fix(install): shell-safe one-liner (curl | bash)

create-use-case/prepare-dataset.mdx

@@ -1,18 +1,18 @@
 ---
 title: "Prepare Data"
 description: "Learn how to prepare and ingest your datasets into tracebloc using containerized data ingestors. Complete guide for CSV, image, and text data with Kubernetes deployment steps."
 ---
 
 ## Overview
 
 Make your data available to the Kubernetes cluster so it can be used for training and evaluation. Whether your client runs on Azure, AWS, Google Cloud, or a local Minikube setup, the process of ingesting datasets works the same way.
 
 The data ingestor is a lightweight service that bridges your raw data and the cluster's persistent storage. It comes with ready-made templates (CSV, images, text) that you can use as starting points and customize for your own dataset. By containerizing the ingestion step, the ingestor validates data format and schema, enforces consistency, and transfers the dataset securely into cluster's SQL storage where it becomes accessible to all training and evaluation jobs.
 
 This guide covers:
 - Customizing ingestor templates for different data types (CSV, images, text)
 - Deploying the data ingestor for training and test data using Kubernetes
 - Managing datasets through the tracebloc interface
 
 **IMPORTANT** Make sure that the data format and ML task is supported and that data standards are met by reviewing the [docs](/create-use-case/prerequisites). You must run the process twice, once to ingest training and once to ingest testing data.
 
@@ -20,17 +20,17 @@
 
 You can ingest data into your client in two ways:
 
 - **Declarative YAML (recommended, simpler)** — describe your dataset in ~8 lines of `ingest.yaml`, then `helm install`. No Dockerfile, no custom Python script. The official ingestor image runs it for you. Use this for any dataset that fits a supported category.
 - **Custom Python template + Kubernetes Job (advanced)** — clone the [data-ingestors repo](https://github.com/tracebloc/data-ingestors), pick a per-category template script, edit it, build and push a Docker image, then `kubectl apply` an `ingestor-job.yaml`. Use this when the declarative schema can't express what your data needs — e.g. non-trivial preprocessing, a custom validator, or a `BaseProcessor` subclass.
 
 Start with the declarative method below. Drop down to the custom-template flow only if you need it.
 
 ## Declarative YAML (recommended)
 
 Describe your dataset in ~8 lines of YAML, then `helm install`. The official ingestor image (published as `ghcr.io/tracebloc/ingestor`) runs it. No Dockerfile, no Python script.
 
 <Note>
-**Before you run any commands in this section:** if you installed the client via the one-liner (`bash <(curl -fsSL https://tracebloc.io/i.sh)`), every later `helm upgrade <workspace> tracebloc/client …` **must** include `--reset-then-reuse-values`, otherwise the upgrade drops the values the installer applied and breaks the workspace:
+**Before you run any commands in this section:** if you installed the client via the one-liner (`curl -fsSL https://tracebloc.io/i.sh | bash`), every later `helm upgrade <workspace> tracebloc/client …` **must** include `--reset-then-reuse-values`, otherwise the upgrade drops the values the installer applied and breaks the workspace:
 
 ```bash
 helm upgrade <workspace> tracebloc/client -n <namespace> --reset-then-reuse-values
@@ -46,11 +46,11 @@
 helm repo update
 ```
 
 The `tracebloc/client` parent chart bootstraps the cluster (jobs-manager, MySQL, RBAC). The `tracebloc/ingestor` subchart submits per-dataset ingestion runs against it.
 
 ### 2. Stage your data on the cluster's shared PVC
 
 The chart **doesn't transport data into the cluster** — it points at data already accessible to the cluster's shared PVC (`client-pvc` by default, mounted at `/data/shared/` inside the ingestor Pod). Before installing, get your raw files there.
 
