|CUDA_VISIBLE_DEVICES={GPU} nohup bash -c 'exec -a pw python main_v2.py \\\\\\n 15| -data Cifar10 -ncl 10 -nc 20 \\\\\\n 16| -m ResNet18 -algo {算法} \\\\\\n 17| -gr 70 -lbs 64 -lr 0.05 -ldg 0.2 -ldm 40 60 -ls 3 \\\\\\n 18| -mu 0.1 -ma 0 -at none -pp none \\\\\\n 19| -did {GPU}' > /home/user-lbhzj/pw/PFLlib-master/system/{算法}_cifar10.log 2>&1 &\\n 20|```\\n 21|\\n 22|**【关键】`exec -a pw python` — 进程名必须是 `pw`，方便 `nvidia-smi` 监控看到哪个GPU在跑训练。**\\n 23|\\n 24|## SSH 验证铁律 ⚠️\\n 25|\\n 26|**报告任何训练结果前，必须先 SSH 到服务器验证日志，再报告！禁止假设结果。**\\n 27|\\n 28|教训：我说\\\"FedNova初始化成功\\\"但没有实际验证就瞎说，小旺骂我蠢货。\\n 29|\\n 30|正确流程：\\n 31|```bash\\n 32|sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"tail -30 /home/user-lbhzj/pw/PFLlib-master/system/{日志名}.log\\\"\\n 33|```\\n 34|\\n 35|## 内置算法测试结果（2026-05-19）\\n 36|\\n 37|| 算法 | 能直接跑？ | 报错 |\\n 38||------|-----------|------|\\n 39|| FedAvg | ✅ | 无 |\\n 40|| FedProx | ✅ | 无 |\\n 41|| FedDyn | ✅ | 无 |\\n 42|| FedNova | ✅ | 无（需 clientnova.py + servernova.py） |\\n 43|| MOON | ✅ | 无 |\\n 44|| Ditto | ✅ | 无 |\\n 45|| FedProto | ✅ | 无 |\\n 46|| PerAvg | ✅ | 无 |\\n 47|| pFedMe | ✅ | 无 |\\n 48|| SCAFFOLD | ❌ | `global_c is None` |\\n 49|\\n 50|**SCAFFOLD 有 bug，优先用 FedAvg/Ditto/FedDyn 作为基线。**\\n 51|\\n 52|## 论文 CIFAR-10 基线结果\\n 53|\\n 54|| 算法 | 准确率 | h5 文件 | 对应 log |\\n 55||------|--------|---------|----------|\\n 56|| FedAvg | 69.84% | `Cifar10_ResNet18_FedAvg_test__acc0.6984.h5` | `train_cifar10_resnet18_fedavg_base.log` |\\n 57|| FedProx（固定μ） | 71.74% | `Cifar10_ResNet18_FedProx_test__acc0.7174.h5` | `train_cifar10_resnet18_fedprox_mu0.01_nopre_nose.log` |\\n 58|| FedProx（动态μ） | 74.59% | `Cifar10_ResNet18_FedProxV2_test__acc0.7459.h5` | **log 已删除** |\\n 59|\\n 60|**注意：CIFAR-10 数据集在 Apr 28 被重新划分，旧 log 和新数据不能直接对比。**\\n 61|\\n 62|## 服务器 log 目录\\n 63|- `/home/user-lbhzj/pw/PFLlib-master/system/*.log`\\n 64|- `/home/user-lbhzj/pw/PFLlib-master/system/final/*.log`\\n 65|\\n 66|## freeze backbone 日志说明\\n 67|\\n 68|日志出现 `>>> [冻结] 已冻结 12 层` 是**正常行为**，不代表代码错误。\\n 69|\\n 70|**冻结逻辑：**\\n 71|- `freeze_backbone()` 在 `has_pretrained=True` 时才冻结\\n 72|- 当 `-pp none` 时 `has_pretrained=False`，**不会执行冻结**（函数直接return）\\n 73|- 如果指定了有效预训练路径（如 `resnet18_imagenet.pth`），才会冻结 layer1\\n 74|\\n 75|**日志判断：**\\n 76|- 如果指定了 `-pp none` 但看到冻结日志 → 检查 `auto_load_pretrained()` 返回值\\n 77|- 如果指定了 `-pp {有效路径}` 看到冻结日志 → **正常，预期行为**\\n 78|\\n 79|## GPU 监控\\n 80|\\n 81|```bash\\n 82|# 查看所有 GPU\\n 83|sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"nvidia-smi --query-gpu=index,name,memory.used,memory.total,utilization.gpu --format=csv\\\"\\n 84|\\n 85|# 查看空闲 GPU（利用率 0%）\\n 86|sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"nvidia-smi --query-gpu=index,utilization.gpu --format=csv,noheader\\\" | grep '0 %'\\n 87|```\\n 88|\", \"total_lines\": 87, \"file_size\": 3399, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_BOCXfhctVTXeaBYm9IVtMX9e" }, { "role": "assistant", "content": "", "reasoning": "**Investigating remote access**\n\nI found some skill documents in Hermes, but now the task requires me to check the latest FedNova log and the relevant source code. I need to locate the actual PFLlib repository, possibly via log references or session dumps. There might be a path for PFLlib in the current environment, but I'm unsure if it's mounted. While the user mentioned not modifying any files on the remote server, I can investigate using accessible artifacts in the session dumps. I could potentially use sshpass if it’s available along with the correct credentials. Let’s try that!\n\n**Using remote tools effectively**\n\nI realize that since the terminal can run shell commands, I can use sshpass to run remote commands without modifying anything. First, I want to inspect the latest log tail and source code snippets remotely. The latest FedNova_cifar10.log is likely the target at the remote path. I’ll try to list the log and relevant files, focusing on the code under /home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py. Since I can't use commands like cat or tail, I can utilize Python on remote to print the relevant lines instead.\n\n**Locating log files remotely**\n\nI need to find the latest log file. I think using `ls -lt` on the remote server could show me modified times, but I'm aware that using `find` in the terminal is prohibited. Since searching files is only allowed locally, I’ll use sshpass with Python to access the remote file system. My plan is to run a command to check the last 80 lines of the log and grep for key markers. I’ll also look at the code around the FedNova classes for context.\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Investigating remote access**\n\nI found some skill documents in Hermes, but now the task requires me to check the latest FedNova log and the relevant source code. I need to locate the actual PFLlib repository, possibly via log references or session dumps. There might be a path for PFLlib in the current environment, but I'm unsure if it's mounted. While the user mentioned not modifying any files on the remote server, I can investigate using accessible artifacts in the session dumps. I could potentially use sshpass if it’s available along with the correct credentials. Let’s try that!\n\n**Using remote tools effectively**\n\nI realize that since the terminal can run shell commands, I can use sshpass to run remote commands without modifying anything. First, I want to inspect the latest log tail and source code snippets remotely. The latest FedNova_cifar10.log is likely the target at the remote path. I’ll try to list the log and relevant files, focusing on the code under /home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py. Since I can't use commands like cat or tail, I can utilize Python on remote to print the relevant lines instead.\n\n**Locating log files remotely**\n\nI need to find the latest log file. I think using `ls -lt` on the remote server could show me modified times, but I'm aware that using `find` in the terminal is prohibited. Since searching files is only allowed locally, I’ll use sshpass with Python to access the remote file system. My plan is to run a command to check the last 80 lines of the log and grep for key markers. I’ll also look at the code around the FedNova classes for context.\n\n", "tool_calls": [ { "id": "call_Is8D6xNVTpEeXVrXCFfq21zb", "call_id": "call_Is8D6xNVTpEeXVrXCFfq21zb", "response_item_id": "fc_Is8D6xNVTpEeXVrXCFfq21zb", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/home/user-lbhzj/pw/PFLlib-master/system/FedNova_cifar10.log')\\nprint('EXISTS', p.exists(), 'SIZE', p.stat().st_size if p.exists() else -1)\\nif p.exists():\\n lines=p.read_text(errors='ignore').splitlines()\\n for i,l in enumerate(lines[-120:], start=max(1,len(lines)-119)):\\n print(f'{i}|{l}')\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } }, { "id": "call_HYD7eCy0RlZz8xu0FBVTRDuJ", "call_id": "call_HYD7eCy0RlZz8xu0FBVTRDuJ", "response_item_id": "fc_HYD7eCy0RlZz8xu0FBVTRDuJ", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python - <<'PY'\\nfrom pathlib import Path\\nfor p in [Path('/home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py'), Path('/home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/servernova.py'), Path('/home/user-lbhzj/pw/PFLlib-master/system/main_v2.py')]:\\n print('FILE', p)\\n print('EXISTS', p.exists())\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"bash: python: command not found\", \"exit_code\": 127, \"error\": null}", "tool_call_id": "call_Is8D6xNVTpEeXVrXCFfq21zb" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"bash: python: command not found\", \"exit_code\": 127, \"error\": null}\n\n[Tool loop warning: same_tool_failure_warning; count=3; terminal has failed 3 times this turn. This looks like a loop; change approach before retrying.]", "tool_call_id": "call_HYD7eCy0RlZz8xu0FBVTRDuJ" }, { "role": "assistant", "content": "", "reasoning": "**Investigating SSH setup**\n\nI need to use SSH with Python 3, since it seems the SSH command failed due to a missing Python command. Maybe I should consider using sshpass if it's available. I think I need to look into whether the local repository is absent. It might be worth trying a remote path via sshpass. I'll check the logs and code to see what's going on, and then run the sshpass command to move forward.\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Investigating SSH setup**\n\nI need to use SSH with Python 3, since it seems the SSH command failed due to a missing Python command. Maybe I should consider using sshpass if it's available. I think I need to look into whether the local repository is absent. It might be worth trying a remote path via sshpass. I'll check the logs and code to see what's going on, and then run the sshpass command to move forward.\n\n", "tool_calls": [ { "id": "call_aRUQKL0CR2oouyxtWyBE3yMZ", "call_id": "call_aRUQKL0CR2oouyxtWyBE3yMZ", "response_item_id": "fc_aRUQKL0CR2oouyxtWyBE3yMZ", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\np=Path('/home/user-lbhzj/pw/PFLlib-master/system/FedNova_cifar10.log')\\nprint('EXISTS', p.exists(), 'SIZE', p.stat().st_size if p.exists() else -1)\\nif p.exists():\\n lines=p.read_text(errors='ignore').splitlines()\\n print('LINES', len(lines))\\n for i,l in enumerate(lines[-120:], start=max(1,len(lines)-119)):\\n print(f'{i}|{l}')\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } }, { "id": "call_469eqgN4O6xNF0CcvrRWekiq", "call_id": "call_469eqgN4O6xNF0CcvrRWekiq", "response_item_id": "fc_469eqgN4O6xNF0CcvrRWekiq", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\nfor p in [Path('/home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py'), Path('/home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/servernova.py'), Path('/home/user-lbhzj/pw/PFLlib-master/system/main_v2.py')]:\\n print('FILE', p)\\n print('EXISTS', p.exists(), 'SIZE', p.stat().st_size if p.exists() else -1)\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"EXISTS True SIZE 6995\\nLINES 224\\n105|------------------------- time cost ------------------------- 154.6066358089447\\n106|-------------Round number: 2 -------------\\n107|Evaluate global model\\n108|Averaged Train Loss: 2.3079\\n109|Averaged Test Accuracy: 0.0932\\n110|Averaged Test AUC: 0.4904\\n111|Std Test Accuracy: 0.1072\\n112|Std Test AUC: 0.0776\\n113|------------------------- time cost ------------------------- 156.08541750907898\\n114|-------------Round number: 3 -------------\\n115|Evaluate global model\\n116|Averaged Train Loss: 2.3023\\n117|Averaged Test Accuracy: 0.1242\\n118|Averaged Test AUC: 0.5254\\n119|Std Test Accuracy: 0.1310\\n120|Std Test AUC: 0.1110\\n121|------------------------- time cost ------------------------- 157.3702940940857\\n122|-------------Round number: 4 -------------\\n123|Evaluate global model\\n124|Averaged Train Loss: 2.3080\\n125|Averaged Test Accuracy: 0.1182\\n126|Averaged Test AUC: 0.5041\\n127|Std Test Accuracy: 0.0825\\n128|Std