Metadata-Version: 2.4
Name: privatemind
Version: 0.3.1
Summary: Official Python SDK for the PrivateMind ML platform.
Project-URL: Homepage, https://docs.privatemind.com/sdk.html
Project-URL: Documentation, https://docs.privatemind.com/sdk.html
Author: PrivateMind
License:                                  Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for describing the origin of the Work and
              reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Support. While redistributing the Work or
              Derivative Works thereof, You may choose to offer, and charge a
              fee for, acceptance of support, warranty, indemnity, or other
              liability obligations and/or rights consistent with this License.
              However, in accepting such obligations, You may act only on Your
              own behalf and on Your sole responsibility, not on behalf of any
              other Contributor, and only if You agree to indemnify, defend,
              and hold each Contributor harmless for any liability incurred by,
              or claims asserted against, such Contributor by reason of your
              accepting any such warranty or support.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright 2026 PrivateMind
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
License-File: LICENSE
Keywords: kubernetes,ml,mlflow,privatemind,ray,training
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Requires-Python: >=3.11
Requires-Dist: httpx[http2]>=0.27
Requires-Dist: pydantic>=2.5
Provides-Extra: dev
Requires-Dist: cloudpickle>=3.0; extra == 'dev'
Requires-Dist: pyright>=1.1.380; extra == 'dev'
Requires-Dist: pytest-cov>=5.0; extra == 'dev'
Requires-Dist: pytest-httpx>=0.30; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.6; extra == 'dev'
Provides-Extra: promote
Requires-Dist: cloudpickle>=3.0; extra == 'promote'
Description-Content-Type: text/markdown

# privatemind

Official Python SDK for the [PrivateMind](https://privatemind.com) ML platform.
Submit distributed training jobs to managed Ray + KubeRay + MLflow GPU
clusters from any Python environment, and promote a notebook function to the
fleet without leaving Python.

```
pip install privatemind
```

In a PrivateMind notebook the SDK is pre-installed and pre-configured, so the
quickstart below runs with nothing to set up.

Full documentation: [docs.privatemind.com/sdk.html](https://docs.privatemind.com/sdk.html)

## Quickstart

```python
from privatemind import submit_training

run = submit_training(
    entrypoint="python train.py --epochs 10",
    image="registry.example.com/trainer@sha256:...",
    workers=4,
    target_cluster="<your-gpu-cluster>",
    gpus_per_worker=1,
)

print(run)            # Run(name='tj-abc12', phase='Pending')
print(run.url)        # link to the platform UI

run.wait()            # block until terminal (Succeeded / Failed)
print(run.phase, run.mlflow_run_id)
```

## Zero-config inside a notebook

A PrivateMind workspace injects everything the SDK needs as environment, so
inside a notebook you do not pass a token, URL, cluster, or image:

| Variable | What it provides |
|---|---|
| `PRIVATEMIND_TOKEN` | per-workspace bearer token |
| `PRIVATEMIND_URL` | gateway API base URL (batch, image generation) |
| `PRIVATEMIND_APP_URL` | app backend base URL (training jobs) |
| `PRIVATEMIND_TARGET_CLUSTER` | the GPU cluster the workspace runs on |
| `PRIVATEMIND_TRAINING_IMAGE` | default worker image |
| `PRIVATEMIND_HOME_VOLUME` / `PRIVATEMIND_HOME_PATH` | the home Volume used to stage promoted code |

Off-cluster (laptop, CI) you supply a `token` (see Configuration); the URLs
default to the production platform, and everything else is an explicit
argument.

## GPU jobs

Set `gpus_per_worker` and the platform pins real GPUs for you. You do not have
to know which physical GPUs your org owns: when you omit `gpu_placements`, the
platform auto-derives a placement from the GPUs your org owns that are free
right now.

```python
run = submit_training(
    entrypoint="python train.py",
    image="...",
    workers=1,
    target_cluster="<your-gpu-cluster>",
    gpus_per_worker=1,        # placement auto-derived
)
```

To pin exact GPUs, pass `gpu_placements` (one placement per GPU worker, each
with exactly `gpus_per_worker` indices):

```python
gpu_placements=[{"host": "<gpu-host>", "indices": [0]}]
```

For multi-node, set `workers` greater than 1: each worker gets `gpus_per_worker`
GPUs. Omit `gpu_placements` and the platform places the workers for you;
explicit placements support single-worker jobs. The platform is the authority
on ownership, conflicts, and quota, and rejects a job that asks for GPUs it
cannot have.

