> ## Documentation Index > Fetch the complete documentation index at: https://docs.freesolo.co/llms.txt > Use this file to discover all available pages before exploring further. # Package & publish > Lay out the environment folder, declare dependencies, and publish it to the managed Hub. Turn your local files into a published environment that training runs reach by id. Structure the folder, declare its dependencies, and push it to the managed Hub. ## Structure the package A published environment is a small folder. Flash packages it, imports its `environment.py` entrypoint on the worker, and calls `load_environment(**params)`, which must return a Freesolo SDK environment. The folder can be named anything as long as it contains `environment.py` at its root. ```text theme={null} math/ environment.py # defines load_environment() helpers.py # optional Python helpers dataset/ train.jsonl # input/output records eval.jsonl ``` Publish the folder: ```bash theme={null} flash env push --name math math ``` A single `.py` upload still works for tiny smoke tests, but push real environments as folders. The `--name` value is normalized to a lowercase hyphen slug, and the published id uses your Freesolo org namespace, for example `your-org/math`. Keep all imports either from sibling files in the folder you publish, from the `freesolo` SDK, or from third-party packages you declare under `[environment].pip` (see below). ### Declare third-party dependencies If your `environment.py` imports a package that isn't part of the `freesolo` SDK, list it under `[environment].pip` so the worker installs it before importing your environment: ```toml theme={null} [environment] id = "your-org/your-env" pip = ["rapidfuzz>=3.0", "pydantic"] ``` This is the supported worker dependency mechanism. A `pyproject.toml`, `requirements.txt`, or lockfile beside `environment.py` can help local development, but Flash does not use those files to install packages for a managed training run. Put runtime packages in the [training config](/reference/configuration) that references the published environment. Only list packages your environment imports. Flash manages the training stack itself, so do not pin packages such as `torch`, `trl`, `vllm`, `peft`, or `bitsandbytes` here unless your environment directly imports them. For example, an environment whose reward calls a judge model should declare the client library and the secret it reads. Here the judge is reached through an OpenAI-compatible client (such as OpenRouter), but the same pattern applies to any provider: ```toml theme={null} [environment] id = "your-org/search-reward" pip = ["openai>=1.0.0"] secrets = ["JUDGE_API_KEY"] ``` If that same reward also queries a database, declare its client library and the exact environment variable name your code reads. Here the database is MongoDB via `pymongo`, but the same pattern applies to any database: ```toml theme={null} [environment] id = "your-org/search-reward" pip = ["openai>=1.0.0", "pymongo>=4.17.0"] secrets = ["JUDGE_API_KEY", "DATABASE_URL"] ``` Do not declare unused packages or secrets. If the active reward only calls the judge model and no longer queries the database, leave out the database client and its secret. ## Publish it Training runs on managed infrastructure, so the environment must be reachable by id. Publish your local environment folder to the managed Environments Hub: ```bash theme={null} flash env push --name math math ``` It prints the published id (`your-org/math`). Put that in your config's `[environment] id`. ## Use an existing environment If you already have a published env id (yours or one shared with you), reference it directly. A config `[environment] id` also accepts a `github:owner/repo@ref:path` ref or a GitHub URL, which Flash resolves at run time. `flash env pull` (below) takes managed `namespace/name` slugs only — it no longer accepts `github:` refs or GitHub URLs. ```toml theme={null} [environment] id = "your-org/your-env" ``` To edit or inspect the source locally, pull the whole environment into a directory: ```bash theme={null} flash env pull your-org/your-env ``` Pull one file by adding its path inside the environment. Use `-o` to choose the destination and `-f` to overwrite an existing output: ```bash theme={null} flash env pull your-org/your-env dataset/train.jsonl -o train.jsonl ``` ## List what you have ```bash theme={null} flash env list ``` Shows local environment sources you can publish, such as `./environment.py` or folders under `environments/`. ## Delete a Hub environment Delete only targets managed Hub ids of the form `namespace/name`; GitHub refs and local paths are not Hub records and cannot be deleted this way. ```bash theme={null} flash env delete your-org/your-env flash env delete your-org/your-env -y # skip the confirmation prompt ``` ## Use API keys If your environment needs an external service, read the key from `os.environ` in `environment.py`: ```python environment.py theme={null} import os SERVICE_API_KEY = os.environ["SERVICE_API_KEY"] ``` Then declare the environment variable names in your training config: ```toml theme={null} [environment] id = "your-org/your-env" secrets = ["SERVICE_API_KEY"] ``` Set the value in your shell before submitting: ```bash theme={null} export SERVICE_API_KEY="..." flash train configs/sft.toml ``` You can also put local development values in `.env` or `.env.local`: ```bash theme={null} SERVICE_API_KEY=... ``` Flash sends declared secret values to the worker out-of-band. Secret values are not stored in the TOML config, run spec, status JSON, logs, or environment artifact. Do not put API keys in `[environment.params]`. If a declared secret is missing when you submit, `flash train` fails before the run starts. ## Next steps Fill in the environment class you just published. Reference the published id from a config and train.