Test AUC: 0.1006\\n129|------------------------- time cost ------------------------- 155.5386779308319\\n130|-------------Round number: 5 -------------\\n131|Evaluate global model\\n132|Averaged Train Loss: 2.3088\\n133|Averaged Test Accuracy: 0.0915\\n134|Averaged Test AUC: 0.4997\\n135|Std Test Accuracy: 0.0906\\n136|Std Test AUC: 0.0708\\n137|------------------------- time cost ------------------------- 154.45581579208374\\n138|-------------Round number: 6 -------------\\n139|Evaluate global model\\n140|Averaged Train Loss: 2.3053\\n141|Averaged Test Accuracy: 0.0985\\n142|Averaged Test AUC: 0.5007\\n143|Std Test Accuracy: 0.0775\\n144|Std Test AUC: 0.1091\\n145|------------------------- time cost ------------------------- 154.92787671089172\\n146|-------------Round number: 7 -------------\\n147|Evaluate global model\\n148|Averaged Train Loss: 2.3034\\n149|Averaged Test Accuracy: 0.1052\\n150|Averaged Test AUC: 0.5158\\n151|Std Test Accuracy: 0.1318\\n152|Std Test AUC: 0.1141\\n153|------------------------- time cost ------------------------- 154.50914025306702\\n154|-------------Round number: 8 -------------\\n155|Evaluate global model\\n156|Averaged Train Loss: 2.3082\\n157|Averaged Test Accuracy: 0.1217\\n158|Averaged Test AUC: 0.4947\\n159|Std Test Accuracy: 0.1269\\n160|Std Test AUC: 0.1103\\n161|------------------------- time cost ------------------------- 155.28406047821045\\n162|-------------Round number: 9 -------------\\n163|Evaluate global model\\n164|Averaged Train Loss: 2.3021\\n165|Averaged Test Accuracy: 0.1250\\n166|Averaged Test AUC: 0.5248\\n167|Std Test Accuracy: 0.1289\\n168|Std Test AUC: 0.1067\\n169|------------------------- time cost ------------------------- 154.76046633720398\\n170|-------------Round number: 10 -------------\\n171|Evaluate global model\\n172|Averaged Train Loss: 2.3040\\n173|Averaged Test Accuracy: 0.1073\\n174|Averaged Test AUC: 0.5047\\n175|Std Test Accuracy: 0.0738\\n176|Std Test AUC: 0.0768\\n177|------------------------- time cost ------------------------- 154.83101344108582\\n178|-------------Round number: 11 -------------\\n179|Evaluate global model\\n180|Averaged Train Loss: 2.3035\\n181|Averaged Test Accuracy: 0.1034\\n182|Averaged Test AUC: 0.5159\\n183|Std Test Accuracy: 0.0702\\n184|Std Test AUC: 0.0815\\n185|------------------------- time cost ------------------------- 155.78869533538818\\n186|-------------Round number: 12 -------------\\n187|Evaluate global model\\n188|Averaged Train Loss: 2.3021\\n189|Averaged Test Accuracy: 0.1671\\n190|Averaged Test AUC: 0.5333\\n191|Std Test Accuracy: 0.1286\\n192|Std Test AUC: 0.1070\\n193|------------------------- time cost ------------------------- 154.56460738182068\\n194|-------------Round number: 13 -------------\\n195|Evaluate global model\\n196|Averaged Train Loss: 2.2988\\n197|Averaged Test Accuracy: 0.1444\\n198|Averaged Test AUC: 0.5405\\n199|Std Test Accuracy: 0.1277\\n200|Std Test AUC: 0.0956\\n201|------------------------- time cost ------------------------- 156.4545497894287\\n202|-------------Round number: 14 -------------\\n203|Evaluate global model\\n204|Averaged Train Loss: 2.3004\\n205|Averaged Test Accuracy: 0.1399\\n206|Averaged Test AUC: 0.5331\\n207|Std Test Accuracy: 0.1086\\n208|Std Test AUC: 0.1146\\n209|------------------------- time cost ------------------------- 157.89477634429932\\n210|-------------Round number: 15 -------------\\n211|Evaluate global model\\n212|Averaged Train Loss: 2.3047\\n213|Averaged Test Accuracy: 0.1274\\n214|Averaged Test AUC: 0.5252\\n215|Std Test Accuracy: 0.1158\\n216|Std Test AUC: 0.0902\\n217|------------------------- time cost ------------------------- 159.44605708122253\\n218|-------------Round number: 16 -------------\\n219|Evaluate global model\\n220|Averaged Train Loss: 2.3054\\n221|Averaged Test Accuracy: 0.1283\\n222|Averaged Test AUC: 0.5240\\n223|Std Test Accuracy: 0.0952\\n224|Std Test AUC: 0.0994\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_aRUQKL0CR2oouyxtWyBE3yMZ" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"FILE /home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py\\nEXISTS True SIZE 3826\\nFILE /home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/servernova.py\\nEXISTS True SIZE 4740\\nFILE /home/user-lbhzj/pw/PFLlib-master/system/main_v2.py\\nEXISTS True SIZE 48594\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_469eqgN4O6xNF0CcvrRWekiq" }, { "role": "assistant", "content": "", "reasoning": "**Investigating code issues**\n\nI need to inspect the log head and some code snippets, so I’ll use remote grep with Python 3. My goal is to figure out if an algorithm isn't running correctly or if there’s a command issue. I’ll compare the log with the FedNova code and search for relevant lines in the files, paying attention to line numbers. I suspect there might be a bug in the parameter launch, so I’ll need to check the main_v2 algorithm selection and the log for round 0 or initial lines. Let's fetch those logs and code segments!\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Investigating code issues**\n\nI need to inspect the log head and some code snippets, so I’ll use remote grep with Python 3. My goal is to figure out if an algorithm isn't running correctly or if there’s a command issue. I’ll compare the log with the FedNova code and search for relevant lines in the files, paying attention to line numbers. I suspect there might be a bug in the parameter launch, so I’ll need to check the main_v2 algorithm selection and the log for round 0 or initial lines. Let's fetch those logs and code segments!\n\n", "tool_calls": [ { "id": "call_7kNlSv7cE5bYFolJuyA27B5K", "call_id": "call_7kNlSv7cE5bYFolJuyA27B5K", "response_item_id": "fc_7kNlSv7cE5bYFolJuyA27B5K", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\np=Path('/home/user-lbhzj/pw/PFLlib-master/system/FedNova_cifar10.log')\\nlines=p.read_text(errors='ignore').splitlines()\\nfor i,l in enumerate(lines[:120], start=1):\\n print(f'{i}|{l}')\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } }, { "id": "call_Uezzpk68wbUUglX0D9mbALdd", "call_id": "call_Uezzpk68wbUUglX0D9mbALdd", "response_item_id": "fc_Uezzpk68wbUUglX0D9mbALdd", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\nfor path in ['/home/user-lbhzj/pw/PFLlib-master/system/main_v2.py','/home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py','/home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/servernova.py']:\\n print('###',path)\\n lines=Path(path).read_text(errors='ignore').splitlines()\\n for kw in ['FedNova','clientnova','servernova','algo','if args.algo','args.algo','global_model','client_class','server_class','send_models','aggregate']:\\n hits=[(i+1,l) for i,l in enumerate(lines) if kw in l]\\n if hits:\\n print('KW',kw)\\n for i,l in hits[:8]:\\n print(f'{i}|{l}')\\n print()\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"1|nohup: ignoring input\\n2|==================================================\\n3|goal = test\\n4|device = cuda\\n5|device_id = 3\\n6|dataset = Cifar10\\n7|num_classes = 10\\n8|model = ResNet18\\n9|batch_size = 64\\n10|local_learning_rate = 0.05\\n11|learning_rate_decay = True\\n12|learning_rate_decay_gamma = 0.2\\n13|lr_decay_milestones = [40, 60]\\n14|global_rounds = 70\\n15|top_cnt = 10\\n16|local_epochs = 3\\n17|algorithm = FedNova\\n18|weight_decay = 0.0001\\n19|join_ratio = 1\\n20|random_join_ratio = False\\n21|num_clients = 20\\n22|prev = 0\\n23|times = 1\\n24|eval_gap = 1\\n25|save_folder_name = items\\n26|auto_break = True\\n27|dlg_eval = False\\n28|dlg_gap = 100\\n29|batch_num_per_client = 10\\n30|num_new_clients = 0\\n31|fine_tuning_epoch_new = 0\\n32|feature_dim = 512\\n33|vocab_size = 80\\n34|max_len = 200\\n35|few_shot = 0\\n36|client_drop_rate = 0.0\\n37|train_slow_rate = 0.0\\n38|send_slow_rate = 0.0\\n39|time_select = False\\n40|time_threthold = 10000\\n41|beta = 0.0\\n42|lamda = 1.0\\n43|mu = 0.01\\n44|K = 5\\n45|p_learning_rate = 0.01\\n46|M = 5\\n47|itk = 4000\\n48|alphaK = 1.0\\n49|sigma = 1.0\\n50|alpha = 1.0\\n51|plocal_epochs = 1\\n52|tau = 1.0\\n53|fine_tuning_epochs = 10\\n54|dr_learning_rate = 0.0\\n55|L = 1.0\\n56|noise_dim = 512\\n57|generator_learning_rate = 0.005\\n58|hidden_dim = 512\\n59|server_epochs = 1000\\n60|localize_feature_extractor = False\\n61|server_learning_rate = 1.0\\n62|eta = 1.0\\n63|rand_percent = 80\\n64|layer_idx = 2\\n65|mentee_learning_rate = 0.005\\n66|T_start = 0.95\\n67|T_end = 0.98\\n68|momentum = 0.9\\n69|kl_weight = 0.0\\n70|first_stage_bound = 0\\n71|fedcross_alpha = 0.99\\n72|collaberative_model_select_strategy = 1\\n73|warmup_rounds = 0\\n74|pretrained_path = none\\n75|model_name = ResNet18\\n76|mixup_alpha = 0.0\\n77|aux_weight = 0\\n78|attention = none\\n79|multi_gpu = False\\n80|client_device_map = {0: device(type='cuda'), 1: device(type='cuda'), 2: device(type='cuda'), 3: device(type='cuda'), 4: device(type='cuda'), 5: device(type='cuda'), 6: device(type='cuda'), 7: device(type='cuda'), 8: device(type='cuda'), 9: device(type='cuda'), 10: device(type='cuda'), 11: device(type='cuda'), 12: device(type='cuda'), 13: device(type='cuda'), 14: device(type='cuda'), 15: device(type='cuda'), 16: device(type='cuda'), 17: device(type='cuda'), 18: device(type='cuda'), 19: device(type='cuda')}\\n81|==================================================\\n82|\\n83|============= Running time: 0th =============\\n84|Creating server and clients ...\\n85|[预训练] -pp=none，使用随机初始化\\n86|>>> [V2增强] 已注入 ColorJitter + RandomErasing 数据增强\\n87|[模型] ResNet18 (num_classes=10)\\n88|Join ratio / total clients: 1 / 20\\n89|Finished creating server and clients.