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SkillAtlasSkill 详情
tao-finetune-nv-tesseract-forecasting
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 72Security 70
项目 README
来源文件：README.md
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Quickstart
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Install One Skill Without Prompts
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Install for a Specific Agent
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
Keep Skills Up to Date
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Browse the Catalog
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Skill Catalog
Getting Help & Contributing
Where to file an issue depends on what's broken:
- Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
- Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
- Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
- Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Verifying Skills
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md— the skill instructions consumed by the agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
Roadmap
- ✅ Public skills catalog with NVIDIA-verified skills across multiple products
- ✅ Automated sync pipeline with skills mirrored from product repos daily
- ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
- ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
- ✅ Skills universal evaluation criteria and task-specific criteria
- ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
- ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
- ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
- 🔲 Syndication to additional MCP hubs and partner channels
Repository Structure
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig— detached OMS-format signature (verifiable againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
License
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
其他
中风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 未检测到高风险命令。
- 扫描发现：4 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-nv-tesseract-forecasting" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-nv-tesseract-forecasting" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-nv-tesseract-forecasting" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-nv-tesseract-forecasting" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-nv-tesseract-forecasting" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: tao-finetune-nv-tesseract-forecasting
description: >-
NV-Tesseract Forecasting — transformer-based multivariate time series forecasting
with DARR (context-enhanced kNN retrieval), interpretability, and fine-tuning.
Use when the user asks to "forecast with NV-Tesseract", "run forecasting inference",
"use perform_forecasting", "DARR mode", "context-enhanced forecasting",
"lag horizon attribution", "interpretability", "fine-tune forecasting",
"fine-tune forecasting with automl", "hyper-parameter optimization with forecasting", or
or mentions "nv-tesseract-forecasting", "moment_head_512_6hr", or "run8_best_model_cr".
license: Apache-2.0
compatibility: Requires Python 3.10+ and uv. CUDA GPU recommended; Apple Silicon (MPS) and CPU supported.
metadata:
author: NVIDIA Corporation
version: "0.2.0"
allowed-tools: Read Bash
tags:
- forecasting
- time-series
- darr
- interpretability
- automl
- finetune
- inference
- nv-tesseractNV-Tesseract Forecasting
Transformer-based multivariate time series forecasting using self-supervised pretraining on diverse temporal data. Three inference modes: standard (direct forecast), DARR (context-enhanced kNN retrieval blending), and interpretability (latent trajectory extraction, semantic flow, lag×horizon attribution, trajectory stability, and diagnostic ratios — full explanation bundle with PDF report). Fine-tuning adapts the forecasting head — and optionally the cross-channel layer — to your domain.
Source code: https://github.com/NVIDIA/NV-Tesseract Pretrained weights: https://huggingface.co/nvidia/nv-tesseract-forecasting
External dependencies
| Dependency | Purpose | Install |
|---|---|---|
| Python 3.10+ | Runtime | https://www.python.org/downloads/ |
| uv | Package + environment manager | pip install uv |
| CUDA toolkit (optional) | GPU acceleration | https://developer.nvidia.com/cuda-downloads |
| matplotlib (optional) | Interpretability PDF report, heatmap PNG, flow + stability charts | uv add matplotlib |
Credentials
nvidia/nv-tesseract-forecasting is a public repo — no token required for downloading weights.
If you hit a 401/403 (gated access or license not accepted) or a 504 on first download, see the Known pitfalls section.
Quick start
git clone --branch main --single-branch https://github.com/NVIDIA/NV-Tesseract
cd NV-Tesseract/forecasting
uv sync --group dev
uv pip install -e . # editable install — required for clean sdk.* imports
# Standard inference (auto-downloads weights from HF on first run, no auth needed)
uv run python sdk/quick_example.py
Inference
Import and call perform_forecasting from sdk/forecasting.py. It auto-downloads weights,
standardizes input, runs autoregressive rollout for long horizons, and returns a DataFrame
with {target_column}_forecast rows for the requested horizon.
