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Who published Env Bootstrap?

mybigday (@mybigday) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Env Bootstrap by mybigday — Agent Skill

Name: Env Bootstrap
Author: mybigday

Env Bootstrap

Recreate the ml-intern-kit Python environment on a new machine (laptop, rented GPU box, Docker, fresh checkout). Triggered when the user is on a new host, sees ImportError on a core dep (torch/transformers/trl/peft/accelerate), or wants to install flash-attn / unsloth / bitsandbytes after the fact.

mybigday daee5e8 3.5 KB Updated Sep 17, 2026

File contents

env-bootstrap — recreate the env anywhere

Default path

bash bootstrap.sh           # auto-detects accelerator (cuda / rocm / mps / cpu)
bash bootstrap.sh --dev     # + pytest, ruff, jupyter
bash bootstrap.sh --eval    # + lm-eval-harness
bash bootstrap.sh --all     # everything except flash-attn / unsloth / mlx
source .venv/bin/activate
make doctor                 # prints torch backend, GPU info, mlx if installed

Force a specific accelerator

make env-cuda    # NVIDIA CUDA 12.4 wheels
make env-rocm    # AMD ROCm 6.2 wheels (gfx942 / gfx110X dGPUs only)
make env-strix-halo  # AMD Ryzen AI Max+ 395 / gfx1151 (TheRock nightly)
make env-mps     # macOS / Apple Silicon, torch-MPS only
make env-mlx     # macOS arm64: torch-MPS + Apple MLX
make env-cpu     # CPU-only wheels

bootstrap.sh picks the right PyTorch wheel index automatically: cu124 for NVIDIA, rocm6.2 for AMD, default (MPS) for arm64 macOS, cpu everywhere else.

When `uv` isn't installed

curl -LsSf https://astral.sh/uv/install.sh | sh

bootstrap.sh will pick it up automatically. If installing uv isn't an option, the script falls back to python3.11 -m venv + pip install -r requirements.txt.

When you need flash-attn or unsloth

These have CUDA / ABI requirements that vary per box, so they are deliberately not in requirements.txt. Install after the core stack works:

make flash      # flash-attn, requires matching CUDA toolkit
make unsloth    # unsloth, Linux only

If make flash fails with a build error, attn_implementation="sdpa" in the training config is a good fallback — slower than flash-attn-2 but works everywhere PyTorch does.

When you need a fully-pinned, identical env (Docker)

docker build -t ml-intern-kit:cu124 .
docker run --gpus all -it --rm \
    -v "$PWD":/workspace \
    -v "$HOME/.cache/huggingface":/root/.cache/huggingface \
    --env-file .env \
    ml-intern-kit:cu124 bash

The Dockerfile pins CUDA 12.4 + Python 3.11 + uv-resolved deps from pyproject.toml. Cache mounts let the host keep model/dataset downloads across container runs.

Common breakage on fresh boxes

Symptom	Fix
`torch.cuda.is_available()` is False on NVIDIA	Wrong wheel. `make env-cuda` re-pulls from `cu124` index.
`torch.cuda.is_available()` is False on AMD	ROCm uses the CUDA API surface — `torch.cuda.is_available()` should be True with a `torch.version.hip` set. If False: `make env-rocm`, then `rocminfo`.
`bitsandbytes` import error on macOS / ROCm	Expected — gated to CUDA Linux. The training scripts auto-substitute `optim="adamw_torch"` on those backends.
`bf16` errors / NaN loss on macOS	`train_sft.py` auto-switches to `fp16` on MPS. If you see this on torch ≤ 2.3, upgrade torch.
`flash-attn` build fails	Skip it; the scripts auto-fall-back to `sdpa`. flash-attn is CUDA-only upstream.
MPS op-not-implemented error	Set `PYTORCH_ENABLE_MPS_FALLBACK=1` to fall through to CPU for that op, or use the `train-mlx` path instead.
HF Hub auth fails	`hf auth login` (or `huggingface-cli login`) and re-run, or set `HF_TOKEN` in `.env`.

