flodl_cli/
init.rs

1//! `fdl init <name>` -- scaffold a new floDl project.
2//!
3//! Three modes, selected by flag or interactive prompt:
4//! - `Mounted` (default): Docker with libtorch host-mounted at runtime.
5//! - `Docker` (`--docker`): Docker with libtorch baked into the image.
6//! - `Native` (`--native`): no Docker; libtorch and cargo provided on the host.
7
8use std::fs;
9use std::path::Path;
10use std::process::Command;
11
12use crate::util::prompt;
13
14#[derive(Debug, Clone, Copy, PartialEq, Eq)]
15enum Mode {
16    Mounted,
17    Docker,
18    Native,
19}
20
21pub fn run(name: Option<&str>, docker: bool, native: bool) -> Result<(), String> {
22    let name = name.ok_or("usage: fdl init <project-name>")?;
23    validate_name(name)?;
24
25    if Path::new(name).exists() {
26        return Err(format!("'{}' already exists", name));
27    }
28
29    if docker && native {
30        return Err("--docker and --native are mutually exclusive".into());
31    }
32    let mode = if docker {
33        Mode::Docker
34    } else if native {
35        Mode::Native
36    } else {
37        pick_mode_interactively()
38    };
39
40    let crate_name = name.replace('-', "_");
41    let flodl_dep = resolve_flodl_dep();
42
43    fs::create_dir_all(format!("{}/src", name))
44        .map_err(|e| format!("cannot create directory: {}", e))?;
45
46    match mode {
47        Mode::Mounted => scaffold_mounted(name, &crate_name, &flodl_dep)?,
48        Mode::Docker => scaffold_docker(name, &crate_name, &flodl_dep)?,
49        Mode::Native => scaffold_native(name, &crate_name, &flodl_dep)?,
50    }
51
52    // Shared across all modes.
53    write_file(
54        &format!("{}/src/main.rs", name),
55        &main_rs_template(),
56    )?;
57    write_file(
58        &format!("{}/.gitignore", name),
59        &gitignore_template(mode),
60    )?;
61    write_file(
62        &format!("{}/fdl.yml.example", name),
63        &fdl_yml_example_template(name, mode),
64    )?;
65    write_fdl_bootstrap(name)?;
66
67    print_next_steps(name, mode);
68    crate::util::install_prompt::offer_global_install();
69    Ok(())
70}
71
72/// Ask the user interactively which mode to generate. Falls through to
73/// `Mounted` when no TTY is attached (the same default as passing no flag
74/// to `--non-interactive` tooling).
75fn pick_mode_interactively() -> Mode {
76    println!();
77    if !prompt::ask_yn("Use Docker for builds?", true) {
78        return Mode::Native;
79    }
80    // 1-based: 1 = mounted (default), 2 = baked-in.
81    let choice = prompt::ask_choice(
82        "libtorch location",
83        &[
84            "Mounted from host (recommended: lighter image, swap CUDA variants)",
85            "Baked into the Docker image (zero host dependencies)",
86        ],
87        1,
88    );
89    match choice {
90        2 => Mode::Docker,
91        _ => Mode::Mounted,
92    }
93}
94
95fn print_next_steps(name: &str, mode: Mode) {
96    println!();
97    println!("Project '{}' created. Next steps:", name);
98    println!();
99    println!("  cd {}", name);
100    match mode {
101        Mode::Mounted => {
102            println!("  ./fdl setup   # detect hardware + download libtorch");
103            println!("  ./fdl build   # build the project");
104        }
105        Mode::Docker => {
106            println!("  ./fdl build   # first build (downloads libtorch, ~5 min)");
107        }
108        Mode::Native => {
109            println!("  ./fdl libtorch download --cpu     # or --cuda 12.8");
110            println!("  ./fdl build                       # cargo build on the host");
111        }
112    }
113    println!("  ./fdl test    # run tests");
114    println!("  ./fdl run     # train the model");
115    if mode != Mode::Native {
116        println!("  ./fdl shell   # interactive shell");
117    }
118    println!();
119    println!("`./fdl --help` lists every command defined in fdl.yml.");
120    println!("Edit src/main.rs to build your model.");
121    println!();
122    println!("Guides:");
123    println!("  Tutorials:         https://flodl.dev/guide/tensors");
124    println!("  Graph Tree:        https://flodl.dev/guide/graph-tree");