\\n90|-------------Round number: 0 -------------\\n91|Evaluate global model\\n92|Averaged Train Loss: 2.3088\\n93|Averaged Test Accuracy: 0.0952\\n94|Averaged Test AUC: 0.4939\\n95|Std Test Accuracy: 0.1239\\n96|Std Test AUC: 0.0944\\n97|------------------------- time cost ------------------------- 147.54365468025208\\n98|-------------Round number: 1 -------------\\n99|Evaluate global model\\n100|Averaged Train Loss: 2.3071\\n101|Averaged Test Accuracy: 0.0911\\n102|Averaged Test AUC: 0.5141\\n103|Std Test Accuracy: 0.0729\\n104|Std Test AUC: 0.0990\\n105|------------------------- time cost ------------------------- 154.6066358089447\\n106|-------------Round number: 2 -------------\\n107|Evaluate global model\\n108|Averaged Train Loss: 2.3079\\n109|Averaged Test Accuracy: 0.0932\\n110|Averaged Test AUC: 0.4904\\n111|Std Test Accuracy: 0.1072\\n112|Std Test AUC: 0.0776\\n113|------------------------- time cost ------------------------- 156.08541750907898\\n114|-------------Round number: 3 -------------\\n115|Evaluate global model\\n116|Averaged Train Loss: 2.3023\\n117|Averaged Test Accuracy: 0.1242\\n118|Averaged Test AUC: 0.5254\\n119|Std Test Accuracy: 0.1310\\n120|Std Test AUC: 0.1110\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_7kNlSv7cE5bYFolJuyA27B5K" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"### /home/user-lbhzj/pw/PFLlib-master/system/main_v2.py\\nKW FedNova\\n37|from flcore.servers.servernova import FedNova\\n820| elif args.algorithm == \\\"FedNova\\\":\\n821| server = FedNova(args, i)\\nKW servernova\\n37|from flcore.servers.servernova import FedNova\\nKW algo\\n722| if args.algorithm == \\\"FedProx（动态μ）\\\":\\n723| args.algorithm = \\\"FedProxV2\\\"\\n725| # select algorithm\\n726| if args.algorithm == \\\"FedAvg\\\":\\n732| elif args.algorithm == \\\"Local\\\":\\n735| elif args.algorithm == \\\"FedMTL\\\":\\n738| elif args.algorithm == \\\"PerAvg\\\":\\n741| elif args.algorithm == \\\"pFedMe\\\":\\nKW if args.algo\\n722| if args.algorithm == \\\"FedProx（动态μ）\\\":\\n726| if args.algorithm == \\\"FedAvg\\\":\\n732| elif args.algorithm == \\\"Local\\\":\\n735| elif args.algorithm == \\\"FedMTL\\\":\\n738| elif args.algorithm == \\\"PerAvg\\\":\\n741| elif args.algorithm == \\\"pFedMe\\\":\\n744| elif args.algorithm == \\\"FedProx\\\":\\n748| elif args.algorithm == \\\"FedProxV2\\\":\\nKW args.algo\\n722| if args.algorithm == \\\"FedProx（动态μ）\\\":\\n723| args.algorithm = \\\"FedProxV2\\\"\\n726| if args.algorithm == \\\"FedAvg\\\":\\n732| elif args.algorithm == \\\"Local\\\":\\n735| elif args.algorithm == \\\"FedMTL\\\":\\n738| elif args.algorithm == \\\"PerAvg\\\":\\n741| elif args.algorithm == \\\"pFedMe\\\":\\n744| elif args.algorithm == \\\"FedProx\\\":\\n\\n### /home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py\\nKW FedNova\\n28| # FedNova: 累计梯度\\n55| # FedNova: 累计梯度 (归一化)\\n64| # FedNova: 计算归一化因子\\n\\n### /home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/servernova.py\\nKW FedNova\\n8|class FedNova(Server):\\nKW clientnova\\n3|from flcore.clients.clientnova import clientNova\\nKW global_model\\n64| self.save_global_model()\\n108| global_model = [p.clone() for p in self.global_model.parameters()]\\n110| for param_idx, _ in enumerate(global_model):\\n123| param = global_model[param_idx]\\n126| for param, new_param in zip(self.global_model.parameters(), global_model):\\nKW send_models\\n35| self.send_models()\\nKW aggregate\\n50| self.aggregate_parameters()\\n103| def aggregate_parameters(self):\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_Uezzpk68wbUUglX0D9mbALdd" }, { "role": "assistant", "content": "", "reasoning": "**Investigating code issues**\n\nI need to inspect relevant code lines to figure out why it's not effective. There might be a missing call to clientNova, so I'll check the surrounding code. I'll read specific lines and log details, noting that it indicates only 0-16 rounds and shows random accuracy. It could be that the algorithm isn’t effective due to the wrong launch command or augmentation. I want to confirm if local training is happening and look for keywords like \"训练\", \"client\", and \"send\" in the logs.\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Investigating code issues**\n\nI need to inspect relevant code lines to figure out why it's not effective. There might be a missing call to clientNova, so I'll check the surrounding code. I'll read specific lines and log details, noting that it indicates only 0-16 rounds and shows random accuracy. It could be that the algorithm isn’t effective due to the wrong launch command or augmentation. I want to confirm if local training is happening and look for keywords like \"训练\", \"client\", and \"send\" in the logs.\n\n", "tool_calls": [ { "id": "call_2LOcyKMdO0bT0VxiROEQCpdP", "call_id": "call_2LOcyKMdO0bT0VxiROEQCpdP", "response_item_id": "fc_2LOcyKMdO0bT0VxiROEQCpdP", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\np=Path('/home/user-lbhzj/pw/PFLlib-master/system/FedNova_cifar10.log')\\nlines=p.read_text(errors='ignore').splitlines()\\nfor kw in ['Train', 'client', 'Client', 'send', 'aggregate', 'loss', 'Updating', 'backprop', 'epoch', 'Local']:\\n hits=[(i+1,l) for i,l in enumerate(lines) if kw in l]\\n print('KW',kw,'COUNT',len(hits))\\n for i,l in hits[:20]:\\n print(f'{i}|{l}')\\n print('---')\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } }, { "id": "call_5TrBFyCx40TzhKh2EpaqmwpL", "call_id": "call_5TrBFyCx40TzhKh2EpaqmwpL", "response_item_id": "fc_5TrBFyCx40TzhKh2EpaqmwpL", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\nfor path in ['/home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py','/home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/servernova.py']:\\n print('###',path)\\n lines=Path(path).read_text(errors='ignore').splitlines()\\n for start in [1,1,1,1]:\\n pass\\n for i,l in enumerate(lines, start=1):\\n if i<=220:\\n print(f'{i}|{l}')\\n print('END',path)\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"KW Train COUNT 17\\n92|Averaged Train Loss: 2.3088\\n100|Averaged Train Loss: 2.3071\\n108|Averaged Train Loss: 2.3079\\n116|Averaged Train Loss: 2.3023\\n124|Averaged Train Loss: 2.3080\\n132|Averaged Train Loss: 2.3088\\n140|Averaged Train Loss: 2.3053\\n148|Averaged Train Loss: 2.3034\\n156|Averaged Train Loss: 2.3082\\n164|Averaged Train Loss: 2.3021\\n172|Averaged Train Loss: 2.3040\\n180|Averaged Train Loss: 2.3035\\n188|Averaged Train Loss: 2.3021\\n196|Averaged Train Loss: 2.2988\\n204|Averaged Train Loss: 2.3004\\n212|Averaged Train Loss: 2.3047\\n220|Averaged Train Loss: 2.3054\\n---\\nKW client COUNT 8\\n21|num_clients = 20\\n29|batch_num_per_client = 10\\n30|num_new_clients = 0\\n36|client_drop_rate = 0.0\\n80|client_device_map = {0: device(type='cuda'), 1: device(type='cuda'), 2: device(type='cuda'), 3: device(type='cuda'), 4: device(type='cuda'), 5: device(type='cuda'), 6: device(type='cuda'), 7: device(type='cuda'), 8: device(type='cuda'), 9: device(type='cuda'), 10: device(type='cuda'), 11: device(type='cuda'), 12: device(type='cuda'), 13: device(type='cuda'), 14: device(type='cuda'), 15: device(type='cuda'), 16: device(type='cuda'), 17: device(type='cuda'), 18: device(type='cuda'), 19: device(type='cuda')}\\n84|Creating server and clients ...\\n88|Join ratio / total clients: 1 / 20\\n89|Finished creating server and clients.\\n---\\nKW Client COUNT 0\\n---\\nKW send COUNT 1\\n38|send_slow_rate = 0.0\\n---\\nKW aggregate COUNT 0\\n---\\nKW loss COUNT 0\\n---\\nKW Updating COUNT 0\\n---\\nKW backprop COUNT 0\\n---\\nKW epoch COUNT 5\\n16|local_epochs = 3\\n31|fine_tuning_epoch_new = 0\\n51|plocal_epochs = 1\\n53|fine_tuning_epochs = 10\\n59|server_epochs = 1000\\n---\\nKW Local COUNT 0\\n---\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_2LOcyKMdO0bT0VxiROEQCpdP" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"### /home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py\\n1|import torch\\n2|import numpy as np\\n3|import time\\n4|import copy\\n5|import torch.nn as nn\\n6|from torch.cuda.amp import autocast\\n7|from flcore.clients.clientbase import Client\\n8|\\n9|\\n10|class clientNova(Client):\\n11| def __init__(self, args, id, train_samples, test_samples, **kwargs):\\n12| super().__init__(args, id, train_samples, test_samples, **kwargs)\\n13| \\n14| self.momentum = args.momentum\\n15| self.weight_decay = args.weight_decay\\n16| # global_params 存CPU节省显存\\n17| self.global_params = [p.detach().cpu().clone() for p in self.model.parameters()]\\n18| \\n19| self.loss = nn.CrossEntropyLoss()\\n20| \\n21| self.optimizer = torch.optim.SGD(\\n22| self.model.parameters(), \\n23| lr=self.learning_rate, \\n24| momentum=self.momentum, \\n25| weight_decay=self.weight_decay\\n26| )\\n27| \\n28| # FedNova: 累计梯度\\n29| self.cum_grad = [torch.zeros_like(p) for p in self.model.parameters()]\\n30| self.local_steps = 0\\n31| self.local_normalizing_vec = 0.0\\n32| \\n33| def train(self):\\n34| trainloader = self.load_train_data()\\n35| start_time = time.time()\\n36| self.model.train()\\n37| \\n38| max_local_epochs = self.local_epochs\\n39| if self.train_slow:\\n40| max_local_epochs = np.random.randint(1, max_local_epochs // 2)\\n41| \\n42| for epoch in range(max_local_epochs):\\n43| for x, y in trainloader:\\n44| x, y = x.to(self.device), y.to(self.device)\\n45| \\n46| self.optimizer.zero_grad()\\n47| with autocast(enabled=self.use_amp):\\n48| output = self.model(x)\\n49| loss = self.loss(output, y)\\n50| \\n51| self.scaler.scale(loss).backward()\\n52| self.scaler.step(self.optimizer)\\n53| self.scaler.update()\\n54| \\n55| # FedNova: 累计梯度 (归一化)\\n56| with torch.no_grad():\\n57| for p, cg in zip(self.model.parameters(), self.cum_grad):\\n58| # 梯度乘以学习率\\n59| if p.grad is not None:\\n60| cg.add_(p.grad.data * self.learning_rate)\\n61| \\n62| self.local_steps += 1\\n63| \\n64| # FedNova: 计算归一化因子\\n65| self.local_normalizing_vec = self.local_steps\\n66| # 如果有动量，需要额外计算\\n67| if self.momentum > 0:\\n68| self.local_normalizing_vec = self.local_steps * (1 - self.momentum) if self.momentum < 1 else self.local_steps\\n69| \\n70| self.train_time_cost['num_rounds'] += 1\\n71| self.train_time_cost['total_cost'] += time.time() - start_time\\n72| \\n73| # 返回用于聚合的信息\\n74| self.weight = self.train_samples\\n75| \\n76| def set_parameters(self, model):\\n77| for new_param, global_param, param in zip(model.parameters(), self.global_params, self.model.parameters()):\\n78| global_param.data = new_param.data.detach().cpu().clone()\\n79| param.data = new_param.data.clone()\\n80| # 重置累计梯度\\n81| for cg in self.cum_grad:\\n82| cg.zero_()\\n83| self.local_steps = 0\\n84| self.local_normalizing_vec = 0.0\\n85| \\n86| def get_grads(self):\\n87| # 返回累计梯度和归一化因子\\n88| return self.cum_grad, self.local_normalizing_vec, self.weight\\n89| \\n90| def train_metrics(self):\\n91| trainloader = self.load_train_data()\\n92| self.model.eval()\\n93| \\n94| train_num = 0\\n95| losses = 0\\n96| with torch.no_grad():\\n97| for x, y in trainloader:\\n98| x, y = x.to(self.device), y.to(self.device)\\n99| output = self.model(x)\\n100| loss = self.loss(output, y)\\n101| train_num += y.shape[0]\\n102| losses += loss.item() * y.shape[0]\\n103| \\n104| return losses, train_num\\nEND /home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientnova.py\\n### /home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/servernova.py\\n1|import time\\n2|import torch\\n3|from flcore.clients.clientnova import clientNova\\n4|from flcore.servers.serverbase import Server\\n5|from threading import Thread\\n6|\\n7|\\n8|class FedNova(Server):\\n9| def __init__(self, args, times):\\n10| super().__init__(args, times)\\n11| \\n12| self.set_slow_clients()\\n13| self.set_clients(clientNova)\\n14| \\n15| print(\\\"Join ratio / total clients:\\\", self.join_ratio, \\\"/\\\", self.num_clients)\\n16| print(\\\"Finished creating server and clients.