 For a single-node workspace (the default install), the PVC is backed by a host directory the installer created at `~/.tracebloc/<workspace>/data/`. Drop your files into a per-dataset subdirectory:
 
@@ -62,15 +62,15 @@
 cp    LOCAL_PATH/labels.csv ~/.tracebloc/<workspace>/data/<prefix>/
 ```
 
 Inside the ingestor Pod those files appear at `/data/shared/<prefix>/...` — that's what you'll put in `ingest.yaml` below.
 
 <Note>
 For multi-node or EKS deployments where the PVC isn't backed by a local host path, use a throwaway `kubectl cp` Pod or a cloud-storage init container instead. See the [client ingestor README](https://github.com/tracebloc/client/blob/develop/ingestor/README.md#stage-your-data-on-the-shared-pvc) for those recipes.
 </Note>
 
 ### 3. Write your `ingest.yaml`
 
 The example below is for `image_classification`. **Other categories require different fields** — e.g. `tabular_classification` has no `images:` and instead needs a typed `schema:` block. Don't copy this one blindly; grab the matching file from [`examples/yaml/`](https://github.com/tracebloc/data-ingestors/tree/master/examples/yaml) (one per category) and edit from there. Per-category sample data and READMEs live under [`templates/`](https://github.com/tracebloc/data-ingestors/tree/master/templates).
 
 ```yaml
 apiVersion: tracebloc.io/v1
@@ -83,11 +83,11 @@
 label: label
 ```
 
 The top-level shape (`apiVersion`, `kind`, `category`, `table`, `intent`, `label`) is the same for every category; the `category` field picks the validator set, file-extension defaults, and column conventions. The data-source fields (`csv:`, `images:`, `schema:`, …) vary per category. The paths are *paths inside the ingestor Pod*, which is the PVC mount you populated in step 2.
 
 ### 4. Install once per dataset
 
 The ingestor runs once: validates your data, copies files into the destination directory on the PVC, inserts rows into MySQL, sends metadata to the tracebloc backend, then exits. **Run it twice per dataset** — once with `intent: train`, once with `intent: test` — using distinct `table:` names. The example below shows both releases:
 
 ```bash
 # Train release — points at the ingest.yaml from step 3 (table: cats_dogs_train, intent: train)
@@ -101,10 +101,10 @@
   --set-file ingestConfig=./ingest-test.yaml
 ```
 
 Each `helm install` is a separate release (the first argument is the release name), so the two runs don't collide. The ingestor Pod picks up `CLIENT_ID` / `CLIENT_PASSWORD` automatically from the Kubernetes Secret the parent `tracebloc/client` chart created in `<workspace>` at install time — you don't pass credentials on the `helm install` command.
 
 <Warning>
 **Validation error like `'<your_category>' is not one of [...]` or `Additional properties are not allowed (<field> was unexpected)`?** This comes from the cluster's `jobs-manager` validating against its own bundled schema at submit time — the deployed schema is older than the ingestor image you're installing. `helm repo update` won't fix it (that only refreshes the local chart index, not the running server). The fix is on the cluster side: upgrade the parent chart so jobs-manager redeploys with the current schema.
 
 ```bash
 helm upgrade <workspace> tracebloc/client \
@@ -118,18 +118,18 @@
 
 ## Custom Python template (advanced)
 
 Use this flow when the declarative schema can't express what your data needs — typically when you have non-trivial preprocessing logic, a custom validator, or a `BaseProcessor` subclass. The sections below — Quick Setup and Detailed Setup — both describe this advanced path.
 
 ## Quick Setup
 
 Use this quick setup if you already have an ingestor configured and just want to switch datasets or toggle between training and testing. If you are setting up for the first time, go to the next section for the detailed walkthrough.
 
 ### Steps
 
 1. Pick a template script and edit it. E.g. `/templates/tabular_classification/tabular_classification.py`
 - Update csv options and data_path
 - Only for tabular data: Update schema
 - Set `schema` and `CSVIngestor()`parameters like category, intent, label_column, etc. to match data type, task and train/test purpose
 
 ```python
 ingestor = CSVIngestor(
@@ -163,9 +163,9 @@
 
 ### 1. Configure a Template
 
 This section walks you through the step-by-step setup of a data ingestor. You will clone the repository, select the right template for your data type, and customize it to match your task. Follow this guide if you are setting up an ingestor for the first time or need full control beyond the quick setup.
 
 ### Clone the Data Ingestor Repository
 
 Clone the public [Data Ingestor GitHub repository](https://github.com/tracebloc/data-ingestors):
 
@@ -230,14 +230,14 @@
         ...
 ```
 
 Both Database, APIClient and other values are configured automatically from the environment variables defined in `ingestor_job.yaml`.
 
