---

name: tensorpack description: "Use Tensorpack, a TF1-compatible TensorFlow training interface with DataFlow, trainers, callbacks, prediction, checkpoints, export, and example workflow guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tensorpack repo skill

Use this repo skill when a task names Tensorpack or clearly needs its graph-mode TensorFlow training interface, pure-Python DataFlow pipeline, callbacks, checkpoint utilities, model export, or documented example workflow patterns. Tensorpack is TF1-oriented: even under a TensorFlow 2 package, guide users toward tensorpack.tfv1 / tf.compat.v1 graph code and explicit eager-mode handling.

Start here

1. Read references/repo-overview.md for the package topology, dependency tiers, TensorFlow compatibility, and public module map.
2. Read references/repo-provenance.md before deciding whether this skill is current for another checkout.
3. Use scripts/check_tensorpack_env.py for a safe import and optional-dependency diagnostic.
4. If the task names a source example family or paper/model recipe, read references/examples-catalog.md and then route to the owning sub-skill.
5. If the user reports an install/import/backend/data failure before you know the owning workflow, read references/troubleshooting.md.

Route by task

User intent or signal	Read
Data loading, augmentation, serializers, dataset loaders, DataFlow, QueueInput, input queue speed, MultiProcessRunnerZMQ, LMDBSerializer, imgaug	sub-skills/dataflow/SKILL.md
Model definitions, ModelDesc, TrainConfig, trainers, callbacks, summaries, symbolic layers, multi-GPU/distributed training, training example recipes	sub-skills/training/SKILL.md
PredictConfig, OfflinePredictor, SmartInit, checkpoint/.npz inspection, Caffe conversion, SavedModel export, compact graph export, inference example patterns	sub-skills/inference-export/SKILL.md
Broad example-family lookup across basics, ResNet/ImageNet, Faster/Mask R-CNN, GAN, RL, speech, NLP, Caffe, saliency/CAM, Keras	references/examples-catalog.md
Unknown install/import/runtime failure	references/troubleshooting.md, then the nearest sub-skill troubleshooting guide

Use this skill when

• The user asks about Tensorpack APIs such as DataFlow, ModelDesc, TrainConfig, SimpleTrainer, InferenceRunner, OfflinePredictor, SmartInit, ModelExporter, argscope, LinearWrap, or Tensorpack model layers.
• The user needs to build or debug a graph-mode TensorFlow training loop while keeping data loading, callbacks, summaries, validation, checkpointing, and export consistent with Tensorpack conventions.
• The user has Tensorpack checkpoint or model-zoo weights and needs variable names, shape matching, .npz conversion, prediction, or export.
• The task is to adapt a Tensorpack example pattern, while preserving dataset, optional dependency, and CPU/GPU verification limits.

Avoid this skill when

• The request is ordinary PyTorch, JAX, modern tf.keras, or TensorFlow 2 eager training with no Tensorpack dependency.
• The user wants a full benchmark or paper-performance reproduction but has not supplied the exact dataset, pretrained weights, hardware, and optional dependencies; provide implementation guidance and state verification limits.
• The task is general TensorFlow Serving, container orchestration, or MLOps deployment beyond Tensorpack export artifact creation.
• The request is only about maintaining this repository checkout rather than using Tensorpack as a package; use a repository-maintenance skill if available.

Install and import guidance

Tensorpack's package dependencies include NumPy, six, termcolor, tabulate, tqdm, msgpack/msgpack-numpy, pyzmq, and psutil. TensorFlow is required for training, models, prediction, checkpoints, and export; DataFlow can be used as a pure Python data pipeline without full TensorFlow workflows. OpenCV is optional but important for image augmentation and most vision examples.

Public install patterns from the project evidence:

pip install --upgrade git+https://github.com/tensorpack/tensorpack.git
# or pin an exact Tensorpack source/release version for reproducible research

Minimal import check:

python - <<'PY'
import tensorpack
print("tensorpack", tensorpack.__version__)
from tensorpack.dataflow import FakeData, BatchData
from tensorpack import tfv1 as tf
print("tf compat", tf.__name__)
PY

For TF2 runtimes, disable eager execution before graph construction when the workflow uses Tensorpack trainers or graph APIs:

from tensorpack import tfv1 as tf
tf.disable_eager_execution()

Safe helper

Run the bundled environment diagnostic from the root of this skill directory or by giving its full path:

python scripts/check_tensorpack_env.py --json
python scripts/check_tensorpack_env.py --require-tf --require-cv2

It reports installed Tensorpack/TensorFlow versions, optional dependency status, and visible TensorFlow GPU devices. It does not download data, train, write outside normal stdout, or inspect original source files.

Verification status

This generated skill was constructed with a CPU inspection and smoke environment. Core package imports, DataFlow construction, TensorFlow graph construction, and selected helper parser checks were verified. CUDA/multi-GPU, Horovod/BytePS, Caffe, Atari/Gym, COCO, TIMIT, and large-dataset example workflows are documented as optional or data/backend-dependent; do not claim they are verified unless the user supplies and verifies the required stack.

Hard boundaries

• Keep runtime answers self-contained. Do not require future agents to open or run original Tensorpack source files, docs, examples, or scripts.
• Link only to files inside this generated skill tree when giving reusable instructions. Source evidence paths live in provenance and review artifacts, not as runtime dependencies.
• Do not reveal private environment prefixes, activation commands, local checkout paths, or package installation locations in user-facing answers.
• Respect the user's current request: this skill was generated for local use and should not be imported unless a later user explicitly requests import.