\\\")\\n17| \\n18| self.Budget = []\\n19| \\n20| def train(self):\\n21| for i in range(self.global_rounds+1):\\n22| s_t = time.time()\\n23| \\n24| if self.args.learning_rate_decay:\\n25| count = 0\\n26| for m in self.args.lr_decay_milestones:\\n27| if i >= m:\\n28| count += 1\\n29| self.learning_rate = self.args.local_learning_rate * (self.args.learning_rate_decay_gamma ** count)\\n30| \\n31| self.selected_clients = self.select_clients()\\n32| \\n33| for client in self.selected_clients:\\n34| client.learning_rate = self.learning_rate\\n35| self.send_models()\\n36| \\n37| if i % self.eval_gap == 0:\\n38| print(\\\"-------------Round number:\\\", i, \\\"-------------\\\")\\n39| print(\\\"Evaluate global model\\\")\\n40| self.evaluate()\\n41| \\n42| for client in self.selected_clients:\\n43| client.train()\\n44| \\n45| self.receive_models()\\n46| \\n47| if self.dlg_eval and i % self.dlg_gap == 0:\\n48| self.call_dlg(i)\\n49| \\n50| self.aggregate_parameters()\\n51| \\n52| self.Budget.append(time.time() - s_t)\\n53| print(\\\"-\\\" * 25, \\\"time cost\\\", \\\"-\\\" * 25, self.Budget[-1])\\n54| \\n55| if self.auto_break and self.check_done(acc_lss=[self.rs_test_acc], top_cnt=self.top_cnt):\\n56| break\\n57| \\n58| print(\\\"Best accuracy.\\\")\\n59| print(max(self.rs_test_acc))\\n60| print(\\\"Average time cost per round.\\\")\\n61| print(sum(self.Budget[1:]) / len(self.Budget[1:]))\\n62| \\n63| self.save_results()\\n64| self.save_global_model()\\n65| \\n66| if self.num_new_clients > 0:\\n67| self.eval_new_clients = True\\n68| self.set_new_clients(clientNova)\\n69| print(\\\"Fine tuning round\\\")\\n70| print(\\\"Evaluate new clients\\\")\\n71| self.evaluate()\\n72| \\n73| def receive_models(self):\\n74| assert len(self.selected_clients) > 0\\n75| \\n76| active_clients = self.selected_clients\\n77| \\n78| self.uploaded_ids = []\\n79| self.uploaded_weights = []\\n80| self.uploaded_grads = []\\n81| self.uploaded_normalizing = []\\n82| tot_samples = 0\\n83| \\n84| for client in active_clients:\\n85| try:\\n86| client_time_cost = client.train_time_cost[\\\"total_cost\\\"] / client.train_time_cost[\\\"num_rounds\\\"] + \\\\\\n87| client.send_time_cost[\\\"total_cost\\\"] / client.send_time_cost[\\\"num_rounds\\\"]\\n88| except ZeroDivisionError:\\n89| client_time_cost = 0\\n90| \\n91| if client_time_cost <= self.time_threthold:\\n92| tot_samples += client.train_samples\\n93| self.uploaded_ids.append(client.id)\\n94| self.uploaded_weights.append(client.train_samples)\\n95| \\n96| cum_grad, norm_vec, weight = client.get_grads()\\n97| self.uploaded_grads.append(cum_grad)\\n98| self.uploaded_normalizing.append(norm_vec)\\n99| \\n100| for i, w in enumerate(self.uploaded_weights):\\n101| self.uploaded_weights[i] = w / tot_samples if tot_samples > 0 else w\\n102| \\n103| def aggregate_parameters(self):\\n104| assert len(self.uploaded_grads) > 0\\n105| \\n106| total_norm = sum(self.uploaded_normalizing)\\n107| \\n108| global_model = [p.clone() for p in self.global_model.parameters()]\\n109| \\n110| for param_idx, _ in enumerate(global_model):\\n111| weighted_grad = None\\n112| \\n113| for cum_grad, norm_vec, weight in zip(self.uploaded_grads, self.uploaded_normalizing, self.uploaded_weights):\\n114| norm_factor = norm_vec / total_norm if total_norm > 0 else 1.0\\n115| scaled_grad = cum_grad[param_idx] * norm_factor * weight\\n116| \\n117| if weighted_grad is None:\\n118| weighted_grad = scaled_grad\\n119| else:\\n120| weighted_grad.add_(scaled_grad)\\n121| \\n122| if weighted_grad is not None:\\n123| param = global_model[param_idx]\\n124| param.data.sub_(weighted_grad)\\n125| \\n126| for param, new_param in zip(self.global_model.parameters(), global_model):\\n127| param.data = new_param.data.clone()\\nEND /home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/servernova.py\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_5TrBFyCx40TzhKh2EpaqmwpL" }, { "role": "assistant", "content": "", "reasoning": "**Analyzing FedNova issues**\n\nI need to compare with the canonical FedNova and check if the server uses `receive_models` and aggregate properly. There might be a bug in `clientnova.train()`: local_steps isn't resetting between rounds. It seems like the model isn't learning with accuracy stuck around 9-16%. There are multiple potential reasons: could the baseline settings or augmentation be an issue? The launch command looks correct, but I need to verify if the algorithm is running as expected.\n\n**Inspecting client/server logic**\n\nThe server class is set to `set_clients(clientNova)`, so that's clear. However, I wonder if there's a logic bug between `clientnova` and `servernova`. I need to check if `client.get_grads` is returning cumulative gradients. In `train()`, after using `scaler.step`, it seems that `p.grad` might be lost. But after the optimizer step, `p.grad` remains, which is good. Still, it’s possible that using the AMP scaler with step/zero could lead to issues with the scaling.\n\n**Diagnosing implementation issues**\n\nI’m considering that the main_v2 default might inject augmentation into all algorithms, but it’s not leading to random accuracy. Maybe I don’t need to compare this with logs from other algorithms. However, my diagnosis should include evidence and a minimal suggestion. I could propose that the issue is more likely with the client/server Nova implementation being mathematically off or incomplete, rather than just something generic like launch or freeze.\n\n**Evaluating client-server reconciliation**\n\nI'm considering how the client updated locally, so the server needs to reconcile based on the delta. If I use raw gradients from local training, I might misestimate and cancel out the learning process, which seems like the strongest diagnosis. I wonder if there's a bug in `train_slow` that's causing only zero or one epoch to be processed. But I’m not seeing that in the logs, so that’s puzzling.\n\n**Analyzing training log**\n\nI’m trying to figure out what happened with the training log, which only shows up to 224 lines, terminating at round 16. It suggests the run could still be ongoing or got truncated. I wonder if there’s anything beyond line 224, or if it stopped due to early stopping or an error, especially since there's no mention of \"Best accuracy.\" I’ll need to investigate the log for any potential errors or abrupt cutoffs to clarify this situation further.\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Analyzing FedNova issues**\n\nI need to compare with the canonical FedNova and check if the server uses `receive_models` and aggregate properly. There might be a bug in `clientnova.train()`: local_steps isn't resetting between rounds. It seems like the model isn't learning with accuracy stuck around 9-16%. There are multiple potential reasons: could the baseline settings or augmentation be an issue? The launch command looks correct, but I need to verify if the algorithm is running as expected.\n\n**Inspecting client/server logic**\n\nThe server class is set to `set_clients(clientNova)`, so that's clear. However, I wonder if there's a logic bug between `clientnova` and `servernova`. I need to check if `client.get_grads` is returning cumulative gradients. In `train()`, after using `scaler.step`, it seems that `p.grad` might be lost. But after the optimizer step, `p.grad` remains, which is good. Still, it’s possible that using the AMP scaler with step/zero could lead to issues with the scaling.\n\n**Diagnosing implementation issues**\n\nI’m considering that the main_v2 default might inject augmentation into all algorithms, but it’s not leading to random accuracy. Maybe I don’t need to compare this with logs from other algorithms. However, my diagnosis should include evidence and a minimal suggestion. I could propose that the issue is more likely with the client/server Nova implementation being mathematically off or incomplete, rather than just something generic like launch or freeze.\n\n**Evaluating client-server reconciliation**\n\nI'm considering how the client updated locally, so the server needs to reconcile based on the delta. If I use raw gradients from local training, I might misestimate and cancel out the learning process, which seems like the strongest diagnosis. I wonder if there's a bug in `train_slow` that's causing only zero or one epoch to be processed. But I’m not seeing that in the logs, so that’s puzzling.\n\n**Analyzing training log**\n\nI’m trying to figure out what happened with the training log, which only shows up to 224 lines, terminating at round 16. It suggests the run could still be ongoing or got truncated. I wonder if there’s anything beyond line 224, or if it stopped due to early stopping or an error, especially since there's no mention of \"Best accuracy.\" I’ll need to investigate the log for any potential errors or abrupt cutoffs to clarify this situation further.\n\n", "tool_calls": [ { "id": "call_YjxX5leHO1weU1StEQJ6RsDO", "call_id": "call_YjxX5leHO1weU1StEQJ6RsDO", "response_item_id": "fc_YjxX5leHO1weU1StEQJ6RsDO", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\np=Path('/home/user-lbhzj/pw/PFLlib-master/system/FedNova_cifar10.log')\\ntext=p.read_text(errors='ignore')\\nfor kw in ['Traceback','Error','Exception','RuntimeError','NameError','CUDA','Killed','Segmentation','nan','inf','best accuracy','Finished','Best accuracy']:\\n if kw.lower() in text.lower():\\n print('FOUND',kw)\\n for i,l in enumerate(text.splitlines(), start=1):\\n if kw.lower() in l.lower():\\n print(f'{i}|{l}')\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } }, { "id": "call_Vn0h4ototl7l3sIHxDd2zoaD", "call_id": "call_Vn0h4ototl7l3sIHxDd2zoaD", "response_item_id": "fc_Vn0h4ototl7l3sIHxDd2zoaD", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\np=Path('/home/user-lbhzj/pw/PFLlib-master/system/FedNova_cifar10.log')\\nlines=p.read_text(errors='ignore').splitlines()\\nprint('LAST5')\\nfor i,l in enumerate(lines[-5:], start=len(lines)-4):\\n print(f'{i}|{l}')\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"FOUND CUDA\\n4|device = cuda\\n80|client_device_map = {0: device(type='cuda'), 1: device(type='cuda'), 2: device(type='cuda'), 3: device(type='cuda'), 4: device(type='cuda'), 5: device(type='cuda'), 6: device(type='cuda'), 7: device(type='cuda'), 8: device(type='cuda'), 9: device(type='cuda'), 10: device(type='cuda'), 11: device(type='cuda'), 12: device(type='cuda'), 13: device(type='cuda'), 14: device(type='cuda'), 15: device(type='cuda'), 16: device(type='cuda'), 17: device(type='cuda'), 18: device(type='cuda'), 19: device(type='cuda')}\\nFOUND Finished\\n89|Finished creating server and clients.\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_YjxX5leHO1weU1StEQJ6RsDO" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"LAST5\\n223|Std Test Accuracy: 0.0952\\n224|Std Test AUC: 0.0994\\n225|------------------------- time cost ------------------------- 159.56295132637024\\n226|-------------Round number: 17 -------------\\n227|Evaluate global model\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_Vn0h4ototl7l3sIHxDd2zoaD" }, { "role": "assistant", "content": "", "reasoning": "**Evaluating aggregation issues**\n\nI'm looking into an issue where they're subtracting weighted_grad from the global model, and it seems like the normalization isn't being done correctly. The local_normalizing_vec is set simply based on local_steps with momentum, which might be too simplistic. There’s a chance the algorithm is ineffective due to behavior being almost random with a training loss around 2.30. I need to check if the command to launch was right and if the join ratio reflects all clients participating in the training. Let’s dig deeper!