 - `config.LABEL_FILE`: Path to local csv label file
 - `config.BATCH_SIZE`: Batch size used during ingestion
 
 ### Customize a Template
 
 Templates provide a starting point, but every dataset has its own format and labels. In this step you adapt the template to your data by tuning CSV ingestion options and setting the ingestor parameters (category, label column, intent, data path and schema). The following example in `templates/tabular_classification/tabular_classification.py` shows how to ingest a tabular dataset, but the setup works the same way for image or text data.
 
 #### Needed for Tabular Data: Define Schema
 
@@ -290,7 +290,7 @@
 ```
 
 #### Set CSV ingestion options
 Customize parsing, memory handling, and data cleaning with the csv_options dictionary:
 
 ```python
 csv_options = {
@@ -305,9 +305,9 @@
 }
 ```
 
 #### Set Up the Ingestor
 
 Define the Ingestor instance with the required configuration. See the tabular data example below:
 
 ```python
 ingestor = CSVIngestor(
@@ -339,7 +339,7 @@
 
 ### Docker Hub Setup (first-time users)
 
 The cluster pulls your ingestor image from a public Docker registry, so you need an account before you can push. If you already have one, skip to [Edit Dockerfile](#edit-dockerfile).
 
 1. **Create a Docker Hub account** at [hub.docker.com/signup](https://hub.docker.com/signup) and verify your email.
 2. **Log in from your terminal** so the `docker push` command can authenticate:
@@ -348,18 +348,18 @@
    docker login
    ```
 
 3. **Push the data ingestor image** to your account using the build/push commands in the next section. The image name takes the form `<your-docker-username>/<image-name>:<tag>` — the username segment must match the account you just created.
 4. **Make the image public** so the cluster can pull it without credentials:
    - Go to [hub.docker.com/repositories](https://hub.docker.com/repositories), open the repository you just pushed.
    - Click **Settings → Visibility settings → Make public**.
 
    Keeping the image private is also fine, but then you must create a Kubernetes `imagePullSecret` named `regcred` in the client namespace (the `ingestor-job.yaml` already references it).
 
 ### Place data files on the client host
 
 Datasets are **not** baked into the Docker image. They live on the client host in the per-workspace data directory and are mounted into the ingestor pod through the shared PVC (`client-pvc` → `/data/shared`).
 
 Copy your dataset into the client's data directory, where `<workspace>` is the workspace name you chose during client install (which is also the Helm release name and the Kubernetes namespace — the chart uses the same value for all three). The directory `~/.tracebloc/<workspace>/data/` is created automatically by the installer; just drop your files into it:
 
 ```bash
 # Host path on the machine where the tracebloc client is installed.
@@ -368,20 +368,20 @@
 cp    LOCAL_PATH/labels.csv ~/.tracebloc/<workspace>/data/
 ```
 
 Inside the ingestor pod this directory is mounted at `/data/shared`, so the same files appear as `/data/shared/images/...` and `/data/shared/labels.csv`. Set `SRC_PATH` and `LABEL_FILE` in `ingestor-job.yaml` to point at those in-pod paths (see [Configure Kubernetes](#3-configure-kubernetes) below).
 
 For tabular data the same rule applies — drop the single `labels.csv` (with features and labels) into `~/.tracebloc/<workspace>/data/`.
 
 ### Edit Dockerfile
 
 The Dockerfile only needs to package the ingestion script — the dataset is mounted at runtime, so do **not** `COPY` data into the image:
 
 ```dockerfile
 # Copy the ingestion script into /app
 COPY templates/tabular_classification/tabular_classification.py /app/ingestor.py
 ```
 
