refactor(cli): remove obsolete printUsage from exec/mod.zig
Exec is now an internal module used by 'ml run', not a standalone command. Remove the misleading 'ml exec' usage documentation and replace with simple internal module message.
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1 changed files with 2 additions and 35 deletions
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@ -50,7 +50,7 @@ pub fn run(allocator: std.mem.Allocator, args: []const []const u8) !void {
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const arg = pre[i];
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if (std.mem.eql(u8, arg, "--help") or std.mem.eql(u8, arg, "-h")) {
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try printUsage();
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std.debug.print("Exec module - used internally by 'ml run'\n", .{});
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return;
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} else if (std.mem.eql(u8, arg, "--json")) {
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flags.json = true;
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@ -95,7 +95,7 @@ pub fn run(allocator: std.mem.Allocator, args: []const []const u8) !void {
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}
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if (job_name == null) {
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try printUsage();
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std.log.err("No job name specified", .{});
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return error.InvalidArgs;
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}
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@ -159,36 +159,3 @@ pub fn run(allocator: std.mem.Allocator, args: []const []const u8) !void {
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},
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}
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}
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fn printUsage() !void {
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std.debug.print(
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\\n
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\\ml exec <job_name> [options] [-- <args>]
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\\
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\\Unified execution - works locally or remotely with transparent fallback.
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\\
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\\Options:
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\\ --priority <1-10> Job priority (default: 5)
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\\ --cpu <n> CPU cores requested (default: 1)
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\\ --memory <n> Memory GB requested (default: 4)
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\\ --gpu <n> GPU devices requested (default: 0)
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\\ --gpu-memory <spec> GPU memory spec
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\\
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\\Execution mode is controlled by execution_mode setting in config.
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\\Use 'ml init --mode=local|remote|auto' to change.
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\\
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\\Research context:
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\\ --hypothesis <text> What you're testing
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\\ --context <text> Background information
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\\ --intent <text> What you're trying to do
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\\ --expected-outcome <t> What you expect to happen
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\\ --tags <csv> Comma-separated tags
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\\
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\\Examples:
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\\ ml exec train.py
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\\ ml exec train.py -- --lr 0.001 --epochs 10
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\\ ml exec train.py --priority 8 --gpu 1
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\\ ml exec train.py --hypothesis "LR scaling helps"
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\\
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, .{});
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}
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