## Promote a notebook function

`@pm.train` turns a function you just validated in the notebook into a
distributed TrainingJob. Calling it runs locally (validate in seconds);
`.promote()` ships it to the fleet.

```python
import privatemind as pm

@pm.train(workers=1, gpus_per_worker=1)
def train(lr=3e-4, epochs=10):
    import torch, mlflow                  # imported on the worker
    assert torch.cuda.is_available()
    ...                                    # your training loop

train(lr=1e-3)                            # runs locally in the notebook
run = train.promote(lr=1e-3)              # runs on the fleet -> Run
run.wait()
print(run.phase, run.mlflow_run_id)
```

Inside a notebook you usually pass only `workers` + `gpus_per_worker`;
`target_cluster`, `image`, and the home Volume come from the notebook context.
`.with_options(...)` returns a copy with overrides (e.g. a different image)
without re-decorating.

Every promote is tracked in MLflow with no setup: the run is named after the
job (not a random name), CPU/GPU/memory **system metrics** are captured
automatically, and anything you log inside the function — metrics, artifacts,
`mlflow.pytorch.log_model(...)` — lands on that run. Pass `experiment="..."`
to group runs under a named experiment.

Three rules make a function promotable:

1. **Take all inputs as arguments.** The function and its bound arguments are
   cloudpickled; closing over a notebook global (a DataFrame, a loaded model)
   either bloats the payload or fails to pickle. Pass data via a mounted
   Volume, not a closure.
2. **Import inside the function.** Imports re-resolve on the worker, so import
   only what the worker image provides.
3. **For GPU work, the body runs on a GPU worker.** A GPU promote dispatches
   your function onto the GPU worker automatically, so plain `torch` code that
   uses CUDA works. For multi-GPU or distributed training, use Ray Train or
   `ray.remote` inside the function as you would in any Ray program.

## The `Run` handle

```python
run.refresh()              # re-fetch status
run.wait(timeout=3600)     # block until terminal, then return self
run.cancel()               # delete the job (idempotent)

run.phase                  # "Pending" | "Running" | "Succeeded" | "Failed" | ...
run.mlflow_run_id          # MLflow run id once tracking starts
run.ray_job_name           # underlying RayJob name
run.start_time, run.end_time
```

```python
from privatemind import list_jobs, get_job

for r in list_jobs():
    print(r.name, r.phase)

r = get_job("tj-abc12")
```

## Image generation

Generate images straight from Python. A prompt goes in, base64 images come
back through the same gateway choke point as training (auth, audit, billing,
rate-limiting).

```python
from privatemind import generate_image

resp = generate_image(
    model="cosmos3-super-text2image",
    prompt="a tropical beach at sunset, dramatic clouds",
    size="1024x1024",
    n=4,
)

for i, img in enumerate(resp.data):
    # b64_json is a base64-encoded PNG; the SDK does not decode it for you.
    import base64
    with open(f"out_{i}.png", "wb") as f:
        f.write(base64.b64decode(img.b64_json))

print(resp.warnings)  # non-fatal notices, e.g. off-allowlist size
```

`size` is required and must be `WIDTHxHEIGHT` (e.g. `1024x1024`). It is a
required argument, so omitting it is a `TypeError`, and a malformed `size`
raises `ValidationError` before the request leaves.

Inference knobs are forwarded verbatim; `extra_args` is a blind passthrough:

```python
resp = generate_image(
    model="cosmos3-super-text2image",
    prompt="...",
    size="1024x1024",
    num_inference_steps=50,
    guidance_scale=4.0,
    flow_shift=3.0,
    negative_prompt="blurry, low quality",
    seed=1143,
    extra_args={"new_backend_param": "value"},
)
```

`generate_image()` returns a typed `ImageGenerationResponse`: `created`,
`data` (each item exposing `b64_json` and `revised_prompt`), and `warnings`.
The SDK returns base64 strings and leaves decoding to you.