125    println!("  PyTorch migration: https://flodl.dev/guide/migration");
126    println!("  Troubleshooting:   https://flodl.dev/guide/troubleshooting");
127}
128
129fn write_fdl_bootstrap(name: &str) -> Result<(), String> {
130    let fdl_script = include_str!("../assets/fdl");
131    write_file(&format!("{}/fdl", name), fdl_script)?;
132    #[cfg(unix)]
133    {
134        use std::os::unix::fs::PermissionsExt;
135        let _ = fs::set_permissions(
136            format!("{}/fdl", name),
137            fs::Permissions::from_mode(0o755),
138        );
139    }
140    Ok(())
141}
142
143fn validate_name(name: &str) -> Result<(), String> {
144    if name.is_empty() {
145        return Err("project name cannot be empty".into());
146    }
147    if !name.chars().all(|c| c.is_ascii_alphanumeric() || c == '-' || c == '_') {
148        return Err("project name must contain only letters, digits, hyphens, underscores".into());
149    }
150    Ok(())
151}
152
153fn resolve_flodl_dep() -> String {
154    // Try crates.io for the latest version
155    if let Some(version) = crates_io_version() {
156        format!("flodl = \"{}\"", version)
157    } else {
158        "flodl = { git = \"https://github.com/fab2s/floDl.git\" }".into()
159    }
160}
161
162fn crates_io_version() -> Option<String> {
163    let output = Command::new("curl")
164        .args(["-sL", "https://crates.io/api/v1/crates/flodl"])
165        .output()
166        .ok()?;
167    let body = String::from_utf8_lossy(&output.stdout);
168    // Extract "max_stable_version":"X.Y.Z"
169    let marker = "\"max_stable_version\":\"";
170    let start = body.find(marker)? + marker.len();
171    let end = start + body[start..].find('"')?;
172    let version = &body[start..end];
173    if version.is_empty() { None } else { Some(version.to_string()) }
174}
175
176// ---------------------------------------------------------------------------
177// Docker scaffold (standalone, libtorch baked into images)
178// ---------------------------------------------------------------------------
179
180fn scaffold_docker(name: &str, crate_name: &str, flodl_dep: &str) -> Result<(), String> {
181    write_file(
182        &format!("{}/Cargo.toml", name),
183        &cargo_toml_template(crate_name, flodl_dep),
184    )?;
185    write_file(
186        &format!("{}/Dockerfile.cpu", name),
187        DOCKERFILE_CPU,
188    )?;
189    write_file(
190        &format!("{}/Dockerfile.cuda", name),
191        DOCKERFILE_CUDA,
192    )?;
193    write_file(
194        &format!("{}/docker-compose.yml", name),
195        &docker_compose_template(crate_name, true),
196    )?;
197    Ok(())
198}
199
200// ---------------------------------------------------------------------------
201// Mounted scaffold (libtorch from host, like the main repo)
202// ---------------------------------------------------------------------------
203
204fn scaffold_mounted(name: &str, crate_name: &str, flodl_dep: &str) -> Result<(), String> {
205    write_file(
206        &format!("{}/Cargo.toml", name),
207        &cargo_toml_template(crate_name, flodl_dep),
208    )?;
209    write_file(
210        &format!("{}/Dockerfile", name),
211        DOCKERFILE_MOUNTED,
212    )?;
213    write_file(
214        &format!("{}/Dockerfile.cuda", name),
215        DOCKERFILE_CUDA_MOUNTED,
216    )?;
217    write_file(
218        &format!("{}/docker-compose.yml", name),
219        &docker_compose_template(crate_name, false),
220    )?;
221    Ok(())
222}
223
224// ---------------------------------------------------------------------------
225// Native scaffold (no Docker; libtorch and cargo live on the host)
226// ---------------------------------------------------------------------------
227
228fn scaffold_native(name: &str, crate_name: &str, flodl_dep: &str) -> Result<(), String> {
229    write_file(
230        &format!("{}/Cargo.toml", name),
231        &cargo_toml_template(crate_name, flodl_dep),
232    )?;
233    // Intentionally no Dockerfile*/docker-compose.yml -- the user opted out
234    // of Docker. They can switch later by regenerating or adding their own.