---name: env-bootstrapdescription: Recreate the ml-intern-kit Python environment on a new machine (laptop, rented GPU box, Docker, fresh checkout). Triggered when the user is on a new host, sees ImportError on a core dep (torch/transformers/trl/peft/accelerate), or wants to install flash-attn / unsloth / bitsandbytes after the fact.---# env-bootstrap — recreate the env anywhere## Default path```bashbash bootstrap.sh # auto-detects accelerator (cuda / rocm / mps / cpu)bash bootstrap.sh --dev # + pytest, ruff, jupyterbash bootstrap.sh --eval # + lm-eval-harnessbash bootstrap.sh --all # everything except flash-attn / unsloth / mlxsource .venv/bin/activatemake doctor # prints torch backend, GPU info, mlx if installed```## Force a specific accelerator```bashmake env-cuda # NVIDIA CUDA 12.4 wheelsmake env-rocm # AMD ROCm 6.2 wheels (gfx942 / gfx110X dGPUs only)make env-strix-halo # AMD Ryzen AI Max+ 395 / gfx1151 (TheRock nightly)make env-mps # macOS / Apple Silicon, torch-MPS onlymake env-mlx # macOS arm64: torch-MPS + Apple MLXmake env-cpu # CPU-only wheels````bootstrap.sh` picks the right PyTorch wheel index automatically:`cu124` for NVIDIA, `rocm6.2` for AMD, default (MPS) for arm64 macOS,`cpu` everywhere else.## When `uv` isn't installed```bashcurl -LsSf https://astral.sh/uv/install.sh | sh````bootstrap.sh` will pick it up automatically. If installing uv isn't an option,the script falls back to `python3.11 -m venv` + `pip install -r requirements.txt`.## When you need flash-attn or unslothThese have CUDA / ABI requirements that vary per box, so they are deliberately**not** in `requirements.txt`. Install after the core stack works:```bashmake flash # flash-attn, requires matching CUDA toolkitmake unsloth # unsloth, Linux only```If `make flash` fails with a build error, `attn_implementation="sdpa"` in thetraining config is a good fallback — slower than flash-attn-2 but workseverywhere PyTorch does.## When you need a fully-pinned, identical env (Docker)```bashdocker build -t ml-intern-kit:cu124 .docker run --gpus all -it --rm \ -v "$PWD":/workspace \ -v "$HOME/.cache/huggingface":/root/.cache/huggingface \ --env-file .env \ ml-intern-kit:cu124 bash```The Dockerfile pins CUDA 12.4 + Python 3.11 + uv-resolved deps from`pyproject.toml`. Cache mounts let the host keep model/dataset downloadsacross container runs.## Common breakage on fresh boxes| Symptom | Fix ||---|---|| `torch.cuda.is_available()` is False on NVIDIA | Wrong wheel. `make env-cuda` re-pulls from `cu124` index. || `torch.cuda.is_available()` is False on AMD | ROCm uses the CUDA API surface — `torch.cuda.is_available()` *should* be True with a `torch.version.hip` set. If False: `make env-rocm`, then `rocminfo`. || `bitsandbytes` import error on macOS / ROCm | Expected — gated to CUDA Linux. The training scripts auto-substitute `optim="adamw_torch"` on those backends. || `bf16` errors / NaN loss on macOS | `train_sft.py` auto-switches to `fp16` on MPS. If you see this on torch ≤ 2.3, upgrade torch. || `flash-attn` build fails | Skip it; the scripts auto-fall-back to `sdpa`. flash-attn is CUDA-only upstream. || MPS op-not-implemented error | Set `PYTORCH_ENABLE_MPS_FALLBACK=1` to fall through to CPU for that op, or use the `train-mlx` path instead. || HF Hub auth fails | `hf auth login` (or `huggingface-cli login`) and re-run, or set `HF_TOKEN` in `.env`. |

Frequently asked questions