### Prompt enrichment (upsampling)

The chat UI enriches sparse prompts into dense, structured text-to-image JSON
before generation. The SDK does **not** bundle that step — it would couple the
client to a specific LLM and a schema that evolves on the backend. Wire it in
yourself with any model you like:

```python
# 1. Ask any strong LLM to expand your description into structured T2I JSON,
#    using a text-to-image template you control.
enriched_json = my_llm.complete(t2i_template.format(description="a cat"))

# 2. Pass that JSON string straight through as the prompt.
resp = generate_image(
    model="cosmos3-super-text2image", prompt=enriched_json, size="1024x1024"
)
```

## Batch jobs

Run bulk chat-completion work asynchronously: upload a JSONL file of requests,
create a batch from it, wait, and iterate the results. The flow mirrors the
OpenAI batch workflow, so an author who knows that API can carry their mental
model over — same parameters, same object fields, flat methods in the SDK's
own style.

```python
from privatemind import Client

# Batch + image generation run against the gateway (PRIVATEMIND_URL).
# Training jobs run against the app backend (PRIVATEMIND_APP_URL); set it
# only if you also call submit_training / list_jobs / get_job.
client = Client()  # token + base_url from env, zero-config in notebooks

f = client.create_file(file=open("in.jsonl", "rb"), purpose="batch")
batch = client.create_batch(
    input_file_id=f.id,
    endpoint="/v1/chat/completions",
    completion_window="24h",
)
batch.wait()                       # poll until terminal; pass timeout= to bound it
for line in batch.results():       # one raw dict per JSONL output line
    print(line["custom_id"], line["response"]["status_code"])
```

The six calls, one-to-one with the gateway's batch endpoints:
`create_file(file=, purpose="batch")`, `get_file_content(file_id)`,
`create_batch(input_file_id=, endpoint=, completion_window=, metadata=)`,
`get_batch(batch_id)`, `list_batches(limit=, after=)`, `cancel_batch(batch_id)`.
Each also exists as a module-level function (`from privatemind import
create_batch`) sharing the lazy process-wide client.

`Batch.wait()` blocks until the batch reaches a terminal status (`completed`,
`failed`, `expired`, `cancelled`), refreshes the object in place, and returns
it. `timeout=None` waits forever; the poll interval doubles up to a 30s cap.
`Batch.results()` downloads the output file and yields each parsed JSON object
(`custom_id`, `response`, `error`); `Batch.error_results()` does the same for
the error file — the per-request failures. Before the batch is terminal both
raise and tell you to `wait()`; on a terminal batch whose requests all failed
there is no output file, so `results()` raises and points you at
`error_results()`.

`list_batches` returns one page as a plain list. Advance with
`after=batches[-1].id` until a page comes back empty; the gateway caps `limit`
server-side, so don't rely on `len(batches) == limit` to detect more pages.
The gateway has no status filter — filter client-side:
`[b for b in client.list_batches() if b.status == "in_progress"]`.

Server-side limits (request count, file size, rate) are enforced by the
gateway, not the SDK, so they can change without an SDK upgrade; a rejected
request surfaces as a normal SDK exception. `max_tokens` is defaulted
server-side; set it per request line if you need a different value.

> **Data retention:** batch input and output files are content-bearing and
> retained server-side for up to 29 days. The batch feature is not eligible
> for zero-data-retention (ZDR) arrangements.

## Configuration

The client resolves auth from, in order:

1. `token=...` kwarg on `Client(...)`
2. `PRIVATEMIND_TOKEN` environment variable
3. `~/.privatemind/auth` file (must be mode `0600`, owned by you, not a symlink)

The base URL comes from `base_url=...` or `PRIVATEMIND_URL` and defaults to
`https://api.privatemind.com`, the production platform API, which serves batch
and image generation. It must be `https://` unless you set
`allow_insecure_http=True` (or `PRIVATEMIND_ALLOW_INSECURE_HTTP=1`).

Training jobs (`submit_training`, `list_jobs`, `get_job`) run against the app
backend, configured via `app_url=...` or `PRIVATEMIND_APP_URL` and defaulting
to `https://privatemind.com` (same `https://` rule).

## Power use: explicit Client

```python
from privatemind import Client

with Client(token="...") as pm:  # URLs default to the production platform
    for run in pm.list_jobs():
        print(run.name, run.phase)
```

The module-level functions share one lazily-created `Client`;
`reconfigure(...)` swaps it (useful in notebooks when credentials change).

## Errors

All raise subclasses of `PrivatemindError`: `AuthError`, `ConfigError`,
`ValidationError`, `ForbiddenError`, `NotFoundError`, `ConflictError`,
`RateLimitError`, `ServerError`. Client-side validation fails fast before any
request.

## License

Apache 2.0.