235    Ok(())
236}
237
238// ---------------------------------------------------------------------------
239// Templates
240// ---------------------------------------------------------------------------
241
242fn cargo_toml_template(crate_name: &str, flodl_dep: &str) -> String {
243    format!(
244        r#"[package]
245name = "{crate_name}"
246version = "0.1.0"
247edition = "2024"
248
249[dependencies]
250{flodl_dep}
251
252# Optimize floDl in dev builds -- your code stays fast to compile.
253# After the first build, only your graph code recompiles (~2s).
254[profile.dev.package.flodl]
255opt-level = 3
256
257[profile.dev.package.flodl-sys]
258opt-level = 3
259
260# Release: cross-crate optimization for maximum throughput.
261[profile.release]
262lto = "thin"
263codegen-units = 1
264"#
265    )
266}
267
268fn main_rs_template() -> String {
269    r#"//! floDl training template.
270//!
271//! This is a starting point for your model. Edit the architecture,
272//! data loading, and training loop to fit your task.
273//!
274//! New to Rust? Read: https://flodl.dev/guide/rust-primer
275//! Stuck?       Read: https://flodl.dev/guide/troubleshooting
276
277use flodl::*;
278use flodl::monitor::Monitor;
279
280fn main() -> Result<()> {
281    // --- Model ---
282    let model = FlowBuilder::from(Linear::new(4, 32)?)
283        .through(GELU)
284        .through(LayerNorm::new(32)?)
285        .also(Linear::new(32, 32)?)       // residual connection
286        .through(Linear::new(32, 1)?)
287        .build()?;
288
289    // --- Optimizer ---
290    let params = model.parameters();
291    let mut optimizer = Adam::new(&params, 0.001);
292    let scheduler = CosineScheduler::new(0.001, 1e-6, 100);
293    model.train();
294
295    // --- Data ---
296    // Replace this with your data loading.
297    let opts = TensorOptions::default();
298    let batches: Vec<(Tensor, Tensor)> = (0..32)
299        .map(|_| {
300            let x = Tensor::randn(&[16, 4], opts).unwrap();
301            let y = Tensor::randn(&[16, 1], opts).unwrap();
302            (x, y)
303        })
304        .collect();
305
306    // --- Training loop ---
307    let num_epochs = 100usize;
308    let mut monitor = Monitor::new(num_epochs);
309    // monitor.serve(3000)?;              // uncomment for live dashboard
310    // monitor.watch(&model);             // uncomment to show graph SVG
311    // monitor.save_html("report.html");  // uncomment to save HTML report
312
313    for epoch in 0..num_epochs {
314        let t = std::time::Instant::now();
315        let mut epoch_loss = 0.0;
316
317        for (input_t, target_t) in &batches {
318            let input = Variable::new(input_t.clone(), true);
319            let target = Variable::new(target_t.clone(), false);
320
321            optimizer.zero_grad();
322            let pred = model.forward(&input)?;
323            let loss = mse_loss(&pred, &target)?;
324            loss.backward()?;
325            clip_grad_norm(&params, 1.0)?;
326            optimizer.step()?;
327
328            epoch_loss += loss.item()?;
329        }
330
331        let avg_loss = epoch_loss / batches.len() as f64;
332        let lr = scheduler.lr(epoch);
333        optimizer.set_lr(lr);
334        monitor.log(epoch, t.elapsed(), &[("loss", avg_loss), ("lr", lr)]);
335    }
336
337    monitor.finish();
338    Ok(())
339}
340"#
341    .into()
342}
343
344fn gitignore_template(mode: Mode) -> String {
345    let mut s = String::from(
346        "/target
347*.fdl
348*.log
349*.csv
350*.html
351
352# Local fdl config (fdl.yml.example is committed; fdl copies it on first run)
353fdl.yml
354fdl.yaml
355",
356    );
357    match mode {
358        Mode::Docker => {
359            // libtorch is baked into the image, nothing on host to ignore.
360            s.push_str(
361                ".cargo-cache/
362.cargo-git/
363.cargo-cache-cuda/
364.cargo-git-cuda/
365",
366            );
367        }
368        Mode::Mounted => {
369            // Mounted libtorch + separate cargo caches per docker service.