 If the cluster enforces the `restricted` Pod Security Standard (see [Run as non-root](#run-as-non-root) below), also add a non-root user to the Dockerfile, **before** the `# Set the entrypoint` line:
 
 ```dockerfile
 RUN groupadd -g 1000 app && \
@@ -480,14 +480,14 @@
 - `image`, your Docker image (imagePullPolicy: Always for DockerHub, IfNotPresent for local)
 - `CLIENT_ID`, `CLIENT_PASSWORD` from the [tracebloc client view](https://ai.tracebloc.io/clients)
 - `TABLE_NAME`, unique per dataset, train and test use different names, no spaces. Different names for train and test data is mandatory
 - `LABEL_FILE`, path inside the ingestor pod (under `/data/shared`) to the CSV with file paths and labels — must match the location of the file you placed in `~/.tracebloc/<workspace>/data/`
 - `SRC_PATH`, root inside the pod where the dataset directory is mounted (`/data/shared`)
 - `BATCH_SIZE` is the number of entries sent to the server per request. Optional — defaults to 4000. Keep it consistent across data types. It depends on available CPU memory, not for example image size. Too large can exhaust memory. It was tested up to 10,000, but 5,000 is a safe default for most systems.
 - `LOG_LEVEL`, "WARNING" for all warnings and errors, "INFO" for all logs, "ERROR" for errors only
 
 ### 4. Deploy
 
 Run the ingestor as a Kubernetes Job:
 
 ```bash
 kubectl apply -f ingestor-job.yaml -n <workspace>
@@ -503,7 +503,7 @@
 
 ### Run as non-root
 
 If the namespace enforces the `restricted` [Pod Security Standard](https://kubernetes.io/docs/concepts/security/pod-security-standards/), `kubectl apply` will be admitted but the pod will be rejected with a warning like:
 
 ```text
 Warning: would violate PodSecurity "restricted:latest":
@@ -529,7 +529,7 @@
     type: RuntimeDefault
 ```
 
 **2. Run the container as a non-root user.** Add the following to the Dockerfile **before** the `# Set the entrypoint` line so the image ships with a UID that satisfies `runAsNonRoot: true`:
 
 ```dockerfile
 RUN groupadd -g 1000 app && \
@@ -541,7 +541,7 @@
 
 Rebuild and push the image, then re-apply the job.
 
 The data ingestor always runs a validation step before ingestion and moving files.
 
 
 #### Verify Deployment
@@ -563,7 +563,7 @@
 **Interface displays:**
 - Dataset name, ID, and record count
 - Data type (Tabular, Image, Text) and purpose (Training/Testing)
 - Namespace and GPU requirements
 
 ## Best Practices
 - Deploy jobs for training and testing simultaneously using different job names

environment-setup/configuration.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Configuration"
 description: "Customize your tracebloc workspace — environment variables, cluster management, GPU support, and manual Helm deployment."
 ---
 
 The installer uses sensible defaults; this page covers what you can change.
@@ -23,7 +23,7 @@
 Example — custom cluster name with two worker nodes:
 
 ```bash
-CLUSTER_NAME=my-cluster AGENTS=2 bash <(curl -fsSL https://tracebloc.io/i.sh)
+curl -fsSL https://tracebloc.io/i.sh | CLUSTER_NAME=my-cluster AGENTS=2 bash
 ```
 
 ## Cluster Management
@@ -43,7 +43,7 @@
 
 ### View logs
 
 The jobs manager is the main tracebloc process. Check its logs when debugging connectivity or job execution issues:
 
 ```bash
 kubectl logs -n <workspace> -l app=manager
@@ -71,7 +71,7 @@
 
 Fully automatic. The installer:
 
 1. Detects NVIDIA GPUs via `nvidia-smi` or `lspci`
 2. Installs drivers if missing (Ubuntu, RHEL/CentOS, Arch)
 3. Installs the NVIDIA Container Toolkit and configures Docker
 4. Deploys the NVIDIA k8s device plugin into the cluster
@@ -81,11 +81,11 @@
 
 ### AMD (Linux)
 
 Auto-detected. ROCm is installed automatically on Ubuntu and RHEL/CentOS. A logout/login may be needed for full GPU access.
 
 ### macOS
 
 CPU only. Docker Desktop on macOS does not support GPU passthrough. For GPU workloads, deploy on a Linux machine with NVIDIA GPUs or use [AWS (EKS)](/environment-setup/eks-client-deployment-guide).
 