\n\n**Examining model configuration**\n\nI think I should check the main_v2 selection and model algorithm mapping to ensure the FedNova server is properly instantiated. I need to look at lines around 820 in the code and also at args.parse to see if they support the algorithm running correctly. In the log, lines 17 discuss the FedNova algorithm, while lines 84-89 detail server creation. I also need to check for any freezes related to the -pp none option, but it looks good since it indicates the pretrained model is not frozen.\n\n**Clarifying algorithm parameters**\n\nI need to consider the baseline settings, which say to use -ma 0. The log from the current run shows mixup_alpha set at 0.0 and no attention applied, which is an interesting mix. The reference I looked at earlier states that for the baseline, I should stick to -ma 0. But now I need to figure out why the new algorithm doesn’t seem to be performing effectively. Exploring this will be crucial to understanding the results better.\n\n**Investigating model updates**\n\nI should check if `global_model` is updated in `aggregate_parameters` and confirm it's copied back to `self.global_model`. It does get updated, but evaluation happens before local training, making logs show the pre-update model, which can make improvements seem delayed. The accuracy fluctuates between 10-16 and does not seem to improve much, possibly staying below 0.17. I might want to look at later rounds and review the latest log, as training is still ongoing but doesn't appear effective.\n\n**Checking remote access issues**\n\nI could check if the process \"pw\" exists, but remote access via SSH failed due to password authentication, which might not be strictly necessary. The user provided the path on the remote server, so it seems the local environment might lack access. However, we did successfully use `sshpass` in the terminal before. It’s puzzling, but maybe I need to look deeper into why the SSH connection isn’t working this time. Let's explore what could be happening!\n\n**Investigating sshpass and processes**\n\nI’m looking into why sshpass worked for us. It seems like it did, which is great news! I’m considering whether I need to check if the process is still running or if that’s necessary at this point. It might help to see if there are any client training logs missing, especially if the logs only show evaluation and timing. I should also see if clientNova differs from the base client, particularly regarding the `train` method. Additionally, I wonder if local_steps are ever reset during rounds.\n\n**Examining model parameter handling**\n\nI'm seeing that the cumulative gradient is reset in `set_parameters`, which is good. The function zips together model parameters and updates the global parameters, which seems fine. However, there's this concern that `client.train()` in the FedNova server doesn’t call `client.set_parameters` in each round. Instead, it looks like `send_models()` sets the client model. I must check if `send_models` is indeed calling `set_parameters(self.global_model)`. It’s crucial to inspect if `global_model` is employed on the GPU too, so I need to search for both `send_models` and `set_parameters` definitions.\n\n", "finish_reason": "tool_calls", "reasoning_content": "**Evaluating aggregation issues**\n\nI'm looking into an issue where they're subtracting weighted_grad from the global model, and it seems like the normalization isn't being done correctly. The local_normalizing_vec is set simply based on local_steps with momentum, which might be too simplistic. There’s a chance the algorithm is ineffective due to behavior being almost random with a training loss around 2.30. I need to check if the command to launch was right and if the join ratio reflects all clients participating in the training. Let’s dig deeper!\n\n**Examining model configuration**\n\nI think I should check the main_v2 selection and model algorithm mapping to ensure the FedNova server is properly instantiated. I need to look at lines around 820 in the code and also at args.parse to see if they support the algorithm running correctly. In the log, lines 17 discuss the FedNova algorithm, while lines 84-89 detail server creation. I also need to check for any freezes related to the -pp none option, but it looks good since it indicates the pretrained model is not frozen.\n\n**Clarifying algorithm parameters**\n\nI need to consider the baseline settings, which say to use -ma 0. The log from the current run shows mixup_alpha set at 0.0 and no attention applied, which is an interesting mix. The reference I looked at earlier states that for the baseline, I should stick to -ma 0. But now I need to figure out why the new algorithm doesn’t seem to be performing effectively. Exploring this will be crucial to understanding the results better.\n\n**Investigating model updates**\n\nI should check if `global_model` is updated in `aggregate_parameters` and confirm it's copied back to `self.global_model`. It does get updated, but evaluation happens before local training, making logs show the pre-update model, which can make improvements seem delayed. The accuracy fluctuates between 10-16 and does not seem to improve much, possibly staying below 0.17. I might want to look at later rounds and review the latest log, as training is still ongoing but doesn't appear effective.\n\n**Checking remote access issues**\n\nI could check if the process \"pw\" exists, but remote access via SSH failed due to password authentication, which might not be strictly necessary. The user provided the path on the remote server, so it seems the local environment might lack access. However, we did successfully use `sshpass` in the terminal before. It’s puzzling, but maybe I need to look deeper into why the SSH connection isn’t working this time. Let's explore what could be happening!\n\n**Investigating sshpass and processes**\n\nI’m looking into why sshpass worked for us. It seems like it did, which is great news! I’m considering whether I need to check if the process is still running or if that’s necessary at this point. It might help to see if there are any client training logs missing, especially if the logs only show evaluation and timing. I should also see if clientNova differs from the base client, particularly regarding the `train` method. Additionally, I wonder if local_steps are ever reset during rounds.\n\n**Examining model parameter handling**\n\nI'm seeing that the cumulative gradient is reset in `set_parameters`, which is good. The function zips together model parameters and updates the global parameters, which seems fine. However, there's this concern that `client.train()` in the FedNova server doesn’t call `client.set_parameters` in each round. Instead, it looks like `send_models()` sets the client model. I must check if `send_models` is indeed calling `set_parameters(self.global_model)`. It’s crucial to inspect if `global_model` is employed on the GPU too, so I need to search for both `send_models` and `set_parameters` definitions.\n\n", "tool_calls": [ { "id": "call_y11OqVhTjiFw3g12CPQgthpy", "call_id": "call_y11OqVhTjiFw3g12CPQgthpy", "response_item_id": "fc_y11OqVhTjiFw3g12CPQgthpy", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\npaths=['/home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/serverbase.py','/home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientbase.py']\\nfor path in paths:\\n print('###',path)\\n lines=Path(path).read_text(errors='ignore').splitlines()\\n for kw in ['def send_models', 'def aggregate_parameters', 'def set_parameters', 'def train(', 'def receive_models', 'def select_clients', 'self.global_model', 'self.clients']:\\n hits=[(i+1,l) for i,l in enumerate(lines) if kw in l]\\n if hits:\\n print('KW',kw)\\n for i,l in hits[:10]:\\n print(f'{i}|{l}')\\n print()\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } }, { "id": "call_e4B6UVkhj2pgbbggMH9dU3wg", "call_id": "call_e4B6UVkhj2pgbbggMH9dU3wg", "response_item_id": "fc_e4B6UVkhj2pgbbggMH9dU3wg", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":false,\"command\":\"sshpass -p 'lbhzj' ssh -o StrictHostKeyChecking=no user-lbhzj@100.78.7.50 \\\"python3 - <<'PY'\\nfrom pathlib import Path\\nfor path in ['/home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/serverbase.py','/home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientbase.py']:\\n print('###',path)\\n lines=Path(path).read_text(errors='ignore').splitlines()\\n for i,l in enumerate(lines[:220], start=1):\\n print(f'{i}|{l}')\\n print('END',path)\\nPY\\\"\",\"notify_on_complete\":false,\"pty\":false,\"timeout\":60,\"watch_patterns\":[],\"workdir\":\"/home/ubuntu\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"### /home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/serverbase.py\\nKW def send_models\\n156| def send_models(self):\\nKW def aggregate_parameters\\n192| def aggregate_parameters(self):\\nKW def receive_models\\n167| def receive_models(self):\\nKW def select_clients\\n147| def select_clients(self):\\nKW self.global_model\\n23| self.global_model = copy.deepcopy(args.model)\\n89| current_model_keys = self.global_model.state_dict().keys()\\n109| self.global_model.load_state_dict(new_state_dict, strict=True)\\n162| client.set_parameters(self.global_model)\\n195| self.global_model = copy.deepcopy(self.uploaded_models[0])\\n196| for param in self.global_model.parameters():\\n203| for server_param, client_param in zip(self.global_model.parameters(), client_model.parameters()):\\n211| torch.save(self.global_model, model_path)\\n217| self.global_model = torch.load(model_path)\\n252| m_name = getattr(self.args, 'model_name', self.global_model.__class__.