370            s.push_str(
371                ".cargo-cache/
372.cargo-git/
373.cargo-cache-cuda/
374.cargo-git-cuda/
375libtorch/
376",
377            );
378        }
379        Mode::Native => {
380            // No docker, no container caches. libtorch/ is still ignored
381            // because `./fdl libtorch download` installs it locally.
382            s.push_str("libtorch/\n");
383        }
384    }
385    s
386}
387
388fn docker_compose_template(crate_name: &str, baked: bool) -> String {
389    if baked {
390        format!(
391            r#"services:
392  dev:
393    build:
394      context: .
395      dockerfile: Dockerfile.cpu
396    image: {crate_name}-dev
397    user: "${{UID:-1000}}:${{GID:-1000}}"
398    volumes:
399      - .:/workspace
400      - ./.cargo-cache:/usr/local/cargo/registry
401      - ./.cargo-git:/usr/local/cargo/git
402    working_dir: /workspace
403    stdin_open: true
404    tty: true
405
406  cuda:
407    build:
408      context: .
409      dockerfile: Dockerfile.cuda
410    image: {crate_name}-cuda
411    user: "${{UID:-1000}}:${{GID:-1000}}"
412    volumes:
413      - .:/workspace
414      - ./.cargo-cache-cuda:/usr/local/cargo/registry
415      - ./.cargo-git-cuda:/usr/local/cargo/git
416    working_dir: /workspace
417    stdin_open: true
418    tty: true
419    deploy:
420      resources:
421        reservations:
422          devices:
423            - driver: nvidia
424              count: all
425              capabilities: [gpu]
426"#
427        )
428    } else {
429        format!(
430            r#"services:
431  dev:
432    build:
433      context: .
434      dockerfile: Dockerfile
435    image: {crate_name}-dev
436    user: "${{UID:-1000}}:${{GID:-1000}}"
437    volumes:
438      - .:/workspace
439      - ./.cargo-cache:/usr/local/cargo/registry
440      - ./.cargo-git:/usr/local/cargo/git
441      - ${{LIBTORCH_CPU_PATH:-./libtorch/precompiled/cpu}}:/usr/local/libtorch:ro
442    working_dir: /workspace
443    stdin_open: true
444    tty: true
445
446  cuda:
447    build:
448      context: .
449      dockerfile: Dockerfile.cuda
450      args:
451        CUDA_VERSION: ${{CUDA_VERSION:-12.8.0}}
452    image: {crate_name}-cuda:${{CUDA_TAG:-12.8}}
453    user: "${{UID:-1000}}:${{GID:-1000}}"
454    volumes:
455      - .:/workspace
456      - ./.cargo-cache-cuda:/usr/local/cargo/registry
457      - ./.cargo-git-cuda:/usr/local/cargo/git
458      - ${{LIBTORCH_HOST_PATH:-./libtorch/precompiled/cu128}}:/usr/local/libtorch:ro
459    working_dir: /workspace
460    stdin_open: true
461    tty: true
462    deploy:
463      resources:
464        reservations:
465          devices:
466            - driver: nvidia
467              count: all
468              capabilities: [gpu]
469"#
470        )
471    }
472}
473
474// ---------------------------------------------------------------------------
475// Dockerfile templates
476// ---------------------------------------------------------------------------
477
478// Docker mode: libtorch baked into images
479const DOCKERFILE_CPU: &str = r#"# CPU-only dev image for floDl projects.
480FROM ubuntu:24.04
481
482ENV DEBIAN_FRONTEND=noninteractive
483
484RUN apt-get update && apt-get install -y --no-install-recommends \
485    wget curl unzip ca-certificates git gcc g++ pkg-config graphviz \
486    && rm -rf /var/lib/apt/lists/*
487
488# Rust
489ENV CARGO_HOME="/usr/local/cargo"
490ENV RUSTUP_HOME="/usr/local/rustup"
491RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain stable \
492    && chmod -R a+rwx "$CARGO_HOME" "$RUSTUP_HOME"
493ENV PATH="${CARGO_HOME}/bin:${PATH}"
494
495# libtorch (CPU-only, ~200MB)
496ARG LIBTORCH_VERSION=2.10.0
497RUN wget -q https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-${LIBTORCH_VERSION}%2Bcpu.zip \
498    && unzip -q libtorch-shared-with-deps-${LIBTORCH_VERSION}+cpu.zip -d /usr/local \
499    && rm libtorch-shared-with-deps-${LIBTORCH_VERSION}+cpu.zip
500
501ENV LIBTORCH_PATH="/usr/local/libtorch"
502ENV LD_LIBRARY_PATH="${LIBTORCH_PATH}/lib"
503ENV LIBRARY_PATH="${LIBTORCH_PATH}/lib"
504
505WORKDIR /workspace
506"#;
507
508const DOCKERFILE_CUDA: &str = r#"# CUDA dev image for floDl projects.