 ### Windows
 
@@ -158,7 +158,7 @@
 ```
 
 <Note>
   **The database must stay on local disk.** MySQL/InnoDB is unsafe on NFS/CIFS, so the database and logs always use the local `/tracebloc` tree. To place large **datasets** on a network mount, set the installer's `HOST_DATASET_DIR` — it relocates only the dataset volume (the chart's `hostPath.datasetPath` → `/tracebloc-data`) and runs ingestion as the mount's owner uid, so writes succeed under NFS `root_squash`.
 </Note>
 
 Platform snippets (drop into your values file):
@@ -247,7 +247,7 @@
 
 #### Docker Registry
 
 The chart pulls the client image from a container registry — credentials are required in production. Use a token, not a plaintext password.
 
 ```yaml
 dockerRegistry:
@@ -284,11 +284,11 @@
   timeout: "10m"
 ```
 
 The CronJob's ServiceAccount is bound to the built-in `cluster-admin` ClusterRole because the chart templates cluster-scoped resources (PriorityClass, StorageClass, ClusterRoleBinding, optionally Namespace). Disable if you need a manual approval gate on upgrades.
 
 #### NetworkPolicy hardening for training pods
 
 Training pods run untrusted ML code. The chart can apply a NetworkPolicy that denies ingress and restricts egress to DNS + external HTTPS only — blocking pod-to-pod, MySQL, and Kubernetes API access from the training pod.
 
 ```yaml
 networkPolicy:
@@ -314,12 +314,12 @@
 Leave `enabled: false` on clusters without an enforcing CNI — silently having no protection is worse than explicitly disabling it.
 
 <Warning>
 The chart's training-pod egress lockdown only blocks traffic if your CNI enforces NetworkPolicy. Verify your CNI before relying on it.
 </Warning>
 
 #### Resource Monitor and node-agents namespace
 
 The `tracebloc-resource-monitor` DaemonSet collects node-level CPU/memory metrics. It mounts `hostPath` volumes (`/proc`, `/sys`) which Pod Security Admission's `restricted` profile bans — so the chart isolates it in a dedicated **privileged** namespace (default `tracebloc-node-agents`).
 
 ```yaml
 resourceMonitor: true          # set false on clusters where metrics-server cannot be installed
@@ -329,7 +329,7 @@
     name: tracebloc-node-agents
 ```
 
 When `create: false`, create the namespace yourself with the required PSA labels:
 
 ```bash
 kubectl create namespace tracebloc-node-agents
@@ -343,7 +343,7 @@
 
 #### Pod Security Admission labels
 
 Training Jobs run untrusted user-supplied ML code. The chart can create the release namespace with Pod Security Admission `warn`/`audit`/`enforce` labels at the `restricted` profile for defense-in-depth:
 
 ```yaml
 namespace:
@@ -354,7 +354,7 @@
     enforce: restricted        # set "" for bare-metal hostPath installs
 ```
 
 When `create: false` (default) and you want PSA labels on an existing namespace:
 
 ```bash
 kubectl label namespace <workspace> \
@@ -379,7 +379,7 @@
 
 #### PriorityClass and PodDisruptionBudgets
 
 The chart pins the MySQL pod with a `tracebloc-data-plane` PriorityClass (value `1000000`) so it survives node-level OOM and scheduling pressure, and applies PDBs to MySQL and the jobs manager. Override only if you run a multi-replica MySQL externally:
 
 ```yaml
 priorityClass:
@@ -394,7 +394,7 @@
 
 ### Deploy
 
 Install the chart into a new namespace:
 
 ```bash
 helm upgrade --install <workspace> tracebloc/client \
@@ -425,7 +425,7 @@
 helm uninstall <workspace> -n <workspace>
 ```
 
 PVCs and the PriorityClass are annotated `helm.sh/resource-policy: keep` so your data and shared cluster resources survive uninstall. To remove them too:
 
 ```bash
 kubectl delete pvc --all -n <workspace>
@@ -445,11 +445,11 @@
 
 ## Security
 
 Tracebloc is designed so your data never has to leave your network. Here's how:
 