__name__)\\nKW self.clients\\n38| self.clients = []\\n129| self.clients.append(client)\\n152| selected_clients = list(np.random.choice(self.clients, self.current_num_join_clients, replace=False))\\n157| assert (len(self.clients) > 0)\\n159| for client in self.clients:\\n308| for c in self.clients:\\n314| ids = [c.id for c in self.clients]\\n324| for c in self.clients:\\n329| ids = [c.id for c in self.clients]\\n426| trainloader = self.clients[cid].load_train_data()\\n\\n### /home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientbase.py\\nKW def set_parameters\\n98| def set_parameters(self, model):\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_y11OqVhTjiFw3g12CPQgthpy" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"### /home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/serverbase.py\\n1|import torch\\n2|import os\\n3|import numpy as np\\n4|import h5py\\n5|import copy\\n6|import time\\n7|import random\\n8|from utils.data_utils import read_client_data\\n9|from utils.dlg import DLG\\n10|\\n11|\\n12|class Server(object):\\n13| def __init__(self, args, times):\\n14| # Set up the main attributes\\n15| self.args = args\\n16| self.device = args.device\\n17| self.dataset = args.dataset\\n18| self.num_classes = args.num_classes\\n19| self.global_rounds = args.global_rounds\\n20| self.local_epochs = args.local_epochs\\n21| self.batch_size = args.batch_size\\n22| self.learning_rate = args.local_learning_rate\\n23| self.global_model = copy.deepcopy(args.model)\\n24| self.num_clients = args.num_clients\\n25| self.join_ratio = args.join_ratio\\n26| self.random_join_ratio = args.random_join_ratio\\n27| self.num_join_clients = int(self.num_clients * self.join_ratio)\\n28| self.current_num_join_clients = self.num_join_clients\\n29| self.few_shot = args.few_shot\\n30| self.algorithm = args.algorithm\\n31| self.time_select = args.time_select\\n32| self.goal = args.goal\\n33| self.time_threthold = args.time_threthold\\n34| self.save_folder_name = args.save_folder_name\\n35| self.top_cnt = args.top_cnt\\n36| self.auto_break = args.auto_break\\n37|\\n38| self.clients = []\\n39| self.selected_clients = []\\n40| self.train_slow_clients = []\\n41| self.send_slow_clients = []\\n42|\\n43| self.uploaded_weights = []\\n44| self.uploaded_ids = []\\n45| self.uploaded_models = []\\n46|\\n47| self.rs_test_acc = []\\n48| self.rs_test_auc = []\\n49| self.rs_train_loss = []\\n50|\\n51| self.times = times\\n52| self.eval_gap = args.eval_gap\\n53| self.client_drop_rate = args.client_drop_rate\\n54| self.train_slow_rate = args.train_slow_rate\\n55| self.send_slow_rate = args.send_slow_rate\\n56|\\n57| self.dlg_eval = args.dlg_eval\\n58| self.dlg_gap = args.dlg_gap\\n59| self.batch_num_per_client = args.batch_num_per_client\\n60|\\n61| self.num_new_clients = args.num_new_clients\\n62| self.new_clients = []\\n63| self.eval_new_clients = False\\n64| self.fine_tuning_epoch_new = args.fine_tuning_epoch_new\\n65| \\n66| \\n67| \\\"\\\"\\\"\\n68| # --- 针对 PFLlib 结构的智能自适应加载逻辑 ---\\n69| import torch\\n70| import os\\n71|\\n72| model_path = os.path.join(\\\"models\\\", self.dataset, f\\\"{self.algorithm}_{self.dataset}.pt\\\")\\n73|\\n74| if os.path.exists(model_path):\\n75| print(\\\"\\\\n\\\" + \\\"=\\\"*40)\\n76| print(f\\\"发现已有模型权重: {model_path}\\\")\\n77| \\n78| try:\\n79| loaded_data = torch.load(model_path, map_location=self.device)\\n80| \\n81| # 情况 A: 如果加载的是整个模型实例\\n82| if isinstance(loaded_data, torch.nn.Module):\\n83| state_dict = loaded_data.state_dict()\\n84| else:\\n85| state_dict = loaded_data\\n86|\\n87| # 情况 B: 自动处理 module. 前缀冲突\\n88| # 获取当前模型的所有 key\\n89| current_model_keys = self.global_model.state_dict().keys()\\n90| # 检查当前模型是否带 module.\\n91| model_has_module = any(k.startswith('module.') for k in current_model_keys)\\n92| # 检查保存的权重是否带 module.\\n93| data_has_module = any(k.startswith('module.') for k in state_dict.keys())\\n94|\\n95| new_state_dict = {}\\n96| if model_has_module and not data_has_module:\\n97| # 模型要前缀，数据没有 -> 给数据加上\\n98| for k, v in state_dict.items():\\n99| new_state_dict[\\\"module.\\\" + k] = v\\n100| elif not model_has_module and data_has_module:\\n101| # 模型不要前缀，数据有 -> 把数据去掉\\n102| for k, v in state_dict.items():\\n103| new_state_dict[k.replace(\\\"module.\\\", \\\"\\\")] = v\\n104| else:\\n105| # 两者一致，直接用\\n106| new_state_dict = state_dict\\n107| \\n108| # 正式加载，设置 strict=False 容错\\n109| self.global_model.load_state_dict(new_state_dict, strict=True)\\n110| print(\\\"权重自适应加载成功！\\\")\\n111| \\n112| except Exception as e:\\n113| print(f\\\"加载出错: {e}\\\")\\n114| print(\\\"程序将尝试继续运行...\\\")\\n115| print(\\\"=\\\"*40 + \\\"\\\\n\\\")\\n116| \\\"\\\"\\\"\\n117|\\n118|\\n119| def set_clients(self, clientObj):\\n120| for i, train_slow, send_slow in zip(range(self.num_clients), self.train_slow_clients, self.send_slow_clients):\\n121| train_data = read_client_data(self.dataset, i, is_train=True, few_shot=self.few_shot)\\n122| test_data = read_client_data(self.dataset, i, is_train=False, few_shot=self.few_shot)\\n123| client = clientObj(self.args, \\n124| id=i, \\n125| train_samples=len(train_data), \\n126| test_samples=len(test_data), \\n127| train_slow=train_slow, \\n128| send_slow=send_slow)\\n129| self.clients.append(client)\\n130|\\n131| # random select slow clients\\n132| def select_slow_clients(self, slow_rate):\\n133| slow_clients = [False for i in range(self.num_clients)]\\n134| idx = [i for i in range(self.num_clients)]\\n135| idx_ = np.random.choice(idx, int(slow_rate * self.num_clients))\\n136| for i in idx_:\\n137| slow_clients[i] = True\\n138|\\n139| return slow_clients\\n140|\\n141| def set_slow_clients(self):\\n142| self.train_slow_clients = self.select_slow_clients(\\n143| self.train_slow_rate)\\n144| self.send_slow_clients = self.select_slow_clients(\\n145| self.send_slow_rate)\\n146|\\n147| def select_clients(self):\\n148| if self.random_join_ratio:\\n149| self.current_num_join_clients = np.random.choice(range(self.num_join_clients, self.num_clients+1), 1, replace=False)[0]\\n150| else:\\n151| self.current_num_join_clients = self.num_join_clients\\n152| selected_clients = list(np.random.choice(self.clients, self.current_num_join_clients, replace=False))\\n153|\\n154| return selected_clients\\n155|\\n156| def send_models(self):\\n157| assert (len(self.clients) > 0)\\n158|\\n159| for client in self.clients:\\n160| start_time = time.time()\\n161| \\n162| client.set_parameters(self.global_model)\\n163|\\n164| client.send_time_cost['num_rounds'] += 1\\n165| client.send_time_cost['total_cost'] += 2 * (time.time() - start_time)\\n166|\\n167| def receive_models(self):\\n168| assert (len(self.selected_clients) > 0)\\n169|\\n170| active_clients = random.sample(\\n171| self.selected_clients, int((1-self.client_drop_rate) * self.current_num_join_clients))\\n172|\\n173| self.uploaded_ids = []\\n174| self.uploaded_weights = []\\n175| self.uploaded_models = []\\n176| tot_samples = 0\\n177| for client in active_clients:\\n178| try:\\n179| client_time_cost = client.train_time_cost['total_cost'] / client.train_time_cost['num_rounds'] + \\\\\\n180| client.send_time_cost['total_cost'] / client.send_time_cost['num_rounds']\\n181| except ZeroDivisionError:\\n182| client_time_cost = 0\\n183| #if client_time_cost <= self.time_threthold:\\n184| if self.time_threthold <= 0 or client_time_cost <= self.time_threthold:\\n185| tot_samples += client.train_samples\\n186| self.uploaded_ids.append(client.id)\\n187| self.uploaded_weights.append(client.train_samples)\\n188| self.uploaded_models.append(client.model)\\n189| for i, w in enumerate(self.uploaded_weights):\\n190| self.uploaded_weights[i] = w / tot_samples\\n191|\\n192| def aggregate_parameters(self):\\n193| assert (len(self.uploaded_models) > 0)\\n194|\\n195| self.global_model = copy.deepcopy(self.uploaded_models[0])\\n196| for param in self.global_model.parameters():\\n197| param.data.zero_()\\n198| \\n199| for w, client_model in zip(self.uploaded_weights, self.uploaded_models):\\n200| self.add_parameters(w, client_model)\\n201|\\n202| def add_parameters(self, w, client_model):\\n203| for server_param, client_param in zip(self.global_model.parameters(), client_model.parameters()):\\n204| server_param.data += client_param.data.clone() * w\\n205|\\n206| def save_global_model1(self):\\n207| model_path = os.path.join(\\\"models\\\", self.dataset)\\n208| if not os.path.exists(model_path):\\n209| os.makedirs(model_path)\\n210| model_path = os.path.join(model_path, self.algorithm + \\\"_\\\" + self.dataset + \\\".pt\\\")\\n211| torch.save(self.global_model, model_path)\\n212|\\n213| def load_model(self):\\n214| model_path = os.path.join(\\\"models\\\", self.dataset)\\n215| model_path = os.path.join(model_path, self.algorithm + \\\"_\\\" + self.dataset + \\\".pt\\\")\\n216| assert (os.path.exists(model_path))\\n217| self.global_model = torch.load(model_path)\\n218|\\n219| def model_exists(self):\\n220| model_path = os.path.join(\\\"models\\\", self.dataset)\\nEND /home/user-lbhzj/pw/PFLlib-master/system/flcore/servers/serverbase.py\\n### /home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientbase.py\\n1|import copy\\n2|import torch\\n3|import torch.nn as nn\\n4|import numpy as np\\n5|import os\\n6|from torch.utils.data import DataLoader\\n7|from sklearn.preprocessing import label_binarize\\n8|from sklearn import metrics\\n9|from utils.data_utils import read_client_data, read_full_test_data\\n10|from torch.cuda.amp import autocast, GradScaler\\n11|from flcore.optimizers.fedoptimizer import PerturbedGradientDescent\\n12|\\n13|class Client(object):\\n14| \\\"\\\"\\\"\\n15| Base class for clients in federated learning.\\n16| \\\"\\\"\\\"\\n17|\\n18| def __init__(self, args, id, train_samples, test_samples, **kwargs):\\n19| torch.manual_seed(0)\\n20| self.model = copy.deepcopy(args.model)\\n21| self.algorithm = args.algorithm\\n22| self.dataset = args.dataset\\n23| \\n24| self.id = id\\n25| # self.device = args.device \\n26| self.device = args.client_device_map[self.id] \\n27| self.model.to(self.device) \\n28|\\n29| self.save_folder_name = args.save_folder_name\\n30| self.mu = args.mu\\n31| self.num_classes = args.num_classes\\n32| self.num_clients = args.num_clients\\n33| self.train_samples = train_samples\\n34| self.test_samples = test_samples\\n35| self.batch_size = args.batch_size\\n36| self.num_workers = getattr(args, 'num_workers', 4)\\n37| self.pin_memory = getattr(args, 'pin_memory', True)\\n38| self.learning_rate = args.local_learning_rate\\n39| self.local_epochs = args.local_epochs\\n40| self.few_shot = args.few_shot\\n41| self.learning_rate_decay = args.learning_rate_decay\\n42| # check BatchNorm\\n43| self.has_BatchNorm = False\\n44| for layer in self.model.children():\\n45| if isinstance(layer, nn.BatchNorm2d):\\n46| self.has_BatchNorm = True\\n47| break\\n48|\\n49| self.train_slow = kwargs['train_slow']\\n50| self.send_slow = kwargs['send_slow']\\n51| self.train_time_cost = {'num_rounds': 0, 'total_cost': 0.0}\\n52| self.send_time_cost = {'num_rounds': 0, 'total_cost': 0.0}\\n53|\\n54| self.loss = nn.CrossEntropyLoss()\\n55| self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)\\n56|\\n57| \\\"\\\"\\\"\\n58| self.learning_rate_scheduler = torch.optim.lr_scheduler.ExponentialLR(\\n59| optimizer=self.optimizer, \\n60| gamma=args.learning_rate_decay_gamma\\n61| )\\n62| \\n63| \\\"\\\"\\\"\\n64| \\n65| # 引入 MultiStepLR\\n66| self.learning_rate_scheduler = torch.optim.lr_scheduler.MultiStepLR(\\n67| optimizer=self.optimizer, \\n68| milestones=args.lr_decay_milestones, # 传入 [60, 120]\\n69| gamma=args.learning_rate_decay_gamma # 传入 0.1 或 0.5\\n70| )\\n71|\\n72| # 混合精度训练支持\\n73| self.use_amp = getattr(args, 'use_amp', False)\\n74| self.scaler = GradScaler(enabled=self.use_amp)\\n75|\\n76|\\n77| def load_train_data(self, batch_size=None):\\n78| if batch_size == None:\\n79| batch_size = self.batch_size\\n80| train_data = read_client_data(self.dataset, self.id, is_train=True, few_shot=self.few_shot)\\n81| return DataLoader(train_data, batch_size, drop_last=True, shuffle=True,\\n82| num_workers=self.num_workers, pin_memory=self.pin_memory, persistent_workers=False)\\n83|\\n84| def load_test_data(self, batch_size=None):\\n85| if batch_size == None:\\n86| batch_size = self.batch_size\\n87| test_data = read_client_data(self.dataset, self.id, is_train=False, few_shot=self.few_shot)\\n88| return DataLoader(test_data, batch_size, drop_last=False, shuffle=True,\\n89| num_workers=self.num_workers, pin_memory=self.pin_memory)\\n90|\\n91| def load_full_test_data(self, batch_size=None):\\n92| if batch_size == None:\\n93| batch_size = self.batch_size\\n94| test_data = read_full_test_data(self.dataset, self.num_clients)\\n95| return DataLoader(test_data, batch_size, drop_last=False, shuffle=True,\\n96| num_workers=self.num_workers, pin_memory=self.pin_memory)\\n97| \\n98| def set_parameters(self, model):\\n99| for new_param, old_param in zip(model.parameters(), self.model.parameters()):\\n100| old_param.data = new_param.data.clone()\\n101|\\n102| def clone_model(self, model, target):\\n103| for param, target_param in zip(model.parameters(), target.parameters()):\\n104| target_param.data = param.data.clone()\\n105| # target_param.grad = param.grad.clone()\\n106|\\n107| def update_parameters(self, model, new_params):\\n108| for param, new_param in zip(model.parameters(), new_params):\\n109| param.data = new_param.data.clone()\\n110|\\n111| def test_metrics(self):\\n112| testloaderfull = self.load_test_data()\\n113| # self.model = self.load_model('model')\\n114| # self.model.to(self.device)\\n115| self.model.eval()\\n116|\\n117| test_acc = 0\\n118| test_num = 0\\n119| y_prob = []\\n120| y_true = []\\n121| \\n122| with torch.no_grad():\\n123| for x, y in testloaderfull:\\n124| if type(x) == type([]):\\n125| x[0] = x[0].to(self.device)\\n126| else:\\n127| x = x.to(self.device)\\n128| y = y.to(self.device)\\n129| with autocast(enabled=self.use_amp):\\n130| output = self.model(x)\\n131|\\n132| test_acc += (torch.sum(torch.argmax(output, dim=1) == y)).item()\\n133| test_num += y.shape[0]\\n134|\\n135| y_prob.append(output.detach().cpu().numpy())\\n136| nc = self.num_classes\\n137| if self.num_classes == 2:\\n138| nc += 1\\n139| lb = label_binarize(y.detach().cpu().numpy(), classes=np.arange(nc))\\n140| if self.num_classes == 2:\\n141| lb = lb[:, :2]\\n142| y_true.append(lb)\\n143|\\n144| # self.model.cpu()\\n145| # self.save_model(self.model, 'model')\\n146|\\n147| y_prob = np.concatenate(y_prob, axis=0)\\n148| y_true = np.concatenate(y_true, axis=0)\\n149|\\n150| try:\\n151| auc = metrics.roc_auc_score(y_true, y_prob, average='micro')\\n152| except ValueError:\\n153| auc = 0.0\\n154|\\n155| return test_acc, test_num, auc\\n156|\\n157| def test_metrics_full(self):\\n158| \\\"\\\"\\\"Evaluate client model on full test dataset (all clients' test data combined).\\\"\\\"\\\"\\n159| testloaderfull = self.load_full_test_data()\\n160| self.model.eval()\\n161|\\n162| test_acc = 0\\n163| test_num = 0\\n164| y_prob = []\\n165| y_true = []\\n166|\\n167| with torch.no_grad():\\n168| for x, y in testloaderfull:\\n169| if type(x) == type([]):\\n170| x[0] = x[0].to(self.device)\\n171| else:\\n172| x = x.to(self.device)\\n173| y = y.to(self.device)\\n174| with autocast(enabled=self.use_amp):\\n175| output = self.model(x)\\n176|\\n177| test_acc += (torch.sum(torch.argmax(output, dim=1) == y)).item()\\n178| test_num += y.shape[0]\\n179|\\n180| y_prob.append(output.detach().cpu().numpy())\\n181| nc = self.num_classes\\n182| if self.num_classes == 2:\\n183| nc += 1\\n184| lb = label_binarize(y.detach().cpu().numpy(), classes=np.arange(nc))\\n185| if self.num_classes == 2:\\n186| lb = lb[:, :2]\\n187| y_true.append(lb)\\n188|\\n189| y_prob = np.concatenate(y_prob, axis=0)\\n190| y_true = np.concatenate(y_true, axis=0)\\n191|\\n192| try:\\n193| auc = metrics.roc_auc_score(y_true, y_prob, average='micro')\\n194| except ValueError:\\n195| auc = 0.0\\n196|\\n197| return test_acc, test_num, auc\\n198|\\n199| def train_metrics(self):\\n200| trainloader = self.load_train_data()\\n201| # self.model = self.load_model('model')\\n202| # self.model.to(self.device)\\n203| self.model.eval()\\n204|\\n205| train_num = 0\\n206| losses = 0\\n207| with torch.no_grad():\\n208| for x, y in trainloader:\\n209| if type(x) == type([]):\\n210| x[0] = x[0].to(self.device)\\n211| else:\\n212| x = x.to(self.device)\\n213| y = y.to(self.device)\\n214| output = self.model(x)\\n215| loss = self.loss(output, y)\\n216| train_num += y.shape[0]\\n217| losses += loss.item() * y.shape[0]\\n218|\\n219| # self.model.cpu()\\n220| # self.save_model(self.model, 'model')\\nEND /home/user-lbhzj/pw/PFLlib-master/system/flcore/clients/clientbase.py\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_e4B6UVkhj2pgbbggMH9dU3wg" }, { "role": "assistant", "content": "我检查了远端训练日志和关键代码路径，结论是：\n\n**FedNova 确实在运行，不像是“没启动”或“卡死/报错”**，但**当前实现大概率把全局更新压得太小，导致模型几乎不学习**。\n\n### 证据\n1. **日志确认算法已正确启动**\n - `FedNova_cifar10.log` 开头显示：\n - `algorithm = FedNova`\n - `Creating server and clients ...`\n - `Finished creating server and clients.`\n - 随后 round 0 到 round 17 连续输出，没有 `Traceback` / `Error` / `Killed`。\n - 训练指标长期停在随机水平附近：\n - Test Accuracy 大致在 **0.091 ~ 0.167**\n - Train Loss 一直在 **2.30 左右**\n - 这说明：**不是 launch command 错、不是冻结住了、也不是直接崩了，而是“在跑但没学到东西”**。\n\n2. **main_v2.py 里确实走的是 FedNova 分支**\n - `main_v2.py` 搜到：\n - `elif args.algorithm == \"FedNova\":`\n - `server = FedNova(args, i)`\n - 所以不是选错算法。\n\n3. **FedNova 的客户端/服务端实现里有明显“更新缩小/公式可疑”的地方**\n - `clientnova.py`\n - `cum_grad` 在本地累计的是 `p.grad.data * self.learning_rate`\n - `local_normalizing_vec` 又被写成 `self.local_steps * (1 - self.momentum)`，而日志里 momentum 默认是 **0.9**，这会直接把归一化量缩到 **0.1 倍**\n - `servernova.py`\n - 服务器端又做一次：\n - `norm_factor = norm_vec / total_norm`\n - `scaled_grad = cum_grad[param_idx] * norm_factor * weight`\n - 这里 **norm_factor** 和 **weight** 都在缩小更新；而 `uploaded_weights` 本身已经归一化了。\n - 结果很像是：**本地更新被“二次/三次缩小”**，全局模型每轮改动太小，最终准确率就一直卡在随机水平。\n\n### 最小诊断\n**问题更像是 FedNova 的聚合公式/归一化逻辑有 bug，而不是算法没跑或命令写错。** \n从现象上看，最符合的是：**更新量过小，导致有效训练几乎没有发生。**\n\n### 最小安全建议\n1. **先对照 FedAvg 同配置跑一次**，看同样数据下是否能正常上升，排除数据/环境问题。\n2. **打印每轮参数变化范数**（FedNova vs FedAvg），确认 FedNova 是否更新过小。\n3. **优先核对 FedNova 原始公式**，重点检查：\n - `cum_grad` 是否该这样累计\n - `local_normalizing_vec` 的计算是否正确\n - `servernova.py` 是否把 `weight` 和 `norm_factor` 重复缩放了\n\n### 我做了什么\n- 读取了最新 `FedNova_cifar10.log`\n- 检查了 `main_v2.py`\n- 检查了 `flcore/clients/clientnova.py`\n- 检查了 `flcore/servers/servernova.py`\n- 顺带核对了 `serverbase.py` / `clientbase.py` 的相关调用链\n\n### 结果\n- **未修改任何文件**\n- **未创建任何文件**\n- 远程 SSH 直接调用 `python` 不可用，我改用 `python3` 完成检查", "reasoning": "**Investigating potential bugs**\n\nI’m thinking there might be a bug in the clientNova code regarding how I calculate `self.local_normalizing_vec`. When `momentum` is set to 0.9, it seems that `norm vec` equals `0.1 * steps`. This raises questions about whether the calculation behaves as expected. I should double-check the logic to see if the condition handles the momentum cases correctly. Is there a scenario I’m missing that could lead to unexpected behavior?\n\n**Examining parameter updates**\n\nI’m trying to understand how client updates and parameter adjustments work. Each client’s norm seems proportional to steps, and I'm thinking the norm factor might be around 1 divided by the number of clients. Cumulative gradients include learning rate and move the global model, but they seem to accumulate squared learning rates. I realize I need to consider that the server should actually aggregate client parameters instead of subtracting these deltas, since each client's model has already adjusted through gradient descent.\n\n**Exploring FedNova updates**\n\nIn FedNova, the server aggregates normalized gradients, probably updating with negative learning rate times that gradient. But I wonder if the server should add these instead of subtracting them. Thinking about SGD, the parameter updates relate to deltas, which should be averaged from client models. The global model should be based on these averaged deltas.\n\nI see that weighted gradients come from cumulative gradients adjusted with a norm factor and weight. It seems like there's no obvious bug in this, but in clientNova, there's something about needing to unscale gradients when using a GradScaler.\n\n**Checking AMP settings and optimizer issues**\n\nI'm noticing that the default use_amp might be set to False, and since it's not being printed in the log, that complicates things. If it's indeed False, then the scaler should work, but it seems it's not enabled. This feels like a potential big bug. In `Client.__init__`, I see that the optimizer is set as SGD with a learning rate, but clientNova is overriding this optimizer to include momentum and weight decay. This discrepancy could lead to unexpected outcomes.\n\n**Analyzing optimizer behavior**\n\nI’m looking at the clientNova optimizer, where the learning rate stays at its initial value of 0.05 and never updates. The server sets the client learning rate before sending models, but since the optimizer is created with this initial rate, it doesn't update. Although the training process uses `self.learning_rate` for gradients, the optimizer still relies on the initial learning rate. This means local optimization is consistently using that initial rate, potentially overlooking any intended learning rate decay. The logs suggest the first rounds seem fine, but there are inefficiencies here.\n\n**Investigating algorithm effectiveness**\n\nI'm considering the `client.set_parameters(self.global_model)` function and whether there’s an issue with it copying parameters and resetting accumulated gradients. The potential bug in `servernova.receive_models` could tie into the `client_time_cost` filters, especially if clients are included when their time cost is below a threshold. The user pointed out that the new algorithm appears ineffective, which might suggest an incorrect implementation or using FedNova on top of the serverbase. I need to check if the algorithm evaluates properly before training, as the logs show little learning with accuracy around chance levels.\n\n**Examining potential issues**\n\nGiven that the training loss is around 2.30, it suggests the network might not be updating correctly or that there are mismatched labels. This could happen due to the parameters `-ncl 10` and `-nc 20` being correct, or maybe there's an issue in dataset preprocessing or data splitting. Since the baseline from the documentation works, it likely points to logic problems in the FedNova algorithm. If the cumulative gradients from clientNova aren’t being used, the server could be receiving zeros. I need to inspect the clientNova flow closely.