509# Requires: docker run --gpus all ...
510FROM nvidia/cuda:12.8.0-devel-ubuntu24.04
511
512ENV DEBIAN_FRONTEND=noninteractive
513
514RUN apt-get update && apt-get install -y --no-install-recommends \
515    wget curl unzip ca-certificates git gcc g++ pkg-config graphviz \
516    && rm -rf /var/lib/apt/lists/*
517
518# Rust
519ENV CARGO_HOME="/usr/local/cargo"
520ENV RUSTUP_HOME="/usr/local/rustup"
521RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain stable \
522    && chmod -R a+rwx "$CARGO_HOME" "$RUSTUP_HOME"
523ENV PATH="${CARGO_HOME}/bin:${PATH}"
524
525# libtorch (CUDA 12.8)
526ARG LIBTORCH_VERSION=2.10.0
527RUN wget -q "https://download.pytorch.org/libtorch/cu128/libtorch-shared-with-deps-${LIBTORCH_VERSION}%2Bcu128.zip" \
528    && unzip -q "libtorch-shared-with-deps-${LIBTORCH_VERSION}+cu128.zip" -d /usr/local \
529    && rm "libtorch-shared-with-deps-${LIBTORCH_VERSION}+cu128.zip"
530
531ENV LIBTORCH_PATH="/usr/local/libtorch"
532ENV LD_LIBRARY_PATH="${LIBTORCH_PATH}/lib:/usr/local/cuda/lib64"
533ENV LIBRARY_PATH="${LIBTORCH_PATH}/lib:/usr/local/cuda/lib64"
534ENV CUDA_HOME="/usr/local/cuda"
535
536WORKDIR /workspace
537"#;
538
539// Mounted mode: libtorch provided at runtime via volume mount
540const DOCKERFILE_MOUNTED: &str = r#"# CPU dev image for floDl projects (libtorch mounted at runtime).
541FROM ubuntu:24.04
542
543ENV DEBIAN_FRONTEND=noninteractive
544
545RUN apt-get update && apt-get install -y --no-install-recommends \
546    wget curl unzip ca-certificates git gcc g++ pkg-config graphviz \
547    && rm -rf /var/lib/apt/lists/*
548
549# Rust
550ENV CARGO_HOME="/usr/local/cargo"
551ENV RUSTUP_HOME="/usr/local/rustup"
552RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain stable \
553    && chmod -R a+rwx "$CARGO_HOME" "$RUSTUP_HOME"
554ENV PATH="${CARGO_HOME}/bin:${PATH}"
555
556ENV LIBTORCH_PATH="/usr/local/libtorch"
557ENV LD_LIBRARY_PATH="${LIBTORCH_PATH}/lib"
558ENV LIBRARY_PATH="${LIBTORCH_PATH}/lib"
559
560WORKDIR /workspace
561"#;
562
563const DOCKERFILE_CUDA_MOUNTED: &str = r#"# CUDA dev image for floDl projects (libtorch mounted at runtime).
564# Requires: docker run --gpus all ...