import sys, pandas as pd
sys.path.append("/path/to/NV-Tesseract/forecasting") # clone NV-Tesseract with --branch main
from sdk.forecasting import perform_forecasting
df = pd.read_csv("your_data.csv") # must have timestamp + numeric target column
results = perform_forecasting(
df=df,
timestamp_column="timestamp", # parseable datetime column
target_column="target", # primary target to forecast
seq_len=512, # input context length (rows consumed)
forecast_horizon=72, # steps ahead to predict (max 512)
model_horizon=72, # native model horizon; change when using custom weights
standardizer_pkl="standardizer.pkl", # auto-downloaded from HF if missing
ckpt="run8_best_model_cr.pt", # auto-downloaded; see Checkpoints table
)
# Returns DataFrame: timestamp | {target_column}_forecast (forecast_horizon rows)
print(results.head())
Checkpoints
| File | Mode | Downloaded when |
|---|---|---|
run8_best_model_cr.pt | Default (cross-channel on) | use_cross_channel=True (default) |
moment_head_512_6hr.pt | Standard (no cross-channel) | use_cross_channel=False |
standardizer.pkl | Both | Always |
Pass use_cross_channel=False to use the standard checkpoint:
results = perform_forecasting(df=df, use_cross_channel=False, ...)
DARR mode (context-enhanced forecasting)
Supply context_df to enable DARR: the SDK builds a kNN memory from historical windows and
blends direct predictions with retrieved neighbors (alpha * direct + (1 - alpha) * kNN).
context_df = pd.read_csv("historical_data.csv") # needs ≥ seq_len + model_horizon rows
results = perform_forecasting(
df=df,
context_df=context_df, # enables DARR
forecast_horizon=72,
alpha=0.2, # 0.2 = 20% direct, 80% kNN (default: 0.01)
k=64, # number of nearest neighbors
temperature=0.05, # kNN softmax temperature
)
Context and input datasets do not need identical columns — the SDK aligns to common features
and warns when columns differ. Both must share timestamp_column and target_column.
Interpretability
Set interpretability=True to activate the Model-Agnostic Interpretability Framework. It produces localized, horizon-specific, time-aware explanations — including lag×horizon attribution, semantic flow, trajectory stability, diagnostic ratios, and (for multivariate inputs) channel-axis attribution and coupling analysis.
For the full parameter reference, output bundle, and component descriptions, see
forecasting/README.md.
Fine-tuning
Fine-tune the forecasting head (encoder/embedder frozen by default) on your own time series.
--ckpt-init auto warm-starts from the published NV-Tesseract checkpoint; --ckpt-init none
trains a fresh head from the base backbone.
cd /path/to/NV-Tesseract/forecasting
# Without cross-channel (uses moment_head_512_6hr.pt)
uv run python examples/finetune_example.py \
--csv /path/to/timeseries.csv \
--timestamp-col timestamp \
--target-cols target \
--seq-len 512 --forecast-horizon 72 \
--epochs 5 --batch-size 8 --lr 1e-4 \
--output-dir artifacts/finetune_my_data
# With cross-channel layer (uses run8_best_model_cr.pt)
uv run python examples/finetune_example.py \
--csv /path/to/timeseries.csv \
--timestamp-col timestamp \
--target-cols sensor_1,sensor_2,sensor_3 \
--use-cross-channel --cross-channel-heads 8 \
--epochs 5 \
--output-dir artifacts/finetune_cross_channel
Fine-tuning arguments
| Argument | Default | Description |
|---|---|---|
--run-config | — | YAML config from AutoMLRunner ({config_path}). CLI flags override file values. |
--csv | required* | Single CSV split temporally into train/val |
--train-csv | required* | Training CSV (mutually exclusive with --csv) |
--val-csv | — | Validation CSV when --train-csv is used |
--timestamp-col | timestamp | Datetime column to exclude from features |
--target-cols | all numeric | Comma-separated columns to forecast |
--model-name | AutonLab/MOMENT-1-large | Backbone model identifier |
--ckpt-init | auto | auto = published NV-Tesseract weights; none = fresh head; or path to .pt |
--standardizer-init | standardizer.pkl | Standardizer pickle used when --ckpt-init auto |
--repo-id | nvidia/nv-tesseract-forecasting | HuggingFace repo for auto-download |
--seq-len | 512 | Input context length |
--forecast-horizon | 72 | Steps ahead to predict |
--stride | forecast_horizon | Sliding window stride (None → horizon) |
--val-ratio | 0.1 | Validation fraction when --csv is used |
--test-ratio | 0.0 | Test holdout fraction when --csv is used |
--no-standardize | false | Disable per-dataset standardization |
--epochs | 5 | Training epochs |
--batch-size | 8 | Per-GPU batch size |
--lr | 1e-4 | AdamW learning rate (OneCycleLR scheduler) |
--weight-decay | 0.0 | AdamW weight decay |
--head-dropout | 0.1 | Forecasting head dropout |
--max-norm | 5.0 | Gradient norm clip |
--num-workers | 0 | DataLoader workers |
--seed | 13 | Random seed |
--output-dir | artifacts/finetune | Output directory |
--local-files-only | false | Do not download backbone weights from HuggingFace |
--unfreeze-encoder | false | Train the transformer encoder too |
--unfreeze-embedder | false | Train the patch embedder too |
--use-cross-channel | false | Add cross-channel attention layer |
--cross-channel-heads | 8 | Attention heads in cross-channel layer |
--cross-channel-dropout | 0.1 | Dropout in the cross-channel layer |
--num-gpus | all available | Number of GPUs for DDP fine-tuning; set 1 to force single-GPU |
*One of --csv or --train-csv is required.