 - **Data stays local.** Training data never leaves your infrastructure. Only metadata and metrics are shared with the platform.
 - **Encrypted.** All communication between client and platform is TLS-encrypted.
 - **Isolated.** Training runs in containers with restricted system access. Kubernetes namespaces separate workloads from each other.
 - **Scanned.** Submitted models are analyzed for vulnerabilities before execution on your infrastructure.
 - **Minimal footprint.** The installer only modifies `~/.tracebloc/` and Docker. No system-wide changes.
 

environment-setup/deploy-local.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Local / k3d"
 description: "Run a tracebloc workspace on a single machine — laptop or on-prem server. Production-capable."
 ---
 
 **When to pick it** — A single machine you own: a laptop to try things, or an on-prem server you run in production. The installer brings up a self-contained Kubernetes cluster (k3d) inside Docker — you don't need a cluster of your own.
@@ -17,17 +17,17 @@
 <Tabs>
   <Tab title="macOS / Linux">
   ```bash
-  bash <(curl -fsSL https://tracebloc.io/i.sh)
+  curl -fsSL https://tracebloc.io/i.sh | bash
   ```
   </Tab>
   <Tab title="Windows (PowerShell as admin)">
   ```powershell
   irm https://tracebloc.io/i.ps1 | iex
   ```
   </Tab>
 </Tabs>
 
 See [Quick Start](/environment-setup/quickstart) for the full walkthrough, including the inspect-first option.
 
 ## Verify
 
@@ -35,17 +35,17 @@
 kubectl get pods -A
 ```
 
 The tracebloc pods should be `Running`, and your workspace should read **Online** on the clients page.
 
 ## Environment-specific config
 
 Set these as environment variables before the install command (full list in [Configuration](/environment-setup/configuration#installer-options)):
 
 ```bash
-CLUSTER_NAME=my-cluster AGENTS=2 HOST_DATA_DIR=/data/tracebloc bash <(curl -fsSL https://tracebloc.io/i.sh)
+curl -fsSL https://tracebloc.io/i.sh | CLUSTER_NAME=my-cluster AGENTS=2 HOST_DATA_DIR=/data/tracebloc bash
 ```
 
 GPUs are auto-detected on Linux (NVIDIA/AMD) — drivers, container toolkit, and device plugin are installed for you.
 
 ## Production notes
 

environment-setup/quickstart.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Quick Start"
 description: "From zero to a running tracebloc workspace in about 10 minutes."
 ---
 
 A running workspace in about 10 minutes, with one command. Deploying on a specific environment instead (EKS, AKS, bare-metal, OpenShift)? See [Deployment environments](/environment-setup/deployment-environments).
@@ -14,7 +14,7 @@
 | You need | Minimum |
 |---|---|
 | A machine | macOS, Linux, or Windows · 2 CPU · 5 GB RAM · 10 GB free disk |
 | A tracebloc account | [Sign up free](https://ai.tracebloc.io) — no credit card |
 
 That's enough to run a workspace. To **train on this machine**, give it 4 CPU · 16 GB RAM · 50 GB disk — a single training job alone reserves ~8 GB. The installer checks before it starts: below the minimum it stops on Linux (on macOS/Windows it warns — raise Docker Desktop's memory in Settings → Resources).
 
@@ -27,12 +27,12 @@
     <Tabs>
       <Tab title="macOS / Linux">
       ```bash
-      bash <(curl -fsSL https://tracebloc.io/i.sh)
+      curl -fsSL https://tracebloc.io/i.sh | bash
       ```
       </Tab>
       <Tab title="Windows (PowerShell as admin)">
       ```powershell
       irm https://tracebloc.io/i.ps1 | iex
       ```
       </Tab>
     </Tabs>

environment-setup/setup-guide.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Setup Guide"
 description: "Detailed walkthrough for deploying a tracebloc workspace — requirements, installer internals, GPU support, and verification."
 ---
 
 Everything the installer does, explained. This page walks through the full setup process — what each step does, what to expect, and how to verify it worked.
@@ -23,7 +23,7 @@
 
 **Supported platforms:** macOS (Intel & Apple Silicon) · Linux (x86_64 & arm64) · Windows (x86_64 & arm64)
 
 **Outbound access needed:** The installer pulls container images, the install scripts, and the Helm chart, then connects to the tracebloc platform. Allow traffic to `*.docker.io`, `ghcr.io`, `raw.githubusercontent.com`, `*.github.io`, `*.tracebloc.io`, and `pypi.org`.
 
 <Warning>
   **Storage must be on a local disk.** The data directory (`HOST_DATA_DIR`, default `~/.tracebloc`) holds the workspace database, which corrupts on network filesystems — the installer **fails fast** if it is on NFS/CIFS/SMB. To keep large datasets on a network mount, point `HOST_DATASET_DIR` at it instead: only the dataset volume moves there, while the database stays local. See [Configuration](/environment-setup/configuration).
@@ -37,7 +37,7 @@
 
 ## 2. Register a Client
 
 A **client** is your workspace's identity on the platform. It ties a specific machine to your account and controls what data and use cases are accessible from it.
 
 Open the [client page](https://ai.tracebloc.io/clients) and click **"+"**.
 
@@ -71,7 +71,7 @@
   <Tab title="macOS / Linux">
 