565ARG CUDA_VERSION=12.8.0
566FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu24.04
567
568ENV DEBIAN_FRONTEND=noninteractive
569
570RUN apt-get update && apt-get install -y --no-install-recommends \
571    wget curl unzip ca-certificates git gcc g++ pkg-config graphviz \
572    && rm -rf /var/lib/apt/lists/*
573
574# Rust
575ENV CARGO_HOME="/usr/local/cargo"
576ENV RUSTUP_HOME="/usr/local/rustup"
577RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain stable \
578    && chmod -R a+rwx "$CARGO_HOME" "$RUSTUP_HOME"
579ENV PATH="${CARGO_HOME}/bin:${PATH}"
580
581ENV LIBTORCH_PATH="/usr/local/libtorch"
582ENV LD_LIBRARY_PATH="${LIBTORCH_PATH}/lib:/usr/local/cuda/lib64"
583ENV LIBRARY_PATH="${LIBTORCH_PATH}/lib:/usr/local/cuda/lib64"
584ENV CUDA_HOME="/usr/local/cuda"
585
586WORKDIR /workspace
587"#;
588
589// ---------------------------------------------------------------------------
590// fdl.yml.example template
591// ---------------------------------------------------------------------------
592
593/// The scaffold ships `fdl.yml.example` (committed) and fdl auto-copies it to
594/// the gitignored `fdl.yml` on first use. Docker modes attach `docker:` to
595/// every command; native mode drops `docker:` so the commands run directly
596/// on the host. Libtorch env vars (`LIBTORCH_HOST_PATH`, `CUDA_VERSION`,
597/// `CUDA_TAG`, etc.) are derived from `libtorch/.active` by
598/// `flodl-cli/src/run.rs::libtorch_env` before each `docker compose run`
599/// (Docker modes) or exported into the child process (native mode).
600fn fdl_yml_example_template(project_name: &str, mode: Mode) -> String {
601    let use_docker = matches!(mode, Mode::Mounted | Mode::Docker);
602    let (cpu_svc, cuda_svc) = if use_docker {
603        ("\n    docker: dev", "\n    docker: cuda")
604    } else {
605        ("", "")
606    };
607    let cuda_note = if use_docker {
608        "(requires NVIDIA Container Toolkit)"
609    } else {
610        "(requires a matching CUDA toolkit on the host)"
611    };
612    let preamble = if use_docker {
613        "# Run any of these with `./fdl <cmd>` (or `fdl <cmd>` once installed\n\
614         # globally via `./fdl install`). Libtorch env vars are derived from\n\
615         # `libtorch/.active` automatically; missing libtorch surfaces as a\n\
616         # clean linker error, with `./fdl setup` one call away."
617    } else {
618        "# Native mode: commands run on the host. Make sure libtorch is\n\
619         # installed (`./fdl libtorch download --cpu` or `--cuda 12.8`)\n\
620         # and that `$LIBTORCH` / `$LD_LIBRARY_PATH` are exported so\n\
621         # cargo can link. `./fdl libtorch info` prints the commands you\n\
622         # need after a download."
623    };
624
625    let shell_block = if use_docker {
626        format!(
627            r#"  shell:
628    description: Interactive shell (CPU container)
629    run: bash{cpu_svc}
630
631"#
632        )
633    } else {
634        // Native mode: no container to drop into; users open their own shell.
635        String::new()
636    };
637
638    let cuda_shell_block = if use_docker {
639        format!(
640            r#"  cuda-shell:
641    description: Interactive shell (CUDA container)
642    run: bash{cuda_svc}
643"#
644        )
645    } else {
646        String::new()
647    };
648
649    format!(
650        r#"description: {project_name}
651
652{preamble}
653
654commands:
655  # --- CPU ---
656  build:
657    description: Build (debug)
658    run: cargo build{cpu_svc}
659  test:
660    description: Run CPU tests
661    run: cargo test -- --nocapture{cpu_svc}
662  run:
663    description: cargo run
664    run: cargo run{cpu_svc}
665  check:
666    description: Type-check without building
667    run: cargo check{cpu_svc}
668  clippy:
669    description: Lint
670    run: cargo clippy -- -W clippy::all{cpu_svc}
671{shell_block}  # --- CUDA {cuda_note} ---
672  cuda-build:
673    description: Build with CUDA feature
674    run: cargo build --features cuda{cuda_svc}
675  cuda-test:
676    description: Run CUDA tests
677    run: cargo test --features cuda -- --nocapture{cuda_svc}
678  cuda-run:
679    description: cargo run --features cuda
680    run: cargo run --features cuda{cuda_svc}
681{cuda_shell_block}"#
682    )
683}
684
685// ---------------------------------------------------------------------------
686// File writing helper
687// ---------------------------------------------------------------------------
688
689fn write_file(path: &str, content: &str) -> Result<(), String> {
690    fs::write(path, content).map_err(|e| format!("cannot write {}: {}", path, e))
691}
flodl_cli/init.rs

flodl_cli/
init.rs