Inference with fine-tuned checkpoint
results = perform_forecasting(
df=df,
timestamp_column="timestamp",
target_column="target",
seq_len=512,
forecast_horizon=72,
model_horizon=72,
standardizer_pkl="artifacts/finetune_my_data/standardizer.pkl",
ckpt="artifacts/finetune_my_data/best_model.pt",
use_cross_channel=False, # set True if trained with --use-cross-channel
)
Data requirements
| Property | Requirement |
|---|---|
| Rows | ≥ seq_len (default 512) for inference; validation split must also have ≥ seq_len + forecast_horizon rows |
| Columns | timestamp + one or more numeric columns; NULLs filled with zeros automatically |
| Timestamp | Parseable by pandas; no NULLs; uniform frequency inferred from mode of diffs |
| Target | Must be numeric; NULLs filled with zeros |
forecast_horizon | Max 512 steps; beyond model's native 72 triggers autoregressive rollout |
| DARR context | ≥ seq_len + model_horizon rows; must share timestamp + target columns with input |
Output structure
Inference (standard / DARR):
DataFrame: timestamp | {target_column}_forecast (forecast_horizon rows)
Fine-tuning (--output-dir artifacts/finetune_my_data):
artifacts/finetune_my_data/
├── best_model.pt # checkpoint with lowest validation MSE
├── standardizer.pkl # normalization statistics for this dataset
├── finetune_metadata.json # model config, channels, best epoch, all args
├── metrics.json # scalar summary: {"val_mse": float, "val_mae": float} — consumed by AutoML runner
└── epoch_metrics.json # per-epoch list: [{epoch, train_mse, val_mse, val_mae}, ...]
Hardware
| Tier | Setup | Notes |
|---|---|---|
| Minimum | 1× CPU | Functional; slow for long horizons |
| Recommended | 1× NVIDIA GPU (≥8 GB VRAM) | Strongly recommended for fine-tuning |
| Apple Silicon | MPS | Auto-detected; on par with CPU for this workload |
| Multi-GPU fine-tuning | 2+× NVIDIA GPUs | Auto DDP via --num-gpus (defaults to all visible GPUs) |
AutoML (HPO: hyperparameter optimization)
This skill is AutoML-enabled for both fine-tuning and DARR inference. When an HPO request arrives, route it through tao-skill-bank:tao-run-automl with this model's skill_dir.
Read references/automl.md when the user asks for AutoML/HPO setup, tunable parameters, runner examples, inference trial scripts, DARR HPO, or AutoML result handoff details.
Known pitfalls
| Symptom | Cause | Fix |
|---|---|---|
ModuleNotFoundError: backbone | Editable install missing | Run uv pip install -e . from forecasting/ |
HfHubHTTPError: 401 / 403 | Model license not accepted or gated fork | Accept license on HF repo page; or huggingface-cli login |
504 / timeout on first weight download | HF CDN throttles unauthenticated requests — public repos are still subject to this on first download | Set export HUGGINGFACE_HUB_TOKEN="$HF_TOKEN" before running; authenticated requests use a more reliable CDN path |
ValueError: DataFrame has X rows but seq_len requires Y | Input too short | Provide ≥ seq_len (512) rows or reduce --seq-len |
ValueError: forecast_horizon must be <= 512 | Horizon too large | Split into multiple perform_forecasting calls |
ValueError: No common numeric columns (DARR) | Context has no overlapping features | Ensure context shares ≥ 1 numeric column with input |
ValueError: Context DataFrame has X rows but requires Y | Context too small | Context needs ≥ seq_len + model_horizon rows |
Interpretability PDF skipped: matplotlib not installed | Missing optional dep | uv add matplotlib or use interpretability_output="json" |
ValueError: No training windows (finetune) | Data too short for windows | Reduce --seq-len / --forecast-horizon, or increase dataset size |
Stale environment errors mentioning backbone package | Old lock file | uv cache clean && uv sync --group dev |
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