 ```bash
-bash <(curl -fsSL https://tracebloc.io/i.sh)
+curl -fsSL https://tracebloc.io/i.sh | bash
 ```
 
   </Tab>
@@ -101,11 +101,11 @@
 **Step 2/4 — Set up secure compute environment**
 Provisions a lightweight local Kubernetes cluster inside Docker. First run takes 1–2 minutes to download components.
 
 **Step 3/4 — Install tracebloc client**
 Prompts for a **workspace name** (e.g. `berlin-team`, `vision-lab`, `ml-mardan`). This identifies the client on your machine and becomes the Kubernetes namespace.
 
 **Step 4/4 — Connect to tracebloc network**
 Prompts for your **Client ID** and **password** from step 2 above. This links your secure local environment to the tracebloc platform so vendors can submit models for evaluation.
 
 When it finishes you'll see a summary like:
 
@@ -150,7 +150,7 @@
 | `mysql-...` | Local metadata store — tracks jobs, metrics, and configuration |
 | `tracebloc-jobs-manager-...` | The client — executes training jobs and communicates with the platform |
 
 Then open [ai.tracebloc.io](https://ai.tracebloc.io) and check that your client status shows **Online**. This confirms the client has established a secure connection to the tracebloc backend.
 
 <Tip>
 Stuck on Offline? Check the [Troubleshooting](/environment-setup/troubleshooting) page — most issues resolve with a pod restart or credential check.

environment-setup/troubleshooting.mdx

@@ -1,6 +1,6 @@
 ---
 title: "Troubleshooting"
 description: "Common issues and debugging commands for your tracebloc workspace."
 ---
 
 Most issues fall into a few categories: pods not starting, client not connecting, or resource limits being hit. Start with the quick checks below — they cover the most common problems.
@@ -11,7 +11,7 @@
 **Stuck? Generate a support bundle.** Re-run the installer with `--diagnose`:
 
