Reinforcement Learning
Transformers
English
post-training
distillation
agentic-coding
composer-2.5
cursor
kimi-k2
grpo
dapo
diloco
openenv
trl
verl
research
methodology
Instructions to use Codeseys/composer-replication-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Codeseys/composer-replication-framework with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,963 Bytes
7453f13 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | #!/usr/bin/env bash
# Build + push the baked SageMaker RL image to this account's private ECR (F3 §3.2).
# One-time (Admin). The one-shot smoke does NOT need this — use
# python examples/gsm8k_grpo/run_sagemaker_launch.py --image dlc
# which runs on the stock DLC + source_dir with no local build. Use this script
# for the repeatable path (no per-job pip-install) and the DiLoCo N-replica
# executor (which passes ContainerEntrypoint and wants the framework baked in).
#
# NOTE: the DLC base is GPU/linux-amd64 (~15 GB). On an Apple-Silicon host you
# must cross-build: pass --platform linux/amd64 (set below). This is slow under
# emulation; prefer building on a linux/amd64 host or CodeBuild for real use.
set -euo pipefail
REGION="${REGION:-us-west-2}"
ACCOUNT="${ACCOUNT:-386931836011}"
REPO="${REPO:-composer-rl}"
TAG="${TAG:-smoke}"
DLC_ACCOUNT="763104351884"
REGISTRY="${ACCOUNT}.dkr.ecr.${REGION}.amazonaws.com"
IMAGE="${REGISTRY}/${REPO}:${TAG}"
REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
echo "[ecr] region=${REGION} image=${IMAGE}"
# 1. Ensure the ECR repo exists (idempotent).
aws ecr describe-repositories --repository-names "${REPO}" --region "${REGION}" >/dev/null 2>&1 \
|| aws ecr create-repository --repository-name "${REPO}" --region "${REGION}" >/dev/null
# 2. Log in to BOTH the DLC registry (to pull the base) and our own (to push).
aws ecr get-login-password --region "${REGION}" \
| docker login --username AWS --password-stdin "${DLC_ACCOUNT}.dkr.ecr.${REGION}.amazonaws.com"
aws ecr get-login-password --region "${REGION}" \
| docker login --username AWS --password-stdin "${REGISTRY}"
# 3. Build (cross-arch on Apple Silicon) + push.
docker build --platform linux/amd64 \
-f "${REPO_ROOT}/docker/Dockerfile.sagemaker" \
-t "${IMAGE}" "${REPO_ROOT}"
docker push "${IMAGE}"
echo "[ecr] pushed ${IMAGE}"
echo "[ecr] launch with: python examples/gsm8k_grpo/run_sagemaker_launch.py --image ${IMAGE}"
|