\n\n**Evaluating model performance**\n\nI'm looking at the average cumulative gradient and wondering if it’s overly damped. In a properly weighted client setup, if all norm vectors are equal, I shouldn’t need to scale by weight. I think the original FedNova method might require normalized updates with sample counts, but not divided by the number of clients. Still, the implementation might be shrinking updates too much, which could explain ineffective training. We could enhance our evidence by reviewing logs, which show no improvement in accuracy or loss over 17 rounds.\n\n**Investigating training issues**\n\nI'm pondering whether the long training is actually complete since it's been ineffective during the first 17 rounds. I'm curious if the auto_break setting might be causing this. I want to check the latest log and code to confirm if the command was launched with `main_v2.py`, which correctly selects FedNova. I'm also considering if the FedNova implementation is utilizing cumulative gradients properly, as there could be a potential bug related to how `self.cum_grad` is handled. If it maintains references to tensors, past data might mutate unexpectedly during aggregation.\n\n**Clarifying model references**\n\nI'm considering whether `self.uploaded_grads` stores references to the client cumulative gradient lists. It seems like if they get zeroed out before the aggregation, that might cause some issues. But, in the order of operations within `server.train()`, it goes send_models, evaluate, then client.train(), followed by receive_models and finally aggregate_parameters. Since `receive_models` stores references and the aggregation happens right after, I think that should be okay. I'm also wondering if the server base might still be using a preloaded model, as there's some commented auto-load logic present.\n\n**Diagnosing code issues**\n\nI think there might be a bug in clientNova with how `self.cum_grad` calculates gradients after the optimizer steps in. If weights are updated in-place, it could lead to using old weights for gradient calculations. Also, FedNova requires local models to start correctly, and the combination of weight decay and momentum might cause wrong aggregations. I’ll note down minimal solutions like verifying against baseline FedAvg and rechecking the aggregation formula for discrepancies.\n\n**Investigating training issues**\n\nI suggest checking client delta norms and comparing them against FedAvg. Disabling momentum could help determine if training begins properly. There are some lines of code I find particularly suspicious: in clientNova.py from lines 55-60, it accumulates `p.grad * lr` after `optimizer.step()`, and in servernova.py from lines 113-124, it aggregates using `cum_grad * norm_factor * weight`. If `norm_vec` is roughly equal across clients, this might be double normalizing, leading to drastically smaller updates.\n\n**Analyzing the log issue**\n\nI noticed that each round takes about 150 seconds, but there doesn’t seem to be any freezing. It could be helpful to mention that there are no exceptions or traceback found in the log. I did some searching and found entries related to CUDA and \"Finished.\" Maybe I should include that information to give a clearer picture of what’s happening. This might help in troubleshooting or understanding the situation better!\n\n", "finish_reason": "stop", "reasoning_content": "**Investigating potential bugs**\n\nI’m thinking there might be a bug in the clientNova code regarding how I calculate `self.local_normalizing_vec`. When `momentum` is set to 0.9, it seems that `norm vec` equals `0.1 * steps`. This raises questions about whether the calculation behaves as expected. I should double-check the logic to see if the condition handles the momentum cases correctly. Is there a scenario I’m missing that could lead to unexpected behavior?\n\n**Examining parameter updates**\n\nI’m trying to understand how client updates and parameter adjustments work. Each client’s norm seems proportional to steps, and I'm thinking the norm factor might be around 1 divided by the number of clients. Cumulative gradients include learning rate and move the global model, but they seem to accumulate squared learning rates. I realize I need to consider that the server should actually aggregate client parameters instead of subtracting these deltas, since each client's model has already adjusted through gradient descent.\n\n**Exploring FedNova updates**\n\nIn FedNova, the server aggregates normalized gradients, probably updating with negative learning rate times that gradient. But I wonder if the server should add these instead of subtracting them. Thinking about SGD, the parameter updates relate to deltas, which should be averaged from client models. The global model should be based on these averaged deltas.\n\nI see that weighted gradients come from cumulative gradients adjusted with a norm factor and weight. It seems like there's no obvious bug in this, but in clientNova, there's something about needing to unscale gradients when using a GradScaler.\n\n**Checking AMP settings and optimizer issues**\n\nI'm noticing that the default use_amp might be set to False, and since it's not being printed in the log, that complicates things. If it's indeed False, then the scaler should work, but it seems it's not enabled. This feels like a potential big bug. In `Client.__init__`, I see that the optimizer is set as SGD with a learning rate, but clientNova is overriding this optimizer to include momentum and weight decay. This discrepancy could lead to unexpected outcomes.\n\n**Analyzing optimizer behavior**\n\nI’m looking at the clientNova optimizer, where the learning rate stays at its initial value of 0.05 and never updates. The server sets the client learning rate before sending models, but since the optimizer is created with this initial rate, it doesn't update. Although the training process uses `self.learning_rate` for gradients, the optimizer still relies on the initial learning rate. This means local optimization is consistently using that initial rate, potentially overlooking any intended learning rate decay. The logs suggest the first rounds seem fine, but there are inefficiencies here.\n\n**Investigating algorithm effectiveness**\n\nI'm considering the `client.set_parameters(self.global_model)` function and whether there’s an issue with it copying parameters and resetting accumulated gradients. The potential bug in `servernova.receive_models` could tie into the `client_time_cost` filters, especially if clients are included when their time cost is below a threshold. The user pointed out that the new algorithm appears ineffective, which might suggest an incorrect implementation or using FedNova on top of the serverbase. I need to check if the algorithm evaluates properly before training, as the logs show little learning with accuracy around chance levels.\n\n**Examining potential issues**\n\nGiven that the training loss is around 2.30, it suggests the network might not be updating correctly or that there are mismatched labels. This could happen due to the parameters `-ncl 10` and `-nc 20` being correct, or maybe there's an issue in dataset preprocessing or data splitting. Since the baseline from the documentation works, it likely points to logic problems in the FedNova algorithm. If the cumulative gradients from clientNova aren’t being used, the server could be receiving zeros. I need to inspect the clientNova flow closely.\n\n**Evaluating model performance**\n\nI'm looking at the average cumulative gradient and wondering if it’s overly damped. In a properly weighted client setup, if all norm vectors are equal, I shouldn’t need to scale by weight. I think the original FedNova method might require normalized updates with sample counts, but not divided by the number of clients. Still, the implementation might be shrinking updates too much, which could explain ineffective training. We could enhance our evidence by reviewing logs, which show no improvement in accuracy or loss over 17 rounds.\n\n**Investigating training issues**\n\nI'm pondering whether the long training is actually complete since it's been ineffective during the first 17 rounds. I'm curious if the auto_break setting might be causing this. I want to check the latest log and code to confirm if the command was launched with `main_v2.py`, which correctly selects FedNova. I'm also considering if the FedNova implementation is utilizing cumulative gradients properly, as there could be a potential bug related to how `self.cum_grad` is handled. If it maintains references to tensors, past data might mutate unexpectedly during aggregation.\n\n**Clarifying model references**\n\nI'm considering whether `self.uploaded_grads` stores references to the client cumulative gradient lists. It seems like if they get zeroed out before the aggregation, that might cause some issues. But, in the order of operations within `server.train()`, it goes send_models, evaluate, then client.train(), followed by receive_models and finally aggregate_parameters. Since `receive_models` stores references and the aggregation happens right after, I think that should be okay. I'm also wondering if the server base might still be using a preloaded model, as there's some commented auto-load logic present.\n\n**Diagnosing code issues**\n\nI think there might be a bug in clientNova with how `self.cum_grad` calculates gradients after the optimizer steps in. If weights are updated in-place, it could lead to using old weights for gradient calculations. Also, FedNova requires local models to start correctly, and the combination of weight decay and momentum might cause wrong aggregations. I’ll note down minimal solutions like verifying against baseline FedAvg and rechecking the aggregation formula for discrepancies.\n\n**Investigating training issues**\n\nI suggest checking client delta norms and comparing them against FedAvg. Disabling momentum could help determine if training begins properly. There are some lines of code I find particularly suspicious: in clientNova.py from lines 55-60, it accumulates `p.grad * lr` after `optimizer.step()`, and in servernova.py from lines 113-124, it aggregates using `cum_grad * norm_factor * weight`. If `norm_vec` is roughly equal across clients, this might be double normalizing, leading to drastically smaller updates.\n\n**Analyzing the log issue**\n\nI noticed that each round takes about 150 seconds, but there doesn’t seem to be any freezing. It could be helpful to mention that there are no exceptions or traceback found in the log. I did some searching and found entries related to CUDA and \"Finished.\" Maybe I should include that information to give a clearer picture of what’s happening. This might help in troubleshooting or understanding the situation better!\n\n" } ] }