 ```bash
-bash <(curl -fsSL https://tracebloc.io/i.sh) --diagnose
+curl -fsSL https://tracebloc.io/i.sh | bash -s -- --diagnose
 ```
 
 It writes a redacted `~/.tracebloc/tracebloc-diagnose-<timestamp>.tgz` — logs, pod status, and versions with **credentials removed** — that you can send to support. The first line of output shows your client version.
@@ -43,7 +43,7 @@
 
 | Error Message | Description | Resolution |
 |---------------|-------------|------------|
 | ServiceBus connection error after Docker restart | When Docker overutilizes local resources and restarts, the ServiceBus connection may fail with `NoneType` errors. | Monitor resources via Docker Dashboard. Restart the jobs manager pod (e.g. in k9s, exit with Ctrl+D) to restore the connection. |
 | MySQL pod in `CrashLoopBackOff` (often ~20 min into install) | The data directory (`HOST_DATA_DIR`) is on a network filesystem (NFS/CIFS/SMB). MySQL/InnoDB corrupts on network storage, and NFS `root_squash` blocks the data-dir setup. | Point `HOST_DATA_DIR` at a **local** disk (the default `~/.tracebloc` is local) and re-run. Keep large datasets on the network mount via `HOST_DATASET_DIR`. Newer installers catch this in preflight before the install starts. |
 
 ## Debugging Commands
@@ -106,8 +106,8 @@
 If training is slow, these are the factors to look at:
 
 - **Batch size** — larger batches increase GPU utilization up to memory saturation
 - **Model complexity** — transformer attention is O(seq_len² x hidden_dim); CNNs scale with kernel x feature map x filters
 - **Precision** — FP16/BF16 can speed up training 2-3x on modern GPUs
 - **Data pipeline** — slow CPU preprocessing (augmentation, tokenization) can bottleneck training
 - **Parallelization** — data parallelism splits batches across GPUs; model parallelism splits the model itself
 

overview/tracebloc.mdx

@@ -1,5 +1,5 @@
 ---
 title: "tracebloc"
 description: "Build better AI together. Without moving data."
 mode: "default"
 ---
@@ -8,7 +8,7 @@
   ![Traceblocfederatedlearning](/images/traceblocfederatedlearning.png)
 </Frame>
 
 tracebloc is **your collaborative AI workspace** you deploy on your own infrastructure. Invite friends, peers, researchers, partners, vendors — anyone — to train, fine-tune, and benchmark models on your private data. Your data never moves.
 
 <CardGroup cols={3}>
   <Card title="Quick Setup" icon="rocket" href="/environment-setup/setup-guide">
@@ -20,24 +20,24 @@
   </Card>
 
   <Card title="Join a Use Case" icon="users" href="/join-use-case/join-use-case">
     Train and submit models on someone else's data.
   </Card>
 </CardGroup>
 
 <br />
 
 ## What is tracebloc?
 
 tracebloc is a **collaborative AI workspace** you deploy on your own infrastructure. Anyone you invite — researchers, partners, vendors, startups — can train, fine-tune, and benchmark models on your data. Your data never moves. Compliance is solved by architecture.
 
 **Who is it for?** Anyone who asks "which model works best on MY data?" and wants external input without the nightmare of NDAs, data-sharing agreements, and security reviews.
 
 **What makes it different?**
 
 - <Icon icon="server" /> **Your infrastructure:** runs on your Mac, Linux box, GPU server, or any Kubernetes cluster
 - <Icon icon="lock" /> **Your data:** stays inside your network. Contributors never see raw records.
 - <Icon icon="user-plus" /> **Invite anyone:** whitelist contributors by email. They see EDA and metadata. Never raw data.
 - <Icon icon="ranking-star" /> **One leaderboard:** every submission benchmarked under identical conditions. Ship the winner.
 - <Icon icon="shield-check" /> **Compliance by architecture:** data never moves. Sign off once on the architecture, not once per partner.
 
 <br />
@@ -51,7 +51,7 @@
     <CodeGroup>
 
     ```shellscript Mac / Linux
-    bash <(curl -fsSL https://tracebloc.io/i.sh)
+    curl -fsSL https://tracebloc.io/i.sh | bash
     ```
 
     ```powershell Windows
@@ -73,17 +73,17 @@
     Contributors submit models and train inside your environment. PyTorch, TensorFlow, custom containers.
   </Step>
   <Step title="Compare and decide">
     Every submission benchmarked under identical conditions. One leaderboard. Ship the winner.
   </Step>
 </Steps>
 
 <br />
 
 ## When Should You Use tracebloc
 
 <CardGroup cols={2}>
   <Card title="Vendor Benchmarking" icon="ranking-star">
     5 vendors claim they have the best model. Invite all five to submit. One leaderboard. One week. Decisions based on measured performance — not claims.
   </Card>
 
   <Card title="Cross-Org Research" icon="building">
@@ -113,7 +113,7 @@
   </Card>
 
   <Card title="Explore Workspaces" icon="compass" href="https://ai.tracebloc.io/explore">
     See what builders are creating on tracebloc.
   </Card>
 
   <Card title="Advanced Setup" icon="gear" href="/environment-setup/setup-guide">