ipfrs_cli/commands/
gradient.rs1use anyhow::Result;
10
11use crate::output::{self, print_cid, print_header, print_kv};
12use crate::progress;
13
14#[allow(dead_code)]
16pub async fn gradient_push(path: &str, model_cid: Option<&str>, format: &str) -> Result<()> {
17 let pb = progress::spinner("Preparing gradient upload...");
18 progress::finish_spinner_success(&pb, "Gradient preparation complete");
19
20 output::warning("Gradient operations require federated learning system integration");
21
22 match format {
23 "json" => {
24 println!("{{");
25 println!(" \"path\": \"{}\",", path);
26 if let Some(mcid) = model_cid {
27 println!(" \"model_cid\": \"{}\",", mcid);
28 }
29 println!(" \"status\": \"not_implemented\"");
30 println!("}}");
31 }
32 _ => {
33 print_header("Gradient Push");
34 println!("Path: {}", path);
35 if let Some(mcid) = model_cid {
36 print_cid("Model CID", mcid);
37 }
38 println!();
39 println!("Gradient push would upload the gradient to the network.");
40 }
41 }
42
43 Ok(())
44}
45
46#[allow(dead_code)]
48pub async fn gradient_pull(cid: &str, output_path: Option<&str>) -> Result<()> {
49 let pb = progress::spinner("Preparing gradient download...");
50 progress::finish_spinner_success(&pb, "Download preparation complete");
51
52 output::warning("Gradient operations require federated learning system integration");
53
54 print_header("Gradient Pull");
55 print_cid("Gradient CID", cid);
56 if let Some(out) = output_path {
57 println!(" Output: {}", out);
58 }
59 println!();
60 println!("Gradient pull would download the gradient from the network.");
61
62 Ok(())
63}
64
65#[allow(dead_code)]
67pub async fn gradient_aggregate(
68 cids: &[String],
69 output: &str,
70 method: &str,
71 format: &str,
72) -> Result<()> {
73 let pb = progress::spinner("Preparing gradient aggregation...");
74 progress::finish_spinner_success(&pb, "Aggregation preparation complete");
75
76 output::warning("Gradient operations require federated learning system integration");
77
78 match format {
79 "json" => {
80 println!("{{");
81 println!(" \"gradient_cids\": [");
82 for (i, cid) in cids.iter().enumerate() {
83 if i < cids.len() - 1 {
84 println!(" \"{}\",", cid);
85 } else {
86 println!(" \"{}\"", cid);
87 }
88 }
89 println!(" ],");
90 println!(" \"output\": \"{}\",", output);
91 println!(" \"method\": \"{}\",", method);
92 println!(" \"status\": \"not_implemented\"");
93 println!("}}");
94 }
95 _ => {
96 print_header("Gradient Aggregation");
97 print_kv("Number of gradients", &cids.len().to_string());
98 print_kv("Method", method);
99 print_kv("Output", output);
100 println!();
101 println!(
102 "Aggregation would combine gradients using the {} method.",
103 method
104 );
105 }
106 }
107
108 Ok(())
109}
110
111#[allow(dead_code)]
113pub async fn gradient_history(cid: &str, limit: usize, format: &str) -> Result<()> {
114 let pb = progress::spinner("Retrieving gradient history...");
115 progress::finish_spinner_success(&pb, "History retrieved");
116
117 output::warning("Gradient operations require federated learning system integration");
118
119 match format {
120 "json" => {
121 println!("{{");
122 println!(" \"model_cid\": \"{}\",", cid);
123 println!(" \"limit\": {},", limit);
124 println!(" \"history\": [],");
125 println!(" \"status\": \"not_implemented\"");
126 println!("}}");
127 }
128 _ => {
129 print_header("Gradient History");
130 print_cid("Model CID", cid);
131 print_kv("Limit", &limit.to_string());
132 println!();
133 println!("History would show gradient updates for this model.");
134 }
135 }
136
137 Ok(())
138}